Factors affecting the price of a Car.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What drives the price of a car?\n",
    "\n",
    "![](images/kurt.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**OVERVIEW**\n",
    "\n",
    "In this application, you will explore a dataset from kaggle. The original dataset contained information on 3 million used cars. The provided dataset contains information on 426K cars to ensure speed of processing.  Your goal is to understand what factors make a car more or less expensive.  As a result of your analysis, you should provide clear recommendations to your client -- a used car dealership -- as to what consumers value in a used car."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CRISP-DM Framework\n",
    "\n",
    "<center>\n",
    "    <img src = images/crisp.png width = 50%/>\n",
    "</center>\n",
    "\n",
    "\n",
    "To frame the task, throughout our practical applications we will refer back to a standard process in industry for data projects called CRISP-DM.  This process provides a framework for working through a data problem.  Your first step in this application will be to read through a brief overview of CRISP-DM [here](https://mo-pcco.s3.us-east-1.amazonaws.com/BH-PCMLAI/module_11/readings_starter.zip).  After reading the overview, answer the questions below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Business Understanding\n",
    "\n",
    "From a business perspective, we are tasked with identifying key drivers for used car prices.  In the CRISP-DM overview, we are asked to convert this business framing to a data problem definition.  Using a few sentences, reframe the task as a data task with the appropriate technical vocabulary. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Understanding\n",
    "\n",
    "After considering the business understanding, we want to get familiar with our data.  Write down some steps that you would take to get to know the dataset and identify any quality issues within.  Take time to get to know the dataset and explore what information it contains and how this could be used to inform your business understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#libraries for preprocessing\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#libraries for evaluation\n",
    "from sklearn.metrics import mean_squared_log_error,r2_score,mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "#libraries for models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import LassoCV,RidgeCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read and save the used car vehicles CSV to a dataframe\n",
    "vehicles = pd.read_csv('data/vehicles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>region</th>\n",
       "      <th>price</th>\n",
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       "      <td>7222270760</td>\n",
       "      <td>worcester / central MA</td>\n",
       "      <td>1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7210384030</td>\n",
       "      <td>greensboro</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>426875</th>\n",
       "      <td>7301591192</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>23590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>maxima s sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>32226.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>1N4AA6AV6KC367801</td>\n",
       "      <td>fwd</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>wy</td>\n",
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       "    <tr>\n",
       "      <th>426876</th>\n",
       "      <td>7301591187</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>30590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>s60 t5 momentum sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>12029.0</td>\n",
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       "      <td>7JR102FKXLG042696</td>\n",
       "      <td>fwd</td>\n",
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       "      <td>sedan</td>\n",
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       "      <td>wy</td>\n",
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       "      <td>wyoming</td>\n",
       "      <td>34990</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>xt4 sport suv 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>diesel</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>1GYFZFR46LF088296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hatchback</td>\n",
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       "      <td>wy</td>\n",
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       "    <tr>\n",
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       "      <td>wyoming</td>\n",
       "      <td>28990</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>lexus</td>\n",
       "      <td>es 350 sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>30112.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>58ABK1GG4JU103853</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>silver</td>\n",
       "      <td>wy</td>\n",
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       "      <td>7301591129</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>30590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>4 series 430i gran coupe</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>22716.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>WBA4J1C58KBM14708</td>\n",
       "      <td>rwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>coupe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>426880 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id                  region  price    year manufacturer  \\\n",
       "0       7222695916                prescott   6000     NaN          NaN   \n",
       "1       7218891961            fayetteville  11900     NaN          NaN   \n",
       "2       7221797935            florida keys  21000     NaN          NaN   \n",
       "3       7222270760  worcester / central MA   1500     NaN          NaN   \n",
       "4       7210384030              greensboro   4900     NaN          NaN   \n",
       "...            ...                     ...    ...     ...          ...   \n",
       "426875  7301591192                 wyoming  23590  2019.0       nissan   \n",
