exercise_ 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.11.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statistics\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from plotly.offline import init_notebook_mode, iplot, plot\n",
    "import plotly as py\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "import statistics as stat\n",
    "from scipy.stats import binom\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib.patches import Rectangle\n",
    "# splitting merge_data into train test and split\n",
    "from sklearn.model_selection import train_test_split, cross_validate\n",
    "from scipy import stats\n",
    "from scipy.stats import normaltest\n",
    "import os \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import preprocessing\n",
    "import matplotlib as mpl\n",
    "import warnings; warnings.filterwarnings(action='once')\n",
    "# to keep track of training time\n",
    "import datetime\n",
    "# logistic regression model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# metrics used for evaluation\n",
    "from sklearn.metrics import f1_score, matthews_corrcoef\n",
    "# visualizations\n",
    "from yellowbrick.classifier import ClassPredictionError, ConfusionMatrix\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "# KNN imputation\n",
    "from sklearn.impute import KNNImputer\n",
    "# normalizer\n",
    "from sklearn.preprocessing import Normalizer\n",
    "# variance threshold\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "# RFECV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "# random forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# evaluation metric\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the file\n",
    "df = pd.read_csv(\"LoanStats.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39786, 7)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the numbers of columns and rows in the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_rate</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.65</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>B</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>credit_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27</td>\n",
       "      <td>2500</td>\n",
       "      <td>60</td>\n",
       "      <td>C</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.96</td>\n",
       "      <td>2400</td>\n",
       "      <td>36</td>\n",
       "      <td>C</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>small_business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49</td>\n",
       "      <td>10000</td>\n",
       "      <td>36</td>\n",
       "      <td>C</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69</td>\n",
       "      <td>3000</td>\n",
       "      <td>60</td>\n",
       "      <td>B</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int_rate  loan_amnt  term grade home_ownership  annual_inc         purpose\n",
       "0     10.65       5000    36     B           RENT     24000.0     credit_card\n",
       "1     15.27       2500    60     C           RENT     30000.0             car\n",
       "2     15.96       2400    36     C           RENT     12252.0  small_business\n",
       "3     13.49      10000    36     C           RENT     49200.0           other\n",
       "4     12.69       3000    60     B           RENT     80000.0           other"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking first 5 rows to see whether the right variables are in the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int_rate          float64\n",
       "loan_amnt           int64\n",
       "term                int64\n",
       "grade                int8\n",
       "home_ownership     object\n",
       "annual_inc        float64\n",
       "purpose            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking datatypes \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_rate</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>annual_inc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39786.000000</td>\n",
       "      <td>39786.000000</td>\n",
       "      <td>39786.000000</td>\n",
       "      <td>39786.000000</td>\n",
       "      <td>3.978600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.027873</td>\n",
       "      <td>11231.360277</td>\n",
       "      <td>42.448499</td>\n",
       "      <td>1.579500</td>\n",
       "      <td>6.897907e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.727466</td>\n",
       "      <td>7464.542832</td>\n",
       "      <td>10.638782</td>\n",
       "      <td>1.388213</td>\n",
       "      <td>6.376263e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.420000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.250000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.050000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.860000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.900000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.590000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.234250e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24.590000</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           int_rate     loan_amnt          term         grade    annual_inc\n",
       "count  39786.000000  39786.000000  39786.000000  39786.000000  3.978600e+04\n",
       "mean      12.027873  11231.360277     42.448499      1.579500  6.897907e+04\n",
       "std        3.727466   7464.542832     10.638782      1.388213  6.376263e+04\n",
       "min        5.420000    500.000000     36.000000      0.000000  4.000000e+03\n",
       "25%        9.250000   5500.000000     36.000000      0.000000  4.050000e+04\n",
       "50%       11.860000  10000.000000     36.000000      1.000000  5.900000e+04\n",
       "75%       14.590000  15000.000000     60.000000      2.000000  8.234250e+04\n",
       "max       24.590000  35000.000000     60.000000      6.000000  6.000000e+06"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36    29096\n",
       "60    10690\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the distribution of loan_terms (approved short term loans is higher than long term loans)\n",
    "df[\"term\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='term', ylabel='int_rate'>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot of int_rate and term (interest rate is higher with longer loan terms)\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.boxplot(data=df, x=\"term\", y=\"int_rate\", palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    18676\n",
       "credit_card            5137\n",
       "other                  4001\n",
       "home_improvement       2985\n",
       "major_purchase         2188\n",
       "small_business         1831\n",
       "car                    1551\n",
       "wedding                 948\n",
       "medical                 695\n",
       "moving                  583\n",
       "house                   382\n",
       "vacation                381\n",
       "educational             325\n",
       "renewable_energy        103\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of purpose of getting loan in the dataset \n",
    "df[\"purpose\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x216dd9e1940>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##distribution of purpose of getting loan in the dataset /\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.catplot(\n",
    "    data=df, y=\"purpose\", kind=\"count\",\n",
    "    palette=\"viridis\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duygu\\AppData\\Local\\Temp\\ipykernel_10828\\2308184235.py:1: UserWarning:\n",
      "\n",
      "Ignoring `palette` because no `hue` variable has been assigned.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x216e5cfeb80>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of the interest rate in the dataset \n",
    "sns.displot(data=df, x=\"int_rate\", kde=True, palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x216dd44e2e0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 563.625x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "sns.catplot(\n",
    "    data=df, y=\"purpose\", hue=\"grade\", kind=\"count\",\n",
    "    palette=\"viridis\", edgecolor=\".6\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='purpose', ylabel='int_rate'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot of int_rate and term (interest rate is higher with longer loan terms)\n",
    "plt.figure(figsize=(25,8))\n",
    "sns.boxplot(data=df, x=\"purpose\", y=\"int_rate\", palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='grade', ylabel='int_rate'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,8))\n",
    "sns.boxplot(data=df, x=\"grade\", y=\"int_rate\", palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='purpose', ylabel='int_rate'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,8))\n",
    "sns.boxplot(data=df, x=\"purpose\", y=\"int_rate\", hue= \"home_ownership\", palette= \"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if there is any missing value in the data/ no missing value\n",
    "df.isna().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_rate</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.65</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>B</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>credit_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27</td>\n",
       "      <td>2500</td>\n",
       "      <td>60</td>\n",
       "      <td>C</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.96</td>\n",
       "      <td>2400</td>\n",
       "      <td>36</td>\n",
       "      <td>C</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>small_business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49</td>\n",
       "      <td>10000</td>\n",
       "      <td>36</td>\n",
       "      <td>C</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69</td>\n",
       "      <td>3000</td>\n",
       "      <td>60</td>\n",
       "      <td>B</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int_rate  loan_amnt  term grade home_ownership  annual_inc         purpose\n",
       "0     10.65       5000    36     B           RENT     24000.0     credit_card\n",
       "1     15.27       2500    60     C           RENT     30000.0             car\n",
       "2     15.96       2400    36     C           RENT     12252.0  small_business\n",
       "3     13.49      10000    36     C           RENT     49200.0           other\n",
       "4     12.69       3000    60     B           RENT     80000.0           other"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int_rate          float64\n",
      "loan_amnt           int64\n",
      "term                int64\n",
      "grade              object\n",
      "home_ownership     object\n",
      "annual_inc        float64\n",
      "purpose            object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# checking the type of the columns to know categorical values that we have to transform on the dataset\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int_rate           float64\n",
      "loan_amnt            int64\n",
      "term                 int64\n",
      "grade             category\n",
      "home_ownership      object\n",
      "annual_inc         float64\n",
      "purpose             object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Changing grade column as categorical value (grade is ordinal) helps to convert each of them into respective numeric value. There are 3 steps to do that.\n",
    "# 1.Changing the type of \"garde\" column to categorical\n",
    "df[\"grade\"] = df[\"grade\"].astype(\"category\")\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.Providing the correct order of categories \n",
    "df.grade =df.grade.cat.reorder_categories([\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.Encoding categories with integer values \n",
    "df.grade= df.grade.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for the nominal variables, there is no order of rank between \"homeownership\" or \"purpose\" columns.The previous approach is not useful anymore. So the transformation applied by using dummy variables approach.\n",
    "# geting dummies and saving them inside new dataframe for \"home_ownership\" and \"purpose\" columns.\n",
    "dummies1= pd.get_dummies(df.home_ownership)\n",
    "dummies2= pd.get_dummies(df.purpose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mergng all dataframes\n",
    "merged =pd.concat([df,dummies1,dummies2],axis=\"columns\")\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping categorical variables:\"home_ownership\",\"purpose\" as already created dummy variables for these two column.\n",
    "#dropping 2 dummy columns to avoid \"dummy variable trap\" in regression model.he Dummy Variable trap van happenif independent variables are multicollinear. In order to avoid this,one of the dummy variable for each categorical values was droped(\"moving and others\")\n",
    "df_final= merged.drop([\"home_ownership\",\"purpose\",\"moving\",\"other\"], axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_rate</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <th>NONE</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>OWN</th>\n",
       "      <th>RENT</th>\n",
       "      <th>...</th>\n",
       "      <th>debt_consolidation</th>\n",
       "      <th>educational</th>\n",
       "      <th>home_improvement</th>\n",
       "      <th>house</th>\n",
       "      <th>major_purchase</th>\n",
       "      <th>medical</th>\n",
       "      <th>renewable_energy</th>\n",
       "      <th>small_business</th>\n",
       "      <th>vacation</th>\n",
       "      <th>wedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.65</td>\n",
       "      <td>5000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27</td>\n",
       "      <td>2500</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.96</td>\n",
       "      <td>2400</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49</td>\n",
       "      <td>10000</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69</td>\n",
       "      <td>3000</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39781</th>\n",
       "      <td>8.07</td>\n",
       "      <td>2500</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39782</th>\n",
       "      <td>10.28</td>\n",
       "      <td>8500</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39783</th>\n",
       "      <td>8.07</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39784</th>\n",
       "      <td>7.43</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39785</th>\n",
       "      <td>13.75</td>\n",
       "      <td>7500</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39786 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       int_rate  loan_amnt  term  grade  annual_inc  MORTGAGE  NONE  OTHER  \\\n",
       "0         10.65       5000    36      1     24000.0         0     0      0   \n",
       "1         15.27       2500    60      2     30000.0         0     0      0   \n",
       "2         15.96       2400    36      2     12252.0         0     0      0   \n",
       "3         13.49      10000    36      2     49200.0         0     0      0   \n",
       "4         12.69       3000    60      1     80000.0         0     0      0   \n",
       "...         ...        ...   ...    ...         ...       ...   ...    ...   \n",
       "39781      8.07       2500    36      0    110000.0         1     0      0   \n",
       "39782     10.28       8500    36      2     18000.0         0     0      0   \n",
       "39783      8.07       5000    36      0    100000.0         1     0      0   \n",
       "39784      7.43       5000    36      0    200000.0         1     0      0   \n",
       "39785     13.75       7500    36      4     22000.0         0     0      0   \n",
       "\n",
       "       OWN  RENT  ...  debt_consolidation  educational  home_improvement  \\\n",
       "0        0     1  ...                   0            0                 0   \n",
       "1        0     1  ...                   0            0                 0   \n",
       "2        0     1  ...                   0            0                 0   \n",
       "3        0     1  ...                   0            0                 0   \n",
       "4        0     1  ...                   0            0                 0   \n",
       "...    ...   ...  ...                 ...          ...               ...   \n",
       "39781    0     0  ...                   0            0                 1   \n",
       "39782    0     1  ...                   0            0                 0   \n",
       "39783    0     0  ...                   1            0                 0   \n",
       "39784    0     0  ...                   0            0                 0   \n",
       "39785    1     0  ...                   1            0                 0   \n",
       "\n",
       "       house  major_purchase  medical  renewable_energy  small_business  \\\n",
       "0          0               0        0                 0               0   \n",
       "1          0               0        0                 0               0   \n",
       "2          0               0        0                 0               1   \n",
       "3          0               0        0                 0               0   \n",
       "4          0               0        0                 0               0   \n",
       "...      ...             ...      ...               ...             ...   \n",
       "39781      0               0        0                 0               0   \n",
       "39782      0               0        0                 0               0   \n",
       "39783      0               0        0                 0               0   \n",
       "39784      0               0        0                 0               0   \n",
       "39785      0               0        0                 0               0   \n",
       "\n",
       "       vacation  wedding  \n",
       "0             0        0  \n",
       "1             0        0  \n",
       "2             0        0  \n",
       "3             0        0  \n",
       "4             0        0  \n",
       "...         ...      ...  \n",
       "39781         0        0  \n",
       "39782         0        0  \n",
       "39783         0        0  \n",
       "39784         0        0  \n",
       "39785         0        0  \n",
       "\n",
       "[39786 rows x 22 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final dataset\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#droping dependent value from dataset before splitting\n",
    "x= df_final.drop([\"int_rate\"], axis=\"columns\")\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keeping dependent value in the dataset before splitting\n",
    "y=df_final[\"int_rate\"]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the dataset by applying random_state to use the same split of data.\n",
    "x_train,x_test,y_train,y_test =train_test_split(x,y,test_size=.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 1: Multiple Linear Regresion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n"
     ]
    }
   ],
   "source": [
    "# linear regresion model fitting to x_train and y_train\n",
    "model = LinearRegression().fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted labels:  [ 8. 18. 18. 11. 16.  8.  8. 18. 11.  8.]\n",
      "Actual labels   :  22900     5.79\n",
      "27960    17.93\n",
      "32773    16.45\n",
      "16463    10.74\n",
      "22822    14.46\n",
      "12833     5.42\n",
      "17036     7.66\n",
      "29184    16.82\n",
      "30104    10.99\n",
      "26804     7.14\n",
      "Name: int_rate, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "np.set_printoptions(suppress=True)\n",
    "print('Predicted labels: ', np.round(predictions)[:10])\n",
    "print('Actual labels   : ' ,y_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Interest Rate Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9022602911185348"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.3401608423994482\n",
      "RMSE: 1.157653161529587\n",
      "R2: 0.9022602911185348\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 2: Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso() \n",
      "\n",
      "MSE: 1.959862622985153\n",
      "RMSE: 1.3999509359206674\n",
      "R2: 0.8570646177997056\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# Fit a lasso model on the training set\n",
    "model = Lasso().fit(x_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(x_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor() \n",
      "\n",
      "|--- feature_2 <= 1.50\n",
      "|   |--- feature_2 <= 0.50\n",
      "|   |   |--- feature_12 <= 0.50\n",
      "|   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |--- feature_0 <= 12837.50\n",
      "|   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 4637.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 3262.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  3262.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  4637.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 52790.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  52790.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 32\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 60800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 3775.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  3775.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  60800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 77500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  77500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 6325.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 28650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 13800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.42]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  13800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  28650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 94500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  94500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  6325.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 8200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 16350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  16350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  8200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 11012.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  11012.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 9537.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 33034.40\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 7237.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 1650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  1650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  7237.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 8250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  8250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.68]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  33034.40\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 36390.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 5575.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  5575.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.42]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  36390.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  9537.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 9587.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  9587.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 61000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 59000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  59000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.32]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  61000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 68100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  68100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 99752.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 11750.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 6800.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_17 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 5200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  5200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_17 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6.54]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  6800.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 11150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 11050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  11050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.90]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  11150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 11350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  11350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.54]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  11750.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 82652.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 12125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  12125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  82652.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6.54]\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 47000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 37998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.88]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  37998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  47000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 12550.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  12550.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |--- feature_3 >  99752.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 12250.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 10000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 158500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.88]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  158500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.29]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 107500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  107500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  10000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 113196.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  113196.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.90]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  12250.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.54]\n",
