Credit Card Fraud Detection Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58d6fa4b",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9f2b535",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt # for data visualization purposes\n",
    "import seaborn as sns # for statistical data visualization\n",
    "%matplotlib inline\n",
    "from collections import Counter\n",
    "import itertools\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, classification_report ,confusion_matrix, ConfusionMatrixDisplay, roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c7030f",
   "metadata": {},
   "source": [
    "### Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44e19058",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dataset\n",
    "df = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df0d310b",
   "metadata": {},
   "source": [
    "### Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a538408d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view dimensions of the dataset\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba66c664",
   "metadata": {},
   "source": [
    "We can see that there are 284807 instances and 31 attributes in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "400138c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<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>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </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>...</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>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the dataset\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e63d78df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "# summary of the dataset\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b64e34",
   "metadata": {},
   "source": [
    "We can see that there are no missing values in the dataset and the data is of the correct type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0feef899",
   "metadata": {},
   "outputs": [
    {
     "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>...</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>3.918649e-15</td>\n",
       "      <td>5.682686e-16</td>\n",
       "      <td>-8.761736e-15</td>\n",
       "      <td>2.811118e-15</td>\n",
       "      <td>-1.552103e-15</td>\n",
       "      <td>2.040130e-15</td>\n",
       "      <td>-1.698953e-15</td>\n",
       "      <td>-1.893285e-16</td>\n",
       "      <td>-3.147640e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.473120e-16</td>\n",
       "      <td>8.042109e-16</td>\n",
       "      <td>5.282512e-16</td>\n",
       "      <td>4.456271e-15</td>\n",
       "      <td>1.426896e-15</td>\n",
       "      <td>1.701640e-15</td>\n",
       "      <td>-3.662252e-16</td>\n",
       "      <td>-1.217809e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.918649e-15  5.682686e-16 -8.761736e-15  2.811118e-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  -1.552103e-15  2.040130e-15 -1.698953e-15 -1.893285e-16 -3.147640e-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   ...  1.473120e-16  8.042109e-16  5.282512e-16  4.456271e-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   1.426896e-15  1.701640e-15 -3.662252e-16 -1.217809e-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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical summary of the dataset\n",
    "\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b0fa1c",
   "metadata": {},
   "source": [
    "### Types of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "08d4dfba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 0 categorical variables\n",
      "\n",
      "The categorical variables are :\n",
      "\n",
      " []\n"
     ]
    }
   ],
   "source": [