       "426876  7301591187                 wyoming  30590  2020.0        volvo   \n",
       "426877  7301591147                 wyoming  34990  2020.0     cadillac   \n",
       "426878  7301591140                 wyoming  28990  2018.0        lexus   \n",
       "426879  7301591129                 wyoming  30590  2019.0          bmw   \n",
       "\n",
       "                           model condition    cylinders    fuel  odometer  \\\n",
       "0                            NaN       NaN          NaN     NaN       NaN   \n",
       "1                            NaN       NaN          NaN     NaN       NaN   \n",
       "2                            NaN       NaN          NaN     NaN       NaN   \n",
       "3                            NaN       NaN          NaN     NaN       NaN   \n",
       "4                            NaN       NaN          NaN     NaN       NaN   \n",
       "...                          ...       ...          ...     ...       ...   \n",
       "426875         maxima s sedan 4d      good  6 cylinders     gas   32226.0   \n",
       "426876  s60 t5 momentum sedan 4d      good          NaN     gas   12029.0   \n",
       "426877          xt4 sport suv 4d      good          NaN  diesel    4174.0   \n",
       "426878           es 350 sedan 4d      good  6 cylinders     gas   30112.0   \n",
       "426879  4 series 430i gran coupe      good          NaN     gas   22716.0   \n",
       "\n",
       "       title_status transmission                VIN drive size       type  \\\n",
       "0               NaN          NaN                NaN   NaN  NaN        NaN   \n",
       "1               NaN          NaN                NaN   NaN  NaN        NaN   \n",
       "2               NaN          NaN                NaN   NaN  NaN        NaN   \n",
       "3               NaN          NaN                NaN   NaN  NaN        NaN   \n",
       "4               NaN          NaN                NaN   NaN  NaN        NaN   \n",
       "...             ...          ...                ...   ...  ...        ...   \n",
       "426875        clean        other  1N4AA6AV6KC367801   fwd  NaN      sedan   \n",
       "426876        clean        other  7JR102FKXLG042696   fwd  NaN      sedan   \n",
       "426877        clean        other  1GYFZFR46LF088296   NaN  NaN  hatchback   \n",
       "426878        clean        other  58ABK1GG4JU103853   fwd  NaN      sedan   \n",
       "426879        clean        other  WBA4J1C58KBM14708   rwd  NaN      coupe   \n",
       "\n",
       "       paint_color state  \n",
       "0              NaN    az  \n",
       "1              NaN    ar  \n",
       "2              NaN    fl  \n",
       "3              NaN    ma  \n",
       "4              NaN    nc  \n",
       "...            ...   ...  \n",
       "426875         NaN    wy  \n",
       "426876         red    wy  \n",
       "426877       white    wy  \n",
       "426878      silver    wy  \n",
       "426879         NaN    wy  \n",
       "\n",
       "[426880 rows x 18 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 426880 entries, 0 to 426879\n",
      "Data columns (total 18 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            426880 non-null  int64  \n",
      " 1   region        426880 non-null  object \n",
      " 2   price         426880 non-null  int64  \n",
      " 3   year          425675 non-null  float64\n",
      " 4   manufacturer  409234 non-null  object \n",
      " 5   model         421603 non-null  object \n",
      " 6   condition     252776 non-null  object \n",
      " 7   cylinders     249202 non-null  object \n",
      " 8   fuel          423867 non-null  object \n",
      " 9   odometer      422480 non-null  float64\n",
      " 10  title_status  418638 non-null  object \n",
      " 11  transmission  424324 non-null  object \n",
      " 12  VIN           265838 non-null  object \n",
      " 13  drive         296313 non-null  object \n",
      " 14  size          120519 non-null  object \n",
      " 15  type          334022 non-null  object \n",
      " 16  paint_color   296677 non-null  object \n",
      " 17  state         426880 non-null  object \n",
      "dtypes: float64(2), int64(2), object(14)\n",
      "memory usage: 58.6+ MB\n"
     ]
    }
   ],
   "source": [
    "vehicles.info()\n",
    "# Understanding attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>min</th>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <th>25%</th>\n",
       "      <td>7.308143e+09</td>\n",
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       "      <td>8.554800e+04</td>\n",
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       "      <th>75%</th>\n",
       "      <td>7.315254e+09</td>\n",
       "      <td>2.648575e+04</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.335425e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.317101e+09</td>\n",
       "      <td>3.736929e+09</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         price           year      odometer\n",
       "count  4.268800e+05  4.268800e+05  425675.000000  4.224800e+05\n",
       "mean   7.311487e+09  7.519903e+04    2011.235191  9.804333e+04\n",
       "std    4.473170e+06  1.218228e+07       9.452120  2.138815e+05\n",
       "min    7.207408e+09  0.000000e+00    1900.000000  0.000000e+00\n",
       "25%    7.308143e+09  5.900000e+03    2008.000000  3.770400e+04\n",
       "50%    7.312621e+09  1.395000e+04    2013.000000  8.554800e+04\n",
       "75%    7.315254e+09  2.648575e+04    2017.000000  1.335425e+05\n",
       "max    7.317101e+09  3.736929e+09    2022.000000  1.000000e+07"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles.describe()\n",
    "# Understanding Numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region               0\n",
       "price                0\n",
       "year              1205\n",
       "manufacturer     17646\n",
       "model             5277\n",
       "condition       174104\n",
       "cylinders       177678\n",
       "fuel              3013\n",
       "odometer          4400\n",
       "title_status      8242\n",
       "transmission      2556\n",
       "VIN             161042\n",
       "drive           130567\n",
       "size            306361\n",
       "type             92858\n",
       "paint_color     130203\n",
