credit_card_fraud_detection_555.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/cxldata/projects/creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "      <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",
       "      <td>0</td>\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",
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       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "      <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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       "      <td>2</td>\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",
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       "      <td>1.0</td>\n",
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       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
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       "      <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",
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       "      <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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       "      <td>4</td>\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",
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       "      <td>1.141109</td>\n",
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       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
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       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
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       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
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       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
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       "      <td>8</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>-0.113192</td>\n",
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       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
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       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
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       "<p>10 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",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \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",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "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",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \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",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
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     "execution_count": 19,
     "metadata": {},
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    "data.head(10)"
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  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <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",
       "      <td>count</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.loc[:,data.columns != 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.loc[:,data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    284315\n",
      "1       492\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data['Class'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid Transactions:  99.83 % of the dataset\n",
      "Fraudulent Transactions:  0.17 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "print('Valid Transactions: ', round(data['Class'].value_counts()[0]/len(data) * 100,2), '% of the dataset')\n",
    "\n",
    "print('Fraudulent Transactions: ', round(data['Class'].value_counts()[1]/len(data) * 100,2), '% of the dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['blue','red']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7a305cfd0>"
      ]
     },
     "execution_count": 41,
     "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": [
    "sns.countplot('Class',data=data,palette=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'train_test_spilt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-fe47d494ea3f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_spilt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'train_test_spilt'"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_spilt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =  train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transactions in X_train dataset:  (199364, 30)\n",
      "Transaction classes in y_train dataset:  (199364, 1)\n",
      "Transactions in X_test dataset:  (85443, 30)\n",
      "Transaction classes in y_test dataset:  (85443, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Transactions in X_train dataset: \", X_train.shape)\n",
    "print(\"Transaction classes in y_train dataset: \", y_train.shape)\n",
    "\n",
    "print(\"Transactions in X_test dataset: \", X_test.shape)\n",
    "print(\"Transaction classes in y_test dataset: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_amount = StandardScaler()\n",
    "scaler_time = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_train['normAmount'] = scaler_amount .fit_transform(X_train['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_train['normAmount'] = scaler_amount .fit_transform(X_train['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_test['normAmount'] = scaler_amount .transform(X_test['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_train['normTime'] = scaler_time .fit_transform(X_train['Time'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_test['normTime'] = scaler_time .transform(X_test['Time'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.drop(['Time', 'Amount'], axis=1)\n",
    "X_test = X_test.drop(['Time', 'Amount'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>V6</th>\n",
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       "      <th>V10</th>\n",
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       "      <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>normAmount</th>\n",
       "      <th>normTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>161145</td>\n",
       "      <td>-0.132066</td>\n",
       "      <td>0.107044</td>\n",
       "      <td>-0.650588</td>\n",
       "      <td>-0.996032</td>\n",
       "      <td>1.814333</td>\n",
       "      <td>1.740740</td>\n",
       "      <td>0.496852</td>\n",
       "      <td>0.633016</td>\n",
       "      <td>0.017181</td>\n",
       "      <td>-0.362707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062489</td>\n",
       "      <td>0.005292</td>\n",
       "      <td>0.251519</td>\n",
       "      <td>-2.466810</td>\n",
       "      <td>-0.889690</td>\n",
       "      <td>0.337462</td>\n",
       "      <td>0.306395</td>\n",
       "      <td>0.074817</td>\n",
       "      <td>-0.165692</td>\n",
       "      <td>0.402560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204520</td>\n",
       "      <td>2.125994</td>\n",
       "      <td>0.014207</td>\n",
       "      <td>-1.514760</td>\n",
       "      <td>0.115021</td>\n",
       "      <td>0.598510</td>\n",
       "      <td>-0.333235</td>\n",
       "      <td>0.199289</td>\n",
       "      <td>-0.264353</td>\n",
       "      <td>0.384111</td>\n",