    "# Explore variables in the dataset\n",
    "\n",
    "# find categorical variables\n",
    "\n",
    "categorical = [var for var in df.columns if df[var].dtype=='O']\n",
    "\n",
    "print('There are {} categorical variables\\n'.format(len(categorical)))\n",
    "\n",
    "print('The categorical variables are :\\n\\n', categorical)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7563bccb",
   "metadata": {},
   "source": [
    "As we can see, the dataset does not have any categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c406492d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 31 numerical variables\n",
      "\n",
      "The numerical variables are : ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount', 'Class']\n"
     ]
    }
   ],
   "source": [
    "# find numerical variables\n",
    "\n",
    "numerical = [var for var in df.columns if df[var].dtype!='O']\n",
    "\n",
    "print('There are {} numerical variables\\n'.format(len(numerical)))\n",
    "\n",
    "print('The numerical variables are :', numerical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a7b9314f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </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>...</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>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the numerical variables\n",
    "\n",
    "df[numerical].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d2a6518",
   "metadata": {},
   "source": [
    "Summary of numerical variables\n",
    "\n",
    "(a) There are 6 numerical variables.\n",
    "\n",
    "(b) All of the numerical variables are of float64 data type, with 30 of these variable being floats and only one variable being an integer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d93caea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values in numerical variables\n",
    "\n",
    "df[numerical].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ccdebb3",
   "metadata": {},
   "source": [
    "We can see that all the 31 numerical variables do not contain missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b1441e",
   "metadata": {},
   "source": [
    "### Declare feature vector and target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7cce6d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# declare feature vector and target variable\n",
    "\n",
    "X = df.drop(['Class'], axis=1)\n",
    "\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ee5053e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Genuine transactions:  284315\n",
      "Number of Fraud transactions:  492\n",
      "Percentage of Fraud transactions: 0.1727\n"
     ]
    }
   ],
   "source": [
    "# let’s check the number of occurrences of each class label and plot the information using matplotlib.\n",
    "\n",
    "non_fraud = len(df[df.Class == 0])\n",
    "fraud = len(df[df.Class == 1])\n",
    "fraud_percent = (fraud / (fraud + non_fraud)) * 100\n",
    "print(\"Number of Genuine transactions: \", non_fraud)\n",
    "print(\"Number of Fraud transactions: \", fraud)\n",
    "print(\"Percentage of Fraud transactions: {:.4f}\".format(fraud_percent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a2591d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "labels = [\"Genuine\", \"Fraud\"]\n",
    "count_classes = df.value_counts(df['Class'], sort= True)\n",
    "count_classes.plot(kind = \"bar\", rot = 0)\n",
    "plt.title(\"Visualization of Labels\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(range(2), labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619e6009",
   "metadata": {},
   "source": [