      "|   |   |   |   |--- feature_0 >  12837.50\n",
      "|   |   |   |   |   |--- feature_0 <= 25725.00\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 19962.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 17475.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 13037.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 58000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  58000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  13037.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 35250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  35250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 31\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  17475.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.17]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.88]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  19962.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 23100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 50171.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 43400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.17]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  43400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  50171.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 80500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  80500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  23100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_14 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 25300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  25300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_14 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 87500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.99]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  87500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.17]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |--- feature_0 >  25725.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 34475.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 254000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 27275.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 26512.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  26512.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.49]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.90]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  27275.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 64250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6.62]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  64250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 128537.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  128537.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  254000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 303000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 272000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  272000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6.62]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  303000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6.62]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.49]\n",
      "|   |   |   |   |   |   |--- feature_0 >  34475.00\n",
      "|   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 105000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 94000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 77250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  77250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  94000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 103000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  103000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.49]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  105000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 147000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.49]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  147000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.90]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.49]\n",
      "|   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 282000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 172616.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 128000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  128000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.66]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  172616.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 224616.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  224616.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  282000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 354000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.29]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  354000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |--- feature_3 <= 96950.00\n",
      "|   |   |   |   |   |--- feature_3 <= 9846.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 5750.00\n",
      "|   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |--- feature_3 >  5750.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 9666.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 3125.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 1775.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 9396.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.51]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  9396.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  1775.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 8802.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  8802.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.99]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  3125.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 3375.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 7000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.59]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  7000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  3375.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.63]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  9666.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |--- feature_3 >  9846.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 33700.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 4825.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 88000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 3687.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 25\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  3687.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 85500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  85500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  88000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 92500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.66]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  92500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  4825.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 85502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 85002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  85002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  85502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 12675.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7012.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7012.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  12675.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 26700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.90]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  26700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |--- feature_0 >  33700.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 90000.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.90]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  90000.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |--- feature_3 >  96950.00\n",
      "|   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 118500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_14 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 99050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 97220.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.17]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  97220.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  99050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_14 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 11000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6.54]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  11000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6.03]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  118500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 119002.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.17]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  119002.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 186000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  186000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.03]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.51]\n",
      "|   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 131000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 111500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.32]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  111500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  131000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 432000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 275000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  275000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  432000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.14]\n",
      "|   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 2487.50\n",
      "|   |   |   |   |   |   |   |--- value: [5.79]\n",
      "|   |   |   |   |   |   |--- feature_0 >  2487.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 7250.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 112600.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 6300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 5500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  5500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  6300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 6800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  6800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  112600.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 117600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.66]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  117600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.49]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  7250.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 162500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 101500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 18000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  18000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  101500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 117500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  117500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  162500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 217000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 202000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  202000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  217000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 391000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  391000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.29]\n",
      "|   |   |--- feature_12 >  0.50\n",
      "|   |   |   |--- feature_0 <= 4250.00\n",
      "|   |   |   |   |--- feature_0 <= 2100.00\n",
      "|   |   |   |   |   |--- feature_3 <= 22000.00\n",
      "|   |   |   |   |   |   |--- value: [7.74]\n",
      "|   |   |   |   |   |--- feature_3 >  22000.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 1250.00\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.59]\n",
      "|   |   |   |   |   |   |--- feature_0 >  1250.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 1650.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 44500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  44500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.32]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  1650.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.94]\n",
      "|   |   |   |   |--- feature_0 >  2100.00\n",
      "|   |   |   |   |   |--- feature_0 <= 3100.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 2900.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 2537.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 48081.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 38581.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.51]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  38581.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.74]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  48081.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 74500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  74500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.88]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  2537.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 2687.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.76]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  2687.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.40]\n",
      "|   |   |   |   |   |   |--- feature_0 >  2900.00\n",
      "|   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |--- feature_0 >  3100.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 3425.00\n",
      "|   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |--- feature_0 >  3425.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 36250.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 3750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 24752.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.68]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  24752.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.59]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  3750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.59]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  36250.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 3550.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.76]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  3550.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 3800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.51]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  3800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.43]\n",
      "|   |   |   |--- feature_0 >  4250.00\n",
      "|   |   |   |   |--- feature_3 <= 51000.00\n",
      "|   |   |   |   |   |--- feature_0 <= 6750.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 12400.00\n",
      "|   |   |   |   |   |   |   |--- value: [7.75]\n",
      "|   |   |   |   |   |   |--- feature_3 >  12400.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 30996.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 27000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 18350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4625.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.94]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4625.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  18350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.32]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  27000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.74]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  30996.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 39002.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  39002.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 5250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.94]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  5250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 40500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.94]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  40500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |--- feature_0 >  6750.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 7900.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 41000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.14]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.51]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  41000.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.32]\n",
      "|   |   |   |   |   |   |--- feature_0 >  7900.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 46500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 43025.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 40400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 8500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.32]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  8500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  40400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.32]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.32]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  43025.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.51]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  46500.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |--- feature_3 >  51000.00\n",
      "|   |   |   |   |   |--- feature_0 <= 12300.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 82500.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 62500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 61000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 57500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.04]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  57500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.88]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  61000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.59]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  62500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 6100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 5500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.74]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  5500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 77500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.51]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  77500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.74]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  6100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.88]\n",
      "|   |   |   |   |   |   |--- feature_3 >  82500.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 85500.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.32]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  85500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 5500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  5500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 103000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 94250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.59]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  94250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  103000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 11000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  11000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.51]\n",
      "|   |   |   |   |   |--- feature_0 >  12300.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 12700.00\n",
      "|   |   |   |   |   |   |   |--- value: [9.32]\n",
      "|   |   |   |   |   |   |--- feature_0 >  12700.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 13900.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.94]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  13900.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.94]\n",
      "|   |--- feature_2 >  0.50\n",
      "|   |   |--- feature_0 <= 15137.50\n",
      "|   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |--- feature_0 <= 7650.00\n",
      "|   |   |   |   |   |--- feature_0 <= 6212.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 1050.00\n",
      "|   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 34900.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 15600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  15600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 975.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.76]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  975.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  34900.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.49]\n",
      "|   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 71582.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 64500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 58300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  58300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.76]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  64500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  71582.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 142500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  142500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.38]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 94500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.71]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  94500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.14]\n",
      "|   |   |   |   |   |   |--- feature_0 >  1050.00\n",
      "|   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_12 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 14244.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 13098.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  13098.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  14244.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 22220.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  22220.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 31\n",
      "|   |   |   |   |   |   |   |   |--- feature_12 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 2175.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 33120.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  33120.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  2175.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 18102.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  18102.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 112000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 24500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 2150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  2150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  24500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 25295.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  25295.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  112000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.88]\n",
      "|   |   |   |   |   |--- feature_0 >  6212.50\n",
      "|   |   |   |   |   |   |--- feature_3 <= 27038.00\n",
      "|   |   |   |   |   |   |   |--- feature_19 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 20700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 19068.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  19068.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 14000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.62]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  14000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  20700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 6450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  6450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- feature_19 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.88]\n",
      "|   |   |   |   |   |   |--- feature_3 >  27038.00\n",
      "|   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 237500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 58760.26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 55850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  55850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  58760.26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  237500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.71]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.18]\n",
      "|   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 7575.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 41500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 37200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  37200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  41500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 95500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  95500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  7575.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |--- feature_0 >  7650.00\n",
      "|   |   |   |   |   |--- feature_3 <= 24998.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 7962.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 22434.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [11.83]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  22434.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 24252.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12.53]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  24252.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12.42]\n",
      "|   |   |   |   |   |   |--- feature_0 >  7962.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 24324.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 21130.60\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  21130.60\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 23000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  23000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 9000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.49]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  9000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.53]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  24324.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 12500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 24780.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  24780.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  12500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.88]\n",
