Credit Card Fraud.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing files\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../data/creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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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",
       "      <td>1.378155</td>\n",
       "      <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>...</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",
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       "      <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",
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       "      <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",
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       "    <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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       "</table>\n",
       "<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       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  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": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
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      "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
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       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First, we grab a heatmap\n",
    "\n",
    "### Just getting a good picture of the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b0cbf378d0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'Time' and 'Amount' cols don't look correlated to the target class, but just to check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time     -0.012323\n",
       "Amount    0.005632\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[['Time', 'Amount']].loc['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets do some Modelling!\n",
    "\n",
    " We will use StratifiedShuffleSplit, as the target class is woefully undersized so train_test_split won't cut it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Class', 'Time', 'Amount'], axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "# Since the classes are very imbalanced, we do a stratified split to ensure even selection\n",
    "split = StratifiedShuffleSplit(n_splits=5, test_size=0.3, random_state=69)\n",
    "for train_index, test_index in split.split(X, y):\n",
    "    X_train, X_test = X.loc[train_index], X.loc[test_index]\n",
    "    y_train, y_test = y.loc[train_index], y.loc[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pahas\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logm = LogisticRegression()\n",
    "logm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, we find the cols which have the highest respective coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V27    0.770330\n",
       "V10    0.760791\n",
       "V4     0.651798\n",
       "V14    0.568365\n",
       "V22    0.491348\n",
       "V28    0.424861\n",
       "V21    0.362416\n",
       "V15    0.246940\n",
       "V20    0.244340\n",
       "V13    0.234875\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefDF = pd.DataFrame(logm.coef_, columns=X.columns).transpose()\n",
    "coefDF.apply(abs)[0].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation\n",
    "\n",
    "Kaggle told us to use \"Area-Under-Precision-Recall-Curve\", so that's what we do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5153104718559123\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "predictions = logm.predict(X_test)\n",
    "print(average_precision_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Could be better..."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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