    "We can observe that the genuine transactions are over 99%! This is not good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fdac12a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let’s apply scaling techniques on the “Amount” feature to transform the range of values. We drop the original “Amount” column and add a new column with the scaled values. We also drop the “Time” column as it is irrelevant.\n",
    "\n",
    "scaler = StandardScaler()\n",
    "df[\"NormalizedAmount\"] = scaler.fit_transform(df[\"Amount\"].values.reshape(-1, 1))\n",
    "df.drop([\"Amount\", \"Time\"], inplace= True, axis= 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52260f93",
   "metadata": {},
   "source": [
    "### Data Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d2863ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['V1',\n",
       " 'V2',\n",
       " 'V3',\n",
       " 'V4',\n",
       " 'V5',\n",
       " 'V6',\n",
       " 'V7',\n",
       " 'V8',\n",
       " 'V9',\n",
       " 'V10',\n",
       " 'V11',\n",
       " 'V12',\n",
       " 'V13',\n",
       " 'V14',\n",
       " 'V15',\n",
       " 'V16',\n",
       " 'V17',\n",
       " 'V18',\n",
       " 'V19',\n",
       " 'V20',\n",
       " 'V21',\n",
       " 'V22',\n",
       " 'V23',\n",
       " 'V24',\n",
       " 'V25',\n",
       " 'V26',\n",
       " 'V27',\n",
       " 'V28',\n",
       " 'Class',\n",
       " 'NormalizedAmount']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List of all numeric columns\n",
    "num = df.select_dtypes('number').columns.to_list()\n",
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0cfbb03f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>NormalizedAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>4.356170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.350151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>-0.975926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.254117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>-0.484782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.081839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>-0.399126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.313249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>-0.915427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>0</td>\n",
       "      <td>0.514355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               V1         V2        V3        V4        V5        V6  \\\n",
       "0       -1.359807  -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
       "1        1.191857   0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
       "2       -1.358354  -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
       "3       -0.966272  -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
       "4       -1.158233   0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
       "...           ...        ...       ...       ...       ...       ...   \n",
       "284802 -11.881118  10.071785 -9.834783 -2.066656 -5.364473 -2.606837   \n",
       "284803  -0.732789  -0.055080  2.035030 -0.738589  0.868229  1.058415   \n",
       "284804   1.919565  -0.301254 -3.249640 -0.557828  2.630515  3.031260   \n",
       "284805  -0.240440   0.530483  0.702510  0.689799 -0.377961  0.623708   \n",
       "284806  -0.533413  -0.189733  0.703337 -0.506271 -0.012546 -0.649617   \n",
       "\n",
       "              V7        V8        V9       V10  ...       V21       V22  \\\n",
       "0       0.239599  0.098698  0.363787  0.090794  ... -0.018307  0.277838   \n",
       "1      -0.078803  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672   \n",
       "2       0.791461  0.247676 -1.514654  0.207643  ...  0.247998  0.771679   \n",
       "3       0.237609  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274   \n",
