Credit_card_fraud_detection.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Credit card fraud detection",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Xcspnz-x5Ki",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "from sklearn import linear_model\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpwTEpf_x5_y",
        "colab_type": "code",
        "outputId": "35650664-286d-42bd-e536-b46b18e7f4d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "df=pd.read_csv('creditcard.csv')\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</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>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</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",
              "    </tr>\n",
              "    <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>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</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",
              "    </tr>\n",
              "    <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>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</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>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</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>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3iRYX_5ykpg",
        "colab_type": "code",
        "outputId": "5f82b952-f1f3-4a31-93d5-0ba1e9fed768",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rndL_y1Xypsa",
        "colab_type": "code",
        "outputId": "ee6805f5-9fd6-43d0-81f5-000483657c7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</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>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>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>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>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>1.758743e-12</td>\n",
              "      <td>-8.252298e-13</td>\n",
              "      <td>-9.636929e-13</td>\n",
              "      <td>8.316157e-13</td>\n",
              "      <td>1.591952e-13</td>\n",
              "      <td>4.247354e-13</td>\n",
              "      <td>-3.050180e-13</td>\n",
              "      <td>8.693344e-14</td>\n",
              "      <td>-1.179712e-12</td>\n",
              "      <td>7.094854e-13</td>\n",
              "      <td>1.875015e-12</td>\n",
              "      <td>1.053488e-12</td>\n",
              "      <td>7.137527e-13</td>\n",
              "      <td>-1.491363e-13</td>\n",
              "      <td>-5.225914e-13</td>\n",
              "      <td>-2.280712e-13</td>\n",
              "      <td>-6.428517e-13</td>\n",
              "      <td>4.958987e-13</td>\n",
              "      <td>7.060712e-13</td>\n",
              "      <td>1.766038e-12</td>\n",
              "      <td>-3.406543e-13</td>\n",
              "      <td>-5.713163e-13</td>\n",
              "      <td>-9.725303e-13</td>\n",
              "      <td>1.464139e-12</td>\n",
              "      <td>-6.989087e-13</td>\n",
              "      <td>-5.615260e-13</td>\n",
              "      <td>3.332112e-12</td>\n",
              "      <td>-3.518886e-12</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>1.088850e+00</td>\n",
              "      <td>1.020713e+00</td>\n",
              "      <td>9.992014e-01</td>\n",
              "      <td>9.952742e-01</td>\n",
              "      <td>9.585956e-01</td>\n",
              "      <td>9.153160e-01</td>\n",
              "      <td>8.762529e-01</td>\n",
              "      <td>8.493371e-01</td>\n",
              "      <td>8.381762e-01</td>\n",
              "      <td>8.140405e-01</td>\n",
              "      <td>7.709250e-01</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>-2.458826e+01</td>\n",
              "      <td>-4.797473e+00</td>\n",
              "      <td>-1.868371e+01</td>\n",
              "      <td>-5.791881e+00</td>\n",
              "      <td>-1.921433e+01</td>\n",
              "      <td>-4.498945e+00</td>\n",
              "      <td>-1.412985e+01</td>\n",
              "      <td>-2.516280e+01</td>\n",
              "      <td>-9.498746e+00</td>\n",
              "      <td>-7.213527e+00</td>\n",
              "      <td>-5.449772e+01</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>-5.354257e-01</td>\n",
              "      <td>-7.624942e-01</td>\n",
              "      <td>-4.055715e-01</td>\n",
              "      <td>-6.485393e-01</td>\n",
              "      <td>-4.255740e-01</td>\n",
              "      <td>-5.828843e-01</td>\n",
              "      <td>-4.680368e-01</td>\n",
              "      <td>-4.837483e-01</td>\n",
              "      <td>-4.988498e-01</td>\n",
              "      <td>-4.562989e-01</td>\n",
              "      <td>-2.117214e-01</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>-9.291738e-02</td>\n",
              "      <td>-3.275735e-02</td>\n",