       "state                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles = vehicles.drop(['id'], axis=1)  # The 'id' attribute will not be used to determine the cost of the vehicle.\n",
    "vehicles.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of Attributes \n",
    "\n",
    "Based on the initial analysis, following are the attributes available to us in the data. These attributes have been manipulated to create statistical models for inferring what factors contribute to teh price of a vehicle. \n",
    "\n",
    "#### Text-based Attributes\n",
    "- Manufacturer: Who built the vehicle (examples: Nissan, Volvo, Cadillac, etc.)\n",
    "- Model: Name of the model (eg: 'maxima s sedan 4d' , 's60 t5 momentum sedan 4d' etc.)\n",
    "- Condition: How the vehicle runs and whether any cosmetic damages ('good', 'excellent', 'fair', 'like new', etc.)\n",
    "- Cylinders: No of cylinders in the engine ('8 cylinders', '6 cylinders', '4 cylinders', '5 cylinders', etc.)\n",
    "- Fuel: Type of fuel ('gas', 'other', 'diesel', 'hybrid', 'electric', etc.)\n",
    "- Title Status: Accident information ('clean', 'rebuilt', 'lien', 'salvage', etc.)\n",
    "- Transmission: What transmission the car uses ('manual','automatic', etc.)\n",
    "- Drive: Whether the car is Rear-wheel drive or other types ('rwd', '4wd', 'fwd', etc.)\n",
    "- Size: Size of the Vehicle ('full-size', 'mid-size', 'compact', 'sub-compact', etc.)\n",
    "- Type: Vehicle Body Style ('pickup', 'truck', 'other', 'coupe', 'SUV', etc.)\n",
    "- Paint Color: Exterior color of the vehicle ('white','purple', 'red', etc.)\n",
    "\n",
    "#### Numerical Attribtues\n",
    "- Odometer: The amount of miles driven on the used vehicle [Min(0); Max(10000000.0); Mean(98043.33); Median(85548.0); std(213881.50)]\n",
    "- Price: The price of the vehicle [Min(0); Max(3736928711); Mean(75199.03); Median (13950.0); std(12182282.17)]\n",
    "\n",
    "#### Spatial Attributes\n",
    "- Region: Cities in the USA\n",
    "- State: States in the USA\n",
    "\n",
    "#### Temporal Attributes\n",
    "- Year: Vehicle Model Year (~1960-2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'price'}>,\n",
       "        <AxesSubplot:title={'center':'year'}>],\n",
       "       [<AxesSubplot:title={'center':'odometer'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle_numerical_att = vehicles[['price','year','odometer']]\n",
    "vehicle_numerical_att.hist(bins=10, figsize=(9,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='odometer', ylabel='year'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle_numerical_att = vehicle_numerical_att.dropna(axis=0)\n",
    "vehicle_numerical_att.plot.scatter(x='odometer',y='year',c='price',\n",
    "                      colormap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='year'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle_numerical_att.plot.scatter(x='price',y='year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "\n",
    "After our initial exploration and fine tuning of the business understanding, it is time to construct our final dataset prior to modeling.  Here, we want to make sure to handle any integrity issues and cleaning, the engineering of new features, any transformations that we believe should happen (scaling, logarithms, normalization, etc.), and general preparation for modeling with `sklearn`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step x: Changing Dtype Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>region</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>VIN</th>\n",
       "      <th>drive</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>prescott</td>\n",
       "      <td>6000</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>az</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fayetteville</td>\n",
       "      <td>11900</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>ar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>florida keys</td>\n",
       "      <td>21000</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>fl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>worcester / central MA</td>\n",
       "      <td>1500</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>ma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>greensboro</td>\n",
       "      <td>4900</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>nc</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426875</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>23590</td>\n",
       "      <td>2019</td>\n",
       "      <td>nissan</td>\n",
       "      <td>maxima s sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>32226</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>1N4AA6AV6KC367801</td>\n",
       "      <td>fwd</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>sedan</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>wy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426876</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>30590</td>\n",
       "      <td>2020</td>\n",
       "      <td>volvo</td>\n",
       "      <td>s60 t5 momentum sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>gas</td>\n",
       "      <td>12029</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>7JR102FKXLG042696</td>\n",
       "      <td>fwd</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>sedan</td>\n",
       "      <td>red</td>\n",
       "      <td>wy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426877</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>34990</td>\n",
       "      <td>2020</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>xt4 sport suv 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>diesel</td>\n",
       "      <td>4174</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>1GYFZFR46LF088296</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>white</td>\n",
       "      <td>wy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426878</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>28990</td>\n",
       "      <td>2018</td>\n",
       "      <td>lexus</td>\n",
       "      <td>es 350 sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>30112</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>58ABK1GG4JU103853</td>\n",