       "4       0.592941 -0.270533  0.817739  0.753074  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -4.918215  7.305334  1.914428  4.356170  ...  0.213454  0.111864   \n",
       "284803  0.024330  0.294869  0.584800 -0.975926  ...  0.214205  0.924384   \n",
       "284804 -0.296827  0.708417  0.432454 -0.484782  ...  0.232045  0.578229   \n",
       "284805 -0.686180  0.679145  0.392087 -0.399126  ...  0.265245  0.800049   \n",
       "284806  1.577006 -0.414650  0.486180 -0.915427  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Class  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0   \n",
       "...          ...       ...       ...       ...       ...       ...    ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731      0   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527      0   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561      0   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533      0   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649      0   \n",
       "\n",
       "        NormalizedAmount  \n",
       "0               0.244964  \n",
       "1              -0.342475  \n",
       "2               1.160686  \n",
       "3               0.140534  \n",
       "4              -0.073403  \n",
       "...                  ...  \n",
       "284802         -0.350151  \n",
       "284803         -0.254117  \n",
       "284804         -0.081839  \n",
       "284805         -0.313249  \n",
       "284806          0.514355  \n",
       "\n",
       "[284807 rows x 30 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numeric df\n",
    "credit_num =  df[num]\n",
    "credit_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53a22ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yErA7yY9mWXfUkUHNUp9tzKGFqpuBmwE2btz4ruWSpIVb0BFGVb3SHl8DvgNsAl5tp5loj6+11fcCpwwNXw+80urrR9QPGZNkFXAccGAhPUuS5mfegZHkg0k+PDUPXAA8DewCtrbVtgL3tvldwJZ259OpDC5uP9JOW72Z5Lx2feKKaWOmtnUp8GC7ziFJWmILOSV1MvCddg16FfAHVfVHSR4F7k5yJfAT4DKAqtqT5G7gGeAgcHVVvdO2dRVwK3AMcF+bAG4Bbk8yyeDIYssC+pUkLcC8A6Oqfgz8sxH1vwLOn2HMdmD7iPoEcNaI+lu0wJEkjZff9JYkdTEwJEldDAxJUhcDQ5LUxcCQJHUxMCRJXQwMSVIXA0OS1MXAkCR1MTAkSV0MDElSFwNDktTFwJAkdTEwJEldDAxJUhcDQ5LUxcCQJHUxMCRJXQwMSVIXA0OS1MXAkCR1MTAkSV0MDElSFwNDktTFwJAkdTEwJEldDAxJUhcDQ5LUZUUERpKLkjyXZDLJNePuR5Lej5Z9YCQ5CvjvwCeBM4DLk5wx3q4k6f1n1bgb6LAJmKyqHwMkuRPYDDwz1q60YBuu+cOx/N2XvvQbY/m70kq3EgJjHfDy0PO9wLnDKyTZBmxrT3+R5LmhxScCf3lEOzwy7PsIyQ0zLlr2vc/AvpfeSu19tr7/yVyDV0JgZEStDnlSdTNw88jByURVbTwSjR1J9r30Vmrv9r30VmrvC+172V/DYHBEccrQ8/XAK2PqRZLet1ZCYDwKnJ7k1CS/BGwBdo25J0l631n2p6Sq6mCSzwD3A0cBO6pqz2FsYuSpqhXAvpfeSu3dvpfeSu19QX2nquZeS5L0vrcSTklJkpYBA0OS1OU9GRhJvpDkp0meaNPFQ8uubT8x8lySC8fZ5yhJ/muSHyV5Msl3kvyDVt+Q5K+HXtPvj7nVd1kpP+GS5JQk30/ybJI9ST7b6jO+b5aTJC8lear1ONFqxyfZneT59rh63H0OS/KrQ/v1iSQ/T/K55bjPk+xI8lqSp4dqM+7f5fSZMkPvi/eZUlXvuQn4AvAfR9TPAH4IHA2cCrwAHDXufqf1eAGwqs3fANzQ5jcAT4+7v1n6Pqrtz18Gfqnt5zPG3dcMva4FPtbmPwz8eXtvjHzfLLcJeAk4cVrtvwDXtPlrpt43y3Fq75WfMfii2LLb58CvAx8b/vdtpv273D5TZuh90T5T3pNHGLPYDNxZVW9X1YvAJIOfHlk2qup7VXWwPf0zBt87WQn+/0+4VNXfAFM/4bLsVNW+qnq8zb8JPMvgFwVWss3Azja/E7hkfK3M6Xzghar6i3E3MkpV/QA4MK080/5dVp8po3pfzM+U93JgfKYdgu0YOnwc9TMjy/mD4t8B9w09PzXJ/07yJ0n++biamsFK27fA4LAc+CjwcCuNet8sNwV8L8lj7WdxAE6uqn0wCETgpLF1N7ctwDeGnq+EfT7T/l1p7/sFfaas2MBI8sdJnh4xbQZuAj4CnA3sA748NWzEppb8vuI5ep9a5/PAQeCOVtoH/OOq+ijwH4A/SHLsUvc+i2Wxbw9Hkg8B3wI+V1U/Z+b3zXLz8ar6GINfcL46ya+Pu6Fe7cu3vwV8s5VWyj6fyYp53y/GZ8qy/+LeTKrqX/asl+TrwP9oT5fFz4zM1XuSrcC/As6vdrKxqt4G3m7zjyV5AfgVYOIIt9trWezbXkk+wCAs7qiqbwNU1atDy4ffN8tKVb3SHl9L8h0Gp0BeTbK2qvYlWQu8NtYmZ/ZJ4PGpfb1S9jkz798V8b5frM+UFXuEMZv2D3TKp4CpOwZ2AVuSHJ3kVOB04JGl7m82SS4C/hPwW1X1f4bqazL4f4OQ5JcZ9P7j8XQ50or5CZckAW4Bnq2qrwzVZ3rfLBtJPpjkw1PzDC5oPs1gX29tq20F7h1Ph3O6nKHTUSthnzcz7d/312fKuO9IOEJ3CtwOPAU8yeAf6NqhZZ9ncCfDc8Anx93riN4nGZwTfaJNv9/qvw3sYXBHxuPAb4671xG9X8zgjqMXgM+Pu59Z+vw1BqcNnhzazxfP9r5ZLhODu9B+2KY9U/sZOAF4AHi+PR4/7l5H9P73gb8CjhuqLbt9ziDQ9gH/l8ERxJWz7d/l9JkyQ++L9pniT4NIkrq8J09JSZIWn4EhSepiYEiSuhgYkqQuBoYkqYuBIUnqYmBIkrr8Pxf5BRPDHG7DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in credit_num:\n",