              "      <td>1.400326e-01</td>\n",
              "      <td>-1.356806e-02</td>\n",
              "      <td>5.060132e-02</td>\n",
              "      <td>4.807155e-02</td>\n",
              "      <td>6.641332e-02</td>\n",
              "      <td>-6.567575e-02</td>\n",
              "      <td>-3.636312e-03</td>\n",
              "      <td>3.734823e-03</td>\n",
              "      <td>-6.248109e-02</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>4.539234e-01</td>\n",
              "      <td>7.395934e-01</td>\n",
              "      <td>6.182380e-01</td>\n",
              "      <td>6.625050e-01</td>\n",
              "      <td>4.931498e-01</td>\n",
              "      <td>6.488208e-01</td>\n",
              "      <td>5.232963e-01</td>\n",
              "      <td>3.996750e-01</td>\n",
              "      <td>5.008067e-01</td>\n",
              "      <td>4.589494e-01</td>\n",
              "      <td>1.330408e-01</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>2.374514e+01</td>\n",
              "      <td>1.201891e+01</td>\n",
              "      <td>7.848392e+00</td>\n",
              "      <td>7.126883e+00</td>\n",
              "      <td>1.052677e+01</td>\n",
              "      <td>8.877742e+00</td>\n",
              "      <td>1.731511e+01</td>\n",
              "      <td>9.253526e+00</td>\n",
              "      <td>5.041069e+00</td>\n",
              "      <td>5.591971e+00</td>\n",
              "      <td>3.942090e+01</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",
              "</div>"
            ],
            "text/plain": [
              "                Time            V1  ...         Amount          Class\n",
              "count  284807.000000  2.848070e+05  ...  284807.000000  284807.000000\n",
              "mean    94813.859575  1.758743e-12  ...      88.349619       0.001727\n",
              "std     47488.145955  1.958696e+00  ...     250.120109       0.041527\n",
              "min         0.000000 -5.640751e+01  ...       0.000000       0.000000\n",
              "25%     54201.500000 -9.203734e-01  ...       5.600000       0.000000\n",
              "50%     84692.000000  1.810880e-02  ...      22.000000       0.000000\n",
              "75%    139320.500000  1.315642e+00  ...      77.165000       0.000000\n",
              "max    172792.000000  2.454930e+00  ...   25691.160000       1.000000\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pbg-wYsiyp3A",
        "colab_type": "code",
        "outputId": "8491f0ef-8ce9-4717-e62c-cb05b495a1a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "print(df.columns)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJ5oC8oNyp5u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#class 1 means fraudlant transaction and class 0 means non frAudlant case\n",
        "#All features in form of PCA for privacy reason"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NibaBVS-yqFo",
        "colab_type": "code",
        "outputId": "5711bc10-5587-4108-8342-e73aee44b0f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rqWDcqJyqPy",
        "colab_type": "code",
        "outputId": "950cb0bc-f7a9-4c53-998c-b805eccce243",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UP4vOFYjyqZ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#So there is no null values in any columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmvTRkRuyqbf",
        "colab_type": "code",
        "outputId": "3ba1532f-3da1-4fc1-c058-f24285bfe6cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df['Class'].value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVbCgDebyqeO",
        "colab_type": "code",
        "outputId": "ed51a35f-8fca-4365-ae0a-87d1c73be4ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df['Class'].value_counts(normalize=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.998273\n",
              "1    0.001727\n",
              "Name: Class, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeDJnvBbyqn_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Here we see that 99% of transaction is not fraud only 0.17% is fraud."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xd8-6kMfyqqT",
        "colab_type": "code",
        "outputId": "951a9c89-e098-4d60-a729-e47d1ff7cd49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(x='Class',data=df)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cfef15f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y3cDOJP863lhVlaR6T6KqrgeuB1i9enX380nSyWqQK5uq2t1e9wIfZ/SdyyPtFhjtdW/r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\nSd39H9VMO5CSsflPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qogrH6pyyqtB",
        "colab_type": "code",
        "outputId": "ce1c3797-8045-455a-dad9-ab51c9bbda3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "corr=df.corr()\n",
