Credit Card Fradulent Modeling 25% Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this session, we are going to create predictions on how to utilize predictive models in credit card modeling. In this session, we are going to use Logistic Regression, SVMs, Decision Trees, among other techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccard = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
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       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccard.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccard.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Counts by Amount and Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.00     13688\n",
       "1.98      6044\n",
       "0.89      4872\n",
       "9.99      4747\n",
       "15.00     3280\n",
       "0.76      2998\n",
       "10.00     2950\n",
       "1.29      2892\n",
       "1.79      2623\n",
       "0.99      2304\n",
       "Name: Amount, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccard['Amount'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccard['Class'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for NA Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "ccard.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x224b082eac8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x224b07c3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(ccard.isnull(),yticklabels=False,cbar=False,cmap='brg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good to go as there are no NA Values in this dataset. However, still have to take some precautions to clean the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis in Target Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x224adb6cc50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x224adb96358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(ccard['Class'], sort = True).sort_index()\n",
    "count_classes.plot(kind = 'bar', color='r')\n",
    "plt.title(\"Fraud Class Histogram\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plot, most of the classes are at zero, in which the information seems to be imbalanced. What we are going to do is do the following:\n",
    "    - Use Kappa and ROC Curves\n",
    "    - Make comparisons in between models for using the simple Logistic Regression classifier.\n",
    "    - F1 Score\n",
    "    - Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting Our Input Variables and Targets, Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>normAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10     ...           V21       V22       V23  \\\n",
       "0  0.098698  0.363787  0.090794     ...     -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425 -0.166974     ...     -0.225775 -0.638672  0.101288   \n",
       "2  0.247676 -1.514654  0.207643     ...      0.247998  0.771679  0.909412   \n",
       "3  0.377436 -1.387024 -0.054952     ...     -0.108300  0.005274 -0.190321   \n",
       "4 -0.270533  0.817739  0.753074     ...     -0.009431  0.798278 -0.137458   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Class  normAmount  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053      0    0.244964  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724      0   -0.342475  \n",
       "2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0    1.160686  \n",
       "3 -1.175575  0.647376 -0.221929  0.062723  0.061458      0    0.140534  \n",
       "4  0.141267 -0.206010  0.502292  0.219422  0.215153      0   -0.073403  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccard['normAmount'] = StandardScaler().fit_transform(ccard['Amount'].reshape(-1, 1))\n",
    "ccard = ccard.drop(['Time','Amount'],axis=1)\n",
    "ccard.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assigning X and Y, no resampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ccard.ix[:, ccard.columns != 'Class']\n",
    "y = ccard.ix[:, ccard.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of normal transactions:  0.5\n",