       "      <td>fwd</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>sedan</td>\n",
       "      <td>silver</td>\n",
       "      <td>wy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426879</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>30590</td>\n",
       "      <td>2019</td>\n",
       "      <td>bmw</td>\n",
       "      <td>4 series 430i gran coupe</td>\n",
       "      <td>good</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>gas</td>\n",
       "      <td>22716</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>WBA4J1C58KBM14708</td>\n",
       "      <td>rwd</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>coupe</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>wy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>426880 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        region  price  year manufacturer  \\\n",
       "0                     prescott   6000  <NA>         <NA>   \n",
       "1                 fayetteville  11900  <NA>         <NA>   \n",
       "2                 florida keys  21000  <NA>         <NA>   \n",
       "3       worcester / central MA   1500  <NA>         <NA>   \n",
       "4                   greensboro   4900  <NA>         <NA>   \n",
       "...                        ...    ...   ...          ...   \n",
       "426875                 wyoming  23590  2019       nissan   \n",
       "426876                 wyoming  30590  2020        volvo   \n",
       "426877                 wyoming  34990  2020     cadillac   \n",
       "426878                 wyoming  28990  2018        lexus   \n",
       "426879                 wyoming  30590  2019          bmw   \n",
       "\n",
       "                           model condition    cylinders    fuel  odometer  \\\n",
       "0                           <NA>      <NA>         <NA>    <NA>      <NA>   \n",
       "1                           <NA>      <NA>         <NA>    <NA>      <NA>   \n",
       "2                           <NA>      <NA>         <NA>    <NA>      <NA>   \n",
       "3                           <NA>      <NA>         <NA>    <NA>      <NA>   \n",
       "4                           <NA>      <NA>         <NA>    <NA>      <NA>   \n",
       "...                          ...       ...          ...     ...       ...   \n",
       "426875         maxima s sedan 4d      good  6 cylinders     gas     32226   \n",
       "426876  s60 t5 momentum sedan 4d      good         <NA>     gas     12029   \n",
       "426877          xt4 sport suv 4d      good         <NA>  diesel      4174   \n",
       "426878           es 350 sedan 4d      good  6 cylinders     gas     30112   \n",
       "426879  4 series 430i gran coupe      good         <NA>     gas     22716   \n",
       "\n",
       "       title_status transmission                VIN drive  size       type  \\\n",
       "0              <NA>         <NA>               <NA>  <NA>  <NA>       <NA>   \n",
       "1              <NA>         <NA>               <NA>  <NA>  <NA>       <NA>   \n",
       "2              <NA>         <NA>               <NA>  <NA>  <NA>       <NA>   \n",
       "3              <NA>         <NA>               <NA>  <NA>  <NA>       <NA>   \n",
       "4              <NA>         <NA>               <NA>  <NA>  <NA>       <NA>   \n",
       "...             ...          ...                ...   ...   ...        ...   \n",
       "426875        clean        other  1N4AA6AV6KC367801   fwd  <NA>      sedan   \n",
       "426876        clean        other  7JR102FKXLG042696   fwd  <NA>      sedan   \n",
       "426877        clean        other  1GYFZFR46LF088296  <NA>  <NA>  hatchback   \n",
       "426878        clean        other  58ABK1GG4JU103853   fwd  <NA>      sedan   \n",
       "426879        clean        other  WBA4J1C58KBM14708   rwd  <NA>      coupe   \n",
       "\n",
       "       paint_color state  \n",
       "0             <NA>    az  \n",
       "1             <NA>    ar  \n",
       "2             <NA>    fl  \n",
       "3             <NA>    ma  \n",
       "4             <NA>    nc  \n",
       "...            ...   ...  \n",
       "426875        <NA>    wy  \n",
       "426876         red    wy  \n",
       "426877       white    wy  \n",
       "426878      silver    wy  \n",
       "426879        <NA>    wy  \n",
       "\n",
       "[426880 rows x 17 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vehicles.info()\n",
    "vehicles_cleaned = vehicles.convert_dtypes()\n",
    "vehicles_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 426880 entries, 0 to 426879\n",
      "Data columns (total 17 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   region        426880 non-null  string\n",
      " 1   price         426880 non-null  Int64 \n",
      " 2   year          425675 non-null  Int64 \n",
      " 3   manufacturer  409234 non-null  string\n",
      " 4   model         421603 non-null  string\n",
      " 5   condition     252776 non-null  string\n",
      " 6   cylinders     249202 non-null  string\n",
      " 7   fuel          423867 non-null  string\n",
      " 8   odometer      422480 non-null  Int64 \n",
      " 9   title_status  418638 non-null  string\n",
      " 10  transmission  424324 non-null  string\n",
      " 11  VIN           265838 non-null  string\n",
      " 12  drive         296313 non-null  string\n",
      " 13  size          120519 non-null  string\n",
      " 14  type          334022 non-null  string\n",
      " 15  paint_color   296677 non-null  string\n",
      " 16  state         426880 non-null  string\n",
      "dtypes: Int64(3), string(14)\n",
      "memory usage: 56.6 MB\n"
     ]
    }
   ],
   "source": [
    "vehicles_cleaned.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step x: Convert attributes to Categorical / Numerical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<StringArray>\n",
      "[          <NA>,  '8 cylinders',  '6 cylinders',  '4 cylinders',\n",
      "  '5 cylinders',        'other',  '3 cylinders', '10 cylinders',\n",
      " '12 cylinders']\n",
      "Length: 9, dtype: string\n",
      "<StringArray>\n",
      "[<NA>, 'other', 'automatic', 'manual']\n",
      "Length: 4, dtype: string\n",
      "<StringArray>\n",
      "[<NA>, 'rwd', '4wd', 'fwd']\n",
      "Length: 4, dtype: string\n",
      "<StringArray>\n",