        "sns.heatmap(corr, cmap='YlGnBu',fmt='g')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cfdead2b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x1IvF43c7r700d3uZ89lx7fzpdJuvUTw18+u3r1G4z0m7Lsrd/rOHiut62ObF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\nWsYNu5lZy7hhNzNrmf8D5IrxYvZ9bIcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39aes8S3yqwk",
        "colab_type": "code",
        "outputId": "0a0e6c32-a7b2-467c-8fff-c8ff2cb7e3be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1171
        }
      },
      "source": [
        "df.hist(figsize=(20,20))\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ObTXNP3vCQF03MIL23MSo6DcUKkL6qhoM3MzMzMzMzMzK8ritZjlu0WRmZmZm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iRJ81tEvBT4ucz8Ubm/BPgIsBFYAawuf79WFtkIXBgRG6h6/DxTfjtsBj7eNF7rEuCSzNwdEc9G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\nYBSCJgAAAABGIWgCAAAAYBT/H9myF8Pnc5U/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 36 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-RDyASazgCW",
        "colab_type": "code",
        "outputId": "d2300596-c3a3-415a-f635-6cfa7a7a77af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "corr = df.corrwith(df['Class']).reset_index()\n",
        "corr.columns = ['Index','Correlations']\n",
        "corr = corr.set_index('Index')\n",
        "corr = corr.sort_values(by=['Correlations'], ascending = False)\n",
        "plt.figure(figsize=(4,15))\n",
        "fig = sns.heatmap(corr, annot=True, fmt=\"g\", cmap='YlGnBu')\n",
        "plt.title(\"Correlation of Variables with Class\")\n",
        "plt.show()\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9+eI8+dmHchg64Es2/7WFyLIR3PHcbcRVLOfL37MzlX53DKDLbZfR8cYLC1Xn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y1v6kPFbnlz4ehkz/nT19Bas32TH3VfqQBAWEgkAMnJySyZu502Heua1ktOTmbv9pM0Sud45+6t\nx9ME1VvXg0y/++4/T6GihiAZFRnD8AFzef/j1lSoWuKptiUez0rpTPGwNBafeWqto4xn3edhyEJR\nSlkDrlrrYKVUZaAysO0FDtMi1KrvyZEDF3iv/URy2NsyaGQX07K+b01j5pKBAHzyRQemjFpGfFwi\nNeqVo2b98gDs2nqC9SsPAFC/cSWata1pWv/0sSvk83ClQGG3NK+7d/tJxn73Qaq2dSsOcMz3IjY2\n1jjndGDwKMN5wXXLD3D7xl0Wz/mDxXP+AGDCDx/imifnI7clREaw6EuV7lNKtQfWAJ5a6wtKKXvg\nmHFxBNBHa33iSdvJjtN2kTX9m0uV9vlvzBSf+wb5W1vUkU+LzzwBtNZrMUzN7z+PBbxe3IiEsBxS\nhsM8WeGYpxBCPHdZIvMUQphPMk/zSOYphBBmkOAphLAISqkWSqm/lFKXlFJfpLM8h1JquXH5YeM3\nDu8vG2ps/0sp1fxZjEem7UJkc5aQQRkvP/wRaArcBI4opdZprc+l6NYTCNVal1ZKeQOTgC5KKS8M\nX6CpgOHr29uVUmW11ul+ceZpWcJ+E0KIWsAlrfUVrXU8sAx4+H6D7YCFxt9XYfjyjDK2L9Nax2mt\n/wEuGbf3n0jwFEJkCinvM2F89EqxuBBwI8Xzm8Y20uujtU4EwgG3p1z3X5NpewpyEx6RHWWWz33K\n+0xYAsk8hRCW4Bap74xW2NiWbh+llA3gAgQ/5br/mgRPIbI5lUkeT3AEKKOUKmG8f683sO6hPuuA\nHsbfOwE7teH75+sAb+PZ+BJAGQy3svxPZNouhMj0tNaJSqmPga2ANTBPa31WKTUG8NNarwPmAouU\nUpeAEIxVJoz9VgDngETgo/96ph2yyI1BnpVrUXJjEJE1FHN++huDHAnKHDcGqZlPbgwihLAgmeWE\nkaWRY55CCGEGyTyFyOYkgzKP7DchhDCDZJ7ikbTW/DTZhyMHzpPD3o7Bo7qkW1ht/o+b+WOjH1ER\nMazbP97UHh+fyOQRS7l4/iY5XRz5amJ38hfMQ0RYNGM/+5W/zt2g2es1+PjzDqZ1vvx4NiF3I0hK\nSqZitRJ8/HkHrK0f/I1ftWg3s77dwMrto3HJ7cSKX3exc/NxAJKSkrjxTyArto8ml4tjBu4ZISw8\n81RK7Xr4DilKqQFKqZlKqS1KqTCl1IYXNT5Ld+TABW7dCGL+2i8YMKwTMyasTrdfnYZefL+wf5r2\nLWsP45zLgQU+Q+nQrSFzZ2wEwDaHDT36tqDXgDZp1vlqYnd+XjaIWSsGEx4azd7tJ03LAv3DOHro\nb9xT1Jbv/E5jfl46kJ+XDuT9j1tRqXpJCZz/klI6UzwsjUUHTx6UHU7J29g+Gej+3EeUhRzcc5am\nrWuglMKzUjGio2IJDopI08+zUjHc8uVK0/7nnrM0bVMDgIZNKnPc9yJaaxwcclCxWgns7GzTrOPk\nbA9AUmIyCQmJqBSngn+e5sMH/dukaktp9xap1y6eH0sPnquA1sZvHGC8f19BYJ/WegcQ+eKGZvmC\nA8PJ5/Egy8vr7kJwUPhTr3836MH61jbWODk7EBF274nrDf1oFp2bjsLR0Z4GTSoDcHD3GfLmc6FU\n2YLprhMbE4/fnxd42dhfiIxm0cFTax2C4WtWLY1N3sAK/S+u/E95J5cl87ZkxDDFvzThx14s2zqC\nhIREThy5RGxMPEvn7aBHn0ffw/bQvnN4VSkuU3YzvOivZT7l1zMznaxwwuj+1N3H+LPnv1k55Z1c\n5BtGhnrpm9YcBqCcVxGCAsJMy+4GhuOWz+Wpt5U3nwtBAWHk83AlKTGJ6KgYcrk+XXCzy2FL3Vcq\n8OeeM+Rxy4n/7RD6dJ0GQFBgOP26Tef7Xz8lT17D4YLdW2XKLp6vrBA8fYDpSqnqgKPW+uiLHpAl\na9u5Pm071wfg8L5z+Kw4QKPmVblw5jpOzvbpHtt8lLqvVOCPDX54VS7O3h2nqFqz9COPVwLE3Ivj\nXnQcbvlykZSYhO/+81SsVoISZQqwcvtoU7/ubcbxw6IBuOR2AiA6MobTxy7z+dddzXzX2Zt8w8g8\nFh88tdZRSqldwDwMWah4Rmq97InvgQu8224iOextGTyqi2lZn67T+HnpQABmf7eBXVuOExebwFst\nx9KifS3e6d2cFu1qMWn4Ut5tN4GcLo58Of5t0/rd24zjXnQsCQlJHNx9lgk/fkguFydGDpxHQnwS\nyTqZqjVK06Zj3SeO88CuM1SvUw4HhxzPficI8QhZ4sYgSqn2wBrAU2t9wdi2DygPOGO4p19PrfXW\nx21Hpu0iq/g3NwY5GbIhU3zuq+RpY1E5sMVnngBa67U8dMxZa93gBQ1HCItiURErE7Hos+1CCPGi\nSPAUQggzZIlpuxDCfFYybzeLZJ5CCGEGyTyFyOYk8TSPZJ5CCGEGCZ5CCGEGmbanIAfORXYkX880\nj2SeQghhBsk8hcjmJPE0j2SeQghhBgmeQghhBpm2C5HNybTdPJJ5CiGEGSR4CiGEGWTaLkQ2J9c3\nm0eCp3gkrTU/TvbBd/95ctjb8dnoLpTxLJym37wfNvPHRj8iI2LYcGC8qf3U0cv8NHUdVy7eYdiE\nbjR8rYpp2Rcfzeb86WtUrFqCcTPS1uz74Zu1bPHxNW0v4E4o34xcRlRkDMlJmg8+bUXtlz3xvx3C\n+x2/oUgxdwA8KxVlwFednvWuECINiwiexhpFE1OW0VBKDQDKASWAOsB+rXWbFMubAJMxHJqIAt7V\nWl96rgO3cL4HLnDrehALfb7g/OnrfDdhNT/82j9NvzoNvWjXpT492k9M1e5eIDefjerCikV70qzT\n+Z1GxMXGs2H1oTTL/jp3g6iI1PXdF8/ZzitNq9D2zXpcu+LPl5/MZfHGrwAoWNiNX5YN/C9vNVuT\nxNM8lnLM83554ZS8je2Tge7prDMT6Ka1rgosAYZl6AizoIO7z9K0TQ2UUnhVLkZUZCzBQRFp+nlV\nLpZuVc38BfNQsmxBrNKZF1avXQYHp7QF25KSkpn17QY+7N8mVbtSinvRsQBER8b+qyqeQmQEi8g8\ngVXA10opO611vFKqOFAQ2Ke11kqpRumso4H7/8JcgNvPY6BZyd3AcPJ5uJqe53N34W5QeIYGLp/l\nB6jbsEKa13indzM+/2gWa5cdIDYmnm9m9jYt878VQu+u03Bysue9fi2oVL1kho1PiPssInhqrUOU\nUr5ASwx12r2BFfrxpT8/ADYppWKACAxTe5GJ3Q0KZ8/2k0yb1TfNsl1bj9P89Rq82b0R505eZeLw\nJcxZOZg8eXOxeNMwXFyd+PvcTUYOms+clUNwcrZ/Ae/AMimVKYpnWhxLmbZD6qn7/Sn74/wPaKW1\nLgzMB6al10kp1Usp5aeU8ls8b8szG6yl8ll+gN7e0+jtPY08+XIRFBBmWhYUGE7efC4Z9tqXLtzi\n9o1g3mk3kW6txxEXm8A7bScAsHmtL680rQqAV5XixMcnEh4WjZ2dDS6uTgCU9SpMgcJu3LwelGFj\nFOI+i8g8jXyA6Uqp6oCj1vroozoqpfIBVbTWh41Ny4F0I6PWehYwC+BGtNRtb9elPu261Afg0L5z\n+Cw/QOPmVTl/+jpOzvYZOmWv08CLlX+MND1vU/9Lfl03FAD3/K4c971I87Y1uXYlgIS4RFxzOxMW\nGkXOXI5YW1tx+2Ywt67fpUAhtwwbY1YkJ4zMYzHBU2sdZTzrPo8nZ52hgItSqqzW+m+gKXA+o8eY\n1dR+2RPf/Rd4p91EctjbMmRUF9Oy3t7TTGe4Z327gZ1bjhMXm4B3i7G0bF+LHn2ac+HsdUYNWkhU\nxD3+3HuOhT9vY+6qIQAMeP9HblwNJCYmDu8WYxk0ojM165V75Fj6DHydaWNXsXrxXpRSDBndBaUU\np45dYeHMrdjYWKOsFAO+7EguF8eM3TFCAOrxhw0zF6VUe2AN4Km1vmBs2weUB5yBYKCn1nqrUuoN\nYAyQjCGYvq+1vvK47UvmKbKKIk6vP3VCeTkic3zuS+V6+jFnBhaTeQJordfy0CxDa93gEX3XYAi0\nQojHkDvJm8eSThgJIUSmIcFTCCHMYFHTdiHEsycZlHlkvwkhhBkk8xQim5MTRuaRzFMIIcwgwVMI\nIcwg0/YUctnaveghCPHcyazdPJJ5CiGEGSR4CiGEGWTaLkQ2J2fbzSOZpxBCmEEyTyGyOUk8zSOZ\npxBCmEGCpxBCmEGm7UJkc+lUhhZPQTJPIYQwg2SeIl1aa6ZOXM3Bfeewt7djxNfdKO9VJE2/n2Zs\nYNM6XyIj7rHHd4qp3f9OCKO/+o3IyBiSkzQfDXid+g0rsGXDERYt2Gnqd+nv2yxaMYSixdwZOmge\nN2/cxcraigavVOTj/7UFYMPaw8yYtpZ87oYa8m92bUD7jvXw8/2b6d88KBZw7Z8Avv7mXRo1qZxR\nuyVLksTTPBYdPI0F4SZqrbemaBsA9AbiUnQtD3gby3iIp3Bw3zluXAti9cbhnDl1lUlfr2D+kkFp\n+jV4pQKduzagY+uxqdrn/bKNJs2r0alLA65cvsP/+v2CT8MKtGhTkxZtagKGwDmk/2zKli9MbEw8\n3d59lRq1ypKQkEi/D37g4L5z1GvgBUDT5tUZ8tWbqV6jRq2yLF71OQDh4dF0bDWWOvXKZ8TuECIN\nS5+2p6zlfp830FtrXVVrXRV4FbgHbHveg7Nke3edplXbWiilqFSlBJGRMdwNCk/Tr1KVEunWclcK\noqNiAYiKjCVvOiWLt20+StOWLwFg72BHjVplAbC1taG8ZxECU9SMf5Kd205Q92VP7B3k/gTi+bDo\nzBNYBXytlLLTWscrpYoDBYF9Kfp0AjZrre+9gPFZrMDAcDzyu5qeu3u4EhgYnm6gTM+H/VrySa+f\nWLlkLzEx8fww+6M0ff7YcowpMz5M0x4ZcY99u8/g3e0VU9vO7Sc5fvQyRYvn43+fdcAjf+5U62zb\ncoy33mn8tG9PpKBUpiieaXEsOvPUWocAvkBLY5M3sEKnrqfszWPqvCuleiml/JRSfgvmbMq4wWYz\nWzcdpU372mzYMZbpP/Vh1JeLSE5ONi0/c+oq9vZ2lCpTMNV6iYlJDPtsIV26NaRQkbwAvNyoIj5b\nR7Lk9y+oVac8o776LdU6d4PCuXzxNnXreWb8GxPCyNIzT3gwdfcx/ux5f4FSqgBQCdia/qqgtZ4F\nzAIIj9+arf8Er1y6l7Wr/wTAq2JRAvwfTJsDA8Jwd3+6rBNg3ZpDzPi5LwCVq5YgLi6RsNBo8rjl\nBGDb5mM0a/VSmvUmjF5GkWL56Nr9QRbp6upk+r1dx7p8P90n1Trbtx6n0atVsLG1furxCfFfWXTm\naeQDNFFKVQcctdZHUyzrDKzRWie8mKFZlje7NmTxqs9ZvOpzXnm1MpvW+aK15vTJf3B2tn/qKTtA\n/vy5OXLobwD+ueJPfHwCufM4A5CcnMyObcdp1qJ6qnVmzthAVFQsAz/vkKo95bHWvbtPU6KkR6rl\n2zYfpVmr1NsST09lkoelsfjMU2sdZTzrPo+00/OuwNDnPyrLV7+BFwf3nqVDqzHY29sx/OtupmXd\nOk0yneWeMc2HbRv9iI1NoE2T4bTtWJde/VrRf0h7xo9axpJFu1BKMeLrbijj7XuOH72MR35X07Qc\nIMA/lPmzt1G8hAfdO08GHlyStHzxHvbuPoO1tRUuLo6MGPu2ab3bt4IJ8A+jeo3Sz2O3CGGiUh8e\ntExKqfbAGsBTa33B2FYcOAAU0VonP3rtB7L7tF1kHS52zZ86mQuMXZcpPvfu9m0tKgG1+MwTwHj9\npnqo7SpQ6IUMSAiR5WWFY55CCPHcZYnMUwhhPouaK2ciknkKIYQZJPMUIpuTDMo8st+EEMIMEjyF\nEMIMMm1PQSn5WyKyHyk9bB6JFkIIYQYJnkIIYQaZtguR7cm83RySeQohhBkk8xQim1OSeZpFMk8h\nhDCDBE8hhDCDTNuFyObk+mbzyF4TQggzSOYp0qW1ZuqEVRzYdxZ7eztGjutOea8iafr99N06Nq7z\nJTLiHnuPTDO137kdwpjhvxEWEkUuF0fGTOyBR/7c/HXhJpPGLiMqKhZrKyve69WcZsba7UcO/8V3\nU4L+LqoAACAASURBVNaQkJCEp1cRho3pho2NoajbUd+/mTppNYmJSbjmdmbWggEAjBn2G/v3niF3\nnpwsX/vVc9gzQhhYdOaplNqllGr+UNsApdR8pdQxpdQJpdRZpVSfFzVGS3Vw3zmuXw/i900j+XJU\nVyaOXZZuvwaNKrFw2ZA07d9NWUPrtrVYuuZLPujbkh+/XQeAvb0to8a/wwqfYcz4pR/TJq0mMuIe\nycnJjPpyEeMmv8fytV+Rv2AeNvocBgx13Cd9vYJpP/Rmhc8wJk41FUilTfs6zPg5bU148W+86NJv\nllkCzqKDJw/KDqfkDcwH6mqtqwK1gS+UUgUfXlk82p5dp2jdthZKKSpVKUFkZEyqKpb3VapSIt2q\nmlcu36FGrXIA1KhVlr27TgNQrLgHRYu5A5DP3ZU8eXISGhpFeFg0trY2FCtuqIxZu255dm4/AcCW\nTX40fq0K+QvkATCVLwaoXqM0uVwcn+E7F+LpWHrwXAW0VkrZganoW0Fgn9Y6ztgnB5b/Pp+7oIAw\nPPLnNj1393AlMCDsMWukVrZcIXYZg9+u7SeJjo4lLCwqVZ+zp6+SkJBI4SJ5cc3tTFJSEufOXANg\nx7YTBPiHAnD9aiAREffo/e63dO88yZSRimdDZZL/LI1FBxWtdQjgC7Q0NnkDK7TWWilVRCl1CrgB\nTNJa305vG0qpXkopP6WU3/w5G5/PwLOB/oPf4JjfJbp1msgxv0u4e7hibfXg43Y3KJwRQ39lxNdv\nY2VlhVKKcZPfY/o3v9PDezJOTjmwMvZPSkrmwrkbfPtTX77/5SPm/rKFa1cDXtRbEwLIGieM7k/d\nfYw/ewJorW8AlY3T9bVKqVVa6zT/4rTWs4BZABEJf2SKEqwvyoqle1i76iAAXhWLmTI/gMCAMNw9\nXJ96W/ncXZn83YcA3LsXx67tJ8iZyzC9joqKYUC/mfT79HUqVSlhWqdy1ZLM/vV/ABw6cJ7r1wIB\nQ9br4uKEg2MOHBxzUO2l0lz865Zpii/Ei2DRmaeRD9BEKVUdcNRaH0250JhxngEavIjBWZLOXV9h\nyeqhLFk9lEavVmbjOl+01pw++Q/Ozg7pHtt8lLDQKJKTkwFYMHsrr79RB4CEhESG9J9Nq7a1adKs\nWqp1QoIjAYiPT2DhvD/o0PllAF5pXJkTxy+TmJhEbEw8Z05fpXjJ/M/iLQvgxZ8osswTRhafeWqt\no5RSu4B5GLJQlFKFgWCtdYxSKjfwMjD9BQ7T4tRvWIED+87yRsvR2DvYMmLs26Zlb3WcwJLVQwGY\nMXUtWzf5ERubQOsmw2jXoS69PmrN0SMX+fHbdSgF1V4qzWfDOgPwx5ZjHD96ifCwaDasPQTAyHHd\nKVe+MIvmb2f/njMka03HLg2oWdtwwqlEqfzUq+/FWx0moKwU7TrWo3QZw/m/r4bM5+iRi4SFRdG6\nyTB69WtFu471nueuEtmU0tryZ6pKqfbAGsBTa31BKdUUmApoDH/SfjBOzx8ru0/bRdaRy7bpU6dy\nmeVz/2/GnBlYfOYJoLVeS4q8X2v9B1D5xY1ICJHVZYVjnkII8dxlicxTCPFfWNRsOdOQzFMIIcwg\nwVMIIcwg03YhsjlL/GpkZiCZpxBCmEEyTyGyOck8zSOZpxBCmEGCpxBCmEGm7UJke5JDmUP2mhBC\nmEEyTyGyOaXkhJE5JPMUQggzSPAUQggzyLRdiGxPpu3mkMxTCCHMIJmnSJfWmqkTVnFg31ns7e0Y\nOa475b2KpOn303fr2LjOl8iIe+w9Ms3Ufud2CGOG/0ZYSBS5XBwZM7GHqZSx/50Qvh6xhAD/UJRS\nfDuzLwULuTHqq0Uc97uEk7M98KA8x56dp/j5+w0oK4WNtRUDv+hE1eqlAPh+2lr27z0