      "|   |   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [11.26]\n",
      "|   |   |   |   |   |--- feature_3 >  24998.00\n",
      "|   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 153502.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 95946.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 58293.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 54643.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 30\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  54643.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  58293.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  95946.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 8600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  8600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_12 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_12 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.21]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  153502.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 157750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.42]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  157750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 158998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  158998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 9275.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 180334.88\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  180334.88\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.75]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  9275.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 10062.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  10062.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 28\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 49998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  49998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 12400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9237.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9237.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  12400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 13400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.71]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  13400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 99200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 49012.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  49012.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  99200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.49]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.74]\n",
      "|   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |--- feature_0 <= 7150.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 1250.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 1100.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 56000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.71]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  56000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.26]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [11.11]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  1100.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [12.53]\n",
      "|   |   |   |   |   |   |--- feature_0 >  1250.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 47500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 12006.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 9940.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.25]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  9940.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.88]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  12006.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 13920.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  13920.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 26886.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  26886.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 3750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  3750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  47500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 54756.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 2350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  2350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 6175.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  6175.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.71]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  54756.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 55500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 3450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.21]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  3450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  55500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 98520.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  98520.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |--- feature_0 >  7150.00\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 68220.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 8100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [10.99]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  8100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 12000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  12000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.25]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 35400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.25]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  35400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.62]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  68220.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [11.89]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 142389.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.75]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  142389.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.65]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 52500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 49329.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 8700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  8700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  49329.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.88]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  52500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 63000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 14650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  14650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.83]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  63000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 11600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  11600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 13750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 11950.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 47700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  47700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  11950.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 57250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  57250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.11]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  13750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12.42]\n",
      "|   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |--- feature_0 <= 5650.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 29400.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 4500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 20760.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.59]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  20760.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.74]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 16980.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  16980.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 26760.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.99]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  26760.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.86]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  4500.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |--- feature_3 >  29400.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 3400.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 36300.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.65]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 32550.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  32550.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.37]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  36300.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 126000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 98400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  98400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  126000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.91]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  3400.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 4062.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 35600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.48]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  35600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 43008.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  43008.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  4062.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 4162.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  4162.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 78600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  78600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |--- feature_0 >  5650.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 124200.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 104000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 10700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 7875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  7875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 89000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  89000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  10700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 91000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  91000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 99875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  99875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  104000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 119619.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 110796.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 8400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.99]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  8400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.11]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  110796.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.11]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  119619.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 121200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.37]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  121200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.65]\n",
      "|   |   |   |   |   |   |--- feature_3 >  124200.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 8500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 163000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  163000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.76]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 136250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.62]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  136250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.88]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  8500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 153000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 11600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.25]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  11600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.37]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  153000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 175404.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.63]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  175404.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.37]\n",
      "|   |   |--- feature_0 >  15137.50\n",
      "|   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |--- feature_0 <= 24625.00\n",
      "|   |   |   |   |   |--- feature_3 <= 28800.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 16450.00\n",
      "|   |   |   |   |   |   |   |--- value: [9.25]\n",
      "|   |   |   |   |   |   |--- feature_0 >  16450.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 23000.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.76]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  23000.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.76]\n",
      "|   |   |   |   |   |--- feature_3 >  28800.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 19100.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 17975.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 17025.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 16875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  16875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 97000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  97000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  17025.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 53750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 17675.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  17675.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.49]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  53750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 59500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.25]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  59500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  17975.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 84500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 37331.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  37331.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 49000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  49000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  84500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 87500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.61]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  87500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |--- feature_0 >  19100.00\n",
      "|   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 24050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 43750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  43750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  24050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 65250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  65250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 86203.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.51]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  86203.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.25]\n",
      "|   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 54769.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 50200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  50200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 38500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.62]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  38500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.88]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  54769.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 70917.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  70917.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 73250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  73250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24\n",
      "|   |   |   |   |--- feature_0 >  24625.00\n",
      "|   |   |   |   |   |--- feature_3 <= 825000.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 241200.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 24775.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.33]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  24775.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 24900.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12.53]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  24900.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 235400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  235400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.83]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 52232.10\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.83]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  52232.10\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |--- feature_3 >  241200.00\n",
      "|   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 255000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.37]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.25]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  255000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 625000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  625000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.91]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.62]\n",
      "|   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.01]\n",
      "|   |   |   |   |   |--- feature_3 >  825000.00\n",
      "|   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |--- feature_0 <= 24587.50\n",
      "|   |   |   |   |   |--- feature_3 <= 54498.00\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 32500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 18100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 15337.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  15337.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.99]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  18100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.37]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  32500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 18112.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 43502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 35940.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  35940.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  43502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 44502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  44502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  18112.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 23837.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 23337.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  23337.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  23837.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.99]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 36900.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 15550.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 29872.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  29872.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  15550.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 17025.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  17025.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 31504.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  31504.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  36900.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 52273.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 20100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 50500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  50500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  20100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 40400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  40400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  52273.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 19000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  19000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 53269.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  53269.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |--- feature_3 >  54498.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 81600.00\n",
      "|   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 18500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 58500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 57250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  57250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  58500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  18500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 79293.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  79293.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 69498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.36]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  69498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 24125.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 75500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 22337.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  22337.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  75500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 20650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  20650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  24125.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 63502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 61000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  61000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.49]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  63502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.37]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.62]\n",
      "|   |   |   |   |   |   |--- feature_3 >  81600.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 84748.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 21000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 84250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  84250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.62]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 83500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.49]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  83500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.74]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  21000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.88]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  84748.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_14 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 20837.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  20837.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_14 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9.62]\n",
      "|   |   |   |   |--- feature_0 >  24587.50\n",
      "|   |   |   |   |   |--- feature_0 <= 31100.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 89000.00\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 29850.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 78500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 27250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  27250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  78500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 87750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  87750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.91]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  29850.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 29587.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 27187.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  27187.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 64500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9.91]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  64500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.65]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  29587.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |   |--- feature_3 >  89000.00\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 2087500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 262498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  262498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  2087500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.62]\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 29000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 26000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  26000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  29000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 30500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.71]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  30500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.42]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 173498.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 26500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  26500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 27500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.36]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  27500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  173498.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.99]\n",
      "|   |   |   |   |   |--- feature_0 >  31100.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 50500.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 49356.22\n",
      "|   |   |   |   |   |   |   |   |--- value: [10.99]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  49356.22\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.99]\n",
      "|   |   |   |   |   |   |--- feature_3 >  50500.00\n",