      "[<NA>, 'gas', 'other', 'diesel', 'hybrid', 'electric']\n",
      "Length: 6, dtype: string\n",
      "<StringArray>\n",
      "[         <NA>,      'pickup',       'truck',       'other',       'coupe',\n",
      "         'SUV',   'hatchback',    'mini-van',       'sedan',     'offroad',\n",
      "         'bus',         'van', 'convertible',       'wagon']\n",
      "Length: 14, dtype: string\n",
      "<StringArray>\n",
      "[<NA>, 'clean', 'rebuilt', 'lien', 'salvage', 'missing', 'parts only']\n",
      "Length: 7, dtype: string\n",
      "<StringArray>\n",
      "[<NA>, 'full-size', 'mid-size', 'compact', 'sub-compact']\n",
      "Length: 5, dtype: string\n"
     ]
    }
   ],
   "source": [
    "#  All the following attributes can be converted to numerical or categorical.\n",
    "print(vehicles_cleaned['cylinders'].unique())\n",
    "print(vehicles_cleaned['transmission'].unique())\n",
    "print(vehicles_cleaned['drive'].unique())\n",
    "print(vehicles_cleaned['fuel'].unique())\n",
    "print(vehicles_cleaned['type'].unique())\n",
    "print(vehicles_cleaned['title_status'].unique())\n",
    "print(vehicles_cleaned['size'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     178976\n",
       "6      94169\n",
       "4      77642\n",
       "8      72062\n",
       "5       1712\n",
       "10      1455\n",
       "3        655\n",
       "12       209\n",
       "Name: cylinders, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cylinders \n",
    "vehicles_cleaned['cylinders'] = vehicles_cleaned['cylinders'].str.replace(r' cylinders','')\n",
    "vehicles_cleaned['cylinders'] = vehicles_cleaned['cylinders'].str.replace(r'other','0')\n",
    "vehicles_cleaned[['cylinders']] = vehicles_cleaned[['cylinders']].fillna('0')\n",
    "vehicles_cleaned.cylinders = vehicles_cleaned.cylinders.astype(np.int64)\n",
    "vehicles_cleaned['cylinders'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    336524\n",
       "0     65238\n",
       "2     25118\n",
       "Name: transmission, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transmission\n",
    "vehicles_cleaned['transmission'] = vehicles_cleaned['transmission'].str.replace(r'other','0')\n",
    "vehicles_cleaned['transmission'] = vehicles_cleaned['transmission'].str.replace(r'automatic','1')\n",
    "vehicles_cleaned['transmission'] = vehicles_cleaned['transmission'].str.replace(r'manual','2')\n",
    "vehicles_cleaned[['transmission']] = vehicles_cleaned[['transmission']].fillna('0')\n",
    "vehicles_cleaned.transmission = vehicles_cleaned.transmission.astype(np.int64)\n",
    "vehicles_cleaned['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    131904\n",
       "0    130567\n",
       "2    105517\n",
       "3     58892\n",
       "Name: drive, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drive\n",
    "vehicles_cleaned['drive'] = vehicles_cleaned['drive'].str.replace(r'4wd','1')\n",
    "vehicles_cleaned['drive'] = vehicles_cleaned['drive'].str.replace(r'fwd','2')\n",
    "vehicles_cleaned['drive'] = vehicles_cleaned['drive'].str.replace(r'rwd','3')\n",
    "vehicles_cleaned[['drive']] = vehicles_cleaned[['drive']].fillna('0')\n",
    "vehicles_cleaned.drive = vehicles_cleaned.drive.astype(np.int64)\n",
    "vehicles_cleaned['drive'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step x: Handling NULL/ NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gas         356209\n",
       "other        33741\n",
       "diesel       30062\n",
       "hybrid        5170\n",
       "electric      1698\n",
       "Name: fuel, dtype: Int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned[['fuel']] = vehicles_cleaned[['fuel']].fillna('other')\n",
    "vehicles_cleaned['fuel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other          114968\n",
       "sedan           87056\n",
       "SUV             77284\n",
       "pickup          43510\n",
       "truck           35279\n",
       "coupe           19204\n",
       "hatchback       16598\n",
       "wagon           10751\n",
       "van              8548\n",
       "convertible      7731\n",
       "mini-van         4825\n",
       "offroad           609\n",
       "bus               517\n",
       "Name: type, dtype: Int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned[['type']] = vehicles_cleaned[['type']].fillna('other')\n",
    "vehicles_cleaned['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f-150                                      8009\n",
       "Other                                      5362\n",
       "silverado 1500                             5140\n",
       "1500                                       4211\n",
       "camry                                      3135\n",
       "                                           ... \n",
       "mustang gt 350                                1\n",
       "sierra 3500hd denali duramax                  1\n",
       "328 m sport xdrive                            1\n",
       "pickup laramie                                1\n",
       "mx 3 i *fr $399 down guaranteed finance       1\n",
       "Name: model, Length: 29648, dtype: Int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned[['model']] = vehicles_cleaned[['model']].fillna('Other')\n",
    "vehicles_cleaned.loc[(vehicles_cleaned.model=='♿'),'model'] = 'other'\n",
    "vehicles_cleaned['model'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clean         405117\n",
       "missing         9056\n",
       "rebuilt         7219\n",
       "salvage         3868\n",
       "lien            1422\n",
       "parts only       198\n",
       "Name: title_status, dtype: Int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned[['title_status']] = vehicles_cleaned[['title_status']].fillna('missing')\n",
    "vehicles_cleaned.loc[(vehicles_cleaned.title_status=='other'), 'title_status'] = 'missing'\n",
    "vehicles_cleaned['title_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other     136903\n",
       "white      79285\n",
       "black      62861\n",
       "silver     42970\n",
       "blue       31223\n",
       "red        30473\n",
       "grey       24416\n",
       "green       7343\n",
       "brown       6593\n",
       "yellow      2142\n",
       "orange      1984\n",
       "purple       687\n",