LQM/eLWjW\n8qWM3jVCABYSPI01iiZqrbemaBsAlANKAHWA/VrrNimWK+Br4E0gCZiptZ7xXAduwQ7uO8f160H8\nvmkkZ05dZeLYZSxYOiRNvwaNKtH5rVfo0Gp0qvbvpqyhddtatGlXhyOH/+LHb9cxZmIPAEYO/ZX3\nezWndj1P7t2LwyrF2d5PB7VPUxiuZp1yNGxcCaUUF/+6xdDB81i1fjj795zhwrkbLF71BQnxifR+\n7zvqNfDC2dkhA/ZI1iVfzzSPpUzb75cXTsnb2D4Z6J7OOu8CRYDyWmtPYFlGDjCr2bPrFK3b1kIp\nRaUqJYiMjOFuUHiafpWqlEi3quaVy3eoUctQwK1GrbLs3XXa1J6UlEztep4AODrmwN7B7rFjcXTM\nYbqcJiYmzvRP/Z/L/lSrURobG2scHHNQpmwh/tx/3ty3LMS/YinBcxXQWillB6CUKg4UBPZprXcA\nkems0xcYo7VOBtBaBz6foWYNQQFhpmk2GGqnBwaEPfX6ZcsVYtf2EwDs2n6S6OhYwsKiuH41kJw5\nHRjSfzbdOk3kuylrSEpKNq3304z1dH1jPNMmrSY+PsHUvmv7STq9Ppb/9fuZ4WO7AVCmnCFYxsbE\nExYahd+Rv1PVmhdPyyqTPCyLRYxYax0C+AItjU3ewAr9+NKfpYAuSik/pdRmpVSZ9DoppXoZ+/jN\nn7Px2Q48G+s/+A2O+V2iW6eJHPO7hLuHK9ZWViQlJXP82GX6D36DhcuGcOvmXVMJ4o8HtGXV+uEs\nXD6EiPBoFs7dbtpe49eqsGr9cCbP6MXPPxj+P9Wp70n9Bl68//ZUvhoyn0pVSmBlbREfaZEFWMQx\nT6P7U3cf48+eT+ifA4jVWtdQSnXAUNe9wcOdjCWJZ0HmKcH6oqxYuoe1qw4C4FWxWKosLjAgDHcP\n16feVj53VyZ/9yEA9+7FsWv7CXLmcsTdw5Wy5QtTuEheABq9WoXTp/6hHZim/3Z2trzevg6/LdiR\nZrvVa5Tm1s27hIVG4Zrbmfd7t+D93i0AGPbZfIoVczfrvQvxb1nSn2kfoIlSqjrgqLU++oT+N4Hf\njb+vQUoRP1Hnrq+wZPVQlqweSqNXK7NxnS9aa06f/AdnZ4d0j20+SlhoFMnJhun4gtlbef2NOoAh\nKEdFxBAaYjjScsT3L0qUyg9gOqaqtWb3zlOULFMQgBvXg7g/ybhw7gYJ8Ym4uDqRlJRMWFgUABf/\nusXFv29Tu175Z7AnsheVSf6zNBaTeWqto4xn3edhyEKfZC3QGPgHeAX4OwOHl+XUb1iBA/vO8kbL\n0dg72DJi7NumZW91nMCS1UMBmDF1LVs3+REbm0DrJsNo16EuvT5qzdEjF/nx23UoBdVeKs1nwzoD\nYG1tRf/B7enX83s0mvJeRXmjU30Ahn++kNDQSLSGsuUKM3Sk4Rzhzj9OsHHdYWxsrLG3t2X8lPdR\nSpGYmEivd74FwMnZnjETe2BjY/08d5PIxtTjDxtmLkqp9hiySE+t9QVj2z6gPOAMBAM9tdZblVKu\nwGKgKBAF9NFan3zc9rP7tF1kHblsmz51KheTeDBTfO4dbOqZnX4qpfIAy4HiwFWgs9Y69KE+VYGZ\nQC4Mly+O01ovNy5bgCHJun9Jybta6xOPfU1LCp4ZTYKnyCr+TfCMTfozU3zu7a3r/pfg+Q0QorWe\nqJT6Asittf78oT5lAa21vqiUKggcxZCIhRmD5wat9aqnfU1LOuYphBCP0g5YaPx9IdD+4Q5a67+1\n1heNv98GAoF85r6gBE8hsj2VKR4pLxs0Pnr9izfhobW+Y/zdH/B47DtWqhZgB1xO0TxOKXVKKTVd\nKZXjSS9oMSeMhBBZW8rLBtOjlNoO5E9n0VcPbUcrpR55KEIpVQBYBPS4/yUaYCiGoGtnHMPnwJjH\njVeCpxDCImitX3vUMqVUgFKqgNb6jjE4pvuNQqVULmAj8JXW+lCKbd/PWuOUUvOBwU8aj0zbhcjm\nFFaZ4vEfrQN6GH/vgeG68NTv0/D17jXArw+fGDIG3Ps3FGoPnHnSC0rwFEJkBROBpkqpi8Brxuco\npWoopeYY+3QGGgLvKqVOGB9VjcsWK6VOA6eBvBjuyPZYcqlSCnKpksgq/s2lSnFJRzLF5z6HdU2L\n+pqRHPNMwQrbFz0EIYSFkGm7EEKYQTJPIbI5qdtuHsk8hRDCDBI8hRDCDDJtFyLbk2m7OSTzFEII\nM0jmKUQ29wy+3ZMtyV4TQggzSPAUQggzyLRdPJLWmskTlnNg3xns7e0YNe5dPL2KpuoTExPP5wN/\n4ebNIKytrGjQqDKf/q8DAMf8/mbKpBVc+vsW4yd/wGvNXjKtd+dOCGNH/EqAfyhKKWbM/JiChfLi\ne+g8305djU7WODjmYPS4dylS1J07d0IY+eV8oiJjSEpK5pP/vcHLDStx5vQ/jBv1m3G80KtfG159\nrdrz20lZgpwwModFBE9j4beJWuutKdoGAOWAEkAdYL/Wuk2K5SWAZYAbhtvtd9daxz/XgVu4A/vO\ncON6IGs3jeXMqX+YMHYxvy4dmqZf9/eaUbNWORISEunTczoH9p2hfoOK5C+Qh9Ffv8uiBX+kWWfk\n0Pm836sldep5ce9eLEoZJkETxi5h2ox+lChVgBXLdjPnl02MHvcuc3/ZSNPmNXjT+xWuXL7Np31/\nYMO2SpQqXYhFy7/ExsaaoKBwunYcS8NGlaUQnMhwljJtv1+zPSVvY/tkoHs660wCpmutSwOhPLnO\nu3jInl0nad22DkopKlUpSVRkDEFB4an6ODjYUbNWOQBsbW0o71mUgABD3a2ChfJSplxhlFXqzObK\n5dskJiVRp54XAI6O9jg42AGGb7tERccCEBUZQz5juWOlFNHRMWnaHRzsTIEyPi5BcigzvOiSw1J6\nOGOtAr5WStlpreOVUsWBgsA+412jG6XsbLwn36vAW8amhcAoDJXzxFMKDAjDI38e03N3D1eCAkJN\ngethkRH32LfnFF3ffvWx2712NZCcOR0Z3H8mt28FU6tOeT75Xwesra0YPro7/ft+Tw57W5ycHFiw\nxFDDq1e/1/mo17csX7KLmJh4Zs4eYNre6VP/MGb4Qu7cDmHMhPck6xTPhUVknlrrEMAXaGls8gZW\n6EffT88NCNNaJxqf3wQKpdcxZd2UeXPWP8thZyuJiUl8+dkcvLs1pnCRx9fUSkpK4vixiwwY3Ilf\nlw3l1s27rF97EIDFv27nu5mfsHnHJNq2r8u0b1YCsHWTL6+3q8fmHZOY8dPHDB86n+RkQwWFSpVL\nsNJnFIuWDWXBnC3ExSVk7JsVAsvJPOHB1N3H+POZTMNT1k2JStidKe5r+CKtWLqLNav2A+BVsTgB\n/iGmZYEBYeTzyJ3ueuNG/UaRou681f2RlRJMPDxyU658EVOQbfRqVU6fukJoSCR//3WTSpVLANC0\nZU0+6T0DAJ/fD/D9z58CULlqKeLjEwgLjSKPWy7TdkuUKoCDYw4uX7yFV8Xi//7NZ1NyYxDzWETm\naeQDNFFKVQcctdZHH9M3GHBVSt3/41AYuJXRA8wKOndtzNLVw1m6ejiNXq3KxnWH0Fpz+uQVnJ0d\n0p2y/zRjLVFRMQz+ovNTvYZXxeJERsQQGhIJwBHfC5QsVYCcuRyJiorh2tUAAA4fPEeJkoZ6X/kL\n5MH38AUA/rl8h7i4BHLnycmtm3dJTEwC4M7tYK7+40+BQnn/834Q4kks6k7ySqnlGM6w+2itR6Zo\nbwQMfuhs+0pgtdZ6mVLqZ+CU1vqnx21fMs/UtNZMGreUg/vPYu9gx6ixPUwZXdeOY1m6ejgB/qG0\neu0LipfIj52d4W9V566NeaPTy5w9fZXBA2YSEXGPHHa2uOXNxUqfUQAcOniO6ZNXodF4ehVj2Ki3\nsbW1Yef24/z84zqslBW5cjkyYuw7FC6SjyuXb/P1yN+4dy8OpeDTgR2pW9+LjesOsWDuFmxsrFFW\nig/7tKFxk6qPeEfZh7Nto6dOJ5P0qUzxubdWlS0qBba04NkeQwEnT631BWPbPqA84Iwh4+yptd6q\nlCqJ4VKlPMBx4G2tddzjti/BU2QV/y54nskUn3trVdGigqclHfNEa72Wh67o1Vo3eETfK0Ct5zEu\nIUT2Y1HBUwjx7FniNZaZgSWdMBJCiExDgqcQQphBpu1CZHsybTeHZJ5CCGEGCZ5CCGEGmbanYGvl\n9KKHIMRzJ1/PNI9knkIIYQbJPIXI9iSHMofsNSGEMIMETyGEMINM24XI5uTrmeaRzFMIIcwgmacQ\n2V5ZST3NIJmnEEKYQYKnEEKYQabt4pG01kwav4h9e09g75CDseN74eVVIlWfmJg4Bv9vBjduBGJt\nZcUrjasxYKC3afnWzYeY+ePvKKUoW74okyZ/BIDP2r3M/tkHgA/7tKNd+4YAvN/ja4KCwrDPYajj\n/vOcz3Fzc+H2rbuMGDaL0NBIXFycGD+pL/nzu3H71l0GfDodnaxJTEyia7dmdPZu8jx2j8jmLCJ4\nKqV2ARO11ltTtA3AUM+oBFAH2P9QDaPFQA0gAUPZ4t5aa6lJ+y/s33uSa9f82bBlKqdOXebr0QtY\nsnx0mn493mtNrdpeJMQn8sH749m39yQNGlbh2lV/5s5ez6+LR5LLxYng4HAAwsOi+PmnNSxbMRal\nFF3eHEbjxi+Ry8Xw9diJ3/SjQsWSqV5j6uQlvN7uZdq1b8jhQ2eZMX0F4yf1JV8+V35bOgo7O1vu\nRcfSod0XNHq1Ou7u6Vf5FOJZsZRp+/2ywyl5G9snA93TWWcxhtpGlQAH4IOMHGBWtGvnUV5v9zJK\nKapUKU1kZDRBQaGp+jg45KBWbS8AbO1s