      "|   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 137998.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 96250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 91250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  91250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  96250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 101000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.42]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  101000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  137998.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.37]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 177500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  177500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 31812.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 31500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.42]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  31500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.69]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  31812.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 180500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  180500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 143000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 78500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  78500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  143000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.99]\n",
      "|--- feature_2 >  1.50\n",
      "|   |--- feature_2 <= 3.50\n",
      "|   |   |--- feature_2 <= 2.50\n",
      "|   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |--- feature_12 <= 0.50\n",
      "|   |   |   |   |   |--- feature_0 <= 6137.50\n",
      "|   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 2937.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.41]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.29]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 25\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
      "|   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 1450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  1450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 2475.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  2475.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.87]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  2937.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 59500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 58053.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 36377.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  36377.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 31\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  58053.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 5500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  5500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.97]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  59500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 67837.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  67837.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 5100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  5100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 87886.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 60681.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 26640.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  26640.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  60681.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 5300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  5300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  87886.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 308000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4425.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4425.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  308000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4075.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4075.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.61]\n",
      "|   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 83500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 1300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.34]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  1300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  83500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 3250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 2150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  2150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10.78]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  3250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 91009.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  91009.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |--- feature_0 >  6137.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 7525.00\n",
      "|   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 7425.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 7050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 23500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  23500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  7050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7137.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7137.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  7425.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 23248.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.34]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11.97]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  23248.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 103000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  103000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 34500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7025.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7025.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.27]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.27]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  34500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 41160.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.22]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  41160.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7225.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7225.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |--- feature_0 >  7525.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 25300.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 24875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 51796.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  51796.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 36\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  24875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 48930.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  48930.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 23000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 17000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.57]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  17000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  23000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 56825.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  56825.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  25300.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 25800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.65]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.96]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  25800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 91002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  91002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 82750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.23]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  82750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.11]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 232500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  232500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.96]\n",
      "|   |   |   |   |--- feature_12 >  0.50\n",
      "|   |   |   |   |   |--- feature_0 <= 1050.00\n",
      "|   |   |   |   |   |   |--- value: [10.28]\n",
      "|   |   |   |   |   |--- feature_0 >  1050.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 52875.27\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 15000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 11000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 19500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.92]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  19500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  11000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [14.22]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  15000.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [11.03]\n",
      "|   |   |   |   |   |   |--- feature_3 >  52875.27\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 165398.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 122950.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 5625.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 3700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  3700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  5625.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  122950.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 18000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [14.26]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  18000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [13.48]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  165398.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [13.16]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [11.91]\n",
      "|   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |--- feature_0 <= 11362.50\n",
      "|   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 1550.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 1150.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_19 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 34500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.06]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  34500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [13.98]\n",
      "|   |   |   |   |   |   |   |   |--- feature_19 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12.68]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  1150.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 17700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [13.61]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  17700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 68080.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.23]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  68080.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [13.61]\n",
      "|   |   |   |   |   |   |--- feature_0 >  1550.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 7100.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 6562.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 195000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 43100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  43100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  195000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.23]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  6562.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 63100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 28002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  28002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  63100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.61]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  7100.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 36750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 35002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  35002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 34536.24\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  34536.24\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  36750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 39998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 37118.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  37118.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  39998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7525.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7525.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 10300.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 26500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 24500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [13.23]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  24500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [14.79]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  26500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 82792.28\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 79790.28\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  79790.28\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [13.98]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  82792.28\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 122500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 99850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.23]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  99850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  122500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.23]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |--- feature_0 >  10300.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 10800.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  10800.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [13.98]\n",
      "|   |   |   |   |--- feature_0 >  11362.50\n",
      "|   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 14262.50\n",
      "|   |   |   |   |   |   |   |--- feature_14 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 30360.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 12187.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 28200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  28200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.79]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  12187.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.49]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  30360.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 36250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  36250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 36700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  36700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |--- feature_14 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 32059.32\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [15.96]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  32059.32\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.96]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [13.61]\n",
      "|   |   |   |   |   |   |--- feature_0 >  14262.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 25100.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 97500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 78998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 61500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  61500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  78998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 20337.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  20337.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  97500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 15750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  15750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  25100.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 34737.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 122000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 74000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  74000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  122000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 29000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  29000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  34737.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 188500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  188500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.06]\n",
      "|   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_3 <= 64105.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 62525.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 26987.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 24812.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 11550.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  11550.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  24812.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 58000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  58000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  26987.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 28100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.96]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  28100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 34737.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  34737.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  62525.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 12250.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [13.49]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  12250.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 13250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.96]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  13250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 14500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.49]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  14500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |--- feature_3 >  64105.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 14037.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 94000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 13150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 13050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  13050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.23]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  13150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 86710.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  86710.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.61]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  94000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 116743.30\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 98000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  98000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  116743.30\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 12875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  12875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  14037.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 98500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 15587.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  15587.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 76608.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  76608.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  98500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 99250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.27]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.23]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  99250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 14825.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  14825.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |--- feature_2 >  2.50\n",
      "|   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |--- feature_0 <= 25337.50\n",
      "|   |   |   |   |   |--- feature_0 <= 17450.00\n",
      "|   |   |   |   |   |   |--- feature_12 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 7687.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 4462.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 4412.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_19 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_19 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  4412.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.49]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  4462.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 7475.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 11000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.17]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  11000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 25\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  7475.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  7687.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 8450.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 8137.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 29886.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  29886.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  8137.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 55300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  55300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  8450.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 13125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  13125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |--- feature_12 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 13620.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 2625.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 11120.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.05]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  11120.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [14.96]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  2625.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [15.65]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  13620.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 3500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 2800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 33388.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  33388.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.86]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  2800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12.49]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  3500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 68700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 6125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  6125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  68700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 168500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  168500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.96]\n",
      "|   |   |   |   |   |--- feature_0 >  17450.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 22775.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 19875.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 70000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 68850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  68850.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.61]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  70000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 19300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  19300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 57900.28\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 18825.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.66]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  18825.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  57900.28\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 59900.28\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  59900.28\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  19875.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 51998.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 50498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  50498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.98]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12.36]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  51998.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 68200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 67095.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  67095.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  68200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |--- feature_0 >  22775.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 22900.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [17.27]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  22900.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 64400.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 61773.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  61773.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  64400.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 184500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 82500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  82500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  184500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 215002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  215002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |--- feature_0 >  25337.50\n",
      "|   |   |   |   |   |--- feature_3 <= 74100.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 61350.00\n",
      "|   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |--- feature_3 >  61350.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 69000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 28200.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.49]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.99]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  28200.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.02]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  69000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 29550.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 71600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.99]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  71600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.62]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [14.54]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  29550.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [14.54]\n",