      "Percentage of fraud transactions:  0.5\n",
      "Total number of transactions in resampled data:  984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:18: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"
     ]
    }
   ],
   "source": [
    "# Number of data points in the minority class\n",
    "number_records_fraud = len(ccard[ccard.Class == 1])\n",
    "fraud_indices = np.array(ccard[ccard.Class == 1].index)\n",
    "\n",
    "# Picking the indices of the normal classes\n",
    "normal_indices = ccard[ccard.Class == 0].index\n",
    "\n",
    "# Out of the indices we picked, randomly select \"x\" number (number_records_fraud)\n",
    "random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)\n",
    "random_normal_indices = np.array(random_normal_indices)\n",
    "\n",
    "# Appending the 2 indices\n",
    "under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])\n",
    "\n",
    "# Under sample dataset\n",
    "under_sample_ccard = ccard.iloc[under_sample_indices,:]\n",
    "\n",
    "X_undersample = under_sample_ccard.ix[:, under_sample_ccard.columns != 'Class']\n",
    "y_undersample = under_sample_ccard.ix[:, under_sample_ccard.columns == 'Class']\n",
    "\n",
    "# Showing ratio\n",
    "print(\"Percentage of normal transactions: \", len(under_sample_ccard[under_sample_ccard.Class == 0])/len(under_sample_ccard))\n",
    "print(\"Percentage of fraud transactions: \", len(under_sample_ccard[under_sample_ccard.Class == 1])/len(under_sample_ccard))\n",
    "print(\"Total number of transactions in resampled data: \", len(under_sample_ccard))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Train Test Split and Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Whole Dataset at 25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions train dataset:  213605\n",
      "Number transactions test dataset:  71202\n",
      "Total number of transactions:  284807\n"
     ]
    }
   ],
   "source": [
    "print(\"Number transactions train dataset: \", len(X_train))\n",
    "print(\"Number transactions test dataset: \", len(X_test))\n",
    "print(\"Total number of transactions: \", len(X_train)+len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Undersample Dataset at 30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample\n",
    "                                                                                                   ,y_undersample\n",
    "                                                                                                   ,test_size = 0.3\n",
    "                                                                                                   ,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number transactions train dataset:  688\n",
      "Number transactions test dataset:  296\n",
      "Total number of transactions:  984\n"
     ]
    }
   ],
   "source": [
    "print(\"\")\n",
    "print(\"Number transactions train dataset: \", len(X_train_undersample))\n",
    "print(\"Number transactions test dataset: \", len(X_test_undersample))\n",
    "print(\"Total number of transactions: \", len(X_train_undersample)+len(X_test_undersample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now going to make calculations based on the following criteria:\n",
    "- Accuracy = (TP+TN)/Total\n",
    "- Precision = TP/(TP+FP)\n",
    "- Recall = TP/(TP+FN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Logistic Regression Machine Learning Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verifying the Ad-Hoc K-Fold Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def printing_Kfold_scores(x_train_data,y_train_data):\n",
    "    fold = KFold(len(y_train_data),5,shuffle=False) \n",
    "\n",
    "    # Different C parameters\n",
    "    c_param_range = [0.01,0.1,1,10,100]\n",
    "\n",
    "    results_table = pd.DataFrame(index = range(len(c_param_range),2), columns = ['C_parameter','Mean recall score'])\n",
    "    results_table['C_parameter'] = c_param_range\n",