8PQqTkCAoVzx6lW76PLWa6ag6OZmqLx54MAp6tatiIur\nM7lcnKhbtyL795987FiuXL5F7doVAKhV24tdO4+aXtPOzhaA+IQEkpMzRTkekQ1YSvBcBbRWStkB\nKKWKAwWBfVrrHUDkwytorTdpIwyZZ+HnN9ysITAwlPz53UzPPTzyEBgQ+sj+ERHR7Nl9nDp1DEHu\n2lV/rl31551uo+nmPZL9+wwBMjAglPwFUmw3f+rtDv9qFm++8SW/zFzD/QKFZcsXZfv2IwDs2O5H\ndHQsYWGG/+3+d4Lp2H4ozV7tz/sftJGsUzwXFhE8tdYhGAJgS2OTN7BCP0XpT6WULYbMdMsjlvdS\nSvkppfzmzF7zrIac7SQmJvH54B956+3mFC7iDkBSUhLXr/kzd8FXTJryEaNHziUiIvqx25nwTT9+\n95nIgt+Gc+zoX6xftx+AQUPe4uiRC3Tu8BV+R87j7pEbKyvDxzd/ATdWr53Ahi1TWeezj+C74Rn7\nZoXAQo55Gt2fuvsYf/Z8yvV+AvZqrfelt1BrPQuYBRCXdCTbz/mWLfmD1St3AVChUkn8/YNNywIC\nQnD3SD+rGzNyLsWK5af7Oy1MbR4eeahUuRS2tjYULuxOsWL5uX7NH3eP3Pj5nn+wXf8QatTyNK0D\n4OTkQKvW9Thz+gpt2zXA3T0302cMAOBedCzb/zhCrlypbyHo7p6b0qULc/ToXzRrLoVTRcayiMzT\nyAdoopSqDjhqrY8+aQWl1EggHzAwoweXVXi/1ZSVa8azcs14Xm3yEut99qO15uTJS+TM6Ui+fGmD\n5/ffrSQyKobPhr6dqr1xk5c4csQQJENDI7l2zZ/CRdypX78yBw+eISI8mojwaA4ePEP9+pVJTEwi\nNNQwFU9ISGTPnuOULl3YtH5ycjIAc2av440OrwDg7x9MbGw8ABHh0Rw/9jfFSxTImJ0jRAoWk3lq\nraOMZ93nYchCH0sp9QHQHGiitU7O6PFlRQ0aVmXf3pO0bjEIe3s7xo7rZVr25htfsnLNePz9g5n9\niw8lShakS8dhAHh3a0rHTo2p/3Jl/jx4mvZtPsPK2oqBg7vi6poTgN592tO183AA+vRtj4urM/fu\nxdLnw0kkJiaRnJRM7boV6PhmYwCO+J5nxvTlKKWoXqMcXw1/F4B/rtxmyjdLUEqhtabHe60oW7bI\nc9xLIrtST3HYMNNQSrUH1gCeWusLxrZ9GM6qOwPBQE+t9ValVCJwjQcnk37XWo953PZl2i6yihzW\nNeUrlxnMooJnRpPgKbIKCZ4Zz5KOeQohRKYhwVMIIcwgwVMIIcwgwVMIIcwgwVMIIcwgwVMIIcwg\nwVMIIcxgMd8weh5srRxf9BCEEBZCMk8hhDCDBE8hhDCDBE8hhDCDBE8hhDCDBE8hhDCDBE8hhDCD\nBE8hhDCDBE8hhDCDXCQvHklrzfhxc9m79xj29jkYP+FjKlQolabfhx+MISgolMSkZGq85MnwER9i\nbW1NWFgkAwdO5datIAoVysf06YNxcXFm/fo9zJm9Fq01Tk4OjBzVi/LlSwDQ5NXeODk5YG1thbW1\nNatWT071WvPn+fDNNws5+OcCcufOZWo/ffoiXb2HMnXqQJq3qJexO0YILCTzVErtUko1f6htgFJq\nplJqi1IqTCm14RHrzlBKRT2fkWYte/ce49q1O2zZ+iOjx/RhzOhZ6fab/u1g1vpMZ/36bwkJiWDL\nlj8BmD17DXXrVGbr1h+pW6cys2f/DkDhQh78umgs69Z/S99+bzJyxM+ptrfw1zGsWTstTeC8c+cu\nBw6cpEDBvKnak5KSmDplEfXqV31Wb12IJ7KI4MmDssMpeRvbJ2Ooy56GUqoGkH6tXPFEO3f40q5d\nI5RSVK1ajoiIaAIDQ9L0c3Y2fK01MTGJhIRElEqxfvtGALRr34gd230BqFa9PC4uzgBUqVI2VXnj\nx5k4YR6Dh3RHkbrCxG+/baJps7q45XEx520KYRZLCZ6rgNZKKTsApVRxoCCwT2u9gwdF3kyUUtYY\nAutnz2+YWUtAQAj5CzzI8vLndyMwIG3wBPig5xherv8eTk4ONG9eF4Dg4DDc3Q112PPly01wcFia\n9Vav2k6DhtVMz5VS9Ow5mo4dBrNi+TZT+44dvnh4uJmm9w/GGMz2Pw7TtWuqiYkQGc4igqfWOgTw\nBVoam7yBFfrx1es+BtZpre88bttKqV5KKT+llN+sWSufzYCzoTlzR7B331zi4xM4dOh0muVKKZRK\nnTEePnSa1at3MGjQO6a2xUvG8fvvU5k1exhLlmzmyJGzxMTEMeuX1Xzy6cOTD5gwfh6DBnfHysoi\nPsoiC7GkE0b3p+4+xp89H9VRKVUQeBNo9KSNaq1nAbMAkvXZbF89c/Hizaxa+QcAFSuVxv/OXdMy\nf/9g3D3yPHLdHDnseLVJTXbuOEL9+lVxc3MlMDCE/7d378FVlHcYx78PBw2BcEdIaa2hZYCKpZSL\n2AqIgDqglSC2oNSpNkJFFBBtLVJEoaJpHC04QqEqXipeikAUKEWBhEuhaARiEKQiqVoGZEAUQjAk\n+fWPsxwjEIgLCR7y+8ycye677777np3MM+9ekrdp00Z88skeGpW5rH7vvXzGjZvK9BnjaNiwbqy8\nWbPGADRu3IDevbvwTu5/qF8viY8/3klqv9FAdLQ54Jq7eOnldPLytnLn6EcA2Lt3H8uX5xCpGaF3\n7y6n7qQ4dwzxFJ6ZwKOSOgC1zSznOHV/DLQE3g9GO7UlvW9mLaugn3Ft8OA+DB4cHeBnZb3FrOf/\nQd8ru7Jhwxbq1q0duww/rKCgkIKCQpo2bURxcQnZ2Tl06ng+AD17diZzXhZDhl5D5rwseva6EIDt\n23cx4vY/kZ4+khYtmsfaOnDgIFZq1ElK5MCBg6xatYFbh/+cVq3PY9W/no7V69XzN8x+JYOGDevx\nxpIvHzaN+f1j9OjR0YPTVYm4CU8z2y9pGfAU0VHo8eouAJIPr0va78H59V1ySUeWL3+bKy6/Nfqq\n0qTbYtv6p45m7rxHKCz8guG3PkhRUTGlVkqXCy9g4KDo/cebh1zD6DseZvYrS2je/BweffROAKZO\nfZm9e/cxYUL06f3hV5J2797L7belA1BcUspVV3WjW7cOVfytnasYHf+24TeLpFRgLvADM9sclK0A\n2gBJwG4gzcz+ecR++80s6UTt+2W7O1PUUFuduJY7GXEVnpXNw9OdKTw8K58/onTOuRA8PJ1zLgQP\nT+ecC8HD0znnQvDwdM65EDw8nXMuBA9P55wLIW7+wqgq1NBZp7sLzrk44SNP55wLwcPTOedC8PB0\nzrkQPDydcy4ED0/nnAvBw9M550Lw8HTOuRA8PJ1zLgR/Sd6Vy8x44IEZZGfnUKtWAg89NJK2bY+e\nzSQtbTy7du2hpKSEjh3bMn78LUQiEdLTn2LZsrWcddZZfPe7yTz44Ejq1Yv+Q//Nm7cxfvzj7N9/\ngBo1ajB79iMkJJwda/OWWyby8cc7mD//cQA2bfqA8eOn8sUXRUQiEe67bxjt2rVi69aPuOeeyWzc\nuJU77riBtLRrqubkuGovLkaekpZJuuKIslGSpklaJGmvpPlHbF8haX3w2S5pXtX2Ov4tX55Dfv52\nFi+ezsSJw7nvvmnHrDd58t28+upjzJ//OJ9++hmLFq0C4OKL2zN//uO89tpjpKR8m+nTZwNQXFzC\nb3/7CPffP5wFC6by7LOTqFkzEmtv8eJ/UadOra8cIyNjJsOHDyIzcwojRw4mI2MmAA0a1GXs2KGk\npfWvjFPgXLniIjz5ctrhsgYF5RnADUfuYGbdzKy9mbUHVgNzKr2XZ5glS9aQmtoTSbRv34bPPy/g\nk0/2HFUvKak2EA3FQ4eKY/Ozd+3aIRaK7du3ZseO6DTGq1ato3XrFNq0aQFAw4b1iESi9QoKCpk5\ncx7Dhg38yjEkUVBQCMC+fQWxWTwbN25Au3atqFnTL6Jc1YqX37jZwB8lnW1mRZJSgObACjMzST3K\n21FSPaAncFNVdPRMsnPnbpKTm8TWk5Mbs3Pn7qOmHwZIS7uX3NwtdO/ekSuu+OlR21955XX69OkG\nwLZt/0OK7rNnz2f07dudIUMGADB58t/49a/7U6tWwlf2v+eeIaSl3Ut6+lOUlpby4osZp/KrOve1\nxcXI08z2AGuBPkHRIOBlq9jsdanAEjP7vLL65+DJJyewcuWzFBUdYs2a3K9smzbtJSKRCFdfI5Hh\nIwAABY9JREFU3QOAkpIScnLeJSPjTmbNSueNN1azevUGNm36gA8/3MFll/3kqPZfeGEhY8bcTHb2\nTMaMuZmxY6dUxddyrlxxEZ6Bspfuhy/ZK+K649WVNFTSW5LemjHjpZPsYvx7/vkF9Os3gn79RnDO\nOY1il9oAO3bsplmzxuXum5BwNr16XcSSJf+Olc2Z8wZZWW/y8MN3xi7nk5Ob0LnzBTRqVJ/ExFp0\n796JjRu3sm7dZvLy3qdnzzSuv/5u8vO3c8MNYwCYO3cpl18eHdH26dOV3NwtlfH1nauweArPTKCX\npA5AbTPLOdEOkpoAFwILyqtjZjPMrJOZdRo6dGB51aqNwYOvJDNzCpmZU+jd+yLmzVuKmbF+/Wbq\n1q191CV7QUFh7D5ocXEJWVlv8r3vfQeIPnB64ok5TJs2jsTELx8Ade3agS1b8iksPEhxcQlvvplH\ny5bncv31fVm58hmWLn2SWbPSSUlpznPPPQhA06aNWLs2D4A1a3JJSWleFafDuXLFyz1PzGy/pGXA\nU1R81HktMN/MDlZez85cl1zSiezst7jssqEkJiYwadLI2LZ+/UaQmTmFwsKDDBs2kaKiYsxK6dKl