       "Name: paint_color, dtype: Int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned[['paint_color']] = vehicles_cleaned[['paint_color']].fillna('other')\n",
    "vehicles_cleaned.loc[(vehicles_cleaned.paint_color==('custom')) | (vehicles_cleaned.paint_color==('missing')),'paint_color'] = 'other'\n",
    "vehicles_cleaned['paint_color'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "missing        306361\n",
       "full-size       63465\n",
       "mid-size        34476\n",
       "compact         19384\n",
       "sub-compact      3194\n",
       "Name: size, dtype: Int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned[['size']] = vehicles_cleaned[['size']].fillna('missing')\n",
    "vehicles_cleaned['size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region               0\n",
       "price                0\n",
       "year              1205\n",
       "manufacturer     17646\n",
       "model                0\n",
       "condition       174104\n",
       "cylinders            0\n",
       "fuel                 0\n",
       "odometer          4400\n",
       "title_status         0\n",
       "transmission         0\n",
       "VIN             161042\n",
       "drive                0\n",
       "size                 0\n",
       "type                 0\n",
       "paint_color          0\n",
       "state                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step x: Removing Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducing The huge variance in numerical attributes\n",
    "first_quartile = vehicles_cleaned['price'].quantile(.25)\n",
    "third_quartile = vehicles_cleaned['price'].quantile(.75)\n",
    "iqr = third_quartile - first_quartile \n",
    "lower = first_quartile - 1.5*iqr\n",
    "upper = third_quartile + 1.5*iqr\n",
    "vehicles_cleaned = vehicles_cleaned.loc[(vehicles_cleaned['price']>lower) & (vehicles_cleaned['price']<upper)]\n",
    "\n",
    "first_quartile = vehicles_cleaned['odometer'].quantile(.25)\n",
    "third_quartile = vehicles_cleaned['odometer'].quantile(.75)\n",
    "iqr = third_quartile - first_quartile \n",
    "lower = first_quartile - 1.5*iqr\n",
    "upper = third_quartile + 1.5*iqr\n",
    "vehicles_cleaned = vehicles_cleaned.loc[(vehicles_cleaned['odometer']>lower) & (vehicles_cleaned['odometer']<upper)]\n",
    "\n",
    "first_quartile = vehicles_cleaned['year'].quantile(.25)\n",
    "third_quartile = vehicles_cleaned['year'].quantile(.75)\n",
    "iqr = third_quartile - first_quartile \n",
    "lower = first_quartile - 1.5*iqr\n",
    "upper = third_quartile + 1.5*iqr\n",
    "vehicles_cleaned = vehicles_cleaned.loc[(vehicles_cleaned['year']>lower) & (vehicles_cleaned['year']<upper)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region               0\n",
       "price                0\n",
       "year                 0\n",
       "manufacturer     12345\n",
       "model                0\n",
       "condition       159841\n",
       "cylinders            0\n",
       "fuel                 0\n",
       "odometer             0\n",
       "title_status         0\n",
       "transmission         0\n",
       "VIN             141377\n",
       "drive                0\n",
       "size                 0\n",
       "type                 0\n",
       "paint_color          0\n",
       "state                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & (vehicles_cleaned.title_status=='salvage') &\n",
    "                         ((vehicles_cleaned.price>=0) & (vehicles_cleaned.price<=500))), 'condition'] = 'salvage'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & (vehicles_cleaned.title_status=='missing') &\n",
    "                         ((vehicles_cleaned.price>=0)&(vehicles_cleaned.price<=500))), 'condition'] = 'salvage'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & (vehicles_cleaned.title_status=='missing') &\n",
    "                         ((vehicles_cleaned.price>=501)&(vehicles_cleaned.price<=2000))), 'condition'] = 'fair'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & (vehicles_cleaned.title_status=='missing') &\n",
    "                         ((vehicles_cleaned.price>=2001)&(vehicles_cleaned.price<=13495))), 'condition'] = 'good'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & (vehicles_cleaned.title_status=='missing') &\n",
    "                         ((vehicles_cleaned.price>=13496)&(vehicles_cleaned.price<=25000))), 'condition'] = 'excellent'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & (vehicles_cleaned.title_status=='missing') &\n",
    "                         ((vehicles_cleaned.price>=25001)&(vehicles_cleaned.price<=40000))), 'condition'] = 'like new'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & (vehicles_cleaned.title_status=='missing') &\n",
    "                         (vehicles_cleaned.price>40000)), 'condition'] = 'new'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & \n",
    "                         ((vehicles_cleaned.price>=0)&(vehicles_cleaned.price<=2000))), 'condition'] = 'salvage'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & \n",
    "                         ((vehicles_cleaned.price>=2001)&(vehicles_cleaned.price<=5000))), 'condition'] = 'fair'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & \n",
    "                         ((vehicles_cleaned.price>=5001)&(vehicles_cleaned.price<=13495))), 'condition'] = 'good'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & \n",
    "                         ((vehicles_cleaned.price>=13496)&(vehicles_cleaned.price<=25000))), 'condition'] = 'excellent'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & \n",
    "                         ((vehicles_cleaned.price>=25001)&(vehicles_cleaned.price<=40000))), 'condition'] = 'like new'\n",
    "vehicles_cleaned.loc[((vehicles_cleaned.condition.isnull()) & (vehicles_cleaned.price>40000)), 'condition'] = 'new'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizations / Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles_cleaned = vehicles_cleaned.drop(columns='VIN')\n",
    "final_data = vehicles_cleaned.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of used cars over the years')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize'] = 25,5\n",
    "sns.countplot(data=final_data, x='year',palette='rocket')\n",