    "\n",
    "    # the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1]\n",
    "    j = 0\n",
    "    for c_param in c_param_range:\n",
    "        print('-------------------------------------------')\n",
    "        print('C parameter: ', c_param)\n",
    "        print('-------------------------------------------')\n",
    "        print('')\n",
    "\n",
    "        recall_accs = []\n",
    "        for iteration, indices in enumerate(fold,start=1):\n",
    "\n",
    "            # Call the logistic regression model with a certain C parameter\n",
    "            lr = LogisticRegression(C = c_param, penalty = 'l1')\n",
    "\n",
    "            # Use the training data to fit the model. In this case, we use the portion of the fold to train the model\n",
    "            # with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]\n",
    "            lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())\n",
    "\n",
    "            # Predict values using the test indices in the training data\n",
    "            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)\n",
    "\n",
    "            # Calculate the recall score and append it to a list for recall scores representing the current c_parameter\n",
    "            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)\n",
    "            recall_accs.append(recall_acc)\n",
    "            print('Iteration ', iteration,': recall score = ', recall_acc)\n",
    "\n",
    "        # The mean value of those recall scores is the metric we want to save and get hold of.\n",
    "        results_table.ix[j,'Mean recall score'] = np.mean(recall_accs)\n",
    "        j += 1\n",
    "        print('')\n",
    "        print('Mean recall score ', np.mean(recall_accs))\n",
    "        print('')\n",
    "\n",
    "    best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']\n",
    "    \n",
    "    # Finally, we can check which C parameter is the best amongst the chosen.\n",
    "    print('*********************************************************************************')\n",
    "    print('Best model to choose from cross validation is with C parameter = ', best_c)\n",
    "    print('*********************************************************************************')\n",
    "    \n",
    "    return best_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "C parameter:  0.01\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.931506849315\n",
      "Iteration  2 : recall score =  0.917808219178\n",
      "Iteration  3 : recall score =  1.0\n",
      "Iteration  4 : recall score =  0.959459459459\n",
      "Iteration  5 : recall score =  0.954545454545\n",
      "\n",
      "Mean recall score  0.9526639965\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  0.1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.849315068493\n",
      "Iteration  2 : recall score =  0.86301369863\n",
      "Iteration  3 : recall score =  0.932203389831\n",
      "Iteration  4 : recall score =  0.945945945946\n",
      "Iteration  5 : recall score =  0.893939393939\n",
      "\n",
      "Mean recall score  0.896883499368\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.86301369863\n",
      "Iteration  2 : recall score =  0.890410958904\n",
      "Iteration  3 : recall score =  0.966101694915\n",
      "Iteration  4 : recall score =  0.945945945946\n",
      "Iteration  5 : recall score =  0.909090909091\n",
      "\n",
      "Mean recall score  0.914912641497\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  10\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.86301369863\n",
      "Iteration  2 : recall score =  0.890410958904\n",
      "Iteration  3 : recall score =  0.966101694915\n",
      "Iteration  4 : recall score =  0.959459459459\n",
      "Iteration  5 : recall score =  0.909090909091\n",
      "\n",