       "      <td>0.028747</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.329368</td>\n",
       "      <td>-0.788150</td>\n",
       "      <td>0.267730</td>\n",
       "      <td>0.066122</td>\n",
       "      <td>-0.135785</td>\n",
       "      <td>0.203841</td>\n",
       "      <td>-0.068267</td>\n",
       "      <td>-0.057678</td>\n",
       "      <td>-0.352239</td>\n",
       "      <td>0.853047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182659</td>\n",
       "      <td>-0.086694</td>\n",
       "      <td>0.166240</td>\n",
       "      <td>1.573127</td>\n",
       "      <td>0.687266</td>\n",
       "      <td>0.222359</td>\n",
       "      <td>1.102606</td>\n",
       "      <td>1.575093</td>\n",
       "      <td>-1.098608</td>\n",
       "      <td>0.763887</td>\n",
       "      <td>1.404677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015324</td>\n",
       "      <td>1.063663</td>\n",
       "      <td>-0.410841</td>\n",
       "      <td>0.722723</td>\n",
       "      <td>-0.171733</td>\n",
       "      <td>-0.613543</td>\n",
       "      <td>-1.201571</td>\n",
       "      <td>-1.139931</td>\n",
       "      <td>0.330886</td>\n",
       "      <td>0.646288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25117</td>\n",
       "      <td>1.352339</td>\n",
       "      <td>-0.534984</td>\n",
       "      <td>0.555143</td>\n",
       "      <td>-0.629355</td>\n",
       "      <td>-1.144170</td>\n",
       "      <td>-0.852967</td>\n",
       "      <td>-0.642128</td>\n",
       "      <td>-0.032659</td>\n",
       "      <td>-0.654482</td>\n",
       "      <td>0.619206</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014814</td>\n",
       "      <td>-0.180379</td>\n",
       "      <td>0.178112</td>\n",
       "      <td>0.347720</td>\n",
       "      <td>0.151810</td>\n",
       "      <td>-0.404361</td>\n",
       "      <td>0.013746</td>\n",
       "      <td>0.016152</td>\n",
       "      <td>-0.336067</td>\n",
       "      <td>-1.290102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>227642</td>\n",
       "      <td>-1.526760</td>\n",
       "      <td>0.647782</td>\n",
       "      <td>0.615391</td>\n",
       "      <td>-0.561114</td>\n",
       "      <td>0.836950</td>\n",
       "      <td>-0.514251</td>\n",
       "      <td>0.984325</td>\n",
       "      <td>-0.097430</td>\n",
       "      <td>-0.062634</td>\n",
       "      <td>-1.033567</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.221533</td>\n",
       "      <td>-0.393158</td>\n",
       "      <td>-0.214990</td>\n",
       "      <td>0.588447</td>\n",
       "      <td>0.679496</td>\n",
       "      <td>0.518434</td>\n",
       "      <td>0.065022</td>\n",
       "      <td>0.147294</td>\n",
       "      <td>0.005211</td>\n",
       "      <td>1.060059</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",
       "161145 -0.132066  0.107044 -0.650588 -0.996032  1.814333  1.740740  0.496852   \n",
       "204520  2.125994  0.014207 -1.514760  0.115021  0.598510 -0.333235  0.199289   \n",
       "182659 -0.086694  0.166240  1.573127  0.687266  0.222359  1.102606  1.575093   \n",
       "25117   1.352339 -0.534984  0.555143 -0.629355 -1.144170 -0.852967 -0.642128   \n",
       "227642 -1.526760  0.647782  0.615391 -0.561114  0.836950 -0.514251  0.984325   \n",
       "\n",
       "              V8        V9       V10  ...       V21       V22       V23  \\\n",
       "161145  0.633016  0.017181 -0.362707  ... -0.062489  0.005292  0.251519   \n",
       "204520 -0.264353  0.384111  0.028747  ... -0.329368 -0.788150  0.267730   \n",
       "182659 -1.098608  0.763887  1.404677  ...  0.015324  1.063663 -0.410841   \n",
       "25117  -0.032659 -0.654482  0.619206  ... -0.014814 -0.180379  0.178112   \n",
       "227642 -0.097430 -0.062634 -1.033567  ... -0.221533 -0.393158 -0.214990   \n",
       "\n",
       "             V24       V25       V26       V27       V28  normAmount  normTime  \n",
       "161145 -2.466810 -0.889690  0.337462  0.306395  0.074817   -0.165692  0.402560  \n",
       "204520  0.066122 -0.135785  0.203841 -0.068267 -0.057678   -0.352239  0.853047  \n",
       "182659  0.722723 -0.171733 -0.613543 -1.201571 -1.139931    0.330886  0.646288  \n",
       "25117   0.347720  0.151810 -0.404361  0.013746  0.016152   -0.336067 -1.290102  \n",
       "227642  0.588447  0.679496  0.518434  0.065022  0.147294    0.005211  1.060059  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before over-sampling:\n",
      " 0    199019\n",
      "1       345\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Before over-sampling:\\n\", y_train['Class'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After over-sampling:\n",
      " 1    199019\n",
      "0    199019\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"After over-sampling:\\n\", y_train_res.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,auc,roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(lr, parameters, cv=5, verbose=5, n_jobs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 14 candidates, totalling 70 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
      "[Parallel(n_jobs=3)]: Done  12 tasks      | elapsed:    3.8s\n",
      "[Parallel(n_jobs=3)]: Done  70 out of  70 | elapsed:   45.2s finished\n"
     ]
    }
   ],
   "source": [
    "k = clf.fit(X_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 1000, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print(k.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_gridcv_best = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pre = lr_gridcv_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix_test = confusion_matrix(y_test, y_test_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the test dataset: 0.9183673469387755\n"
     ]
    }
   ],
   "source": [
    "print(\"Recall metric in the test dataset:\", (cnf_matrix_test[1,1]/(cnf_matrix_test[1,0]+cnf_matrix_test[1,1] )))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pre = lr_gridcv_best.predict(X_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix_train = confusion_matrix(y_train_res, y_train_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the train dataset: 0.9191132504936714\n"
     ]
    }
   ],
   "source": [
    "print(\"Recall metric in the train dataset:\", (cnf_matrix_train[1,1]/(cnf_matrix_train[1,0]+cnf_matrix_train[1,1] )))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['Not Fraud', 'Fraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_confusion_matrix(k, X_test, y_test,  values_format = '.5g', display_labels=class_names)\n",
    "plt.title(\"Test data Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_confusion_matrix(k, X_train_res, y_train_res,  values_format = '.5g', display_labels=class_names) \n",
    "plt.title(\"Oversampled Train data Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_k =  k.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_auc = auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC: 0.9840059171295605\n"
     ]
    }
   ],
   "source": [
    "print(\"ROC-AUC:\", roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% 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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}