    "plt.title('Count of used cars over the years')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price of used cars over the years')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize'] = 25,10\n",
    "sns.barplot(data=final_data, x='year', y='price', palette='rocket')\n",
    "plt.title('Price of used cars over the years')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Usage of Cars over the years')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 25,5\n",
    "sns.barplot(data=final_data, x='year',y='odometer' ,palette='rocket')\n",
    "plt.title('Usage of Cars over the years')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price of Cars per Manufacturer')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 25,5\n",
    "sns.barplot(data=final_data, x='manufacturer',y='price' ,palette='rocket')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Price of Cars per Manufacturer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fuel type of used cars and price impact')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fuel type of used cars and its impact to price\n",
    "rcParams['figure.figsize'] = 20,5\n",
    "sns.barplot(data=final_data, x='fuel', y='price', palette='rocket')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Fuel type of used cars and price impact')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='Density'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(final_data.price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling\n",
    "\n",
    "With your (almost?) final dataset in hand, it is now time to build some models.  Here, you should build a number of different regression models with the price as the target.  In building your models, you should explore different parameters and be sure to cross-validate your findings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_col = ['price', 'year', 'cylinders', 'transmission', 'drive', 'odometer']\n",
    "categorical_col = ['region', 'model', 'condition', 'fuel', 'title_status', 'size', 'type', 'paint_color', 'state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region              0\n",
       "price               0\n",
       "year                0\n",
       "manufacturer    12345\n",
       "model               0\n",
       "condition           0\n",
       "cylinders           0\n",
       "fuel                0\n",
       "odometer            0\n",
       "title_status        0\n",
       "transmission        0\n",
       "drive               0\n",
       "size                0\n",
       "type                0\n",
       "paint_color         0\n",
       "state               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cleaned.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 347852 entries, 27 to 426879\n",
      "Data columns (total 16 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   region        347852 non-null  int64  \n",
      " 1   price         347852 non-null  float64\n",
      " 2   year          347852 non-null  float64\n",
      " 3   manufacturer  337857 non-null  string \n",
      " 4   model         347852 non-null  int64  \n",
      " 5   condition     347852 non-null  int64  \n",
      " 6   cylinders     347852 non-null  int64  \n",
      " 7   fuel          347852 non-null  int64  \n",
      " 8   odometer      347852 non-null  float64\n",
      " 9   title_status  347852 non-null  int64  \n",
      " 10  transmission  347852 non-null  int64  \n",
      " 11  drive         347852 non-null  int64  \n",
      " 12  size          347852 non-null  int64  \n",
      " 13  type          347852 non-null  int64  \n",
      " 14  paint_color   347852 non-null  int64  \n",
      " 15  state         347852 non-null  string \n",
      "dtypes: float64(3), int64(11), string(2)\n",
      "memory usage: 45.1 MB\n"
     ]
    }
   ],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()\n",
    "final_data[cat_cols]=final_data[cat_cols].apply(label_encoder.fit_transform)\n",
    "\n",
    "final_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = final_data.drop(columns=['state','manufacturer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm = StandardScaler()\n",
    "final_data['price'] = np.log(final_data['price'])\n",
    "final_data['odometer'] = norm.fit_transform(np.array(final_data['odometer']).reshape(-1,1))\n",
    "final_data['year'] = norm.fit_transform(np.array(final_data['year']).reshape(-1,1))\n",
    "final_data['model'] = norm.fit_transform(np.array(final_data['model']).reshape(-1,1))\n",
    "\n",
    "#scaling target variable\n",
    "q1,q3=(final_data['price'].quantile([0.25,0.75]))\n",
    "o1=q1-1.5*(q3-q1)\n",
    "o2=q3+1.5*(q3-q1)\n",
    "final_data=final_data[(final_data.price>=o1) & (final_data.price<=o2)]\n",
    "\n",
    "final_data['region'] = norm.fit_transform(np.array(final_data['region']).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "X = final_data.drop(columns='price', axis=1)\n",
    "y = final_data['price']\n",
    "print(type(X), type(y))\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_negatives(y_test,y_pred):\n",
    "    ind=[index for index in range(len(y_pred)) if(y_pred[index]>0)]\n",
    "    y_pred=y_pred[ind]\n",
    "    y_test=y_test[ind]\n",
    "    y_pred[y_pred<0]\n",
    "    return (y_test,y_pred)\n",
    "\n",
    "#function for evaluation of model\n",
    "def get_results(y_test,y_pred):\n",
    "    results=[]\n",
    "    results.append(mean_squared_log_error(y_test, y_pred))\n",
    "    results.append(np.sqrt(results[0]))\n",
    "    results.append(r2_score(y_test,y_pred))\n",
    "    results.append(round(r2_score(y_test,y_pred)*100,4))\n",
    "    return (results)\n",
    "\n",
    "final_results=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearReg MSE: 0.0001786251912641226\n",
      "LinearReg RMSE: 0.01336507355999669\n",
      "LinearReg R2 Score: 57.99992427184784\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'X_train Values')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_regression = LinearRegression()\n",
    "linear_regression.fit(X_train, y_train)\n",
    "y_pred = linear_regression.predict(X_test)\n",
    "linreg_results = get_results(y_test, y_pred)\n",
    "print(f'Linear Regression MSE: {linreg_results[0]}')\n",
    "print(f'Linear Regression RMSE: {linreg_results[1]}')\n",