      "Mean recall score  0.9176153442\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  100\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.86301369863\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:37: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration  2 : recall score =  0.890410958904\n",
      "Iteration  3 : recall score =  0.966101694915\n",
      "Iteration  4 : recall score =  0.959459459459\n",
      "Iteration  5 : recall score =  0.909090909091\n",
      "\n",
      "Mean recall score  0.9176153442\n",
      "\n",
      "*********************************************************************************\n",
      "Best model to choose from cross validation is with C parameter =  0.01\n",
      "*********************************************************************************\n"
     ]
    }
   ],
   "source": [
    "best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion Matrix',\n",
    "                          cmap='Accent'):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=0)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        #print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        1#print('Confusion matrix, without normalization')\n",
    "\n",
    "    #print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True Label')\n",
    "    plt.xlabel('Predicted Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running a Test Set on the Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how effective the predictive model becomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.931972789116\n"
     ]
    },
    {
     "data": {
      "image/png": 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XXFycHA6HJkyYIH9//yuuQwQBADeE2267TevWrZMk3XXXXUpLS7vkPrGxsYqNjb3qNbkc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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x224b1648ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use this C_parameter to build the final model with the whole training dataset and predict the classes in the test\n",
    "# Dataset\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
    "y_pred_undersample = lr.predict(X_test_undersample.values)\n",
    "\n",
    "# Compute Confusion Matrix\n",
    "cnf_matrix = confusion_matrix(y_test_undersample,y_pred_undersample)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "# Plot non-normalized Confusion Matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the analysis, the accuracy rate is 93.2% which is really good on the first try by running a 25% data on the test set. However, it is an udersampled test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the Information on the Whole Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.925\n"
     ]
    },
    {
     "data": {
      "image/png": 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2aVpLj3DBSYtO4+cKr8ef06aRFp3WTHv6YyM8xqON8BhNr8EzzAEDBpz1zdbX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\nAIABggkAgAGCCQCAAYIJAICB/w/nZweJzUU2xwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x224b25966d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use this C_parameter to build the final model with the whole training dataset and predict the classes in the test\n",
    "# dataset\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
    "y_pred = lr.predict(X_test.values)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is still a very good accuracy predictor as we are going to apply this information on a larget set of information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precision Recall and ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z7N271xPZiIgUJ2VnQf/WUITHdobqzz9gfmsMMrbtgv3xekpHI7otl8U+d+5c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\nyL8Ujg88ZZHBAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x224b2658470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC CURVE\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "y_pred_undersample_score = lr.fit(X_train_undersample,y_train_undersample.values.ravel()).decision_function(X_test_undersample.values)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test_undersample.values.ravel(),y_pred_undersample_score)\n",