      "|   |   |   |   |   |--- feature_3 >  74100.00\n",
      "|   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 97000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 30375.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  30375.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 88000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  88000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  97000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 110305.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  110305.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 208816.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.49]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  208816.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [15.62]\n",
      "|   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 120000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 85000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.89]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.77]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  85000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 108491.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.91]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  108491.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.99]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  120000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 137500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.49]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  137500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [14.54]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 32500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.62]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  32500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.49]\n",
      "|   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |--- feature_0 <= 11687.50\n",
      "|   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 24798.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 5187.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 24300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  24300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.19]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 15600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  15600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.20]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  5187.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 7100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  7100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.58]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  24798.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 7775.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 3112.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 1050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.46]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  1050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  3112.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 33300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  33300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  7775.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 34394.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  34394.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 35212.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  35212.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 32\n",
      "|   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 8200.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 52891.20\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.65]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  52891.20\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 83141.20\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.21]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  83141.20\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.83]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  8200.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 9862.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9162.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9162.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.49]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  9862.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 74000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.57]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  74000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.95]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 55000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 49000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 44000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.89]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  44000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.27]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  49000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.02]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  55000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 9950.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  9950.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.58]\n",
      "|   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 6500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 4650.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 4325.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4012.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4012.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  4325.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [14.46]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  4650.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_14 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.49]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_14 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.77]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  6500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 10850.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 28700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 8225.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.89]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  8225.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.77]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  28700.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 40000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  40000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  10850.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17.58]\n",
      "|   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |--- value: [14.46]\n",
      "|   |   |   |   |--- feature_0 >  11687.50\n",
      "|   |   |   |   |   |--- feature_0 <= 18225.00\n",
      "|   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 28750.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 12937.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 24500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  24500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 12400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  12400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  12937.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 25082.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 13875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  13875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  25082.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.65]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  28750.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 31750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.32]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 12987.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  12987.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  31750.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 34750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 33800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  33800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  34750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 149000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  149000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 37850.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [18.49]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  37850.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 48919.96\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 42919.96\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.02]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  42919.96\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.49]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  48919.96\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 71500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 66500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  66500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  71500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 123500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  123500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.49]\n",
      "|   |   |   |   |   |--- feature_0 >  18225.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 24850.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 22775.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 22362.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 19862.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.28]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  19862.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 21100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  21100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  22362.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 66098.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  66098.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 22575.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.46]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  22575.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14.91]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  22775.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 23100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.49]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 65000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  65000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  23100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 105421.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 85222.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  85222.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  105421.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |--- feature_0 >  24850.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 25100.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 47760.44\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_14 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_14 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.58]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  47760.44\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 95250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 69000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  69000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  95250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  25100.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 56500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 49998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.89]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  49998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 28800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  28800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.49]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  56500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 64256.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 26912.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  26912.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  64256.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 113664.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  113664.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |--- feature_2 >  3.50\n",
      "|   |   |--- feature_2 <= 4.50\n",
      "|   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |--- feature_0 <= 25900.00\n",
      "|   |   |   |   |   |--- feature_0 <= 4662.50\n",
      "|   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 40998.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_12 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_12 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 8100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.95]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  8100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 3475.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  3475.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.32]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  40998.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 2900.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 47750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 46540.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  46540.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13.75]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  47750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 74502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  74502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  2900.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 3100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 57500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  57500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  3100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 91000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  91000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 2850.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 2500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [18.79]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  2500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [16.77]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  2850.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 3450.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [20.69]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  3450.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 4100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.64]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  4100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.91]\n",
      "|   |   |   |   |   |--- feature_0 >  4662.50\n",
      "|   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 90184.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 74693.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 74351.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  74351.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.91]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  74693.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 45000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 7625.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  7625.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  45000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 19875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  19875.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  90184.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 146556.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  146556.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 126500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  126500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 97500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.42]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.51]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  97500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 147500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  147500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 6500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 4900.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [15.95]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  4900.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  6500.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 39700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17.99]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  39700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 79250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  79250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.88]\n",
      "|   |   |   |   |--- feature_0 >  25900.00\n",
      "|   |   |   |   |   |--- feature_0 <= 33900.00\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 28400.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [19.03]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  28400.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 30500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.29]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  30500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.42]\n",
      "|   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 102500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 85920.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.03]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  85920.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.42]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  102500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 126998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 120000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  120000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  126998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.79]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 30300.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 66500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [18.39]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  66500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17.99]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  30300.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [18.79]\n",
      "|   |   |   |   |   |--- feature_0 >  33900.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 34900.00\n",
      "|   |   |   |   |   |   |   |--- value: [16.77]\n",
      "|   |   |   |   |   |   |--- feature_0 >  34900.00\n",
      "|   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 154080.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.88]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.39]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  154080.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17.14]\n",
      "|   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 135500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.29]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  135500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 173000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.64]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  173000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17.99]\n",
      "|   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |--- feature_0 <= 11262.50\n",
      "|   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 6425.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 6362.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 5900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 5050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  5050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  5900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 129576.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 90348.10\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  90348.10\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.99]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  129576.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.95]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  6362.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 66500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 36002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.29]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  36002.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 48500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.14]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  48500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  66500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |--- feature_0 >  6425.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 53190.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 6600.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.64]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.69]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  6600.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 7650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 33500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  33500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  7650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  53190.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 147000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 117150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  117150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  147000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.77]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 6712.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 6650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.42]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  6650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.30]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  6712.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 10250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  10250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 10300.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 7350.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 5975.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 35000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.40]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  35000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.95]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 5200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.88]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  5200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.51]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  5975.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 7150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.03]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.42]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  7150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.56]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  7350.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 69000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.64]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 9350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.51]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  9350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  69000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.79]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 83436.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.56]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  83436.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |--- feature_0 >  10300.00\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 47500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 10775.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.45]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  10775.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.40]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  47500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17.93]\n",
      "|   |   |   |   |--- feature_0 >  11262.50\n",
      "|   |   |   |   |   |--- feature_0 <= 26200.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 14950.00\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 150000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 30498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  30498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  150000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 247498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  247498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.99]\n",
      "|   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 61800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.82]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  61800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 36561.60\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.99]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  36561.60\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.03]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 33500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [19.29]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  33500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 52000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.56]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  52000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 13250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  13250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |--- feature_0 >  14950.00\n",
      "|   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 99700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 53982.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  53982.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  99700.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 15300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 133220.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  133220.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  15300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 15987.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  15987.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19\n",