    "print(f'Linear Regression R2 Score: {linreg_results[2]*100}')\n",
    "\n",
    "final_results.append({'Model_name': 'Linear Regression', 'MSE': linreg_results[0], 'RMSE': linreg_results[1], 'R2 Score': linreg_results[2]*100})\n",
    "\n",
    "linreg_coeffs = pd.Series(linreg.coef_, index=X_train.columns)\n",
    "linreg_coeffs.plot(kind='bar')\n",
    "plt.title('Linear Regression Coefficients')\n",
    "plt.xlabel('X_train Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso Grid MSE: 0.0001821880447894458\n",
      "Lasso Grid RMSE: 0.013497705167525544\n",
      "Lasso Grid R2 Score: 57.17088011593279\n",
      "Best alpha: 0.001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'X_train Values')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lasso Regression\n",
    "\n",
    "param_dict = {'alpha': [0.001, 0.1, 1.0, 10.0, 100.0]}\n",
    "lasso_grid = GridSearchCV(Lasso(), param_grid=param_dict, scoring='neg_mean_squared_error', cv=5)\n",
    "lasso_grid.fit(X_train, y_train)\n",
    "y_pred2 = lasso_grid.predict(X_test)\n",
    "lasso_grid_results = get_results(y_test, y_pred2)\n",
    "print(f'Lasso MSE: {lasso_grid_results[0]}')\n",
    "print(f'Lasso RMSE: {lasso_grid_results[1]}')\n",
    "print(f'Lasso R2 Score: {lasso_grid_results[2]*100}')\n",
    "\n",
    "lasso_best_alpha = lasso_grid.best_params_\n",
    "print(f'Best alpha: {list(lasso_best_alpha.values())[0]}')\n",
    "\n",
    "final_results.append({'Model_name': 'Lasso Regression', 'MSE': lasso_grid_results[0], 'RMSE': lasso_grid_results[1], 'R2 Score': lasso_grid_results[2]*100})\n",
    "\n",
    "lassoreg = Lasso(alpha=0.001).fit(X_train, y_train)\n",
    "lassoreg_coeffs = pd.Series(lassoreg.coef_, index=X_train.columns)\n",
    "lassoreg_coeffs.plot(kind='bar')\n",
    "plt.title('Lasso Regression Coefficients')\n",
    "plt.xlabel('X_train Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Grid MSE: 0.0001786252947221173\n",
      "Ridge Grid RMSE: 0.013365077430457234\n",
      "Ridge Grid R2 Score: 57.999911458596245\n",
      "Best alpha: 10.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'X_train Values')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ridge Regression\n",
    "param_dict = {'alpha': [0.001, 0.1, 1.0, 10.0, 100.0]}\n",
    "ridge_grid = GridSearchCV(Ridge(), param_grid=param_dict, scoring='neg_mean_squared_error', cv=5)\n",
    "ridge_grid.fit(X_train, y_train)\n",
    "y_pred1 = ridge_grid.predict(X_test)\n",
    "ridge_grid_results = get_results(y_test, y_pred1)\n",
    "print(f'Ridge MSE: {ridge_grid_results[0]}')\n",
    "print(f'Ridge RMSE: {ridge_grid_results[1]}')\n",
    "print(f'Ridge R2 Score: {ridge_grid_results[2]*100}')\n",
    "\n",
    "ridge_best_alpha = ridge_grid.best_params_\n",
    "print(f'Best alpha: {list(ridge_best_alpha.values())[0]}')\n",
    "\n",
    "final_results.append({'Model_name': 'Ridge Regression', 'MSE': ridge_grid_results[0], 'RMSE': ridge_grid_results[1], 'R2 Score': ridge_grid_results[2]*100})\n",
    "\n",
    "ridgereg = Ridge(alpha=10.0).fit(X_train, y_train)\n",
    "ridgereg_coeffs = pd.Series(ridgereg.coef_, index=X_train.columns)\n",
    "ridgereg_coeffs.plot(kind='bar')\n",
    "plt.title('Ridge Regression Coefficients')\n",
    "plt.xlabel('X_train Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN MSE: 7.618346463552377e-05\n",
      "KNN RMSE: 0.008728313962932575\n",
      "KNN R2 Score: 82.27577915993989\n"
     ]
    }
   ],
   "source": [
    "#  KNN Regressor\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# mses=[]\n",
    "# for i in range(1,10):\n",
    "#     KNN=KNeighborsRegressor(n_neighbors=i)\n",
    "#     KNN.fit(X_train,y_train)\n",
    "#     y_pred=KNN.predict(X_test)\n",
    "#     results=np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "#     mses.append(results)\n",
    "# print(mses)\n",
    "\n",
    "# Finding best k=6 using the code above.\n",
    "KNN=KNeighborsRegressor(n_neighbors=6)\n",
    "KNN.fit(X_train,y_train)\n",
    "y_pred3=KNN.predict(X_test)\n",
    "KNN_results = get_results(y_test, y_pred3)\n",
    "print(f'KNN MSE: {KNN_results[0]}')\n",
    "print(f'KNN RMSE: {KNN_results[1]}')\n",
    "print(f'KNN R2 Score: {KNN_results[2]*100}')\n",
    "\n",
    "final_results.append({'Model_name': 'KNN Regression', 'MSE': KNN_results[0], 'RMSE': KNN_results[1], 'R2 Score': KNN_results[2]*100})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation\n",
    "\n",
    "With some modeling accomplished, we aim to reflect on what we identify as a high quality model and what we are able to learn from this.  We should review our business objective and explore how well we can provide meaningful insight on drivers of used car prices.  Your goal now is to distill your findings and determine whether the earlier phases need revisitation and adjustment or if you have information of value to bring back to your client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>Model_name</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.013365</td>\n",
       "      <td>57.999924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.013498</td>\n",
       "      <td>57.170880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.013365</td>\n",
       "      <td>57.999911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN Regression</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.008996</td>\n",
       "      <td>81.184521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Model_name       MSE      RMSE   R2 Score\n",
       "0  Linear Regression  0.000179  0.013365  57.999924\n",
       "1   Lasso Regression  0.000182  0.013498  57.170880\n",
       "2   Ridge Regression  0.000179  0.013365  57.999911\n",
       "3     KNN Regression  0.000081  0.008996  81.184521"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results =pd.DataFrame(final_results)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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    "### Deployment\n",
    "\n",
    "Now that we've settled on our models and findings, it is time to deliver the information to the client.  You should organize your work as a basic report that details your primary findings.  Keep in mind that your audience is a group of used car dealers interested in fine tuning their inventory."
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