    "roc_auc = auc(fpr,tpr)\n",
    "\n",
    "# Plot ROC\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.2f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has been determined that it is best to run more data to get a better sample. Records have come from the majority class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Skewed Data Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "C parameter:  0.01\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.46835443038\n",
      "Iteration  2 : recall score =  0.586666666667\n",
      "Iteration  3 : recall score =  0.634920634921\n",
      "Iteration  4 : recall score =  0.58904109589\n",
      "Iteration  5 : recall score =  0.463414634146\n",
      "\n",
      "Mean recall score  0.548479492401\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  0.1\n",
      "-------------------------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:37: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration  1 : recall score =  0.518987341772\n",
      "Iteration  2 : recall score =  0.64\n",
      "Iteration  3 : recall score =  0.666666666667\n",
      "Iteration  4 : recall score =  0.643835616438\n",
      "Iteration  5 : recall score =  0.524390243902\n",
      "\n",
      "Mean recall score  0.598775973756\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.518987341772\n",
      "Iteration  2 : recall score =  0.626666666667\n",
      "Iteration  3 : recall score =  0.698412698413\n",
      "Iteration  4 : recall score =  0.671232876712\n",
      "Iteration  5 : recall score =  0.560975609756\n",
      "\n",
      "Mean recall score  0.615255038664\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  10\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.518987341772\n",
      "Iteration  2 : recall score =  0.613333333333\n",
      "Iteration  3 : recall score =  0.698412698413\n",
      "Iteration  4 : recall score =  0.671232876712\n",
      "Iteration  5 : recall score =  0.560975609756\n",
      "\n",
      "Mean recall score  0.612588371997\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  100\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.518987341772\n",
      "Iteration  2 : recall score =  0.613333333333\n",
      "Iteration  3 : recall score =  0.698412698413\n",
      "Iteration  4 : recall score =  0.671232876712\n",
      "Iteration  5 : recall score =  0.560975609756\n",
      "\n",
      "Mean recall score  0.612588371997\n",
      "\n",
      "*********************************************************************************\n",
      "Best model to choose from cross validation is with C parameter =  1.0\n",
      "*********************************************************************************\n"
     ]
    }
   ],
   "source": [
    "best_c = printing_Kfold_scores(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.658333333333\n"
     ]
    },
    {
     "data": {
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Hjqlz587av3+/JOnKK69UZGSk5syZ49nms88+02effaYrrriilY8E33ejkkdJkj7966ee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\nZZMdks2QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x224b088bac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use this C_parameter to build the final model with the whole training dataset and predict the classes in the test\n",
    "# dataset\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train,y_train.values.ravel())\n",
    "y_pred_undersample = lr.predict(X_test.values)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_undersample)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Changing Classification Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  1.0\n",
      "Recall metric in the testing dataset:  1.0\n",
      "Recall metric in the testing dataset:  1.0\n",
      "Recall metric in the testing dataset:  0.972789115646\n",
      "Recall metric in the testing dataset:  0.931972789116\n",
      "Recall metric in the testing dataset:  0.863945578231\n",