\nHYMGRe+uTJw4naKiQ9x00zgAfvSj1kyYMJz69ZO48cZUrr12NJLo3r0TPXp0Pm5fJk68jUmT/kpx\ncQkJCWczYcJtAOza9SkDBtwRe+XpmWdeZeHCqbGHWM5VFlXstuE3g6RUYC7wAzPbHJStANoAScBu\nIM3M/hlsywIeMrNFFTvClvg5Gc4dVyud7h6c6eIqPCufh6c7U3h4VrZ4uufpnHPfGB6ezjkXgoen\nc86F4OHpnHMheHg651wIHp7OOReCh6dzzoXg4emccyH4S/KuykkaamYzTnc/nDsZPvJ0p8PQ090B\n506Wh6dzzoXg4emccyF4eLrTwe93urjnD4yccy4EH3k651wIHp7OOReCh2c1JylZ0ouStkrKkbRQ\nUqtKOE6WpE4nqDNKUu0y6wslNTjVfXHuVPDwrMYUnc5yLpBlZt83s47AGKBZBfatecS6JJ3s79Mo\nIBaeZtbXzPaeZJvOVQoPz+rtUuCQmf3lcIGZbQBWSsqQlCfpHUkDAST1kLRC0qvAu5JSJL0n6Vkg\nDzhX0uWSVkt6W9LfJSUdeVBJ04LpnjdKuj8oGwE0B5YFE/0hKT+YARVJo4P+5EkaFZSlSNok6a9B\nW4slJR5uT9K7knIlvViJ59BVV2bmn2r6AUYAjx6jfADwOhAhOgr9EPgW0AMoAFoE9VKAUuCiYL0J\nsByoE6zfDdwbLGcBnYLlRsHPSFDeLljPB5qU6Ud+0GZH4B2gDtGJ/jYCPw6OXwy0D+q/DPwyWN4O\nJATLDU73ufbPmffxkac7lq7AC2ZWYmY7gWzg8NzAa81sW5m6/zWzNcHyRcD5wCpJ64FfAecdo/1f\nSHobWAe0DfY5UX/mmlmBme0H5gDdgm3bzGx9sJxDNFABcoHnJf2SaMA6d0rFzbztrlJsJDq3/ddR\ncJx1Aa+b2XXl7SypBXAX0NnMPpX0NFDra/ahrC/KLJcAicHylUB34GfAWEk/NDMPUXfK+MizelsK\nJEiK/aMOSe2AvcBASRFJ5xANobUVaG8NcLGklkFbdY7x5L4e0cD9TFIzoE+ZbfuAusdodwWQKqm2\npDpA/6DsmIIHV+ea2TKitw7qE73cd+6U8ZFnNWZmJqk/8GdJdwMHid5nHEU0bDYABvzOzHZIanOC\n9nZJuhF4QVJCUPwHYEuZOhskrQM2Ax8Bq8o0MQNYJGm7mV1aZp+3gxHq4QB/wszWSUoppysR4G+S\n6hMdDU8xf2rvTjH/80znnAvBL9udcy4ED0/nnAvBw9M550Lw8HTOuRA8PJ1zLgQPT+ecC8HD0znn\nQvg/gtZT7HmuFfQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIHBY_-szlbL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "c543bebb-dc5d-449d-edeb-738a871334de"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from collections import Counter \n",
        "from sklearn.datasets import make_classification\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ue_7_c3pzldg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x=df.drop('Class',axis='columns')\n",
        "y=df.Class"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_T7A1CBYzli3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyCUlPGy3CKx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kv6BBiH-3JRx",
        "colab_type": "code",
        "outputId": "9ead0d25-249f-4cfc-eab7-f04df7221942",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Before applying SMOTE',x_train.shape)\n",
        "print('Before applying SMOTE',x_test.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before applying SMOTE (199364, 30)\n",
            "Before applying SMOTE (85443, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDB2ozOW3U69",
        "colab_type": "code",
        "outputId": "8a511836-f6e7-4ce0-eb18-5dbb8e7fde03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Before applying SMOTE count of 1 is',format(sum(y_train==1)))\n",
        "print('Before applying SMOTE count of 0 is',format(sum(y_train==0)))\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before applying SMOTE count of 1 is 345\n",
            "Before applying SMOTE count of 0 is 199019\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gk_sTKUm3wdS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sm=SMOTE(random_state=20,ratio=0.7)\n",
        "x_train_res,y_train_res=sm.fit_resample(x_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qynt_zN48h7",
        "colab_type": "code",
        "outputId": "72ba224f-c12e-4371-a819-d9f91a836a21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('After applying SMOTE count of 1 is',format(sum(y_train_res==1)))\n",
        "print('After applying SMOTE count of 0 is',format(sum(y_train_res==0)))\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After applying SMOTE count of 1 is 139313\n",
            "After applying SMOTE count of 0 is 199019\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlJ9Xlvb5_xr",
        "colab_type": "code",
        "outputId": "af95b6fc-5620-4539-b9f9-42556798b2d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('After applying SMOTE',x_train_res.shape)\n",
        "print('After applying SMOTE',y_train_res.shape)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After applying SMOTE (338332, 30)\n",
            "After applying SMOTE (338332,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ez8W5Agv7pi9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lz3JUQLt846r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=LogisticRegression()\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "parameters = {    'C': np.linspace(1, 10, 10)}\n",
        "clf = GridSearchCV( model,parameters, cv=5, verbose=5, n_jobs=3)\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37VKxlnm9EKS",
        "colab_type": "code",
        "outputId": "06aaba00-19fb-49b4-9e5d-dbae7ec99cd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "source": [
        "clf.fit(x_train_res,y_train_res)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
            "[Parallel(n_jobs=3)]: Done  12 tasks      | elapsed:   49.5s\n",
            "[Parallel(n_jobs=3)]: Done  50 out of  50 | elapsed:  3.0min finished\n",