      "|   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 72500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 50300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 17000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.79]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  17000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.42]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  50300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.14]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  72500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 74300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  74300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.77]\n",
      "|   |   |   |   |   |--- feature_0 >  26200.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 51300.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 46750.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [19.29]\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [19.42]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  46750.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 26900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.77]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  26900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.88]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.32]\n",
      "|   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [16.40]\n",
      "|   |   |   |   |   |   |--- feature_3 >  51300.00\n",
      "|   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 74250.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 68500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 28050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  28050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  68500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 28650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  28650.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  74250.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 107000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 100952.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  100952.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  107000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 85000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 68794.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.88]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  68794.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.40]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  85000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 102000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [20.30]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  102000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.39]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.14]\n",
      "|   |   |--- feature_2 >  4.50\n",
      "|   |   |   |--- feature_2 <= 5.50\n",
      "|   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |--- feature_0 <= 11500.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 199000.00\n",
      "|   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_12 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 106150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 2900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  2900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  106150.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_12 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 100000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16.33]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  100000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17.90]\n",
      "|   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 2312.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 73000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.13]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  73000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.01]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  2312.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 78623.04\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  78623.04\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 61498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  61498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.78]\n",
      "|   |   |   |   |   |   |--- feature_3 >  199000.00\n",
      "|   |   |   |   |   |   |   |--- value: [15.01]\n",
      "|   |   |   |   |   |--- feature_0 >  11500.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 12775.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 51780.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [18.62]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  51780.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [15.33]\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15.76]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 67901.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.58]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  67901.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.26]\n",
      "|   |   |   |   |   |   |--- feature_0 >  12775.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 15825.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 15087.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 70500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 63500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  63500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [22.35]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  70500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  15087.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [21.74]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [19.13]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  15825.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 36696.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [15.96]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  36696.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 32500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_12 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_12 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16.08]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  32500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [20.25]\n",
      "|   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |--- feature_0 <= 25225.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 15025.00\n",
      "|   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 3350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 40500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  40500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  3350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 12600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 73998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  73998.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.41]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  12600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 58000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.17]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  58000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [17.80]\n",
      "|   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 39584.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 34500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18.30]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  34500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 8000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.62]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  8000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  39584.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 11550.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  11550.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 62500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  62500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |--- feature_0 >  15025.00\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 15925.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 58749.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 37848.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.89]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  37848.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.25]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  58749.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 15725.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  15725.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [21.28]\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 15400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [22.06]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  15400.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [21.67]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  15925.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 20100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 41750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  41750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  20100.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 24125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 51300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  51300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  24125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 100502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  100502.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |--- feature_0 >  25225.00\n",
      "|   |   |   |   |   |   |--- feature_3 <= 282500.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 43400.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [18.62]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  43400.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 60492.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 30600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  30600.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  60492.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 30200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  30200.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
      "|   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 27725.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [21.28]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  27725.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 100498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18.25]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  100498.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.36]\n",
      "|   |   |   |   |   |   |--- feature_3 >  282500.00\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [22.35]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 34250.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 295000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [22.35]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  295000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [21.74]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  34250.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [20.25]\n",
      "|   |   |   |--- feature_2 >  5.50\n",
      "|   |   |   |   |--- feature_1 <= 48.00\n",
      "|   |   |   |   |   |--- feature_3 <= 135000.00\n",
      "|   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 95000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 61450.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [20.86]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 52500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  52500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  61450.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 9100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [21.59]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  9100.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [21.27]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  95000.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 107725.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17.34]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  107725.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 121500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.16]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.86]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  121500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 127500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  127500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.11]\n",
      "|   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 23125.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 21925.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 46840.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 6750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [21.27]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  6750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  46840.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 16125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [22.11]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  16125.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  21925.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [23.22]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  23125.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 68826.48\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [18.72]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  68826.48\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 78826.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.03]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  78826.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 24500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.03]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  24500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.66]\n",
      "|   |   |   |   |   |--- feature_3 >  135000.00\n",
      "|   |   |   |   |   |   |--- feature_0 <= 21000.00\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 220000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [22.11]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  220000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [22.48]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [21.21]\n",
      "|   |   |   |   |   |   |--- feature_0 >  21000.00\n",
      "|   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [21.27]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [21.64]\n",
      "|   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 24500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [20.03]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  24500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [20.53]\n",
      "|   |   |   |   |--- feature_1 >  48.00\n",
      "|   |   |   |   |   |--- feature_0 <= 25437.50\n",
      "|   |   |   |   |   |   |--- feature_0 <= 24962.50\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 24125.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_17 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 9500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 4900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  4900.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  9500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 23350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  23350.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |--- feature_17 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [24.40]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  24125.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [23.91]\n",
      "|   |   |   |   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [23.91]\n",
      "|   |   |   |   |   |   |--- feature_0 >  24962.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 142800.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 67572.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [21.59]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [22.48]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [20.48]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  67572.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.03]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19.66]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.53]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  142800.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 146500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [24.11]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  146500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 188000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  188000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.85]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 176500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.90]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  176500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |--- feature_0 >  25437.50\n",
      "|   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 215000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 158300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 115050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  115050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  158300.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 30512.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [23.91]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  30512.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  215000.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [21.59]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 287500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [20.48]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  287500.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [21.22]\n",
      "|   |   |   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [20.11]\n",
      "|   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 127500.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [20.48]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  127500.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [20.11]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Decision tree\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_text\n",
    "\n",
    "# Train the model\n",
    "model = DecisionTreeRegressor().fit(x_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Visualize the model tree\n",
    "tree = export_text(model)\n",
    "print(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.9565152830294135\n",
      "RMSE: 1.3987549045595562\n",
      "R2: 0.8573087437452268\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(x_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor() \n",
      "\n",
      "MSE: 1.1910779735837609\n",
      "RMSE: 1.0913651880025133\n",
      "R2: 0.9131331026022446\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Train the model\n",
    "model = RandomForestRegressor().fit(x_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(x_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor() \n",
      "\n",
      "MSE: 1.008508914000633\n",
      "RMSE: 1.0042454450982752\n",
      "R2: 0.9264481064210915\n"
     ]
    },
    {
     "data": {
      "image/png": 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nz0atVhMeHk5CQgKzZ89GpVIREhKieBzWGjZsGMuXL+fJJ5/k2WefpX79+vz9998sXLiQxx9/HEdHRxo3bszDDz/MzJkzycvLIzQ0lM2bN3P27FmT1/Xx8WHMmDH88MMPODk50blzZ06cOMGqVat47bXXsLOz02V5t27dStu2bQ2WIzz55JP89NNPjB07lhdeeAEvLy82bdrEgQMH+PjjjxUfXtKyZUtcXFx47bXXmDRpEnXq1OHvv/8mMjKyXA40EUIUkgBXCFEmZ86c4eGHHwYK1xu6u7vTvHlz3n33XUaMGKHr17hxY2bMmMHcuXN57rnnqF27Nu3atWPZsmWMHj2aw4cP6x29ak67du3w8vLivvvuw8nJSdFzvv76a77++mugcCmAp6cnrVq14rvvvtNlYi1xdXXl7bff5plnnmHBggVMnDiRTz75hG+//ZbVq1dz7do1fH19ue+++5g8eTL29vYmrzVlyhRCQ0NZt24dH374IWq1mlq1atGxY0fWr19vVWDp6enJ/Pnz+fzzz3nppZdwd3cnNDSU5cuXM378eA4fPmz2GNvJkyfj5+fHypUrWbRoEbVr16Zr165MmTIFT09PxeOwlpubGytWrGDGjBl88cUX3Lx5k4YNGzJ16lTGjRun6/fOO+9Qp04dli9fTlpaGj179uTZZ59l1qxZJq/96quv4uvry+rVq1m0aBEBAQG89dZbjBo1Ciisnbx582Zef/11HnroIb0DN6Bwje+qVauYMWMGH374Ibm5uYSEhDB//nzuvvtuxe/R2dmZxYsXM2PGDD766CPS09MJCgri/fffL9PdByGEeSqt0vuCQghRRZw4cYKRI0eyefPmcs0wCiGEqJ4kgyuEqDaKSmlt2rSJHj16SHArhBDCKNlkJoSoNlJSUvj++++pU6cOH374YWUPRwghRBUlSxSEEEIIIcRtRTK4QgghhBDitiIBrhBCCCGEuK1IgCuEEEIIIW4rUkWBwpNmtFotjo6OlT0UIYQQQghhRG5uLiqVivbt21vsKxlcCo8ILcteO61Wi0ajKdM1agKZJ8tkjpSRebJM5kgZmSfLZI6UkXmyrKxzZE28Jhlc0GVu27RpU6rnZ2ZmEhkZSbNmzXBzc7Pl0G4rMk+WyRwpI/NkmcyRMjJPlskcKSPzZFlZ5+jUqVOK+0oGVwghhBBC3FYkwBVCCCGEELcVCXCFEEIIIcRtRQJcIYQQQghxW5EAVwghhBBC3FYkwBVCCCGEELcVCXCFEEIIIcRtRQJcIYQQQghxW5EAVwghhBBC3FYkwBVCCCGEELcVCXCFEEIIIcRtRQJcIYQQQghxW5EAVwghhBBC3FYkwBVCCCGEELcVCXCFEEIIIcRtRQJcIYQQQghxW5EAVwghhBBCWCcD6AuMB5IreSxGSIArhBBCCCGUWw14AL8Di4BdlTscYxwqewBCCCGEEKIa0AANgKQS7YMrYSwWSAZXCCGEEEKYtwNwRj+4bQLkAm6VMiKzJMAVQgghhBDGFQBtgIEl2n8EYqiyawGq6LCEEEIIIUSl2g90K9HmAqT89+8qTDK4QgghhBDiFi1wD4bB7WIgiyof3IJkcIUQQgghRJF/gNZG2tOAWhU8ljKQDK4QQgghhIAnMAxuP6cwo1uNgluQDK4QQgghRM0WS2FFhJKuAXUrdii2IhlcIYQQQoia6n8YBrevUJi1rabBLUgGVwghhBCi5knEeAB7AQiq2KGUB8ngCiGEEELUJNMxDG7HUJi1Darw0ZQLyeAKIYQQQtQE6UBtI+2nMF45oRqTDK4QQgghxO3uewyD2/4UnlR2mwW3IBlcIYQQQojbVzbg/d+/i/sb6Frxw6koksEVQgghhLgdbQZc0Q9uWwP53NbBLUgGVwghhBDi9pIHNKewIkJx24EBFT+cyiABrhBCCCHE7eJPoE+JNh/gKuBU4aOpNLJEQQghhBCiutMC3TEMblcCSdSo4BYkgyuEEEIIUb0dAzoYaVcD7hU8liqiUjO4CQkJvPjii3Tu3JmePXvyySefkJOTA8Dx48cZNWoU7du3595772XdunVmr9WxY0datGih909GRkZFvA0hhBBCiMoxAsPgdg6FGd0aGtxCJWZwtVotL774IrVq1WLFihWkpaXxxhtvYGdnx7hx4xg/fjyPPPIIn376Kf/88w/Tpk3Dz8+PPn36GFwrISGBmzdvsmvXLlxcXHTtbm5uFfiOhBBCCCEqyDkKN5KVlEThmtsartIC3JiYGI4fP86+ffuoU6cOAC+++CKfffYZjRo1ok6dOkyZMgWAoKAgDh48yJYtW4wGuNHR0fj5+REYGFiRb0EIIYQQouK9AMwr0fY28F4ljKWKqrQA18/Pj0WLFumC2yJqtZqePXsSGhpq8By1Wm30WufPn6dJkyblMk4hhBBCiCrhCtDQSPtlE+01WKUFuLVq1aJnz566nwsKCli+fDldunQhICCAgIAA3WNJSUls27aNSZMmGb1WdHQ0WVlZjB49mgsXLhAaGsobb7xhVdCr1WrJzMws1XvJysrS+7cwTubJMpkjZWSeLJM5UkbmyTKZI2XKe54cPnXA6QP9Ugi5T+eSOzu38IfShTAVqqxzpNVqUalUivqqtFqttlSvYmOfffYZK1asYP369TRvfmtRSXZ2NuPGjSMpKYlNmzbh6upq8NzRo0dz7do13nvvPTw8PFi4cCEnT55k27ZteHh4WHztU6dOodFobPp+hBBCCCHKyj7dnnZ92xm0n95wmpzGORU/oErm5OREmzZtLParEgHuF198wffff8/MmTO59957de0ZGRk8//zznDt3jpUrVxIUFGT0+RqNhtzcXNzdC7cL5uTk0Lt3b958802GDBli8fVPnTqFVqulWbNmpRp/VlYWsbGxBAUFGQ3ARSGZJ8tkjpSRebJM5kgZmSfLZI6UKY95cljggNPL+lnbvCF5aFZXz6RcWefo/PnzqFQqRQFupdfB/eCDD1i1ahVffPGFXnCrVqt5+umnuXTpEkuWLDEZ3EJhNO/kdOsD4OzsTEBAAAkJCYrHoVKpylx1wdXVVSo3KCDzZJnMkTIyT5bJHCkj82SZzJEyNpmnTIyX+DoMDnc64FD54VuZlHaOlC5PgEqugzt37lxWr17Nl19+yaBBg3TtBQUFvPDCC1y+fJlly5Zxxx13mLyGVqulX79+bNy4UdeWmZnJxYsXCQ4OLtfxCyGEEELY1BoMg9suQAFwZ8UPp7qqtK8A0dHRzJ8/nwkTJnDnnXdy/fp13WO///47Bw8e5Ouvv6ZWrVq6xxwdHfHy8kKj0ZCWloaPjw/29vb06dOHOXPm0LBhQ3x8fJg9ezb16tWjd+/elfX2hBBCCCE4GJvIxI0RpGdrqOXixLxhnQgP8jfsmAsEAIkl2n8D7ir/cd5uKi3A3b17N/n5+Xz99dd8/fXXeo/16NGDgoICnnnmGb32zp07s2zZMo4dO8aYMWPYvXs3AQEBvPrqqzg4ODB16lTUajVdunRhwYIF2NvbV+RbEkIIIUQNE5eiZtaeKK7fzKQg6yZv129Eczc3sjV5hE3fQkySmuKbnbrP2UmwrwcnXxmCi9N/YdgvwL36173qncmlQ2rCmxkJhoVFlRbgTpgwgQkTJpTqueHh4Zw9e1b3s7OzM6+//jqvv/66rYYnhBBCiBqqKGhNycrB29WZyb1CCPTWr8qUl1/AhHX72R55hUR1tq59d9zvDGzZkL0xicQkGdbv1wLRSWrCZmzh3/89CO2Bk/p9HhryBz/eEYfqawyDYaGIzJYQQgghBKaD1pVHLzAwtAELRnTFwb5w+9KEdftZGhFDyVJUiRk5LImIsfhadU65QIkbzRq7fLwnriHbMR8oEQxPe7Asb63GkQBXCCGEEBZdTs3g21/PmM1qVncmg1Z1NksjYgAVi0d1Iy5FzfbIKwb9FNHCth/7MiBW/+ix8f33s7jNeaNPibmh5mBsovG1u8IoCXCFEEIIYVJefgHv7Y8nIjGaxIxbBwsYy2pWZ5aCVi2wIypet3yheIZXqdCk2pxeMtSg3XviatKdc00+Twu8sDGCiCmDTPYR+iTAFUIIIYRJL2w+ys8X0ixmNas7JUFrws1sZu8tXJtrrUU7u/LkP/oHSs0aeIb5Pc6SnmQ6uC2Slm26j5I1wzWNBLhCCCGEMCouRc2v/15TlNWs7gGV0qA1OVODt6uz4usGprsRu2i4QXv9Z9bRt7s/ta47GXmWodoujgZt1qwZrmlq5rsWQgghhEWz9kTpLUswpiirWd0pDVp93JyY3CsEfw8Xi30/2tveILid2eEM9lOWkeiejbOjI/OGdVL0unON9CtaM1wy81yUXZ+w7oCia9+OJMAVQgghhFHWZDWrOyVBa11PF17qWXj7f2BoA0wdHFsn05n8L0fzekRrvfamT/3IK32O6H72cXOiQW037CycQGungga19Y+2tWbNcE0kAa4QQgghjLImq1ndWQpaVcCAkAa6pRgLRnRlTKem1PXUD4rfOtWGhG9G6rWtCInBfsoyYmvfCjaLguVZe6IosFCOoUCLQZbcmjXDNZGswRVCCCGEUZN7hbDySIzZZQpFgVppVaUNUgtGdAVU7IiKJ+HmreCxrqcLA0Ia/Pd4IQd7O13JsFl7olAn5vDthO4G12w7egun/VL12ooHy6XNklv7vKo0zxVBAlwhhBCihlEa7AR6e9C/eT1WHrto9FZ4yaxmyevHpaqJScqgqa8HAV7ueq9TFTdIlQxaU7I0/625DSXAy93ocwK9PZiR1hFKHM6a36eA0P6bDU4zU1F4Otn8YeFA6bPkSp/n5erIuNX7qtQ8VwQJcIUQQogaojRB5dz7O5CWlsqhhGy9TK6xrGbR9X8+E8/1Yn2PxSf/9zoxDAxtyIIRXRUfqlAZAr09mHF/R8sdcwBfIKNE+18wPm4/MRFqg/enBWKS1Dy/8RCLR3UrzJIfvWB2uYGxLLnS58WnZrLh5KUqOc/l6fYL2YUQQghhVGl23V+7mYWnkz3t6tfGz92Zxt5udGjow6axvVk8qrteQFx0/esmljQkqnNYGhHDY8v3Vv8NUlsAF/SC26zgLDLTM4lrqXwDmLVrf4soeV6PJn7siUms3vNcShLgCiGEEDWAtbvu8/ILGLd6H72+/o1VUcn8cj6R6xk5XEzJ5Gh8Mvct+o1xq/eRl1+g6PrFX+fnyPjqu0EqH7gDKHEgWfaGbM6sPQP21m8AM7Vhra6nC2M6BetlyYt7q18YIXVr4+JgZ/R5DWu7V995LiNZoiCEEELUANYEXdOHdjS5hKBISlYuSyJiKCjQ8sOjPaw6vjYzN19RvypXfmwP0LtEmxeQAAV5BRBZ2GTtBjBr1/6aWmri4mBHsK8HW57uS5CPJ+NW77NqHLcTCXCFEEKIGsCaoEtpNhYK1+9+MLBdqY6vtaSyyo8ZbMLrGULgAx6wt0THZcDj//133q3m0m4cU7r219SXj+y8AiIT0nn/l1MsHtVN8ThOXUlh3Op9VlVXqOpVGSTAFUIIIWoAe5WF0wT+42CnLNtbJF8Lr289Sr1abpY7W0EFjAhrZNNrWmIsMxp23ZsZDxgJOm8CJuK50m4cU8KapSYj2zZi9p5Ii19UjsYnc1S3EdB8dYWqWP3CmMofgRBCCCHKnbLwtrCftdnYX85eZXKvEIO1oGWhBdadvAQUBnVTNx9m3Op9TN18uNw2RZXchLdqa0+OLRus32n2f4Mzk6ws7cYxJaxZarL2hGH1BEssHfNbXY4HlgyuEEIIUQPkaZWFOrkFym+xF8lXeG1rJalzKqyGa/HMaHCqB+cWP2jQp9Wrm9kx+m4Ci0W3Rbfqr9/MpCDrJm/Xb0RzNzerDo2whnXre0v3eylZ5aGINdnjyl6uIAGuEEIIUQNYsy70pZ4hfPP3WbLzChQ9J6C2G7P2RCnur9ThyzeITEg3WsN1SUQMGTl5rHmi5K6v0inKjM78vSMvHgvVe+zD8JO80/0E5KPbhGfqVv3uuN8Z2LKw1q+1h0YoYc3vsSzfO4pvOCxi7UbFyiQBrhBCCFEDlOe60Mbe7iRlKluzq5QKuJKWZTYHueHkJUYu+YOVj/cqcya34EoB+V+ONmhvPH4Dlz0zdT8XVRwweVBFRo7eAQqKD41QyNrfo6W+5pT1eODKJGtwhRBCiBrAmnWh1mZjt0dd4Uhcsk3GWUQLpGbnWuyz4WRc2dd9fgQzn++s17Sgzb/YT1mmF9xC4Sa8uBQ1P5+JN3urfnvkZeJS1DZfP2zN79FSX0tKezxwZVW/KE4yuEIIIUQNoXRdqLWbzLQULhvwcXUiOavis3dFwaTV6z5TAW/D5tCxm/nXJ93oU1QU3qo3dVpbkUR1Dvct3M2NDI3N1w8X/R63/hNHUrFsqa+bE4NbBeit7zX1O1dhfoVuWY4HLs1dAFuTAFcIIYSoIZQeKGDtJjOAGxk5tKxbi5QsjcnAaWBIPY5cTi31LXNTEtU51q/7/BZ4Vr9pS3AcD9z/h9mSE7kFEJeqLAt7JsEwSC6qNlC0hKH0tKhKlH4r+TOY/p3HpajZcDLO6O/K0vHApg4AKUt1CFuTAFcIIYSoYSytC1WSqTOmY0AdOjXyM5shtnRCWhFfNydUKhU3LGRKi1xKyVA2yEzAyB6vmdPP8MqVIxaf7uPmxMkrKcpey4SyVhswNYc3Sqz/La7k7zwvvwD3dQesrvJQXtUhbE0CXCGEEELoKcrULYmIsep5vh7OTB/a0WyGuChAWnPsgsl1vipgcKsAAMVjuJCkIKu6HhhRoq0jcBAeSmvE57P+UXT7PS4lg2PxZVtzXNpqA7Yq1WXt8cBlfV5FkwBXCCGEEAYWjOjKoUtJRCakKepffO2luQxxUYD0Vr82DFn8OxeSbuoFur5uTvh7uqAtAC83JxrUcuFKuuVMcrCvmUxoLtAIuFaifRdwN7oxK739bqtArjTVBmxdqqu0VR5sXR3C1iTAFUIIIWzoVmYrB29XZyb3CqkSaxKt5WBvx/bxfbnzy216G5mMKc3ayyZ1PDn92lDiUtS8/8tJ/o69ztX0LDI1+UQmpBP53/pVV0d7RdcL9DYRdO4C+pdoawhcABz1mxeM6EpGTh4/R8aTmZuvay95+71wCUcMiWrrNuOVVJpqA9WpVFdlkgBXCCGEsAFThf/L49StihLo7cGAFvVZeeyiyVviLg52PNw+yOzay4OxiUzcGEF6toZaLk7MG9aJ8CB/8vILeGfnCYM5Ky6rWKBpSh13Z8Od+wUULj84pt9844cs6jzhanCNot/fH9EJesGti4MdPZv46f3+CrO9Dc1me50d7MyWWitttYHqVKqrMkmAK4QQQtiAycL/Nts1r4ytM8hz7+9AWloqhxKySSy24cvFwY5gXw+2PNWXIF9Po8/N1uQRNn0L0SXWx3abs5Omvh50DfJjxZELpTxQ9hZ1Ti7v7DxxKwiNAPTL2pKvKqDWC6upFePIwNWGXzjGr93P0sOG632z8wpYfzIOt7X7+f6R7rp2U5ut/N2dGdiyIQVaLcsPG39vZak2UJ1KdVUmCXCFEEKIMrKm8H95LVcorwyyg70db3dpiGf9RnwbcVFvU5FWqzUbTLf54idiko1XN4hOUhOTpLYquLVXQb6RJ2TnFbAkIgZ1dh5r1/eGbfqPP9vvAAvDzhX2VecbfOGIS1Gz9nis2ddee/wi7w9oy5W0TL1s9DfDO/NnzHVu3MykIOsmbw8O5476dcjLL8BOZWfzagPVqVRXZZIAVwghhCgjpYX/S278sWW2tbwzyAFe7rpNRUqC6SNxN0wGt0WszdyqzDwhJKkWa5/sbdDu8/xq0lz0T0TTAltOXyIuJYxAbw8++OWkxZPbsvPyafPFFtQ5eXrjHvbDHoJ9PTjw/N1ciD5Hw9puQPlWG6gupboqkwS4QgghRBkpLfxfVKvV1tlWW5WOUkpJMB1x6UaZX6ekPBPtC37pwlOn79Bre7P7MT4NP23yWslZudy3cDfHpg5h/0VlY72ZYzgCLYXZ6PB5u1g9oLHB4+VRbaC6lOqqTBLgCiGEEGUUk6TskIGiWq22zrbaunSUOUqD6UyNqXDUdgJuunFx4XCD9gbPrCPB3XJpsTMJ6UxYd4CcvLKP9UJyBqeuZxAaWuZLKVbVS3VVpuq1nVMIIYSogpqaq8FaTLCvh1XZVqUqsnSU0mA6J89y9YOyeH9fO4Pgdnb7SOynLFMU3BbZHhlPfr755QlKaIEvDpcstCsqi2RwhRBCiDJSels40Nu9XLKt9iqVon4ONkhrKQ2mXRzs0OTbNshVAT5ZziR+PdLgsWbjfuSCl/IvBUUS1dl4OimrtWuJOrfsgbKwDcngCiGEEGU0uVcI/h7m65P6exSWbrJltjUuRc3UzYfZE5Og6JrmwuCia41bvY+pmw+bzCArrcPq72FYa7asXj3RyiC4XdMiFvspy0oV3BaxtMFMKQ9HCauqCsngCiGEEGWkpPD/wNDC0k22KNRvapOaJcYSjJY2vM0a1Favv5I6rP4eLuTaMHvrrnEgfe4jBu3tR2/lpF9Kma+vMAFu/hrAqx3rlf1Cwibkq4YQQghhAwtGdGVMp6bU9XTRa6/r6cKYTsF6x7z6e7gYu4Tec8wV6i/apGZNcAvGg2ZT1yra8DZp81G99qI6rKZiQhXg6ezAxdQsq8ZmymNnmhgEt78HXMP+5WVmg1svF0eTj5VUv1bZs81NfNxp4ycVDKoKyeAKIYQQNqC0dFNZC/Vb2qRmirGgWcmGt1/+vcbIRoEULw5grg5rzyb+/BmtbMmEOU55dlz7ZgS1NfpBea+Hd7Cv4XWLz//pqT48u/4QZxLSLPbt17w+yw9fIEfBZjMV+vV7VaBXB1dUDRLgCiGEEDZUvHRTXIqamX9GGhzkUJZC/Uo2qZVkKmhWcq3EjBxWn03mrmL73cwF8zP/jLR46IUlg2Ia8tOmvnptsX5q7nhsEwV2lkP7YB93ugfX4+fxfWk/fSsp2bkm+/p7OPNW/zD+vnCdyMR0i9ce2a4R/15Xk56dS20XR+YP70ynxn5kZmZafmOiwkiAK4QQQtiYkoMcSluoX+kmtSLmgmal10rXGF9Pa6wOq7XjK86uQMXpJUNokVJbr330qL9Y2eCComt4Ojlw6tWhuvENbRNoYW10QwK9PejYyFdRgOvi6MjhKYMUjUVUHglwhRBCCBtTepBDaQr1K92k1rJube4NaWA2aFZ6rVpWlNFSek0oLCVWVMGgW7wfe9cM0Hs83SmXus+uReOgvMrB013vwMXpVnijNFvu62Z+XXQRc5v/RNVRqZvMEhISePHFF+ncuTM9e/bkk08+ISen8JtfXFwcY8eOpV27dtx333389ddfZq+1detW+vXrR9u2bZk4cSLJyckV8RaEEEIIPdYe5KC0PFcRpZvUfh7fl+lDO5rNCCu5lr+7M6Na+JjtY+01ARp7u/NQWGNUWti1tr9BcPvEgL+oP9m64NbYOuOi5RQHXxrI5F6hPNGpKS/3DuXQ5PtYPKq77khkW2z+E1VHpWVwtVotL774IrVq1WLFihWkpaXxxhtvYGdnx2uvvcbEiRNp3rw5GzZsYNeuXbzwwgv8/PPPNGjQwOBaJ0+e5M033+S9994jJCSEjz76iGnTpvHtt99WwjsTQghRk/1v61FFBznM/DOS1GwNm05eIi3n1lGx3x88xwNhjVgwoqsu+CpStKTB09mBRBNxcNF6W4Cpmw8brP8tTsmGt/7N61HP3XjW8tYSC/3XMHfNIpdSMgh0dCdv5miDx2q/sIocl3xy85RvpTO1zrjkGN+7N8zoBr6icS+JiLHq+qJqqrQANyYmhuPHj7Nv3z7q1KkDwIsvvshnn31Gr169iIuLY/Xq1bi5udG0aVP279/Phg0bmDRpksG1li9fzsCBA3nggQcA+Pzzz7nrrruIi4sjMDCwIt+WEEKIGiguRc30P87ww6HzqE2sVy1p0YFzZOQa9k3LyWNJRAx5BVqWPtoDML2mt+SO/rqeLtzbogEFWi2dZ203uf63eOD8Vv82RFxKIibppt6BB0W38F/p0YyPtx/GLjIDP083JvcKoX4tNyas28+mU3GkFdvA9f2h8zzQJpD5w8JR5+Sx8eQlk0Husm09eORsE722l/tE8FWHqMIfrDx7QaWCgS1u1aEtmrOSXyDm7onkjrq12DKuL03qeOr112rByQ40JV7bxcGOEe0as2BEV5NBvahaKi3A9fPzY9GiRbrgtoharebEiRO0bNkSNzc3Xfudd97J8ePHjV7rxIkTjB8/Xvdz/fr1adCgASdOnJAAVwghRKkdjE1k4sYI0rM11HJxYt6wToQH+eseL+2BC4DR4La4FUcukJObT4CXO5dTM9hgJFgs+rmprydDWwcwuVcob+84zrKICxbX/5oau4uDHcG+Hmwa24ePfjvN3Qv+JDEjByhc+rfy6AXcney5kJxhMOa07NzC4Dy/gEAvd6PBbZNUD84vftCg3f+5tSS5ln6DWoEWRi3/m6fXHiL+7eE8/+NBVhyJNeiXB0QmpBPyySYe+68+sYO9HU+v/Ztlh41vZMvOK6BAi1UHYojKVWkBbq1atejZs6fu54KCApYvX06XLl24fv06/v7+ev19fX25du2a0WslJiZa1V8IIYQwJ1uTR9j0LcQkqfWCtO5zdhLs68HJV4bg4uRgcjOZraw/eUlRv5s5GkaENeK9nSdYedj0eLTA9sjLxKWoeWfnCaNjz84rIDIhnYGLfjN4/4CiQH7F0ViGhxkmmKb/cScvH22p1/Zx51O81eO4xWsqpdbk0eC99WTlmf8CkQcsiYihQAsfDGjL2mMXzfZfdfQCWi0mvzjk5+XzYqgc9FBVVJkqCl988QVnzpxh/fr1/PDDDzg56a/3cXJyQqMxfi53dna2Vf2N0Wq1pa5hl5WVpfdvYZzMk2UyR8rIPFkmc6SMqXlqO3MHMUYylFogOklNmy9+YtuTPdl2Oq7cgltrJKpz6Pv1r3pLDMz1fWf7UX6OumY2EDYW3Fpjz/lbSaa6GS5c+XaEQZ+gpzcQV8v29WMtZceLW3csloysbIuHPBSYmQwtsPPfq4xs1Igg+X/OpLL+uaTValEpPFe5SgS4X3zxBUuWLGHmzJk0b94cZ2dnUlNT9fpoNBpcXIzvbnR2djYIZjUaDa6uyo/ey83NJTIy0uqxFxcbG1um59cUMk+WyRwpI/NkmcyRMsXn6dT1DKO334u7kJzBpLV/cSPL9AECFU1JcFtk99l4rmfkme1T1sD9embh3PzvUGs+/qu93mPftT7HhHsOlPEVbCM7v4AdUVfKfJ3rGRpWn02mnnts2Qd1myvLn0slE5qmVHqA+8EHH7Bq1Sq++OIL7r33XgDq1q3L+fPn9frduHHDYBlCkbp163Ljxg2D/n5+forH4ejoSLNmzawcfaGsrCxiY2MJCgqyKqiuaWSeLJM5UkbmyTKZI2WMzdP433dZDO60wP6r5oPgquxmBcTltbMdSZ4/yqC91RObifK1fKBCRcpSnvA1K12TL//PmVHWP5dKxobmVGqAO3fuXFavXs2XX37JgAG36t+1bduWBQsWkJ2drcvaHjlyhDvvvNPoddq2bcuRI0cYNmwYAFevXuXq1au0bat8wbdKpdLb1FYarq6uZb5GTSDzZJnMkTIyT5bJHClTfJ6UVkHIsKKEVZWj8DZvaT19shnf7tI/Oe3nJpcZ8sDvhaUfqhhb/SZrOdnL/3MKlHaOlC5PgEoMcKOjo5k/fz4TJkzgzjvv5Pr167rHOnfuTP369Zk2bRrPP/88v//+OydPnuSTTz4BCpcfpKWl4ePjg729PY888gijR4+mXbt2tGnTho8++og+ffpIBQUhhBBWq+Wi7BZoyRJd1Ym/h7NeeS9bccm1J23uKBy0+vV7uzzyMxH1k2z+elWJtQdiiPJVaSeZ7d69m/z8fL7++mt69Oih94+9vT3z58/n+vXrDBs2jJ9++ol58+bpDnk4duwYPXr04OrVqwC0b9+e999/n3nz5vHII49Qu3ZtXTAshBBCWGPesE4Wk4wqwMO50lf5lYq/hws9g+va/LrD/m1ExpxH9YLbI/5JOLy87LYIbu3MfCgsHYghKl6l/d85YcIEJkyYYPLxxo0bs3z5cqOPhYeHc/bsWb22YcOG6ZYoCCGEEKUVHuRPsK8H0Ummj8wN9vWgY6Ava46bLy1VFQ0IacDb94Sx5Z/LXM8wXXfWz92J3k3rsSc64b86uIXsVPoVBewLVEQvepBAtX6JrLFP7uPHuhfRKi9oZJGzvYqcfMt580Et63Pgwg2SbLgJ8NEOTbC3s2NHVDwJN2+VSis6EGPWoLac+/esmSuIilRpGVwhhBCiqjr5yhCa1jF+OlXT/+rgfja4g9msXlWlpfBY2vtaNjSZqVYB97UMYM0Tvfnz2bt4JMSHx9s35uXeoYT619b163upHppZj+sFt1fdM3F+aTn/tE4l+vUHzL7GQ0Zq5ZribG/HkFaBipbwerk4U9u1dNlUpxK/VF83J57oFMx3D3dj8ahu/Di2N+0b+tDU14MODX3Y9GQfFo/qbnCssqhc1fP+ihBCCFGOHOzt6NHEn+SMHFKKZQF9XJ3oEeyPg70dgd4eBHl7EJNsOtNbkr0KFCQgS8UOZafb/nL2CnEpahaM6AqoTGYkCx+HAC93Xu5Qj9DQUNzc3DgYlkiPr3by98qBdErQP4101KA9rGtxERUwd1gnus3babbW7rH4FJ7o1JRtZy5zw0w2GWBEu8Z893A3tMv3ssHCARirjsXiUMpvH6M7BePp7ERKlgYfNycm9wolwMudvPwCxq3eZ3CS2f2L/5CTzKogCXCFEEKIEkydUJacpdEdd/vevWHczLHuFni+Fhp6OhN/s/RH0pri7+7MNQtBIkDCzWxm741i+tCOLB7VjbgUNbP2RBkEdKaE3/And+Zog3aPSSvJciysQBHsW5j9jjGzzKPo8WWPdue9e8MYsGA3UYmmy4dpKfzioSRwLdCCphTfJOp6uvBW/zACvQ2z96Y+E3KSWdUkAa4QQghRTFyKmu2RV8xmHndExeOgwuwaVlPSNeYPWCgtJcFtkeTMWwtjA709mHF/R8tP0gL3Az/pNz9/9wG+bXsOKFx2UHSUcY+5prO3xS/5wsYINj7Zm1gLmfANJy7y0cB2/Hr2quWxloKKwvXJxoJbJZ+JX/69xshGgYSWy+iEtSTAFUIIIYqZtSdK7xa0MQk3s9l/8YbZPqbkWHHiWHnxcbNufapzrDNuHQ3rlh4+fp1Du5Nomu1JbRdH5g/vTKfGhYcspWcr212Wlp3L+7+ctHgSW3ZeAR/8epJ8re3XeJRcllGSks9EYkYOq88mc5eC7wqi/EmAK4QQQhSTkqUsE1raMMvFwR5NfvlkcZWo6+nCSz1DFPd3nORI68Wt9RvfB96CjvhxuO0go89TWk+4tosj+y9et9wR2B97g4a13UjLTlPU35I76ngwuFWgxWUZSj8T6QoPCRHlT7b8CSGEEMV4uzor6tctqA7+Hi5WX7+ep/XPsSVTt+ENXAZU4LjYUb89HnjL8tOV1hOeO6wTKiuON1s0souifk725q/p4mDHr8/2Z/rQjmaDW1D+majlZK+onyh/EuAKIYQQxUzuFWIxcC3ajDQwtIHVJ8+2aeBj87983RztLY5ZBQwPCzR5G17P20CJCl65z+UWpq0bKBtTUT1hc4J9PQgP8qdr4zpm+xXpFlSn8Lo+5gPSYB93RrVvYrbPyHZBygJ9lH0m5CSzqkUCXCGEEKKYQG8Ps4Fr8c1IC0Z0ZUynptS1Iivr7eqISmFU7OZoOSPo4eTA1XceshhsD2/biLVP9DFfrzWJwjf4gX7z6R9Pkzvd+kMTiuoJlxyXilv1hAHeuicMFwfzIYmLgx3/1z8MgFOvDjVbp/jUq0NZOLIrT3RqSh13/exrHXdnnugUzMKRCgL9/yj5TMhJZlWLrMEVQgghSlBaI9bB3k5XauuDX06y4ugFs5ul6nq6UKBVVgvX1dGeXc/2Y+2JS7oSXi39PHlj+wmyc/NxdbRn4cguDG7dyKoxmzQHeLFE2wjI/CGTnMjSlTVzcXLg32kPcjA2kYkbI0jPzjXYjAaFAeTIdkEsPRxj8lrFM65Kr1uaMmimWJpfOcmsapEAVwghhCiheOCqJDgK9PZgwcPdyNNqjdZKhVuZX6VVABrUciU8yJ/wIH+99nFdW9hkzDoZgLFk6DGgHZCpaLhmhQf5c3iK8c1oRRaO7IpKpTI49KGOuzODWjY0GqArua7iMmgWWJrfzEwbTJSwGQlwhRBCCBOsDY6UZFH/t/WoomsNbhVg7XABK8e8EnisRFsPYA9Yvbi4jEodoFcwWwXMonxJgCuEEELYiJIgbXKvEFYevWC2rmpdTxde7lWORwZogLpAaon2P4Fe5feySkgAKWxBAlwhhBDCxswFaUUbliwtZVC6w99q24H7SrQ1A6IAqXIlbhMS4AohhLht3Mqc5uDt6szkXiHlFyiWQdFShq3/xJFU7NhcXzcnBrcKUFbKy4JbG7A01HJxYt79nQgf4g+RJTpuBobqNxWfRw9HO+71w+QRtNVlzkXNIgGuEEKICmdNUKSkb15+ARPW7WfbmXi9DUo/HDpP/dqudAqog4976YOv4mNwUKnQAvlabRkDOi05efonX+Xk5aPOyeXVLUdIy9aU6vrZmjzCpm8hJkmtyxB3veJH+FT9zWq4UVgWrFiFs6J53B55RW8JxSpnewZeymXxIz10ZcaK+m4+FUdq9q0SYj8cOs/9bQrr7ZotSSZEOZIAVwghRJlYE6wWBUUbT17iZs6t42q/O3iOYWGN9IKior5bTl8mOetWlnNpRDRDWgfo9R2/dr/RElOp2bmkZucSmZAOwMqjFxgYWrjZ62p6Jl/8doqLV6/TOFbDq33bmAycfzodR0qW8TqwJa9Zci4AvbaRbRsxbvV+oq6nG1xLrclnw8k4vbblR2J0VQSUBIxh07cQnaQu/EELOzf0o9+l+vqdvgfGGj53wrr9RpdOJOfks/LYRRwcHFg8qhsAT635m+VHLhhcIzU7lyURMeQXaFnyaA+L4y0LyR4LUyTAFUIIUSqmsn3FA76SAdnYlftYdTzW4Fo3c/JYEhGDJq+A5Y/3BODptX+z7LBhAJWcpWFJRAwFWvjhke7EpahZa+SaxiSqs1kaEcNfFxJJydDcCpwvpLHq2CWDwPmp1X+z/KjhGEpec0lEDJtOxZGTl69XB/ebvwvrohZvm70n0ujaW1NuZOSwJCIGdU4uDnZ2/Hr2KvlaLQ1ru7FoZBddGbGfTl1k7Oq/Scsu/OLQ6oYXJ5cOMbhe7RdW8Uufuzm9P4UXNx0mL1+Lg72Kd/u3YXvkFZNj0wI7ouKJSykMnldamJeVRy/w4cB2igJOawPV0nz2RM0iAa4QQohSMZXtKwr4/opJZEirQF2wEpeiNhrcFrfqWCyfDGoPwNpj5vuuPXaBDwa05YNfTpo9XKEkLRB9Q23QbixwXnnMfBBXXFq2YYbX2LisCW6LK5nZTctOo9ucnQR5uXIjU4Nac2u5w9Kfu/NYVLBe/1d6HWZmx8IFuN3m7NR7LC9Py+vbT1gcQ8LNbGbvjeJKagYFFt5IgRZe33qEFaN7m+xT2mUO5j57SyNiAJUu0yxqJglwhRBCWC0uRW022wcQnaRm1p5IXVatKPNnydNr9tPI250cC8d95eRr+eDXk+y/eMOKkVu29lgsHwxoy/+2HrUYxFUFsalZuv9unOZOzHfDDPrUfXYtN9xKdxpZScmZGn6OuqKo78+R5vuVZpmDpc9e8UyzLFeouSR/L4QQwmqz9kSZreNaXFFW7bfziYr6/3b+Gr/9qyyA2n32CtpS50SNy8kvoN/Xv/DL2as2vW55++zPDgbB7WedTmM/ZZnNglsABzv01k+bY65fXIpa0TKHkl+MlHz2ijLNouaSDK4QQgirpWRZFzBZE4JqtXC5WFbSnMupWYzp3EC3icxWzidl4GBXwUd5lZJ/hgtXvx1h0N7k6Y1cqpVh89fLUBjcgvnf+/+2HCnVMgeln73kYuXXRM0jGVwhhBBW83Z1LrdrO9lBvuVuQGG/t+8Jw8XB9n+d5VWD9QmvHmplENz+0Oo89lOWlUtwC3Dyaqrivua+IvwcFa/oGiWXOSj97Pm4OSnqJ25PEuAKIYSw2uReIfh7uFjuWApzhnVWnD11sFMR6O3BiHaNy2UsVVWtHEfyvxzNp3910Gtv/cRPPHXv/nJ97dx8pV8/wMPJ9I3iDI2y66g1+hljJZ+9up4uvNQzRNH1xe1JAlwhhBBWKzpu1tY38e1V8FTXFjTxcVfUv4lvYb9FI7vxRKem+NaArN2Tp5uSMm+UXtuOoHjsX15GpG9aub++k73y83wHtWxo8jF7lbJPT8l+lj575X7UsagWZA2uEEKIUik6bnZHVDwJN5VtODNHBSS+W3i7/YdHuhuUsjLmh1HdAXCwt2PxqG7Epah5/5eTrDx6warSYdWBc54dKfNG4ZyvH2B2G7Wdgw1sW0nCnJC6tbmanqV3+IYxdir4dHAHk48HebtzLslyZQ1jX3ZMffbqerowIKSBTY46FtWbBLhCCCFKpXhQOWtPFFv+ibt1gpaV3BzgzOsP4vXfrefwIH+CfTyISTZ9vWAfD90hB0UCvT1Y+HA3/oxOKPVYdGNytCczV/nt+PL0wLlANmzpo9d2ok4yd47ehraC98I18nZnSOsAlkQYnhxX3KPtm5jNoi55VOGXmEe6G7SV/OylZGnwcXNicq9QAryUZf/F7U0CXCGEEGUS6O3BjPs78tngDkxYd8BoVs3DycFkwKkCRrQPNgiGTr06hLAZW4i5odbbja8Cgn09OPmK4SldUFh+Kt3IoQvFOdvbkZ9fgKl6ACpgYGgD9kQncj3D/K59Tyc77glpSF1PVxbsP2fTzWn2BSrOffcAjW/qz83AYbv4Jajiy5gVrW2tX8uNAi2sOx5rkCl3slcxqn0TFo40n0UND/KniY87F5JNb4Zr4uNu8CWmuKLPnhAlSYArhBDCJsxl1ep5uhoNfl0c7Gji68Fb/cIMrufi5MC/0x7kYGwiEzdGkJSRTVZuPp0b1eEOv1pcz8gm0MkwQzhrT5TFoDQnv4CRbRvx69mrpJQIhn3dnBjcqvDIXlMnZhWxU8GX93diXJfmAMy8vxMPLP6NX89eI09btkC3d1xdflt3j15boms2jSZsINe+bMsvGnu5clFhKbYiJde2/vBIdz4Y0JbXthxh17lr5BdoaeTlzoIR4XQ2E5QWd/rVoYTN2GL0ZLmmZr7ECGGJBLhCCCHK5KdTF5mw7iBZufm4OtqzYES40aza4lHdWLAvkok/HtbVP83OKyAyIZ1mn2zCTgXzHuzIhO6hALpA+UBsIsfik3XX2RYZD5HxLDrwL8PbNmbBiK78fCaOCesOkpaVg0Zh7Lf2xCWj7cWrBCwY0ZX8Aq3R07agsE7r+HUHeXnzEeLfHo6HqxNbx/cjLkXN9D/O8MOhaIMqAFC4w9vkMLXw1+oBdL3qp9f8yKA9rG1xUclbsygp03yG25gmPu7MHxau+zkvv4B3dp7gp9NxZP936typa6ncNf8XHu7QxOQxu8WV/BKTnp1LbRdH5g/vTKfGfmafK4Q5EuAKIYQoFXWWhobvb9AL4NSaPB78YQ8eTg66gM9U35IKtPDcxsO88ONhRrYN4pd/r5Bkpli/WpPPkogYlh+OwcKpvlZJz8nXHRM778HOrDAR3OqPJY96765F/dnjQOGt8xvqbJPv11Rw2z7Bh8MrBhm01560CrWj8gMWLDH3ezAlJjmDZ9cf4If/js59bNke1p+KM+iXna9lSUQMGdm5rBnbR9G1w4P8OTzF8H0LUVpSJkwIIUSpNHhvvclASa3Jo8H763U/Wwpui8vXwqrjsWaD25L9y8OKIxfwf3ut4lPYsvK0/HSqMMMal6Jm9XHrsq0bNvc2CG4n9j2I/ZRlNg1uy6Lo6Ny4FLXR4La49afiDI7ZFaKiSAZXCCGE1X46dZEMCxUGMjT5uoCvNBnDyqYFcqzcMPbQD3vQzBjNU6v/VvycO1I8ifr+AYP2Os+tIcW1ah03m6+F17ce5cy1FEX9hy7azbFX7y/nUQlhSAJcIYQQVhu3WtlpWU+t3l+4E6uGKAr5d59PUNR/7u7OPHeihV7bu11P8EHXkzYeme3siLpCqoUqFUVOXksv59EIYZwEuEIIIaymNCN7U5On+BZ/TdLgpitxCx8yaA+YsJ6rHtZVN6hombnVLxsvah5ZgyuEEMJqdgqzsvZ2KgrKWC7rdvPW/jCD4HZ+27PYT1lW5YNbAFdH5Uf1ClFZJMAVQghhtXua11fcr56HczmPpnrwznIi/8vRvLu/rV57iyc3MenuQ5U0KusNCGmgeNVJDVqdIqoYCXCFEEJYbc6wzor6fTWsMz2b1ivn0VQd3i6OAPi7Oeq1P3e8OTe+flivbWOzi9hPWcZ575sVNr6yUgGfDb6Tb4Yr+/0b6xeXombq5sOMW72PqZsPS6UFUS5kDa4QQgirBXp78Gj7IFYeizXZ59H2QQR6e+DuVHP+qmni6wmAr6criZm5uOU6cHPOIwb97nx8K8f9lVUiqEqCfNwI9Pbgqa4teGb9IbPrq1XAU11vbaDLyy9gwrr9bI+8QqL61ml2K49eYGBoA0UHQwihlHyShBBClMrCkV1pWsfwqFwoPGZ14ciuAOSXYg2un0vlr/O8v1VDrL3DPndYJwC6Nvbj4aggg+B2X4NE7F9eVmnBbS2Xsn3ZyNAU6Org+rg6mu3r6+aol50tOva4eHALkKjOZmlEDBPWHSjT2IQoTgJcIYQQpTJh/X6ibxi/vRydpGbC+sKAxdvV+jW417PN19itCG5OjjzWoYni/ioKT+RCA98835WVP/fUe7zviF/oNWonVkfNNpSenYdzGbKkiepsZu+NYtaeKJKyzJcKu5GZy+y9UUDhsoTtkVdMZny1wI6oeFmuIGxGAlwhhBBWi0tRs/JIrNk+K48Unno1uVdIxQzKxlYdi+Wp8GDF/bXA/q8TwBnsk29FsTG1b+I0eTl/BiqrjVvecvJNHRSsTHKmhsupGYr6xqUU9pu1J8ogc1tSws1sXUAsRFnVnIVRQgghbOa1LUcs1rfVAv/beoS7m1XfTWaDFv2uqJ9KC8eWDqZNkrde+7Chf7C5mfkjbasbRzs4Hp+sqO+J//qlZOUo6p+s8HhmISypEgGuRqNh2LBhvPXWW4SHh/P666/z448/GvQLDw9n6dKlBu1paWl07qy/U9PLy4uDBw+W25iFEKIm23k2XlG/HVFX2Hz6cjmPpvxk5lrOdoZfqcPfqwfqtWXZ5+E7cQ05DmXLllZFWuB6hrKAtaif0mUqPm5OpR2WEHoqPcDNyclh6tSpnDt3Ttf25ptvMnXqVN3P8fHxjB49mjFjxhi9xvnz5/Hy8mLr1q26Njs7WX0hhBDlJUtB4FfYL5+C2y/GK6SF7Rvv5p6LDfTbF4PH6VWVM6YKkFeA4sM7ijYYTu4VwsqjF8wuU6jr6cJLPavnchZR9VRqFHj+/HlGjhzJpUuX9No9PT3x8/PT/TNnzhwGDBhAv379jF4nJiaGJk2a6D3H19e3It6CEELUSG6OyvIjbo4OONjfftX+W96oTf7M0QbBrdfE1fBkJQ2qgvi4OeGocKOa03/9Ar09GBjawOT+OhWFB0gEehuvyiGEtSo1wD106BDh4eGsWbPGZJ/9+/cTERHBlClTTPY5f/48QUFB5TBCIYQQxtwb0sByJwqDlq8e6FjOo6lY3+/oxqmlQ/Xa/tfzCPZTllGrTmF5s+e6KN+cVp2ogBFhjejcyEdR/06NbiWbFozoyphOTanr6aLXp66nC2M6BbNgRFdbDlXUcJW6ROHRRx+12GfBggU8+OCD1K9v+ljI6Oho8vLyeOihh0hISKBjx45MmzYNf39/xWPRarVkZmYq7l9cVlaW3r+FcTJPlskcKSPzZFl5z9EznYJYc/yixX4TOgXRqVEdnt8YQV6B9fVwq5JG6e5cWDTMoL3+M+tIdC+89d6veV0yMzP5fFB7vj4QU9FDLHdaYOWRGJr6KMu0NvPx0Pu7de7QdlzudQdz958nLSsXb1cnJnZrRsPabmhysqnOW8zkzyXLyjpHWq0WlUrZHSGVVluKCtzloEWLFixdupTw8HBdW1xcHPfccw9btmyhWbNmJp/bt29ffHx8mDZtGlqtlpkzZ5KVlcW6deuwt7dcLPzUqVNoNNX5fyshhKhYM49eY1WU5Z30j4b4MLlDPdKy8xjw47/kV4m/caz3yZ72vHa4tV7b9Dv/4X+9j+q1fde/MW383AG4lp7D0K3RZX5tFVDXzZ5rmaZrA/cL9OBoYhbJOeVfP3hwcG0mtPFjzM/RpOaa/oV6OapYel9T6rnLxjFhO05OTrRp08Ziv0rfZGbOzp07CQ0NNRvcAmzbtg2VSoWLS+Ftj6+++ooePXpw4sQJOnTooOi1HB0dLb6OKVlZWcTGxhIUFISrq2uprlETyDxZJnOkjMyTZeU9R3aRGYDlANfO1ZPQ0FAAUtu3YfHB87y09YTNx+OoAjOxlp7hrRtw8moa55Is13L1y3Th2jcjDNqDn9rIxdqGz9+TpGJkr8L3GwrcDG/Hy5siWHTk1l4TLyc7UjWWd9452aloWa82swa3487Awlv9M/88w3u7IynQgp0KPujXkkn/vd7l1AxGrdzPiatpFq9dFkH1/LmrYxiDLuWy8thFo+XiVMCg1o24q2Pbch1LVSJ/LllW1jk6f/684r5VOsDdu3cvd999t8V+JSfJ19cXLy8vEhKUF9VWqVS4ublZPcaS4yjrNWoCmSfLZI6UkXmyrLzmyM9T2TX9arnpvf4Ld4Xx2Z6zXEk3X/TfWp6ujiRnmj9ZCwrXe854IJxZe6KYtSfSbN8ph1vyxZ479dqWhUYzduDfJp8TEZ9iMN/fPtqbb4utyDsYm0j3OTvN1hFWAX9MvKfwZLRi3hzYkTcHGl/T3NzNjc1P9aXzrO1mqxWowGINY1PqerrwSt/WuLm5sfiRHjg4OLAjKp6Em9l6fQaENGDBiK44lOHUtOpK/lyyrLRzpHR5Athok1lysrKCz9bQarWcOnXKYgZWrVbTqVMnDhy4dYZ1QkICKSkpBAffnov8hRCisk3uFYK/h4vZPqbKPg0IaajoNazJwPQJrscTnZri4mD6r7XiO/XNHTzgmeNI/pejDYLbsDFbzAa3SoUH+RPsa34Na7Cvh0Fwq4SSagXBvh6lOi24ZKUDB3s7Fo/qxsGXBjK5VyhPdGrKy71DOTT5PhaP6l4jg1tRdVj96UtPT+ett97i7Nmz5Ofn8+STT9K9e3cGDhxIXJztTmuJj48nIyPD6LKB7Oxsrl+/DoCHhwd33nknn3zyCSdPnuSff/7h5ZdfpmfPnrRo0cJm4xFCCHFLWco+vX1PGE525kMsJzsV94cFKh5PY18PFo/qxunXhtCybm1cHPT3X5TcqW/q4IEn/gkmdd4ovbbTbVLxeHUl/9RJtTiObkF1FI335CtDaFrHMNBUAU19PTj5yhBF1zHGUrWCk68MMfn46I5NGNMx2OAxH2d7Hm3f2Gilg0BvD2bc35HFo7oxfWhHArzcSz12IWzF6iUKn3zyCYcPH2bs2LH8+uuvHD58mM8//5yff/6Zzz//nDlz5thkYElJSQDUrl3b4LGff/6ZadOmcfbsWQA+++wzPv30UyZMmIBGo+Huu+/m//7v/2wyDiGEEMYVBjsqs7eojQn09uDhDkEsO3zB5LUf7hDEopHdeHT5XjacvGSyH4C/h7MuU9zEtxanXhtKXIqaWXuiSMnS4OPmxOReoXqBV8mDB5zz7Eia9zCu+fp/LSbsyKT1vV7sju1Hz7k7zW6Sc3Gw4//6h5kdq66vkwP/TnuQg7GJTNwYQXp2LrVdHJk/vDOdGvspuoYpRZlVc3Ng6fHij9VytOMePy19OraVrKyoNqwOcP/880/mzZtH06ZNWbhwId27d2fIkCG0aNGCxx57rNQDKQpWi7Rt29agrciwYcMYNuxWqZbatWvzySeflPq1hRBCWE9JIGXKopHdsFPZsfWfOJIyb1Wx8XVzYnCrAN36zbVP9ObhJX+y3kSQqwIGhjY0yBQXZRVNKcpAL42IYcj5AH786S69x0/5pjDrqzN8d293oHBZwWN3BrP0sOnSXyPbBVl9UEF4kD+Hpwyy6jlKKZkDU48XfywzM5PISPPrlYWoaqwOcDMzM3U1afft28f48eMBcHFxIT+//MuTCCFEdXQrCMzB29WZyb1CqvypTUrHXBQMFfV/e8dxi++xeHD8wS8n2X/xBqCla2M/3ronTC9TuOLxnrivO2CQKXZxsCPY14O3+huWDCoay+XUDKKT1AT7uhPo5aE3pgUPduXTFzvgf11/o/Kjj+3BZbC9QQb67f5hRMQlEZN0k5y8W1UQ6rg7M6hlQ5MZa2t/97eyuhpquTgxb1gn3Xrc6vg5EqIyWB3gNm3alD/++IP69etz/fp1evXqBcDatWtp2rSpzQcohBDVWV5+ARPW7Wd75BW9ne0rj15gYGjV3Gluasyz9kRSy9mBrU/fRffgehb7G3uPRQHtnzEJJGdoUGdr0BS77X8mIZ31Jy7yQFgj3uoXxtx9Z3VBan13J70ANzuvgDMJ6dzx8WZUKnBxsMfdyYFWdWtxOO4G6mJ1w47FF26GXnEkhl5N/ekYXYfXPmiNP7eC2xRXDfe9u4vZD3eic7FlAqbeX1GAveXpvgT5eCqeR1O/+2xNHmHTtxCTpNarctB9zk6a+HrQLciPLafjSMvJ0z32/cFzPBDWiAUjurLk0Dle3HSYvHwtDvYqvnqgI091lb0oomayOsB98cUXmTRpErm5uQwePJigoCA++eQTVqxYwbx588pjjEIIUSlskS2bsG4/SyNiDMoyJaqzWRoRA6hYPKpbqV+v6DlxqWoiE9LJzc/H0d6eTgFeDA9wJLQU1zU1ZoD0nDx6zfuVYB8PTr06BBcnB+5fvJsdUdcM+ha9x4ycPBrUdmPbP5e5kKLG0oFmaTl5LImIYUmEspPAtIBWC5m5+WTm5vNHzHWTfa+rc3jpjZZ0v6JfoeCx+/ayOiQWEuD+xX/Q0r8W/ySkkZWbT25+ATn5hnVriwLsx5fv5a8X7zN4XMnv/pkuzXTZ2kupGeQaWeSrBWKS1MQkqQ0eK5qrkq+Tl6dlwvpDTFh/CDcHO9ydHVkwIpyhbRrr+pj7XBTPIns42fNiay/+K2csRLVQqpPMUlJSSEhIICSkcFH/yZMncXd3r7YZ3FOnTgEoOhnDmKL1SaGhoVL7zgyZJ8tkjpQp73kylXnz93CxKusal6K2WJO0rqcL+ybdywe/nrLq9YrG+POZeK5nGC955aiCAC931Jo8vT7Fr3s1PVMvyBnZthEPfP+n2TEXaezlBioVF1MsH5hQFbRL9ObI8sEG7Z6TVpHpmGfkGcrZqyDx3RF4/Vc6LS5FTeeZP5No4ncDhQc1aLWlr0lbGh5ODlx880GmbD1i9PPWv0U9DsTeMMgiq4AmPu6cenUoLk5VuoR+pZE/vy0r6xxZE6+V6lPq7e2Nt7e37uewMGW7RoUQojqwJutqzqw9URYDxYSb2Qz97nciE9Kter1Hl+9hw0nzpRlztXDBSPCZqM5mSUQMf8UkcjMnT2+M3/x9luw8y6dsAVxMzVTUrypYs6UXD51rrNc26a5DzG9vfDOztfK1UPe99eR88Tjw3+/eTHALWMxklwe1Jo+6760nv0Br9PO24kis0edpgZjkDMJmbOHfaQ/KWmBR5SkKcPv27av49Ijdu3eXaUBCCFGZ4lLUbI+8YjKrpgV2RMUTl6K2+Be6ucMEiiuZLTP3enn5BTy2fC8bLQS3SkQbueWtNLitLpqmePLv9w8YtPs9t4ZkV43hE8ogr0DLd/vPck9IQzadMl/arDLllSGyjrmhZvDCXRy5nFJt1pSLmklRgPvggw9adTyaEEJUV0qzrrP3RjF9qOkSTGD6MIGSLAWVxV9vwrr9JktmCX2zf+vEC8f1T1J7v8sJ3ut2stxe89kNh6jj7qJoiUd1pAW2R101aLf27oYQ5U1RgDtp0qTyHocQQlQJSrOuyZmWs38lDxMoi+RMjS67LMyrr3bl8oKHDNoDx6/nimdWub52gZbbNri1xJq7G0KUt1LdR4iKimLatGmMGjWKhIQEVqxYwaFDh2w9NiGEqHBKs64+bk4W+1g6ztYajnbKsss13ZsH2hgEt9+EncV+yrJyD27FrbsNQlQ2qwPc06dPM2LECC5fvszp06fRaDRERkYybtw4/vzzz/IYoxBCVJjJvULw/28nvCl1PV10R8NasmBEV8Z0akodd2WBsylalGeXayKvbCfyvxzN+3+302sPeXITE/tJAqYiKbm7IUR5szrAnT59OuPGjWPZsmU4OjoC8OGHH/LYY48xZ84cmw9QCCEqkqWsqwoYENJA8S3YohO7hrYKKNO48gqUZ5drmmdO3EHS/If12jY1vYT9lGWc875ZSaOquZTc3RCivJUqg/vAAw8YtD/22GNER0fbYkxCCFGpirKudT31M7l1PV0Y0ynY5JGs5uRbX3Jcj4+bk6Lsck3immtP/pejmb+7i157p8e2Mfz+mnVHMdjHnVrOyip/OgAPhTUy+vl+7M4gmvp6GHzBUwGBtV3xczcfvFpzd0OI8mR1HVxHR0fUasPSMlevXsXV1dXIM4QQonopyrreqvWp+S/ADCXAy71U1yxL9rUoaCjKLps6ZcwWnO1VuDg6kJada7GvPZCv4Joj2way/mScTeu+jjzbmFXbeum17a9/nR6jdmCTRc/lwE4Fof61CfLxYFtkvOLnqUB3VO/mU5e4qbk1655O9gxr21jv0I4t/8QZLQFX5JGOTfjhkR5mP9+3TjLLxdPZnpda1eahXh154adjJj9/1t7dEKI8WR3g9uvXj1mzZjFz5kxdW3R0NB999BF9+vSx5diEEKJSBXp7MON+86XAlCpLRYUODb11QUNh9ljFjqh4Em4qu5bSQBRg3ZgeDP1+r6K+ax/rwfAVf5ntU8fNkc+HdOTQxRvEplq3yeu74R15asNhvTaHfBWXFg6nbqZ+QuXuh37Ba5ADL/uEMrlXKMcuJvDA0n1WvZ4xjb3d8HVzoX4tV6uCUmMCa7tx8rWhTN18GBRcq7aLI019PZk/vDOdGvsBmA1Kiz6vnw3uwIR1B9j6TxxJxdbD+ro5MbhVgO4OhLnPd3iQP4enDAJunT4Fpj9/dT1dGBDSoFR3N4QoD1YHuP/73/94+umn6dKlCwUFBQwbNgy1Wk1ISAivvfZaeYxRCCGqPSXZV2d7FTn5ho9uj7pK849/5OQrQ3BxctDLLkclprLDSF3S4pQGt4Di4FYFFoNbgBuZuZy5mmR1cAsYBLf9LtZn54Z+em0XPdXc8dQm8u20cAryZ/QFoPEHZQ9uAWL+bziA4qDUnIupmcSlqBV92anr6cLBlwYaZEOVfOkqjzsQFXFtIWzJ6gDXw8OD1atXs3//fs6cOUNBQQHNmzenZ8+e2NnJ6SVCCGGKpezX7n+vcDnNeNATnaSmzRc/ce7NYQD4ubtYvBVdnqxZbXDfd3vK9FoqLUQsH0T76z567Q8N+YMf79A/0e2JZX9w+OL1Mr1ecS+s28fcEd1tVtN46KLdHHv1frNfdmx1q9+WdyAq8tpC2ILVAW6RgIAAMjMzcXJyolmzZhLcCiGEBeayX/GpGSyJiDH7/JjkDH46dZGhbRrTZvoWYiopuK1Ina76cmDVfXptGrt8vCeuIdvRMDe9/HjZjzAu7usDMcwd0d1m659PXksH5Fa/EOXN6gA3LS2NN954g99++w3tf7uC7ezseOCBB3jnnXdwdpYyNkIIYY6x7Fe/+b8oeu6wH/YwIKT+7R/camHbj30ZENtQr3lC//181+Z8pQypKChddji6zBvm5Fa/EOXL6gD3ww8/JDo6mu+++46wsDAKCgo4fPgwH374IZ999hlvv/12eYxTCCFua7EpGYr6aSlck3s7C02qzeklQw3avSeuJt3ZcnWH8lIUlB6+dIN/EtJsck251S9E+bB6XcFvv/3Ghx9+SLdu3fDw8KBWrVr07duXDz74gG3btpXHGIUQ4rZX1jq5t4uFO7saBLev9ziK/ZRllRrcFrdwZJeqWolMCPGfUtXB9fAwXPju4+NDQUGBTQYlhBA1jYeTA+k5eZU9jEoTmO5G7KLhBu31n1lHorvyjV0OQHnPYniQP8G+HpW2wU8IYZnVGdwxY8bw8ccfk5ycrGvLzMxk5syZPProozYdnBBC1BQDQxpU9hAqzYd/tTMIbmd2OIP9lGVWBbcAwX4e+Fs4bcsazib+ljz5yhCa1jE88csSRzvJ/QpRERRlcPv27YtKdet/yvj4ePr27Uvjxo2xt7fnwoUL5OTkkJZmmzVJQghR03w25E7Wnbxk09O+ylsdNyduFDtIwOrnZzqT8M1Ig/amT/1IbO3SZUfb1PchJklNYkay5c4KNPOrbbTdxcmBf6c9qDvxKy5FrWgu5g3rZJNxCSHMUxTgPvjgg3oBrqh8t45R1FDLxYl5wzoRHuRf2cMSQpRSoLcHD7QKYOPpy4r6O9mr0Bg5FKIiOahK//qTj4Qy40/9zVUrQy4w+j7Lh0eYE+DlRpYmj2NlusotC0d2Mft48RO/HKYuM1tCTAU81bWFjUYmhDBHUYA7adKk8h6HUChbk0fYf/Uvi/9B2n3OToJ9PXQnHQkhqh+lwa2KwlvdlR3gXsuwftOXh8aBtLmPGLS3Hb2F036pZR5TfGom26OulPk6RaxJHLg52ZOhMX1unJuTvS2GJIRQoFSRUFRUFP/++69uU5lWq0Wj0XDq1Ck+/PBDmw5Q6AubvsXoxgYthScdhc3Ywr/THqz4gQkhyuTNLRGK+rnYQ987GvCzDYO4ijL6TDA/7Oiu17Y78Cr3PLQLW5Ul2B4ZV6aDGEr6bv9ZRVnXn05dNBvcAmRo8nUHdQghypfVAe7333/PZ599BoBKpdId9qBSqejYUWr5laeDsYkWi7vH3FBzMDZRlisIUc189keUon7Z+cpr5lYVTnl2XP96JB65jnrtPR/ewd8NbXesLkBGrm2z2hM3HlIU4D65er+i6z25ej9JEuAKUe6srqKwYsUKxo8fz4kTJ/D29ubPP/9k8+bNNG3alLvvvrs8xij+M3FjhMXMhBZ4YaOyTJAQouqwJiyLT8sst3HY2uDoALK+ekwvuD3jk4rj5OU2D27LQ67C6pdp2cqWayjtJ4QoG6sD3GvXrjFixAicnZ0JCQnh1KlTtGjRgtdff53169eXxxjFf9Kzle1Wlj9Ahah+lN6hVwH21WDTr12BiqjF97N581167YMf2E2bsVsosKtG5SIUUPorqQa/OiFuC1YHuG5ubuTnF64zatSoEefPF54J3rRpU+Lj4207OqGnlouy2o61XRwtdxJCVCnPdglW3K9/i/rlPJqy6XHZn9xZj3NHai1dW4pzDi4vrWB7cPmuHXZ3tG0EGVjb1ab9GinsJ4QoG6sD3A4dOrBgwQKysrJo2bIlv/32GwUFBRw5cgR3d/fyGKP4z7xhnSxmeVTAXKmzKES14+zkrKifi7Mznw3uQJU8L0ALv625hz/X3qvXPGbAX9SZuJZc+/I97dLBTsVD7ZrY9JprxvRS1G/VaGX9VirsJ4QoG6sD3ClTprB3715WrFjBoEGDuHHjBp07d+Z///sfw4YNK48xiv8UHQ9pTrCvh2wwE6IaSsnKUdQvOVNDoLcHj7a3bSBXVmHXvcmfOZre8XX12mu9sIoVLS+U++s72KlIeOchFozoStM65v+cVKqJt7viP0/Dg/wJ8jKfnQ3ycpU/n4WoIFZXUWjevDm7du0iMzMTd3d31q5dy9atW6lXrx4DBgwojzGKYk6+MoSwGVuIuaFfB1cFujq4QtxO4lLUzNoTRUpWDt6uzkzuFYKvqfNTK2Esgd7KgilLz/V2VZbBPXUlhbgUNd+N6oa9vR1bTseRnFX608SKq+fmyLVM69fwr9rak5H/Bum1Te4TwZwOyipDlIUKWDQinLFdmuvahrQMZNaeyDJdt2kp/jz9538PEDZjC9E3DKvdlOZ6QojSK1UdXBcXF1xcXACoU6cOY8eO5dKlS8ydO5cXXnjBpgMU+oqOh/zp1EUmrDtIdm4+ro72LBwRzmApPSOqqNIEhnn5BUxYt5/tkVdIVGfr2r/d/y/1PV3oVMeZZ9z9+OlsIlGJKRyJS8HNyR4fNxceDgtkxp4osnLzsbcDb1cntECmJp+OgT608PcyGMN3+88y6ccINPlaVCq4q2ldvhvVjUBvD91Ytpy+rBdMfrU3EjdHB5r5eTJ/WGfCg/wN3usL3Vvwwa6TbDsTz42MW1naWXsiqe/mSOy7I7mankl6tgYXBzuy88zfxj8an0yTD3+kbX0voq6nW+xvjdGdgvnqr3Pk5Cu7ZnCqB+cWG9bd9ntuDcmut+bJz82J62U40tecWi4O+LjrfzmY3CuElUcv6H1uSvJ1c2Joq0D+jL5GTLJ+2TVHoEewPw72lr9Ilfx97362P79ExfPipsPk5WtxsFcx94GOPCknmAlRoVTaokK2ZbR3714mTJhAZGTZvjVXhlOnTgHQpk2bUj0/MzOTyMhIQkNDcXNzs+XQDJj6S9/fw4WBoQ1YMKKroj+UK0NFzlN1dbvNkTWf15KBQlyqmo0nbVu0vzjv/wIje5Ud/964abLfqHaNsbdXseJIrMVrejg54OxgR1KxYM7Z3s5iwOhhD2rzZwRUOTN/78iLx0L12j4MP8k73U9Uyng8nByIf3s4Hq6Fm3EfWfona09cMtl/UEh9ziXd5N/rpmuLP9y+MSsfN75mtuizXfKLi50KCox8aJtW45Mmb7c/l8qLzJNlZZ0ja+K16vd/Wg03Yd1+lkbEGPyln6jOZmlEDKBi8ahulTE0IQwo+bwuGNHFaBCswrrasNZKyc4jJTvPYr/Vxy8qvqZak4e6RKJSSTa0OgW39dSuxC94yKC98fgNXPasvPq8ak0eDd5bR/qnjwGwzkxwC7At6qrFa645dpHPBqmN3m0Yv3Y/Sw/HGLQbC26h8KTJNl/8xLk3Za+KEBWhaqb6hFFxKWq2R14x+Ze+FtgRFU9civnTzoSoCEo/r48u38PSiBiD28m3V5XU28O0g60NgtuFbc5hP2VZpQa3RTJyC/jp1EW+2HXSZp+fcav+MmiLS1Gz9nis1deKSc7gYGyiDUYlhLBEMrjVyKw9UWbXlAEk3Mxm9t4opg+VY5NF5VL6ef3ZTBAsqoba2Y4kzx9l0N5y7GbO+qRXwohMG7NyHxm5tkuJ/xZteNraB7+cLPXa57Gr9hE5zXDdshDCthQFuFeuWC7MnZSUVObBCPOsKSMkRGVT+nnNsmEwImxvwsk7+HpXF722LcFxPHD/H8qPX6tANzXl/3naf/FGqZ8bm5JhuZMQoswUBbh9+/ZFZeF8Qa1Wa7GPKBulZYR83JSdeCZEeVL6eRVVk2uuPeo5jxq0d350G0fqJVfCiKoOrdxzEKLKUxTgLl26tLzHIRRQUvqmrqcLL/UMqcBRCWGcks+rm6M9mZLBrXKG/9uItVt767VF1L1B10e3o60GeQwnoDzvY3Vt7EdkQumWZgT5yImfQlQERQFu586dy3scQoFAbw8GhjYwuisdCu8WDghpoLjwvBDlScnn9b7QhuyJSbS4VldUDId8FRcWDaNBhn75nv4P/cpvja5V0qisU3Ra2Borql+Y072xr0Hb2/eEsfLohVKtw/1hVHdbDEsIYUGVqKKg0WgYPHgwBw8e1LV9+OGHtGjRQu+f5cuXm7zGDz/8QM+ePWnfvj1vvPEGWVlZFTH0CrdgRFfGdGpKXU8Xvfa6ni6M6RTMghFdK2lkQhiy9Hld8XhPBoY2qIpLOWucvpfqkTP7cb3g9rJHBs4vLa82wS3AytG9+GxwB5tdb8Vowzq4gd4ejGhn/cE6wT5ylLoQFaXSqyjk5OQwdepUzp07p9ceHR3N1KlTefDBW7tNPTyMZyZ37tzJ3Llz+eKLL/D19WXatGl88cUXvP322+U69srgYG/H4lHdihXF1+Dj5sTkXqEEeMmtL1G1KPm8Fn4pU7EjKp6Em7cyuUpO9SqL8q6zaw07oPzeqXkqLRxccR93JupnKkcM/pONzc3Xkq1qmni76QLIxzs0YfnRC2W63si2jUzeEVs0sht2Kju2/hOnd7CHsd+lHKUuRMWr1AD3/PnzTJ06FWOHqUVHR/PUU0/h5+dn8TpLly7liSee4K677gLgvffe46mnnuLVV1/F1dXV5uOuCgK9PZhxv5QCE9WDuc+rqSD4he4teP/XUwYBhDn2gKUVvZ5O9gxr25i3+rfh412n+ftCItFJanJNVehXyMfVCW83J1Iyc0jOyr31eg5w08x5EiPCGvHJ4PZ8+ttpFh2ILtMYrNXxmi8HV96n15avKqDWC6vJdqy8tdEhvu6EB9fD1d6Obw6cs/wEbp0UVuS7Ud2wt7djy+k4veOVvV0c8fFwJiVDo9denJuDihHtm5i9I2buy1t8agYTN0aQnp1LbRdH5g/vTKfGlv8uE0LYTqUGuIcOHSI8PJyXX36Zdu3a6drVajUJCQkEBQVZvEZ+fj6nTp3ihRde0LW1a9eO3NxcoqKiaN++fTmMXAhha8aC4KIA4r6FuzljZlNPU18PhrYO1AXFJbPBbo721K/lypBWAbzcu6Uue7zw4Vun/h2MTWTwd7+RnJlrcP0iTvZ2aIqdTOZsb0eAl5vedU1lq9/cEsGnf0TpntupoRfrx/XVjeXbEd34dkQ3g37lQgs/bbqLQRcC9Jqf7XeAhWHnymXJiL1KRdfGvuyLvaGXOa/j7syglg1NHjM+b0QXvth1kjd3nKBAW3gU7nNdm7HvYrLZANLS3YOi9hs3M7mZloqXtzfY2Vt9R8zY5zbAy53DUwZZPUdCCNtRFOBGREQovmCnTp0U9330UcMSNFCYvVWpVHzzzTfs2bMHLy8vnnzySb3lCkXS09PJycnB3//WuiYHBwe8vLy4dk35ujGtVktmZulO4ila73u7rvu1FZkny2SODPk627HvubuZtPkov/x7jcSMW/V1/dyduKd5febe30EXHM0d2o7Lve5g7v7zpGXl4u3qxMRuzWhY+9baUmP/r7fx9yD6tcFGX6douYSmxLG7OfkFxCSpuXEzCx8nFZmZmfg62/FB/5Z6/TIzM3nz7la8eXcrg9ctOZbi/fLyC5i0+Sjbz15VnMU2xkEFLevW5quh7QlIcKdJbx+DPj7PrybNpTC493N35vnwJrz3W5TFZRzO9nbk5hdYXGJRoNUS5O3OwuEdjf5uNDnZJisfTOzWjIndmpm9vqk/v039Poras7KyiI2NJSgoSO+OX2n/PrgdyZ9Lysg8WVbWObKmJK2iAHf06NGoVCqDCxctLSjeFhkZac1YjYqJiUGlUhEcHMzjjz9OREQEb731Fh4eHvTv31+vb3Z2YZbGyUm/9quTkxMajfK/EHJzc8s89tjY2DI9v6aQebJM5sjQi6HujGwUyKqzydzU5FPLyZ5HWvhQ192Jc/+eNej/RJAThQWjIP3KRdItn1dj9HUcVCp+v5SGqToPWuDnM5f5vZEj9dxtX4O6cDyNWHU2mcTMXP66rCbHzHIKRxXUc3NAq1Lh4WjH/zrWo5VfYTay0esu+G3SD27f7H6MT8NP67UlZuTw75VERoX4sCrKfM3bnPwC7m9amwxNPr/FqU0GukXzNLKRY6l/N+VJ/p+zTOZIGZkny8oyRyXjPVMUBbi7d+/W/ff+/fuZP38+b7zxBh06dMDBwYFTp07x8ccfM378+NKNtoQHHniAu+66Cy8vLwBCQkKIjY1l1apVBgGus3NhMfmSwaxGo7Fq/a2joyPNmpnPEJhiKgMg9Mk8WSZzZF4ocFfH8p+notcBeH37CdKiU832T87J55frKj7pGGrzsRQfz+vbT7D70k2zfXO1cH/bJrzd5w7dHLklueHawnCeGjyzjgR346G7navnf9lby4c6eNbyIsDVkV1x5832K+95Kg35f84ymSNlZJ4sK+scnT9v/s+Y4hQFuA0bNtT998KFC/noo4/o2vXW4vvu3bvzzjvv8Prrr/PAAw8oH6kJKpVKF9wWCQ4O5sCBAwZ9vby8cHZ25saNGzRt2hSAvLw8UlNTFW1QK/6abm5uljua4erqWuZr1AQyT5bJHClTEfOkzlVW3+BmbkGVGkvRXx61Pq+F4+eOeo/Pbh/JlLsOm72GXy03jOz/Ndk3OVPZ0cwVMU+lIf/PWSZzpIzMk2WlnSNrTsy1ug5uYmKi3nrXIrVq1SI1NdXayxk1e/Zsxo4dq9cWFRVFcHCwQV87OzvatGnDkSNHdG3Hjx/HwcGBkBA50UsIUTZV6Yhsq8ZyA+7seKdBcHvlSAafDjlt4pmFik5EnNwrBH8PF0V9q9I8CSGE1QFuWFgYs2fPJiMjQ9eWmprKF198YbMTz+666y4iIiL47rvvuHTpEitXrmTTpk2MGzcOKFx3e/36dV3/Rx99lO+++45du3Zx8uRJ3n33XUaOHCm3CIQQZWZNkFdVxvLmP21wa1wiO/IwoIUGHdzNHq5R/ETEotPolPStSvMkhBBWlwn7v//7P8aOHUvPnj0JCgpCq9USGxuLr68vS5YsscmgioLor776itmzZ9OwYUNmzJihK/n1888/M23aNM6eLdxYMmjQIOLj43n77bfRaDTcc889vPrqqzYZixCiZqtKR2RbGouHxoEr740wfOA40PbWj6YO16jr6cKAkAZ69V+V9q1K8ySEEFYHuHfccQc7d+5k69atnDt3DpVKxWOPPcagQYPKlDEtClaL9OvXj379+hntO2zYMIYNG6bXNmHCBCZMmFDq1xdCCFOsCQgrayzPXWjO3B/D9frevPMm9n/a4+aun8215kREa/pWpXkSQtRspTrowcPDg2HDhnH58mUCAwOBwioEQghxO6pKR2SXHMvN9FxmP9sJ10z9P86zf83mX+9/CVWZrlhgzYmISvpWpXkSQtRsVge4Wq2WGTNmsGzZMnJzc9m5cyczZ87E1dWVd999VwJdIcRtqyodkR3o7cEMh44wpsQDzYEzUJBTAGUvS14qVWmehBA1k9WbzJYtW8bmzZt55513dMV2+/Xrx65du5g7d67NByiEEKKEfCAEGFyifQtwFrCv8BEJIUSVYnWAu2bNGt5++22GDRumq0d233338eGHH7JlyxabD1AIIUQx+yi891Z824InkI1hwCuEEDWU1QHu5cuXCQ01XNMVEhKiV7pLCCGEDWmBu4AeJdqXAOmAsjK0QghRI1i9Brdhw4acOnWKgIAAvfY9e/boNpwJIYSwoZPolfnSSacweyuEEEKP1QHuU089xXvvvcf169fRarXs37+fNWvWsGzZMl5//fXyGKMQQlSqn05dZMK6g2Tl5uPqaM+CEeEMbdO41Ne7VWUgBweVCi2Qr9Xi7erM5F4h+rViHwVWlbjATGByqV9eCCFue1YHuMOHDycvL4+vv/6a7Oxs3n77bXx8fJg8eTKPPPJIeYxRCCEqhTpLQ8P3N6DW5N1q0+Tx4A978HByIP7t4Xi4Kj96Ni+/gAnr9rPl9GWSszRG+6w8eoGBoQ1Y0LErDncYWUV2Hahj7Tu5pXhwbTSgFkKI24DVAe6VK1cYMWIEDz/8MMnJyWi1Wnx9fcnLy+PkyZOEhYWVxziFEKJMSpOFLRncFqfW5NHwgw2kfWz+i/3B2EQmbowgPVtDcmY2KVnGr1ckUZ1N2BfeOBwtEdy+AXGv6AenI9s2Yu2JS4qC1aLgetuZeG5k5Ojalx+JYVDLhiwY0RUHe6u3ZQghRJVkdYB79913s2/fPnx8fPDx8dG1X758mdGjR3PixAmbDlAIIcqitFnYn05dNBnc6q6Tk8dPpy4aDZSzNXmETd9CdJJa8VjrZrhw5VvDo3bzYgqYcHA/W2boZ35n7dEvdKvL/ho5MWz82v0sPRxj0H4jI4clETFotfD9I90Vj1UIIaoyRQHuihUrWLx4MVB40MPw4cOxs9P/pp+enk6DBg1sP0IhhLDA3G330mZhJ6w7qOi1n1l30GiA2+aLn4hJzlD8Hl471IpP/uqg13aw33XCf/Xj6VV/s+zwBYvXSFRnszQiBlAxd2g7XXtcipo1x2PNPnft8VjeH9BWlisIIW4LigLcYcOGkZKSglarZd68eQwYMAB3d/1jF93d3bnnnnvKZZBCCGFMXn4B41bvY3vkFRLV2br2okzm4NAGpcrC5uUXkJSZY+ZZt6Tn5Bq0HYxNVBzc1spxJGXeKIP2Vk9sJqi7O9+kdGHtsYuKrgWF1cR2RMVzudcdurYPfjlJTl6B2edl5xXw4a8n+XZkN8WvJYQQVZWiANfV1ZUXXngBAJVKxVNPPYWrq6vucY1GozvVTAghKsqTaw+y6cwVg/aiTObqY7GKrvPU6v2MibmuywBfSlFToFU2hmwjgePTaw8oe91TzVjwq/5ygp+bXGbIA7+DCq5fyub9X06Sk28+OC0p4WY28/ZHMyao8Oj03eeuKnre7n+V9RNCiKrO6jW4Tz/9NO+99x5BQUE888wzAAwYMIDu3bvz1ltvSaArhCiT4puyark4MW9YJ8KD/PX65OUXMG1vHLvjbpq8jhYsZi2LJGfnGqxntYbjK8uw10KBCvzcXUgolk02xiXXnrS5o3DQ6i/16vLIz0TUT9L9nJOXz56YhFKNKTVLAxQGuFdvmh9Pkfj0rFK91ptbIvjsjyi0gAr4X58QPhrSqVTXEkIIW7B6y+ynn37K4cOHad++va5t2rRpHDx4kJkzZ9p0cEKImiNbk0fzj3+k+5ydHItPJjpJzbH4ZLrP2Unzj38ku9hSg/EbDpkNbitagRZygXwtXFNnYy75O+zfRmTMeVQvuD3in4TDy8v0gluA3AItyRnGy4lZ4lVs45yjnUrRc/KVpq3/cy1Fjf3UZXz6X3ALhV8sPv0jCvupy7iWonyDnRBC2JLVAe6vv/7K559/TufOnXVt/fv356OPPmLbtm02HZwQouYoqjhQMsTSAtFJasKmbwEKN0ytPxVf4eMrK/sCFbELhrFua2+99nuH76Lz4z+jNRKD1nayx9e9dHfFJnZtqvvvxgo3juVrC+dXqYYf/limx4UQorxYHeBmZmZSq1Ytg3YfHx/S0tJsMighRM1yMDbRYjmt6CQ1B2MTGbdqXwWNynbuulQPzazHCVTf2px71T0T55eWs6ux6XWv6Tm5NK9TurN4d5+7pvvvl3q2UPy8FzceUtTvzS0RNu0nhBC2ZHWA265dOxYtWkRBwa21bVqtliVLltCmTRubDk4IUTOMV7gpa8K6A/wenVjOo7EhLfy9ciC71vfXax41aA8Bz2wgz978koDsfIhMTC/VS7/w03Hdf3/y2z+Kn7ctUll2/NM/omzaTwghbMnqTWYvv/wyTzzxBAcPHqR169YA/PPPP6Smpupq5QohhDUup2Uq6heXmml2fWtVcuc1Hw6tHGTQ7jFpJVmO+YqvcylFeS1dU2KtqMebX10mWAghzLA6wA0LC+Onn35i7dq1nDt3DgcHBwYPHsxjjz2Gv7+/5QsIIUQJyrZAKe9XqbTw4099GBodqNf8/N0H+LbtOasvl2eDgFNiViFETWN1gAsQGBjI1KlTbT0WIYQZ5k7rKs9rW3pdY48DVo21jrszqdmGByaUlJevPPNZGZon1yLyh/sN2n2fX0OqS+mqIQghhLCeogB32rRpvPnmm3h4eDBt2jSzfT/55BObDEwIUSgvv4AJ6/bz48mLpOfcCvAWH/yXB8Mas2BEVxzszS+nLxmEtvT35M3tJ8jKzSc3vwBHexVqza1rf/P3WQaFNsTN2YEtpy/rBZ9F9WI7B3hxM7eA+NQMvXEZqyf7zd9naeLrwZZxffnt3BVe3HSYvHwtDvYqJvdozuVUZTv31bnWHXhQkebvCueZk8312t7qdpyPu5wq03UdVLbJ4irl7eKoqJ+7ox0ZCn4f7o5Wb/UQQogyUxTgXr58Wbep7PLly+U6ICGEvidX7WOlkRO50nPyWRIRQ15+AUsf66lrLx7M1nZx4kpaJntiEvWOsi0pp0RiNDuvgA2n4syO69DlVMXvITuvgMiEdJp9skmvPS9PW+03ITW86calhcMN2gMmrOeqR+kOTiiuIoNbgKw822bJs/MKmLr5sE3vOAghhCWKAtxly5YZ/W8hRPmKS1EbDW6LW3E0lo/ua0/9Wm5MWLef7ZFXzAazwnbe3deWtw6G6bXNaRfF5L5VqzSWCivW4WqV9VR6Sly+tjCrv/LoBQaGNlB0x0EIIcpKUYB75YrhWe+mNGjQoNSDEULoe2HDQUX9Jm04iI+HC0sjYmRDUQXwyXLi+tcPG7Q3f3IT0d5V54S1ItZ8JrIVllGwNrOcqM5maUQMoGLxqG7WPVkIIaykKMDt27cvKpWy/cuRkaU/z10IoW9nlLIvlzuiruDt7iLBbQWYeKwFX/3eWa9tXfNYRg3eW0kjqj60wI6oeOJS1LJcQQhRrhQFuEuXLtX9d1RUFPPmzeP555+nffv2ODo6curUKebOncvzzz9fbgMVoiZSmiXL1SLLEsqZW64DN+c8YtDe4fGtnPBPqYQRVU8JN7OZvTeK6UM7VvZQhBC3MUUBbufOt7IVH3/8MR9++CH9+986mSc0NBQ/Pz8+//xzRo0aZftRClFDOdqr0Ci4ZWzVGkthtUcig1i+vade296GCfQZ+Us1Kc5btSRnSsk0IUT5sroO7oULF2jWrJlBe6NGjbh61fSZ6kII693bogFbzlg+OjXI250LNjjxSuhzzLfj6jcP4Z3jrNfeZ+RO9gZUoyODqxgfN6fKHoIQ4jZn9VbWFi1asHTpUrTFdtrm5eXx7bff0qZNG5sOToiabs6wzpY7ASse74G/h0s5j6ZmGXChAdmzH9MLbs/XTsdx8vLbOrh1tVfWz7NUxwRBXU8XXuoZUronCyGEQlb/EfXaa6/x1FNPsXfvXlq2bElBQQGnT58mKyuLJUuWlMcYhaixAr09eLR9kNlSYY+2DyI8yJ+BoQ2kioIN2BWoOL5sMK2SvPTaH7j/d7Y0rT51wFc/Eg4UVnRoWMuF+HRla7R3P3+von6nXn+QoA9/tGpMKmBASAPZYCaEKHdWZ3A7duzI1q1bGThwIBqNhry8PB588EG2bNlCSIh8KxfC1r5/pDtPdGqKV4kTprxcHHmiUzDfP9IdgAUjujKmU1Pqekomt7S6XvEjd9bjesFthkMebi+uqLTgtq67spPFinO2g0EtA3Q/dw3yV/g8FeEK+wZ6e9C/ubK+UJi5HdMpmAUjuip+jhBClFapbjIFBgYydepUNBoNjo6OikuICSGs52Bvx+JR3Yi7N+y/E8o0+Lg5MblXKAFe7ob9dCeZafByddSdZJZw81YGz8vFATs7FV6uzqRmaUjJ1Bhkfge0qM/FlAyib6SjscEJuR0a+vDjuD58vfcMn/0RpXu9ED9P3JwcaVjbhS1nlNfctikt7NzQj36X6us1j7t3H0taxVTOmIDG3m7c37oRX+1Vftqbo52KxPdHgjZP11b8c2LOhO7NLXcqZuvT/Zmw7gBLIqINHhseFkjD2u6kZeca/bwKIUR5KlWAu2rVKhYtWsTVq1fZuXMn3333Hf7+/lImTIhyFOjtwYz7LZdWMtaveNBrLNgw93jRY5dTM4hJUhPs60FMkpqj8clWjb9NA28CvNz5aEgnPhrSyWS/L3ad5M0dJ8jXFt7S7tDQi3YBviw/ckHx6VnWaHXDi5NLhxi0e01czU3nXJu/njWGhTXmpZ4hrD4Wq6gMnKeTHadeux8PVycyM28FuJN7hbDyaAyJ6hyTz/X3cGFq75ZWja/oS9V7Fr58CSFERbM6wN2yZQszZszgiSeeYNGiRQAEBwczffp0XFxcGDdunM0HKYQoG0vBsbnHjT02bvU+qwNcpTvnX+0Xxqv9wgza1xy7SA62DXB/2N6N0ZFN9dpe6XWYmR1tc2BNU18PTr4yBBenwj9qx63ex5IIZRlhf4/CzViB3h6K1lergGFtg4yuby28RkOT11ABA0NLvzZW6ZcvIYSoKFYHuIsXL+bNN9/kwQcfZPHixQCMGTMGNzc3Fi5cKAGuEDWAt6uz5U7F2GLnvKujPWpNnuWOCjROcyfmu2EG7XWfXcsNN9NZTkv83J2p5eJEbRdH5g/vTKfGfnqPWzNvxQPOwnWrKrZHxhvN5Pp7ODMwtKHZ9a1F19gRFa+3XKWupwsDQhrI2lghxG2lVHVwO3Y0/KYeHh7O+++/b5NBCSGqtsJb3hcU3Ta31c75BSPCefCHPWW6BsBX+zsxcb9+sP1Zp9O80fNYma7rZK8i4uX7zL5PJfOmAoaHNdILOEuury6+XCTQ213RkgBja7RlOYEQ4nZldYBbp04dLly4QGBgoF77sWPH8PdXvqNWCFF9Kb1tbsvs4NA2jfFwcih1Ftc/w4Wr344waG/y9EYu1Sr7IRnN6nhaDOKVzNvwsEDWPNHb5PPLuhRAlhMIIWoCqwPchx9+mPfff59p06YBEBMTw19//cWsWbN44oknbD5AIUTVZOqWt5ujPfVruTKkVQAv925p0+xg/NvDafjBBtQ5hkGug50KD0c7UnPyDR57JaIln+29U6/tr16JPNT7d65nGB4bqwKCfT3oEuTH9jPxJGeZP1rW2d6On566S9F7kKUCQghR/qwOcMePH8/NmzeZMmUKOTk5PPPMMzg4ODBq1CieffbZ8hijEKIKqoxb3h6uTqR9/AjrDv/LcxsjyNXa4eZoz8KRXRjcupHeWBzswDXTgdlPGjkN7h/o0dKfiJRBzNoTRVRiKkfiknF3csDHzVlv/WxcipqBC3cTmZBuclyjOgTRxLeWovcgSwWEEKL8WR3gHj58mEmTJvHcc89x/vx5tFotwcHBeHjIyTRC1ESVcct7UMsAdqpuEhoaipubm/GxLAaeKvHEAcDPFKZoS/Y3IdDbg+NThzBh3QGbZl1lqYAQQpQfqwPcSZMmsWjRIlq1akWbNm3KY0xCCFF62UBtoOSqgv1Al9JdUrKuQghRvVgd4Pr4+HDz5k2bDkKj0TBs2DDeeustwsPDATh+/DiffvopZ8+exd/fn6effpoRIww3iBTp2LGjwbiOHj2Ku7v85SNEjbEJeLBEWxhwjFIcTG5Isq5CCFE9WB3g9urVi2eeeYbevXvTuHFjnJ316zq+8MILVl0vJyeHqVOncu7cOV3b9evXGT9+PI888giffvop//zzD9OmTcPPz48+ffoYXCMhIYGbN2+ya9cuXFxcdO3Fb10KIW5jeUAz4GKJ9h3AvRU/HCGEEJXL6gB3586d+Pr6cvr0aU6fPq33mEqlsirAPX/+PFOnTkWr1S+Ys2vXLurUqcOUKVMACAoK4uDBg2zZssVogBsdHY2fn59B6TIhRA3wB1CygIEfcBlQdniaEEKI24zVAe5vv/1msxc/dOgQ4eHhvPzyy7Rr107X3rNnT0JDQw36q9Vqo9c5f/48TZo0sdm4hBDVgBac73KGQyXaVwMPV8aAhBBCVBWKA9xr167x66+/4uzsTO/evalbt26ZX/zRRx812h4QEEBAQIDu56SkJLZt28akSZOM9o+OjiYrK4vRo0dz4cIFQkNDeeONNyToFeI2pTqm4s4edxo+oAZk2b0QQtR4igLcw4cP8/TTT5OdXVgex83Nja+++ooePXqU6+AAsrOzmTRpEnXq1OHhh42nZWJiYkhLS2PKlCl4eHiwcOFCxo4dy7Zt2xSXL9NqtWRmZpZqjFlZWXr/FsbJPFkmc2SZ0yNOuP7kqtemmakhb8J/hz+U7n/j2458lpSRebJM5kgZmSfLyjpHWq0WlUqlqK9KW3IBrBGjR4/Gw8OD9957D3t7e95//32io6PZunVrqQZoTIsWLVi6dKmuigJARkYGzz//POfOnWPlypUEBQUZfa5GoyE3N1dXMSEnJ4fevXvz5ptvMmTIEIuvferUKTQa8ycVCSEql/MlZ1oPa23Qfnz3cfJrG55eJoQQ4vbj5OSkqEytogzumTNnWLNmDf7+/gC88cYb9OnTB7VaXW4HPKjVap5++mkuXbrEkiVLTAa3UPhmnZxu7SZxdnYmICCAhIQExa/n6OhIs2bNSjXWrKwsYmNjCQoKwtXV1fITaiiZJ8tkjoxznOyI40JHvbYrz1zB+SNnmrs2r6RRVW3yWVJG5skymSNlZJ4sK+scnT9/XnFfRQFuZmYmXl5eup/r1q2Lo6MjaWlp5RLgFhQU8MILL3D58mWWLVtG06ZNTfbVarX079+f559/nmHDhunGe/HiRYKDgxW/pkqlKnNZMVdXVylNpoDMk2UyR/+JBwIMm7POZ3E19SqhrqEyTxbIZ0kZmSfLZI6UkXmyrLRzpHR5AigMcI2tebC3t6egoMC6kSm0fv16Dh48yNdff02tWrW4fv06UJhl9fLyQqPRkJaWho+PD/b29vTp04c5c+bQsGFDfHx8mD17NvXq1aN3797lMj4hRAV4D3i3RNvzwDzQZmohtcJHJIQQopqwukxYRdi5cycFBQU888wzeu2dO3dm2bJlHDt2jDFjxrB7924CAgJ49dVXcXBwYOrUqajVarp06cKCBQuwt7evpHcghCi1ZMDXSPu/wB0VPBYhhBDVkuIAd/HixXrrJfLy8li6dCm1a9fW62ftSWZFzp49q/vv7777zmzf8PBwvf7Ozs68/vrrvP7666V6bSFEFTEfmFiibRiwoRLGIoQQotpSFOA2aNCA7du367X5+fmxe/duvTZrTzITQggAMgBjy/mPAu0reCxCCCGqPUUBri1PLxNCCD2rgUdKtHUD/gKU7ycQQgghdKrkGlwhRA2gARoASSXafwf6VPhohBBC3EbsKnsAQogaaCfgjH5w2wTIRYJbIYQQZSYBrhCi4hQAYcCAEu0/AjHIPSUhhBA2IX+dCFENxaWombUnipSsHLxdnZncK4RAb+sPXbHVdRQ5AHQt0eZMYT1bl/J5SSGEEDWTBLhCVCN5+QVMWLef7ZFXSFRn69pXHr3AwNAGLBjRFQd7yzdmbHEdxcGxlsKM7S8l2r8DxlkcqhBCCGE1CXCFqEYmrNvP0ogYtCXaE9XZLI2IAVQsHtWtXK9TFBxvOX2Z5CyNrn1pRDRDWgewYERXfj4Tx4R1B2l8xZ2D3w0yvEgaUMviMHUOxiYycWME6dkaark4MeO+NkarigkhhBAgAa4Q1UZcipqfz8QbBKVFtMD2yMvEpajNLjMovM5ls9f5+Yzp6zy99m+WHb5g0J6cpWFJRAzLD8eQr4XFO7rxxJmmen1yPszD+U2HwuzvZsvZ32xNHmHTtxCTpNYb790L/6ShhyPHmt6BHPkuhBCiJAlwhagmZu2J4npGjtk+ieocZu+N4qWeISaXDxReR2P2OtczCq8zfWhHvfa4FDXrjl80+9yGae5cWDTMoL3+M+vIVOcyfHVjxUsjwqZvITpJbXAtLXBZnUv4vF2ce9PwtYQQQtRsEuAKUcmKr2X1cLTjXj8INdYv1TDQM2bNsQt88/e/ZOXm69qKB5BnE1MVXScqwbDf+7+cJDuvwORzPtnTntcOt9Zrm37nP/yv99HCHzSwJCLG4HnGlkYcjE0kxkhwW9yF5AwOxiYC6C1hmDesE+FB/mafK4QQ4vYlAa4QlaRoLeu2M/HcKJaZXe5sz+BLuSx+pIdeNjMmKUPRda+kZxu0FQ8gD8TeUHSdg0b67b943Whfv0wXrn0zwqA9+KmNXKytbNyFSyPidEsjJm6MMLmMovhz+sz/hdx8rV7f7nN2EuzrwclXhuDiJH/MCSFETSN1cIWoJOPX7mdJRIxecAuQlpPPimMXeXTZXr32pr5l21ZVtEY3Jz/fYl+AnALDTK3KyNm5Lx8ONQhul4VGYz9lmeLgtsj1DA2z90YBkKjOUvQcTYngFgrfa3SSmrDpW6x6fSGEELcHCXCFqARxKWrWHo8122fDqUuMXPIHefmFgWZtF8cyv26iOoesXNNLDIrLNbIUoXiQ7ZnjSP6Xo5m+R3+dbtiYLYwd+Hepx3g8rvB4syQL642ViE5S65YwCCGEqDkkwBWCwoBz6ubDjFu9j6mbDxOXomy9qyUHYxPp+OU2mn/8Ix2/3KYLtj6wsJa1yIaTcYxfux/A4u16pVSGSVijcgu0BsFhZEIaAE/8E0zqvFF6j/3a+Ar2Ly/jnzqpZRrfnzGFr+nqoHCgFkxYd8Am1xFCCFF9yOI0UaPZ6uCEkkyVtypaG+poxTXXHr/I+wPakq+1TYhboPAyWgzXsqan5aKe/Qiu+fp/dHQftZ0DDZSt7bU4vv/+nZlnm/d7McW6ZRJCCCGqPwlwRY1mq4MTSjJX3io6SY2jnfLsZHZePh/+ehJvV2erx1FWurWsX2zh3zYPcvXzkXqPn/ZNpd2YLWhtk2zVY2+ja2oUZMqFEELcXmSJgqix4lLUbI+8YvbAgx1R8QbLFUwtOyj+uLHgtrhcpWnU//wde4ORbRtZ9RxbsStQsf2LfnC/fvt9D+6m7RPlE9yOX/M3uTbK4HpKFQUhhKhx5E9+UWPN2hOltyzBmISb2boDD4qWHZQMXrvN2UnTYrfxJ26MsPlYk9RZfLzrlM2va0nPy/78sfZe/bG45NDwmfXk2pdfZnTxoWibXetGlvlDLYQQQtx+JIMrapyiDWVb/olT1D85szBAavPFTyYzs9FJatpM/wmAeIUHMlgjIUPD1sgrNr+uSVrYs/peg+D2sfv24v/82nINboUQQoiykgyuqDFMbSizJFmdWXiqVrL5zUoxSRm4vLrc6uUHVU3bRG+OLh9s0O45aRWZjnmVMCIhhBDCOhLgihrD1IYyS/ZeuEH0WmWlpqp7cLtmSy8eOtdYr23SXYeY3/5sJY1ICCGEsJ4EuKJGiEtR8/OZ+FLVkk3NziW3wPbLDqqSpime/Pv9Awbtfs+tIdlV1rAKIYSoXiTAFTXCrD1RXC/DyVgZGmXH21ZHJ5YMoXWSl17b+11O8F63k5UzICGEEKKMJMAVNUJcOWz8qu7uSPEkykjWttH4DcR7Zlb8gIQQQggbkQBX1AgxSXKaVXE7NtxN/4sN9Np2B17lnhG7KmlEQgghhO1IgCtqhKa+HhyLT67sYVS6empX4hc8ZNBuy6N2hRBCiMomAa6oEQK83Ct7CJVuwS9deOr0HQbt9lOWVcJohBBCiPIjAa6oESb3CmHl0RgS1aXfaFZd1cpxJGXeKIP2++//na1NL1fCiIQQQojyJSeZiRoh0NuDgaENUVX2QCrY23+HGQ1uHV5eJsGtEEKI25ZkcEWNsWBEV0DFjqh4Em4qP8msOnLKsyPrq8cM2p++52++bx1dCSMSQgghKo4EuKLGcLC3Y/GobsSlqJm1J4q41Aw2nLyk6LlOdio01eSUsmdO3MH83V0M2l1eWkGufUEljEgIIYSoWBLgihon0NuDGfd3BGDE97+z8bTpW/WNarkwvH0Tbmbnsujg+YoaYqnYFajInfW4Qfub3Y/xafjpShhR1VDL2b6yhyCEEKKCSYArarRVY3ozYd0BVkZEk1us3Ql4pFMwC0Z0xcHejrgUNT+djiPRzGloKijVUcC28MC5QDZs6WPQXvuFVaid8ip+QFVItyb+lT0EIYQQFUwCXFGjFS1beO/eMGbtiSIlS4OPmxOTe4XqlRYL9PZgYMuGLI2IMRrEqoDhYY0I8HInJUvD0ohomwa7wT7uBPt6sOtcgv4DWsifOdqg//y2Z5l09yEbjqD6Cq3rVdlDEEIIUcEkwBUC/WULppjapFbX04UBIQ102V6ALwd3oO5768kzsm5XBTzcrjH1arnyx79XOH4t3eL4lj/Wgwa13WjxyWZy8gvX0fa47M+fa+816Fvv2XVcd7P9Jro9z9/Dq1sOcTAu1WJfO2B0p2C+ur8THWZtI+aGukwBv4uDHdl51q8f9nN34qWeIWV4ZSGEENWRBLhCKFRyk5qpbC+Al4cLOV88znf7z/LipsPk5WtxtFcx98FOjO3SXK9vkw82cCk10+TrBvt6EB5UeJv94fZBLD0cQ8L8EdTJdtHrtzX4Mvc/8LuN3q2+pr4edG9al65N6isKcMeFN+Xbkd0A+HfagxyMTWT82gP8k5Bm1esWfXl4q38b5v71L1v+iSM6Sa3ouSrgvpYBBHp7WPWaQgghqj8JcIWwkpJsb5Gnurbgqa4tzPaJ/N/9hM3YYpDlVFEY3J58ZYiubWFoV75/tLvBNTpP3Ear7l48rm3C+hOXyM7LN/l6Pq6OeLs7czM7j0T1rUyvnUpFgVY/z1pyDJN7hbD00DmSs02v6/V1deD/+ofptYUH+XPytaGMXfUXyw5fMPncB9sE0Njb0+iXhxn3d+SzwR2YsO4A289c1lsPXXL9c/GsuhBCiJpHAlwhKpmLk4Muy/nc+gMk3cykTi13vnmoC50a+93qGA4Oh/TPZrkckMmseWfY2KuPLhD8cKCaD345yf6LNwAIq++Fu5MDeVr0gkZjmej41AwmbowgPTuX2v/f3p3HRVXufwD/jOyIghuKaOISS6GgoLiWmha5a2lJLmSldjVTTBPL++umXc0WNZfU3OW6a7mWmdebS0EoCaSgrIobiyyKLAPM8/tjYmSYGeawzeDweb9evnKeeeac7/l2OHx95jnPsbbAuld6qMXQtokdhnd+qsK5yMM8n9I5arppXG80kDXAsSspuJ8nV7U3s7XEsGfbqE3z0KZ0FP36nXR8evwPmNk0QovGthjX5SnsjbxZ4ag6ERHVHyxwieoIPxdHnP/HIMTExMDDwwO2trbKN24AcNHygUtAm262+BLqo8ltm9hh42u99e5P20h0G4eGuBg0tMLPSZmLrEtlpnlUpI1DQ8zp1kotTz1cuFoCEREpscAlqsteA7CvXJsNAN1TdmtdTRSplZnmQUREVFkscInqovsAtNWKvwB4wcCx6MAilYiI6ioWuER1jPNKZ9iG2Gq+oYBykisRERFVSPfdHAYkl8sxbNgwhIWFqdpSUlIQGBgIb29vDBkyBOfPn69wG8eOHcOgQYPg5eWFGTNmIDMzs7bDJqpZeYBtQ1u0Cmml3r4byiUCWNwSERFJYvQCt7CwEEFBQYiLi1O1CSEwY8YMNG/eHAcPHsTIkSMxc+ZM3LlzR+s2oqKi8NFHH2HmzJnYu3cvHjx4gODgYEMdAlH1fQ3tUxKKALxu4FiIiIiecEadohAfH4+5c+dClFt7MzQ0FCkpKdizZw9sbW3RsWNH/P777zh48CDee+89je2EhITg5ZdfxqhRowAAy5cvx4ABA5CSkoK2bdsa4lCIqqYYgIVms3y5HJbzLA0eDhERkSkw6gjuH3/8AT8/P+zdu1etPTIyEs8888zjZZIA+Pj44PLly1q3ExkZCV/fxze7ODk5oXXr1oiMjKyVuIlqxH+gtbiNOB+B4hm6H6RAREREFTPqCG5AQIDW9vT0dDg6qq9p2axZM9y7d09r/7S0tEr110YIgby8qq29lJ+fr/Zf0o55+psAbO00byIrmlOEBx89gEgWzJEePJf0Y46kYZ70Y46kYZ70q26OhBCQyaTdkFInV1HIz8+HpaX617OWlpaQy+Va+xcUFFSqvzZFRUWIiYmpfLBlJCcnV+vz9UV9zlOj0EZwnemq0X759GWU2JcAycrX9TlHlcE86cccScM86cccScM86VedHJWv93SpkwWulZUVsrOz1drkcjmsra119i9fzMrlctjY2Ejep4WFBTp16lTpWAFlQZ6cnAwXF5dK7bO+qe95srG3gaxY/V+exeOKId8qhyuURW99z5FUzJN+zJE0zJN+zJE0zJN+1c1RfHy85L51ssBt2bKlxkFkZGRoTEMo2z8jI0Ojf4sWLSTvUyaTqc35rQobG5tqb6M+qHd5ugigu5b2m4B5W3OYa/kxrHc5qiLmST/mSBrmST/mSBrmSb+q5kjq9ASgDiwTpo2XlxeuXLmCgoLHz7m/dOkSvLy8dPa/dOmS6vXdu3dx9+5dnf2JDMYdmsVtbyjXteUCH0RERLWiTha4PXr0gJOTE4KDgxEXF4eNGzciKioKr776KgDl9IP09HSUlJQAAMaPH4/Dhw9j//79iI2Nxfz589G/f38uEUbGEw/lgxmulWv/C8AFw4dDRERUn9TJAtfMzAzr1q1Deno6xowZgyNHjmDt2rVo3bo1AODPP/9E3759cffuXQBA165d8emnn2Lt2rUYP3487O3tsXTpUmMeAtVnLwN4ulxbKyhHbZ81fDhERET1TZ2Zg3vtmvpQV7t27RASEqK1r5+fn0b/MWPGYMyYMbUWH5Fe9wA4aWk/D6CPgWMhIiKqx+rkCC7RE2cqtBe3CrC4JSIiMrA6M4JL9ER6AMBeS/thACMMHAsREREB4AguUdV9Cu3FbQlY3BIRERkRR3CJKqsQgLZnjnwH4G0Dx0JEREQaOIJLVBmboL24LQCLWyIiojqCBS6RFAoo17V9p1z7p1Au/2Vl8IiIiIhIB05RINLnMIBRWtpzADQ2bChERESkH0dwiXQRUI7ajirXPvXv91jcEhER1UkcwSXS5gKAvlra70L5VDIiIiKqsziCS1SeEzSLW38oR21Z3BIREdV5HMElKnUFgKeW9jgAnQwcCxEREVUZR3CJAOXjdMsXt25QjtqyuCUiInqicASX6rcUAE9paQ8H4GvgWIiIiKhGcASX6q8AaBa35lCO2rK4JSIiemJxBJfqn0wAzbS0nwTwooFjISIiohrHEVyqXz6E9uJWARa3REREJoIjuFQ/5AFoqKU9BMAbBo6FiIiIahVHcMn0rYL24rYILG6JiIhMEAtcMl3FUD5qd3a59q+gvJGM318QERGZJP6KJ9O0B8B4Le2PANgaOBYiIiIyKI7gkmkRUI7ali9u5/79HotbIiIik8cRXDIdvwAYrKU9A9pXTiAiIiKTxBFcMg3W0Cxux0E5asviloiIqF7hCC492SIA+GhpvwHtj+AlIiIik8cRXHpyeUKzuPWDctSWxS0REVG9xRFcevIkAOikpT0KQGcDx0JERER1Dkdw6ckyDJrFbXMoR21Z3BIRERE4gkt1QEpWLlaejUVWfiGa2Fhh9nPuaNvETr1TKoBWWj58FkA/AwRJRERETwwWuFRryheu07q3U3u/uESBqft/xw9RN5FTWKxq3xoWh1FdnsLGsb1gbtYA+AeAb7XsQAHlmrdViEVrEU1EREQmgQUu1bjSwvXHmDtIyy1Qte+6lIjujtYIcXUDAEzZewH/uZSs8fmcwmJsD0+E+aMG2Ph2L80dHAIwupqxRCThZY/Wj4toIiIiMhkscKnGTd3/O3aEJ0KUa097VIgTSYV473AElgztht0RyTq3sSDME5993VXzjWIAZjUQS24BdoQnApBhy+u9AVR+lJejwkRERHUTC1yqUSlZufgx5o5GQVlKAPj5+j1k5YdCoaWTRUkDFKx6Q/ON9QCm1XwsP8XeRtL9B1h8KhpH/kpBVn6R6v3t4fEY4dlWY5SXo8JERER1GwtcqlErz8aqFX3apD0qxC/x9zTa3/yrIzb93FvzAwUArCofy6c/R+mNJfVhAYZtOoPYtAca72XlF2F7eCIUCoFtAX1V7bU5KkxERETVxwKXalRWfqGkfgrF47/LBFC8YqJGn3/1jMSqgTHItHq9UjGUjrDu/TNZUn9txW1ZuyKSsPhlb7RtYoeUrFycuHq7wlHhH2NuISnjIRb/EsVRXiIiIiPgb1iqUU1spA21NrK2AAAMTXTWWtw2mbEHn/aOQp68GL5fH0dYcprkGEpHWAuKFfo7S1AigAXHIgAoR6jTH1VcxKflFmLElv9iR3iixghy6Sjv1P2hNRIbERERaWKBSzVq9nPucLSzrrCPY0MrbB7XEyVfT8SRHwaqvbfZMw5mQTvxwEo5F7ZIIfDn7Uz0WX0Srv/+HgXyYm2bVNE377Y8iauM4edrd5Xbz86V1D8+/YHeub8pWdK2RURERJXDApdqVNsmdnjZo7XOwlEG4F24YkSXdhrvOU89gKkvah/ZFAAS7ueiy1dHK9y/lDnAZWMxbyCtxC0RynI18f4jSf3legaPUx8WYNW5WEnbIiIiosrhHFyqUFVukto4thcAGX6KvY3Uh4+LTceGVohePRzNs23U+p9qdwf+r5yWFE9iRi7CktPg5+Ko9X2pc4CtzRvgta4uuJCYhvj7+kdSW9opp150bGaHP29nStqHPpl58hrZDhEREaljgVtP6Stcq7MUlrlZA2x5vbdqH3FpOciKkOPc1y9rdr4GNLY0R9dDTXHlbhbk2tYOK0MAmHkoHOFBQ7W+L3UO8ASfDtgwrhdGfPeLpALXtUUjAEAbh4aSti9FU1vLGtsWERERPcYCt56RWrhWZiksXVo0tMbRKynYvK43+t1uqfbejaa5aHnXGtaW5vCDIy4GDUWHJQdxIytP7zHcz9M9BWH2c+7YFZFU4TSFlo2s8fHgzgCAaxkP9e4PAK6lK/uN83oKq87GVDjHVwbA3toC2QVFOvu0bGSN9/u5S9o3ERERVQ7n4NYzpYVrRXf3S31Agr6bpAYvOoXrH43WKG57jj+BDoHfa8ynfZCvuyAs62EF/do2scNgt1YVfn6QayvVaHXmI2nTBO7/3W9f5E29N7AJAK3tbSqch+zv3prr4RIREdUSjuAagdR5rfr6hSWnYcahcDwokKOxtSX+OdgTvyam6+wvpXA9HH0TZjJIekDCklNR2DBOfRS3NOZhS51xLkxzSoJZ0E7V38vPp21sbY6sCkY9SzW2qfir/dDkDMnv21g0APL17lLZD9JXUXBztEf3p1pozENu2cga/u6t/56nTERERLWhzha4hw4dQnBwsEa7TCZDbKzm3ecjRozAtWvX1NqOHj0KV1fXWouxskqnBxz96xYy8x+PHO4IT8Bwzzaq6QGl/Y5fvY2MMmuuhlxKxNBnnPHNyO7otuI4EsrNHR297aza621/xGNk58ePmpWywkB2QRF2XkyUdDxbwhJQpBCqYm3q/t9xOvQObqwaq9F3yOjTONn+jlqbANB/7c941qkJ1o7pjkyJI7i2lrpP2yPRNzTyUl7C/Vwcib6BEZ3bIVfPsmOlSvtJXUXhRuYjhAf1V/tHiL21Jda+0gM92rWQtA0iIiKqmjpb4A4ZMgT9+vVTvS4uLsbkyZPRv39/jb4lJSVITk5GSEgIXFxcVO1NmjQxQKTSvb3vN+y8mKTRnpkvVz4SVgDbxvfBO/t+xw4tRWbGo0JsD0/EzvBESHmEQXaB8lGzJQqB7QF9Ja8wUKTnRq9SCgDbwxOxPVwZ65Lz3tjyRx+NfmZzdupccFb+9zq3vVeflLRPAJDJlCPFZUenLyTew6it/0NmnrQiefS2s3ilS1vV8l/6lPydE6mrKLg0bYgpey6oclOq1zc/YXL3DnySGRERUS2qswWutbU1rK0fPzBgw4YNEELggw8+0Oh769YtFBUVoUuXLrCyknYXvaGlZOVi9yXN4rasPRGJeLfX09gVUfEIamWfzxVyKQknYm4jq5aWpbIuMsOj1QEa7ZP9zyPkmYqPuSqu3MvBM599j7G+HXSOZktxMCpFct9ceQnmHr6Ixn8/gU2fH6JTdP5/2h6eiIxH+Tjy1iDJ+yciIiLp6myBW1Z2dja+++47LFmyBJaWmvMv4+Pj4eTkVGeLWwD49OcoFOsZLCxSAIG7L6CGnjCrprbWXP3Hn25YfaaHRrvV+yEoNpP6PLHKyxPKQvFg5A3kyktqbT9lrTwbg+YNpZ1j+v4XHr96V2MUmoiIiGrGE1Hg7t69G46OjvD399f6fkJCAiwsLDBt2jT89ddfaN++PebPn48uXbpI3ocQAnl5+peo0iY/P1/tv9rsDE+QtK3rGU/G41vNFDLIV07QaJ/33CV87XvVYHEYqrgtVXZOdHV1//IwEj8ardYm5Vwi5kkK5kga5kk/5kga5km/6uZICAGZTNoTSOt8gSuEwP79+/H222/r7JOUlIScnByMHTsWs2bNwr59+zB58mScOHECTk5OkvZTVFSEmJiYasWanJyse/u1N5hpcGOvtcOe489ptDd6bzfyLKTdtEVAeoFC5zlX0blEjzFP+jFH0jBP+jFH0jBP+lUnR9q+ydemzhe40dHRSE1NxdCh2p9cBQCLFy9GQUEB7OyUX/d+8skniIiIwOHDhzF9+nRJ+7GwsECnTp2qFGN+fj6Sk5Ph4uICGxsbHb0MN6pZawRQsmKiRvOqrjEIGnDRCAE9+Tw8PNReSzuXiHnSjzmShnnSjzmShnnSr7o5io+Pl9y3zhe4586dg6+vL+zt7XX2MTc3VxW3gHIpsQ4dOiA1NVXyfmQyGWxtbasVq42NTbW3UVf1v9kSpw+8qNHu+O4+3Lepua/t6xtd54spn0s1iXnSjzmShnnSjzmShnnSr6o5kjo9AXgCnmQWFRWFbt26Vdhn4sSJWLNmjeq1QqHAtWvX0KFDh9oOr17IXv26RnF78OkbMAvayeKWiIiI6pw6P4IbFxeHESNGqLWVlJQgMzMT9vb2sLS0xMCBA7F27Vp4eHigffv22LFjBx4+fIjRo0fr2CpJ4ZXWBBEhwzTaO7x1CDfspT3wgIiIiMjQ6nyBm5GRgcaNG6u13b17Fy+88AJ27NgBPz8/BAYGorCwEEuWLEFGRga8vLywdetWtWkLVDmXdg6Fd3pT9TbH++gx4YSRIiIiIiKSps4XuFFRURptbdq0UXssr0wmw/Tp0yXfUEa6ueTYIWGz5sh314nHENUiywgREREREVVOnS9wyXAOHe6PkQlt1dqyrArRfMY+4wREREREVAUscAkt8qxxb/1YjfaBY3/Gr22lr0RhDA1kgMJAawwv6O+Ok7F38Oe9BzW2PSIiIqp5dX4VBapdq/7bXWtxazZnZ50vbv3dWyGga3uD7W96Xw+sHdurRrdHRERENY8juPVUQ7k5HqwZr9E+dtivOOR60wgRVY6TnSWOvzMYxSUKmJk1wPGrt2r0MbrarDoXC1GDo8WrzsXiyxG+NbdBIiIiAsACt16a98ezWHZec21hi9khUDSo+88UdmpkhfiFYwAA5mYNsOX13kjJysXKs7HYfzkJtx8U1Mp+M/PkAGouP8rtERERUU1jgVuPmJfIULhqgkb7jIFhWO993QgRVV6rRta49YnmlIq2Tezw1UhfzH7OHR0++75S83KlzuNtamtZoyO4TW2lPU+biIiIKodzcA1E6g1F7/asnaevTb7SQWtxazvrP09McWtjJkPCwoof3tG2iR0Cukmfl/t613YY5dlWb7+Wjazxfj93zH7OHY521hX2bd7QCk1tKi5eS7dHRERENY8FroF8Nry7pH5rxvbB697tamSfMgAyAZR8PRFbTvZRj8cvGmZBO1ForqiRfdW2Ie5OyF4aAGtL/V86bH6tNyZ37wgHawudfWzNZZjcvQO2j++L3ROfQ8fmFT8UxN+9Ndo2sUPbJnZ42aM1dD0NWwZg6DPOGO7ZpsI+pdsjIiKimscpCgaSkpWLJlYNkFWou6BsYmWGlKxcbA/oCysLcxy8nITcosf9bcyAghLds0CtGwC21hZoYmMFe2tL/KdpX7i+aa/Rb9fP8Zjk2x6ZZwuRkv0ISfdz4dK0IU7H3UNOQZFG/+Y25ngoL0FhSe3NzzWTAS+5OeH7KQNx90EeVp6NRVa+HE1tLTH7OQ+0cWgoeVuqebkvdVFtx6KBDEIIFAto3ebV+SMREHIOP8bcRl5Riaq9ZSNr+Lu3xsYyqyco/y7DT7G3kfqwoIK+UvoQERFRTWOBayArz8ZWWNwCQFZhierO+i2v98a/yhRoZYuyzb9fw6wfLqKoRLk9v7bN4NfeUb1o0zZ8GAhgKxCATgCAr0Zq3sEflpyGGYfC8aCgCPbWFlj3Sg90b9cCAFQ3cpXGM67LU9gbeVMtvtvZj1SfN5MJQCZDYXEJsvLkeFRYDJkMaGRlgS+HdUV02kOtRWzpfNrqqsx2zM0aYN/k5zWOUVtxXf7GNl19pfQhIiKimscC10Cy8qUtYVX2znpdBdpbvdzwVi837RsIA9BTS/ttAK3179/PxREXg4ZqfU9bPD1cHNVet3FoqPPzeXl5iImJgYeHB2xtbfUHYwSVKYql9K2pYp2IiIik4xxcA2liYyWpX7XurHeBZnE7AMo5DRKKWyIiIiJTwALXQKTcfV/lO+tjoZyScKNcewyA/1Z+c0RERERPMha4BiLl7vsq3Vk/EED5J762g3LUlqtQERERUT3EObgGJP3uewnuAHDW0h4KwK+agRIRERE9wVjgGpDUu+/1ehPANi3tdf8pu0RERES1jgWuEVT5zvpsAE20tB8HMKR6MRERERGZCs7BfVIsgvbitgQsbomIiIjK4AhuXVcAwEZL+1YoH9xARERERGpY4NZlJwH4a2mXA7AwcCxERERETwhOUaiLFAC6QLO4XQrljWQsbomIiIh04ghuXRMKQNtqYbkAKrHQAhEREVF9xRHcukIAeAmaxe2mv99jcUtEREQkCUdw64KrAJ7V0p4DoLGBYyEiIiJ6wnEE19gCoVncfg7lqC2LWyIiIqJK4wiusdwA4KKl/R6AloYNhYiIiMiUcATXGK5Cs7idC+WoLYtbIiIiomrhCK4x7Cj3OhFAe2MEQkRERGR6WOAawzQAD6BcGeELI8dCREREZGJY4BpDewDrjB0EERERkWniHFwiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiIiIiEwKC1wiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiIiIiEwKC1wiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiIiIiEwKC1wiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiIiIiEyKTAghjB2EsUVEREAIAUtLyyp9XgiBoqIiWFhYQCaT1XB0poN50o85koZ50o85koZ50o85koZ50q+6OZLL5ZDJZOjWrZvevuZVCdDUVPdElMlkVS6O6xPmST/mSBrmST/mSBrmST/mSBrmSb/q5kgmk0mu2TiCS0REREQmhXNwiYiIiMiksMAlIiIiIpPCApeIiIiITAoLXCIiIiIyKSxwiYiIiMiksMAlIiIiIpPCApeIiIiITAoLXCIiIiIyKSxwJTp16hTc3NzU/syaNUtr399++w3Dhg2Dl5cXJk2ahJSUFANHaxyHDh3SyJGbmxvc3d219h8xYoRG3+vXrxs4asORy+UYNmwYwsLCVG0pKSkIDAyEt7c3hgwZgvPnz1e4jWPHjmHQoEHw8vLCjBkzkJmZWdthG5y2PF2+fBmvv/46unbtipdeegn79++vcBu+vr4a59ajR49qO3SD0ZajJUuWaBxzSEiIzm1s27YN/fr1Q9euXbFw4ULk5+cbInSDKp+nBQsWaL1GTZo0Sevnc3JyNPr6+fkZ8hBqTWpqKmbNmoUePXqgX79+WLp0KQoLCwHwulRWRXnidUmpohwZ9bokSJJ169aJadOmibS0NNWfnJwcjX63b98W3t7eYvPmzeL69evi/fffF8OGDRMKhcIIURtWfn6+Wn7u3LkjBg8eLD777DONvsXFxaJz587ijz/+UPtMUVGRESKvfQUFBWLGjBnC1dVVhIaGCiGEUCgUYvjw4WLu3LkiPj5erF+/Xnh5eYnbt29r3UZkZKTo0qWL+P7770VMTIyYMGGCmDp1qiEPo9Zpy1NaWprw9fUVX331lUhKShLHjh0TnTt3FmfOnNG6jXv37glXV1dx8+ZNtXPLVH4GteVICCECAwPFhg0b1I45Ly9P6zZ++ukn4ePjI/773/+KyMhIMWTIEPGvf/3LUIdgENry9ODBA7X8/Pnnn8LT01OcOnVK6zYuXrwoevToofaZjIwMQx5GrVAoFGLcuHHi7bffFtevXxfh4eFi8ODBYtmyZbwulVFRnnhdUqooR0IY97rEAleiuXPniq+++kpvv5UrV4oJEyaoXufl5YmuXbuq/SKqL9avXy8GDRokCgsLNd5LTk4W7u7uoqCgwAiRGVZcXJwYMWKEGD58uNov299++014e3uLR48eqfpOnjxZfPPNN1q3M2/ePPHhhx+qXt+5c0e4ubmJmzdv1u4BGIiuPO3atUv4+/ur9V20aJEICgrSup0LFy6IPn361Hq8xqArR0II0a9fP3Hu3DlJ2wkICFA7z8LDw0WXLl10/uJ50lSUp7KmTJkiPvjgA53b2bdvn3jttddqK0yjiY+PF66uriI9PV3VdvToUdG3b19el8qoKE+8LilVlCMhjHtd4hQFiRISEuDi4qK3X2RkJHx9fVWvbWxs8Oyzz+Ly5cu1F1wdlJ2dje+++w5z586FpaWlxvvx8fFwcnKClZWVEaIzrD/++AN+fn7Yu3evWntkZCSeeeYZ2Nraqtp8fHx0nivlzy0nJye0bt0akZGRtRK3oenKU+lXXuXl5uZq3U58fDzat29fKzEam64c5ebmIjU1VdI1qqSkBNHR0Wrnkre3N4qKihAbG1vTIRuFrjyV9fvvvyM8PBxBQUE6+8THx0vK6ZOmRYsW2LRpE5o3b67Wnpuby+tSGRXlidclpYpyZOzrknmVPlXPCCGQlJSE8+fPY8OGDSgpKYG/vz9mzZqlUbylp6fD0dFRra1Zs2a4d++eIUM2ut27d8PR0RH+/v5a309ISICFhQWmTZuGv/76C+3bt8f8+fPRpUsXA0da+wICArS2V/ZcSUtLM+lzS1ee2rRpgzZt2qhe379/H8ePH8d7772ntX9CQgLy8/MxceJEJCUlwcPDAwsXLjSJXy66cpSQkACZTIb169fj7NmzcHBwwJtvvonRo0dr9H3w4AEKCwvVziVzc3M4ODiY/LlU1saNGzF69Gg4OTnp7JOQkIDi4mK8+uqrSE1Nha+vL4KDgzV+Dp80jRs3Rr9+/VSvFQoFQkJC0LNnT16XyqgoT7wuKVWUI2NflziCK8GdO3eQn58PS0tLrFy5Eh9++CGOHj2K5cuXa/Qt7VeWpaUl5HK5ocI1OiEE9u/fjwkTJujsk5SUhJycHIwdOxYbN25Ex44dMXnyZNy9e9eAkRpXZc+VgoKCen9uFRQU4L333kPz5s3x2muvae2TmJiInJwcvPvuu1i3bh2sra0RGBioc2TFFCQmJkImk6FDhw7YuHEjxo4di0WLFuHUqVMafQsKCgCgXp9LKSkpCA0NxcSJEyvsl5iYiNzcXAQHB2PFihVIS0vD9OnTUVJSYqBIDeOLL77A1atXMWfOHF6XKlA2T2XxuvRY2RwZ+7rEEVwJnJ2dERYWBnt7e8hkMnh4eEChUGDevHkIDg6GmZmZqq+VlZXG/wy5XI7GjRsbOmyjiY6ORmpqKoYOHaqzz+LFi1FQUAA7OzsAwCeffIKIiAgcPnwY06dPN1SoRmVlZYXs7Gy1NrlcDmtra539tZ1bNjY2tRVinfLo0SP84x//QHJyMnbt2qXzuDdv3oyioiI0bNgQAPDll1/i+eefx5kzZzB8+HBDhmwwo0aNwoABA+Dg4AAAcHd3R3JyMnbv3o3Bgwer9S2dFlSfz6WTJ0/Cw8MDnTp1qrDf8ePHIZPJVD+T33zzDfr27YvIyEh069bNEKHWui+++ALbt2/HihUr4OrqyuuSDuXzVIrXpcfK5+jpp5826nWJI7gSOTg4QCaTqV537NgRhYWFyMnJUevXsmVLZGRkqLVlZGSgRYsWBomzLjh37hx8fX1hb2+vs4+5ubmquAWg+ldeamqqIUKsE3SdK7q+/qzP51Zubi7eeustxMXFYfv27RXO6bK0tFT9EgGUF842bdqY9Lklk8lUv0RK6fp5cnBwgJWVldq5VFxcjOzs7HpxLgHKa9QLL7ygt5+NjY1aYdesWTM4ODiYzLm0ePFibN26FV988QVeeuklALwuaaMtTwCvS2Vpy5Gxr0sscCU4d+4c/Pz81NZji4mJgYODA5o2barW18vLC5cuXVK9zs/Px9WrV+Hl5WWweI0tKipK7+jGxIkTsWbNGtVrhUKBa9euoUOHDrUdXp3h5eWFK1euqL6aAYBLly7pPFfKn1t3797F3bt3Tf7cUigUmDlzJm7duoWdO3fi6aef1tlXCIFBgwbh0KFDqra8vDzcuHHDpM+tVatWITAwUK0tNjZW6zE3aNAAnTt3VjuXLl++DHNzc51rVpsSIQSio6P1XqNyc3PRvXt3hIaGqtpSU1ORlZVlEufSmjVrsGfPHnz99ddq37bxuqROV554XXpMV46MfV1igStB165dYWVlhY8//hiJiYn49ddfsXz5crz99tsoKSlBenq6alj9lVdeQUREBDZu3Ii4uDgEBwejTZs2JrM4uBRxcXEaX/2Vz9PAgQOxbds2nD59GomJifj000/x8OFDrZPPTVWPHj3g5OSE4OBgxMXFYePGjYiKisKrr74KQPnVTHp6umq+3/jx43H48GHs378fsbGxmD9/Pvr374+2bdsa8zBq3YEDBxAWFoYlS5agcePGSE9PR3p6uupr1LJ5kslk6N+/P1avXo2wsDDExcVh/vz5aNWqFZ5//nnjHkgtGjBgAMLDw7F582bcvHkTu3btwg8//IApU6YAUM5vS09PV/UPCAjA5s2b8csvvyAqKgqffPIJxo0bZ3JfK2tz+/ZtPHr0SOv0hLJ5srOzg4+PD5YuXYqoqChcuXIFc+bMQb9+/eDm5mbosGtUQkIC1q1bh3feeQc+Pj6qn6n09HRel8qoKE+8LilVlCOjX5eqtLhYPXT9+nURGBgovL29RZ8+fcTq1auFQqEQKSkpGuss/u9//xMvvvii6NKli5g8ebJJrAdYGZ07dxZnz55VayufJ4VCIb799lvRv39/4enpKd544w1x7do1Y4RrUOXPleTkZPHGG28IT09PMXToUHHhwgXVe6GhocLV1VWkpKSo2g4ePCief/554e3tLWbMmCEyMzMNGr+hlM3TlClThKurq8af0vWmy+epoKBALF26VPTp00d4eXmJadOmiTt37hjtWGpL+XPp1KlTYvjw4aJz587C399fnDx5UvXewYMHhaurq9rnN2zYIHr16iV8fHxEcHCwya5JXT5Ply9fFq6urlrX5y6fp+zsbLFgwQLh5+cnunbtKj744AORnZ1tkLhr04YNG7T+TJUeO69LShXlidclJX3nkjGvSzIhhKiJKp6IiIiIqC7gFAUiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiIiIiEwKC1wiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiKgKcnNz4eXlhd69e6OoqKhSn7106RIuXrxYY7HcunULbm5uCAsL0/p+WFgY3NzccOvWrSrvY/Xq1Rg4cGCVP19T2yAikoIFLhFRFRw/fhzNmjXDw4cPcerUqUp9NiAgADdv3qylyIiIiAUuEVEVHDx4EP369UPPnj2xZ88eY4dDRERlsMAlIqqkhIQEREZGok+fPnjxxRcRFhaGpKQk1ftFRUVYtWoVBgwYAC8vL4wZMwYXLlwAALi5uQEAgoODsWDBAq3TC8q3yeVyfP755xg4cCA8PT3Ro0cPvP/++8jMzKyxY7p+/TqmTZuG7t27w9PTEy+88AK2bNmi0W/t2rXw8/NDt27d8MEHHyA7O1v13sOHD7Fo0SL07NkTPj4+mDRpEqKjo3Xu89dff8WYMWPg5eWFXr16YcGCBcjJyamxYyKi+osFLhFRJR04cAC2trZ47rnnMHjwYFhYWKiN4n722WfYs2cPPvzwQxw9ehT9+vXD9OnTkZiYiPPnzwMAFi5ciI8++kjS/pYvX46ff/4Zy5Ytw8mTJ7Fs2TKEhobi22+/rZHjyc/Px5QpU+Dg4IA9e/bg2LFj8Pf3x+eff46YmBhVv9u3byM0NBRbt27F+vXrER0djeDgYACAEALvvPMOUlJSsGHDBuzbtw/e3t4YP348rl69qrHPzMxMzJw5E6+88gpOnDiBNWvWIDw8HMuXL6+RYyKi+s3c2AEQET1JiouLceTIEQwcOBDW1tawtrZG37598cMPPyAoKAhFRUU4cOAAFi1aBH9/fwDAnDlzIIRAbm4uOnToAABo1KgRGjVqJGnEsnPnzvD394evry8AwNnZGb1798b169dr5Jjy8/MxadIkvPHGG2jYsCEAYNasWdi0aROuXbsGDw8PAICVlRVWrFiB5s2bAwD++c9/YsqUKbhx4wbu3LmDy5cvIzQ0FA4ODgCAoKAgREREYMeOHVi2bJnaPlNTUyGXy9G6dWs4OzvD2dkZ69evR0lJSY0cExHVbyxwiYgq4ddff0VGRgaGDh2qahs6dCjOnDmDH3/8ER07dkRRURG8vLzUPhcUFFTlfY4cORK//fYbvvzySyQnJyMxMRFJSUmqgre6mjZtioCAABw7dgxXr17FzZs3ERsbCwBQKBSqfu3atVMVtwBUxxgXF4fk5GQIITBgwAC1bcvlchQWFmrs08PDA8OGDcP06dPRokUL9OnTB/3798fgwYNr5JiIqH5jgUtEVAmHDh0CAMycOVPjvT179uCTTz6p9j7Kj2L+85//xMmTJzFq1CgMHDgQM2bMwObNm5GamlrtfQFAeno6XnvtNTRt2hQDBw5E37590blzZzz//PNq/czMzLTGaWFhAYVCATs7O1V+yrK0tNS636+++gozZszA2bNn8dtvv2HevHnw8fHB9u3ba+S4iKj+YoFLRCTR/fv3VTdGvfnmm2rvbdu2DQcPHgSgLPiio6Ph7u6uen/cuHEYMmQIAgMD1T5nYWEBQLmubqnk5GTV37OysrB3716sWLECQ4YMUbUnJibC1ta2Ro7r2LFjyM7OxsmTJ1XxXLt2DYBybm3ZuHJzc2FnZwdAuZ6vTCZDp06dVFMwioqK0KlTJ9VnPv74Y7i7u2PChAlq+4yMjMTx48excOFCdOjQAYGBgThy5AjmzZuH+/fvo1mzZjVybERUP7HAJSKS6MiRIyguLsY777yjmktbavr06fj++++xb98+TJgwAatWrULTpk3x9NNP48CBA7h+/bpqHqqtrS0SEhKQlZUFR0dHODs7Y/v27XBxcUF2djZWrVoFmUwGALCzs0OjRo1w+vRpPPvssygoKEBISAiuXLmiMQ1Cn/DwcCQmJqq1tWvXDq1atUJ+fj5++ukn+Pj4IDExEUuXLgWgnGJQqrCwELNnz0ZQUBCysrKwePFijBo1Cs7OzmjVqhU8PDwwZ84cfPTRR3BycsKuXbtw6NAhbN68WSMWOzs77Nq1CxYWFhg3bhwKCwtx4sQJuLi4oEmTJpU6LiKi8ljgEhFJdOjQIfTu3VujuAWAp556CoMGDcKRI0dw5swZmJmZ4f/+7//w8OFDuLu7Y+PGjarPTZkyBZs2bUJCQgLWr1+P5cuX49///jdGjhyJdu3aITg4GFOnTgWgHOFdtWoVli1bhuHDh8Pe3h5+fn4ICgrChg0bkJ+fLzn+BQsWaLTNnDkTM2fOxJUrV7Bs2TLk5ubC2dkZY8eOxenTpxEdHY3x48cDADw9PeHh4YFJkyZBJpNhyJAhqm2amZlhy5Yt+OKLLzB79mzk5+ejY8eOWLNmDXr16qWx344dO2L16tVYs2YNdu3ahQYNGqBnz5747rvv0KABF/ghouqRibLfPxERERERPeH4z2QiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiIiIiEwKC1wiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiIiIiEwKC1wiIiIiMikscImIiIjIpLDAJSIiIiKTwgKXiIiIiEzK/wPPaVChdl1L0wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train the model\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# Fit a lasso model on the training set\n",
    "model = GradientBoostingRegressor().fit(x_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(x_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}