      "Recall metric in the testing dataset:  0.823129251701\n",
      "Recall metric in the testing dataset:  0.761904761905\n",
      "Recall metric in the testing dataset:  0.591836734694\n"
     ]
    },
    {
     "data": {
      "image/png": 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44447+PrrrzGZTGRlZZGbm8tf//pXtmzZAkBqaiqdO3eu1NgVvw1PRK7KmoEV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x224b266b9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(C = 0.01, penalty = 'l1')\n",
    "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
    "y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values)\n",
    "\n",
    "thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "j = 1\n",
    "for i in thresholds:\n",
    "    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i\n",
    "    \n",
    "    plt.subplot(3,3,j)\n",
    "    j += 1\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall)\n",
    "    np.set_printoptions(precision=2)\n",
    "\n",
    "    print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "    # Plot non-normalized confusion matrix\n",
    "    class_names = [0,1]\n",
    "    plot_confusion_matrix(cnf_matrix\n",
    "                          , classes=class_names\n",
    "                          , title='Threshold >= %s'%i) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pattern is very directed: the more you lower the required probability for the class \"1\" category, more records will be placed in that list. \n",
    "\n",
    "This implies an increase in recall (we want all the \"1\"s), but at the same time, there is a decrease in precision as well.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the Precision-Recall Tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AX33Vzm9/e+1yQMufu8d/fJDvjs1nyvCZDE+4o859g4L+hNn8Cbm503E6R1fb\nJmz4YLR7dpN58Dgi3OrXvvuCKp4BTmVxOXI+k+27vuYO50xS8lMB2GfshabT80S3v6NaF26XR3Dw\nnGBnmsLxi16DEn2JC36CTHzEtZtkuh7FszyV48suqVVV3gIwq/ZVVcnFuaolblWOLDMjRgRx8aLE\n5Ml2Ro26Njmg5WM7mp1K/1k30M7agVV3b0CWas9Q0GgOYrXehMNxO3l586ptY/7vOwT981XyPv4C\nx10T/N7/ulDFM8CpSVzO5xSyfPNMBrtm0DNnBwDH9CnkJT1Fq56TQK7eCi67ULDrlMKukwrZxUvL\nIy3QM0Gme7xMiB9d8H3hlyaeNVGfVVVWo5EWBckcemcUKDKvfJrG6FvMxFzlVVWVY3vip8eYm/ot\nk4dMYXSbumuwh4Xdila7o8byxJrDh7AOuBH7mHHkf/G1P7vuE6p4Bjh1XXy5NgfzN31Pb/s0Bmat\nQUaQrm3O2ZaPknDzk6CteusH3kmQExne0eiB8i74xaWW28ZeuQu+L6jiWTuFLpe3VlWlVVWn8nJR\njibCN/eC0Q6PfklIbGGlPFXvI4DGWlVVObYTOcfoO7M3bcKSWTNhMxq59hmtsvLEL2OzvVy1gRBY\nb+iKlJlJ5uGToK/6bLQxUcUzwPH14nO4PMzetIbEgq8Zkf0DesVJtiaMQ5GTaDPgD0imiBr3LXIK\n9p5R2HlScK7YBT/IUDzJ1FompoEu+L6gimfDKFlV9b8pEtPfSsAck0+zZ2Zx2nMBd6UFADWtqmoV\nHILGzwsAnl31BDMPT+eT2yYzPuXuOt6h7vLEQX97CfNnH5Mz+ztcg+rOI/UnqngGOPW9+BRFYf72\nnVguf82ded8R7MrHJpvYEzKOxIEvoQmNr3X/8zllBiUlLvgtrRI9W0t0jZMx+nmSSRXPK+fNN/X8\n5z/eHNBZc/K54MypkE51JCuT4znZOJWKCwBMWi1JYeHlTFW8WQCtQ8N8WlVVXWyn8tLoM6MH8SGt\nWX/PNrQ1PD4qoa7yxLoN6wgbN5Kihx+j4M1/+/Bp+A9VPAOcK7n4lu1PpfD0/xhfOJcoewZuScOu\noKE07/sXDDGda93XrQgOp3sNSlIviNJSyx1b+O6C7wuqeF45QsCTTxqZO1fHiBEuvvyyag6oW1E4\nlZdbLq2qrlVVYRVHqlYriaHhFVZV1RTbH9Y8y7SDX/H+4E+4p92va+17neWJXS4iOiQhLBaydh24\nqmWJVfH0+m3HAAAgAElEQVQMcPxx8W05fo5jh6cw1vEtrQtOArDH2JfQ3i9iaT2ozj/I3CLB7jSF\nnWnlXPCDvKWWe9Tigu8Lqnj6B6cT7rnHxIYNWn7zGyevveaoeyfqt6pKI0kkhoaXjlB7xbegmTao\nyqqqs/lnuOmb7sRamrNp4k50mtoWBwjCw29Go0mtsTxx8G8fwTh/DlkrN+Dp3MWnuPyBKp4Bjj8v\nvkPns9i0ewZ3uGfROWcfAEf0nRCdniOi43ioI71ECEHa5WIX/LMCV7ELfpsY72i0Q3MJraZ+QqqK\np//IzYWRI80cOaLhH/+w8/jjDc8BFUJwroGrqlKsEfyY9iGLjk/nP4M+YFKHB2o9lsn0CRbLn2os\nT2xYOJ+Qxx6k8MU/Y/vjSw2Oqb6o4hngNMbFdy7HxvdbF3CLZwb9Lq8H4Iw2jpykJ2nW62HQ1L3i\nxe4S7D/jna0vccE36ctc8JuH+Saiqnj6l7NnvT6gGRkS//ufnTvu8G8OqBCCDFshR7KzSHcWsPNM\neqmwZtnLr6rKA95DI4UwoOW/aGeNKTVXSQmPIMRQ9jdWVp44juzsHVQuTyzl5RLRPhF3h07krKjZ\nhd7fqOIZ4DTmxZdrczBr04/0cH3DkMs/ohVuLmsiOd3iYeJufgahD6n7TfC64O9MU9idplBQPChp\nHubNHe0aJ2OupdSyKp7+Z/9+by14RYH582306qXUvVMDqBzb5SJbcZ6qd6T6w4mPSC9cAdwB9K6w\nb2yQpUI61YSub9AyfEmN5YlDfzUG/drVZO45hNK8RaPEUxlVPAOcq3HxOVwevtm8kXjbNEZnfY/Z\nbaNAtnA4YgLxA16EoFif3qfEBX/HSYUj5V3wW3gNSpKqccFXxbNxWLlSw6RJJsLCBEuW2EhM9P/l\nXFdsGbYMek/vTIg+jI9v+4G0vIIaV1Xd0voEqx6Yyqz9Pflo6xOlt/8lq6pazZhG8J9fIP/td7E/\nWHMBOX+iimeAczUvPkVR+HbHPkyZU7krbx7hjmwckp79ISOJG/AXpPDq7cOqI98u2FNcarmyC37P\n1jLWYhd8VTwbj6lTdfzxj0YSExWWLLEREeHfS9qX2P6+6W98tOc9Xu/3Fo91+V2FbflOh3epalYm\nqdmXePmWx7Casmn27z+Q56hYLbVLURF733qLfd26s/idd0vNqhtzVZUqngHOtbr4lu4/TvbZafzK\nNocWhWdQkNgfNIjIPn9G37yqEUlNCCE4k+Udje47I3AWP4JLjPaORgd2CyE3u+q67uuFa/3l8M9/\n6vnvfw307u1h7lwbpuoXnDUIX2LLLMqk1/TOBOmC2PbrvZh1NRuRmkz/xmL5O5k577AnfUxZnmrx\nSHXeW2/S/tIlIl98kYLiZ6YhegPJ4dbSWlU3xbagR4xvd0q+xNcQNK+88sorfunBVcBWks19HRIU\nZLgm8SXHWOmSPIjNhnF8Z48gRneBjvk7CDkxjdOHl4MpGl14mzrTnCRJItQs0b65zM3JMpHBEjYn\nnLzkdcRfvd9BdqHAYvRa5wWSC74vXKvzV0K/fh5OnJBZuVLLsWMyo0a5/ZYq6UtsZp0Zm6uQladX\nEGYMp3ezmr94FSUBk+kTdNqLhGqepmNkFANaxjE2uR2PdulOZHY2QZs30nvYSKJ69CTCaMbhcXM4\nK5O9lzJYc+YUMw8f4L6OXbDornwpZ1CQb1aBlVFHnk2Eaz1yKeFgejar9s1npDKL3pe9pRHSdG1w\nd/g9oZ3vrdGIpCYu53tXMu05I8gp9P6pxYR4J5m6xctYrpNSy03h/DkcMGGCiU2btPz2t05efdW3\nHNC68DW2bHsWvaZ3waDRs23SPiw6S41ty8oTr8Pt7lZhm3bvbsJvH4j97onkf/hZ6etOj4eTuTl8\nvm8X0w7u56tho7gj0fdHTDWhjjwDnGs9cikhKtjEDW16UBA5no/yOiDMRXTN30XkxSUUHJjC5QIX\nQbFdQfbNLd1skEiKkRl9UwgRRhceBU5fhtQLgo2pCudzBHqtNxk/kEstN4Xzp9XCsGFuli/Xsny5\njvBwQY8eVz4D72tsJq0Jh9vOT6d/JFgfzI211HkXIgijcS6gwems6EqvxDTDOH0K2qNHKPrtU6Vl\niTWyTKTJjFmrY/aRg7QKDmFgq9qXIfuCOvIMcJrCyKU6sm0Opm1eS3fPDO7IWIJBcZArh3K6+f3E\n3vw8wlizEUl5ysdX6BDsOa2ws5ILfskkU2QTc8H3haZ0/s6c8eaAXrok8fXXdoYPv7Ic0PrElufI\npef0zshI7LhvP8E1psG5sVo7Ikm2assTW154DtOUL8lZtAzXTTdX2FbgdJI0+UP6NG/Jd3fWZUpS\nN+rIM8BpCiOX6jDptNyYmESzuFF8nD+AE0YNHeyHiMtZj3TwM9LOnSA4phMYwmp9n/Lx6bUScREy\nNyTKtIuVkGWJ9BzB8QzB5mMKxzMESBBh4arY5fmDpnT+QkOhb18Pc+fqWLxYy8CBbmJjGz5Gqk9s\nBq0Rj+JmxenlmLQmbm7er4aWMpKUjcGwGo8nGY+nkgeDRsY4bzZKWDiuW26tsEmv0bD4eCpHsrJ4\nqnvvK75jUUeeAU5TGrnUhqIozNhxEG3ODO7OnUtM0QXcaDgYMoTYfn+ByOrXJNcVn8vjnVjaebJY\nPAG9Frq0kujZWibuGrrg+0JTPH8rVmi47z4TVqtg6VIbrVs37FKvb2wFznx6Te+MW/Gw8779hNbw\nxVpWnvhmcnOXVdxotxPZLgFPTAzZW3ZXmbB8bvWPfHPoZ1bdfR+dIquuk68PTW7kqSgK//d//8en\nn37KokWL6NmzJ2FhZR/iokWLeOmll5gzZw4ej4fOnWt3/wF15NkUkCSJLi2i6ZR0K4uUkfyohNNK\nc452edsIOvo/Th9dgyE4DjkkvsIffF3xaWSJZqESPYpNSIw672TTiUuwM02w74yCyw3hQU2jJlNl\nmuL5S0oSREUJFi7UsWqVlnHjXA1KYapvbHqNAQGsOLUMnUZHvxYDqm0nRDg63Wb0+vU4HHchRLlH\nQFot2r170G/bgmPsXYiIio+HLhfZWJ52gs6R0XSNjql/UOVo6Miz4Q6pdfDTTz/hdDr59ttv+cMf\n/sCbb75ZYfvbb7/NV199xcyZM/nqq6/Izc1trK6oNBJjOifwyLCX2d1pGf8X/RZ7I7rT3raZZqtH\nkf9tH+xH5kIlb0lfCA+SuK2jhj+O0PLwAA1dWknkFMKy/QpvL3EzdYObg+cUPErA3DRdMx54wMXT\nTzs4flzm/vtN2KsW+2wUHu70GJGmKD7f+wlZ9swa29nt9wNet/nKOIaNAEC/bGmVbT1ivGW5d148\n74/uNohGE8+dO3fSv39/ALp168bPP/9cYXvbtm3Jz8/H6XQihGjSt2QqtdO/TQxPDfsdWd0X8lLM\nx6yLGUSi8yCttj6Ma1YXCnZ9Bu76p83IkkSbGJl7btLy8igto7vLNAuFw+cF0zd5eHOxm6V7PVzM\nU0W0Nv7yFydjx7rYtk3LU08ZURpnCXwFgnRBPNvjeQpc+Xy8+4Ma2zkco1CUcIzGGUDF0a3ztqEI\nWcawvKp4tg2PIEinY9fFC/7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ai/bgp+RdTkMf1R5hqH0G90riK3HB79NGJiHK64J/JrPY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\ntVgcNsh8gwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x224b2aaec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "\n",
    "lr = LogisticRegression(C = 0.01, penalty = 'l1')\n",
    "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
    "y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values)\n",
    "\n",
    "thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "colors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal', 'red', 'yellow', 'green', 'blue','black'])\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "\n",
    "j = 1\n",
    "for i,color in zip(thresholds,colors):\n",
    "    y_test_predictions_prob = y_pred_undersample_proba[:,1] > i\n",
    "    \n",
    "    precision, recall, thresholds = precision_recall_curve(y_test_undersample,y_test_predictions_prob)\n",
    "    \n",
    "    # Plot Precision-Recall curve\n",
    "    plt.plot(recall, precision, color=color,\n",
    "                 label='Threshold: %s'%i)\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.title('Precision-Recall example')\n",
    "    plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}