            "/usr/local/lib/python3.6/dist-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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
              "                                          fit_intercept=True,\n",
              "                                          intercept_scaling=1, l1_ratio=None,\n",
              "                                          max_iter=100, multi_class='warn',\n",
              "                                          n_jobs=None, penalty='l2',\n",
              "                                          random_state=None, solver='warn',\n",
              "                                          tol=0.0001, verbose=0,\n",
              "                                          warm_start=False),\n",
              "             iid='warn', n_jobs=3,\n",
              "             param_grid={'C': array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_z84zgsaC9hD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c69e810a-a579-4662-b5fe-b5ffca51cb93"
      },
      "source": [
        "clf.best_params_"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYxf6OLVDLy1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "dbdf0976-3359-41ad-8683-c077dc85d742"
      },
      "source": [
        "model1=LogisticRegression(C=4)\n",
        "model1.fit(x_train_res,y_train_res)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=4, 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)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTI0MVWQ9MXw",
        "colab_type": "code",
        "outputId": "adb2255d-c5a5-4550-f751-20a8f8d8ed52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model1.score(x_test,y_test)\n",
        "  "
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9924277003382372"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hL_W0w1F9RBr",
        "colab_type": "code",
        "outputId": "ed9a9441-3e3d-4280-8597-c6d60fae4c21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_predict=model1.predict(x_test)\n",
        "y_predict"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0OXPFop-y6V",
        "colab_type": "code",
        "outputId": "d79fdd9b-90af-4a16-b613-a801d895a0eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(y_test,y_predict)\n",
        "cm"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[84672,   624],\n",
              "       [   23,   124]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALD1z87X-2Cb",
        "colab_type": "code",
        "outputId": "25366b1d-78b6-4a7d-ba6c-870d6838d0a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.heatmap(cm,annot=True)\n",
        "plt.xlabel('predict')\n",
        "plt.ylabel('truth')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T+3H2CvYiIqDMXkQkFFJ96WWFeA9ARCQR5OVZzCUWZpZmZivNbGbwfayZfWhmq4LSNKg3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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJlqTg54-5oP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_predict = model1.predict(x_test)\n",
        "probs= model1.predict_proba(x_test)[:,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IGQ8TFE_H-z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tpr=cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "fpr=cm[1,1]/(cm[1,1]+cm[1,0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G15-Vblc_K2K",
        "colab_type": "code",
        "outputId": "bb8d0cae-0c60-48eb-a7dd-54ccc5278528",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tpr    #True positive rates"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.999728437333963"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PaOZm3A_QUS",
        "colab_type": "code",
        "outputId": "e919c036-b979-409d-fe46-07a4ea7931ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "fpr   #False positive rates "
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8435374149659864"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Me1BHw3_eiw",
        "colab_type": "code",
        "outputId": "553fa48e-636a-4cc3-f470-e7fcf8e0089a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "from matplotlib import pyplot\n",
        "from sklearn.metrics import auc,roc_auc_score,roc_curve\n",
        "fpr,tpr,thresholds=roc_curve(y_test,probs)\n",
        "pyplot.plot([0,1],[0,1],linestyle='-')\n",
        "pyplot.plot(fpr,tpr,marker='.')\n",
        "pyplot.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NhbPCfdA4yFxaeSwNqrTMxT++2sLTn2ykRZMQnrw2gWvP7KHWASKNjLPCPXEi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\nuIuIBCCFu4hIAFK4i4gEoP8HJYwBPcm3fQIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDwaO0lN_h3K",
        "colab_type": "code",
        "outputId": "60fe5e5f-a762-4433-dc31-d60207738bdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "roc_auc_score(y_test,probs)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9330783429485093"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbrqscvm_k73",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}