Credit Card Fraud Detection by ML Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, precision_recall_curve, auc, f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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       "      <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",
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       "  </thead>\n",
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       "      <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",
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       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       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",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data\n",
    "df = pd.read_csv('creditcard.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
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      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffle the entire data set (applies to both train & test)\n",
    "df = df.sample(frac=1).reset_index(drop=True)\n",
    "x= df.iloc[:,:-1].values\n",
    "y= df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.29238000e+05 -1.72900916e+00  1.54577371e+00 -1.55904756e+00\n",
      "  -4.68547191e-01  6.90220696e-01 -9.37532588e-01  1.42542518e+00\n",
      "  -4.72525218e-01  1.07964655e+00  1.05209706e+00 -7.21706071e-01\n",
      "   1.21001202e-01  1.34082138e+00 -1.72014687e+00  8.03159160e-01\n",
      "   7.23708012e-02 -5.15650139e-02  6.35674492e-01  5.34461411e-03\n",
      "   2.51638478e-01 -1.42677208e-01  1.01577661e+00 -1.40244941e-01\n",
      "  -6.81823472e-01  5.72008555e-03 -1.95072211e-01 -1.98216255e-01\n",
      "  -5.64002736e-01  8.49500000e+01]\n",
      " [ 1.61379000e+05  2.05946298e+00  5.64049655e-01 -2.35834631e+00\n",
      "   1.45734465e+00  1.03761584e+00 -9.87555947e-01  6.79705298e-01\n",
      "  -3.55059986e-01  1.24168604e-01 -3.51692794e-01 -6.81927352e-01\n",
      "  -6.42619018e-03 -1.97047970e-01 -1.02256934e+00 -3.72303751e-01\n",
      "  -1.41817724e-01  9.02945087e-01  1.47141942e-01 -3.16908056e-01\n",
      "  -2.25573434e-01 -5.51987741e-02  6.16025649e-02 -5.89952811e-03\n",
      "   5.40487474e-01  5.15182863e-01 -5.20206751e-01 -2.76309281e-03\n",
      "  -2.37167568e-02  1.00000000e+00]\n",
      " [ 8.45330000e+04  1.44216883e+00 -1.27495080e+00  1.08908192e+00\n",
      "  -1.18650669e+00 -1.87864300e+00 -1.21957423e-01 -1.47434800e+00\n",
      "  -1.99014929e-02 -1.17590344e+00  1.21187774e+00 -1.02264832e+00\n",
      "  -1.87577608e-01  1.60041189e+00 -1.19751722e+00 -3.24545665e-01\n",
      "  -4.27630545e-01  5.35681896e-01  3.64568808e-02  4.07443997e-02\n",
      "  -2.17426544e-01 -2.42101442e-01 -1.12489726e-01 -7.26842385e-03\n",
      "   5.69456524e-02  3.26100667e-01 -1.78965054e-01  8.22021746e-02\n",
      "   3.46403680e-02  3.19400000e+01]]\n"
     ]
    }
   ],
   "source": [
    "print(x[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the best k for KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimalK():\n",
    "    Ks = 10\n",
    "    mean_acc = np.zeros((Ks - 1))\n",
    "    for n in range(1, Ks):\n",
    "        # Train Model and Predict\n",
    "        neigh = KNeighborsClassifier(n_neighbors=n).fit(x_train, y_train)\n",
    "        y_pred = neigh.predict(x_test)\n",
    "        mean_acc[n - 1] = roc_auc_score(y_test, y_pred)\n",
    "    print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1)\n",
    "    return mean_acc.argmax()+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# creating scaler object for encoding\n",
    "scaler = StandardScaler()\n",
    "# applying StandardScaler on x_train and x_test\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = np.array([['Precision-Recall AUC','Receiver Operating Characteristic(ROC) AUC','F1-score'],['','',''],['','',''],['','','']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = LogisticRegression(C=0.1, solver='saga',max_iter=3000,random_state=0)\n",
    "classifier.fit(x_train, y_train)\n",
    "y_pred = classifier.predict(x_test)\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred)\n",
    "metric[0,:] = np.array([['{:.2%}'.format(auc(recall, precision)),'{:.2%}'.format(roc_auc_score(y_test, y_pred)),\n",
    "                            '{:.2%}'.format(f1_score(y_test, y_pred))]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = DecisionTreeClassifier(criterion = 'entropy',max_depth=4, random_state = 0)\n",
    "classifier.fit(x_train, y_train)\n",
    "y_pred = classifier.predict(x_test)\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred)\n",
    "metric[1,:] = np.array([['{:.2%}'.format(auc(recall, precision)),'{:.2%}'.format(roc_auc_score(y_test, y_pred)),\n",
    "                            '{:.2%}'.format(f1_score(y_test, y_pred))]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = SVC(kernel='rbf')\n",
    "classifier.fit(x_train, y_train)\n",
    "y_pred = classifier.predict(x_test)\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred)\n",
    "metric[2,:] = np.array([['{:.2%}'.format(auc(recall, precision)),'{:.2%}'.format(roc_auc_score(y_test, y_pred)),\n",
    "                            '{:.2%}'.format(f1_score(y_test, y_pred))]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.886991268557789 with k= 1\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=optimalK())\n",
    "classifier.fit(x_train, y_train)\n",
    "y_pred = classifier.predict(x_test)\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred)\n",
    "metric[3,:] = np.array([['{:.2%}'.format(auc(recall, precision)),'{:.2%}'.format(roc_auc_score(y_test, y_pred)),\n",
    "                            '{:.2%}'.format(f1_score(y_test, y_pred))]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot metric\n",
    "fig, ax = plt.subplots(figsize=(14,4)) \n",
    "columns = ('Precision-Recall AUC','Receiver Operating Characteristic(ROC) AUC','F1-score')\n",
    "rows = ('LogisticRegression', 'DecisionTree', 'SVM','KNN')\n",
    "rcolors = plt.cm.BuPu(np.full(len(rows), 0.1))\n",
    "ccolors = plt.cm.BuPu(np.full(len(columns), 0.1))\n",
    "table = plt.table(cellText=metric,rowLabels=rows,rowColours=rcolors,rowLoc='right',colColours=ccolors,colLabels=columns,loc='center',figure=fig)\n",
    "table.set_fontsize(31)\n",
    "table.scale(2, 2)\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "plt.box(on=None)\n",
    "plt.subplots_adjust(left=0.2, bottom=0.2)\n",
    "plt.title('Metrics',fontdict={'fontsize': 51})\n",
    "plt.show()\n"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}