    "    plt.hist(credit_num[col])\n",
    "    plt.title(col)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698e48f1",
   "metadata": {},
   "source": [
    "### Correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0507044e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the correlation matrix\n",
    "sns.heatmap(df.corr() ,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efbf9dab",
   "metadata": {},
   "source": [
    "### Correlation table for a more detailed analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "828fc3ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musa Masango\\AppData\\Local\\Temp\\ipykernel_20368\\2390052987.py:3: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n",
      "  corr.style.background_gradient(cmap='coolwarm').set_precision(2)\n"
     ]
    },
    {
     "data": {
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       "  color: #f1f1f1;\n",
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       "#T_89f9e_row0_col3, #T_89f9e_row0_col10, #T_89f9e_row0_col19, #T_89f9e_row0_col20, #T_89f9e_row0_col25, #T_89f9e_row0_col26, #T_89f9e_row0_col27, #T_89f9e_row1_col3, #T_89f9e_row1_col10, #T_89f9e_row1_col19, #T_89f9e_row1_col20, #T_89f9e_row1_col25, #T_89f9e_row1_col26, #T_89f9e_row1_col27, #T_89f9e_row1_col29, #T_89f9e_row2_col3, #T_89f9e_row2_col10, #T_89f9e_row2_col19, #T_89f9e_row2_col20, #T_89f9e_row2_col25, #T_89f9e_row2_col26, #T_89f9e_row2_col27, #T_89f9e_row3_col10, #T_89f9e_row3_col19, #T_89f9e_row3_col20, #T_89f9e_row3_col25, #T_89f9e_row3_col26, #T_89f9e_row3_col27, #T_89f9e_row4_col3, #T_89f9e_row4_col10, #T_89f9e_row4_col19, #T_89f9e_row4_col20, #T_89f9e_row4_col25, #T_89f9e_row4_col26, #T_89f9e_row4_col27, #T_89f9e_row5_col3, #T_89f9e_row5_col10, #T_89f9e_row5_col19, #T_89f9e_row5_col20, #T_89f9e_row5_col25, #T_89f9e_row5_col26, #T_89f9e_row5_col27, #T_89f9e_row6_col3, #T_89f9e_row6_col10, #T_89f9e_row6_col19, #T_89f9e_row6_col20, #T_89f9e_row6_col25, #T_89f9e_row6_col26, #T_89f9e_row6_col27, #T_89f9e_row7_col3, #T_89f9e_row7_col10, #T_89f9e_row7_col19, #T_89f9e_row7_col20, #T_89f9e_row7_col25, #T_89f9e_row7_col26, #T_89f9e_row7_col27, #T_89f9e_row8_col3, #T_89f9e_row8_col10, #T_89f9e_row8_col19, #T_89f9e_row8_col20, #T_89f9e_row8_col25, #T_89f9e_row8_col26, #T_89f9e_row8_col27, #T_89f9e_row9_col3, #T_89f9e_row9_col10, #T_89f9e_row9_col19, #T_89f9e_row9_col20, #T_89f9e_row9_col25, #T_89f9e_row9_col26, #T_89f9e_row9_col27, #T_89f9e_row10_col3, #T_89f9e_row10_col19, #T_89f9e_row10_col20, #T_89f9e_row10_col25, #T_89f9e_row10_col26, #T_89f9e_row10_col27, #T_89f9e_row11_col3, #T_89f9e_row11_col10, #T_89f9e_row11_col19, #T_89f9e_row11_col20, #T_89f9e_row11_col25, #T_89f9e_row11_col26, #T_89f9e_row11_col27, #T_89f9e_row12_col3, #T_89f9e_row12_col10, #T_89f9e_row12_col19, #T_89f9e_row12_col20, #T_89f9e_row12_col25, #T_89f9e_row12_col26, #T_89f9e_row12_col27, #T_89f9e_row13_col3, #T_89f9e_row13_col10, #T_89f9e_row13_col19, #T_89f9e_row13_col20, #T_89f9e_row13_col25, #T_89f9e_row13_col26, #T_89f9e_row13_col27, #T_89f9e_row14_col3, #T_89f9e_row14_col10, #T_89f9e_row14_col19, #T_89f9e_row14_col20, #T_89f9e_row14_col25, #T_89f9e_row14_col26, #T_89f9e_row14_col27, #T_89f9e_row15_col3, #T_89f9e_row15_col10, #T_89f9e_row15_col19, #T_89f9e_row15_col20, #T_89f9e_row15_col25, #T_89f9e_row15_col26, #T_89f9e_row15_col27, #T_89f9e_row16_col3, #T_89f9e_row16_col10, #T_89f9e_row16_col19, #T_89f9e_row16_col20, #T_89f9e_row16_col25, #T_89f9e_row16_col26, #T_89f9e_row16_col27, #T_89f9e_row16_col28, #T_89f9e_row17_col3, #T_89f9e_row17_col10, #T_89f9e_row17_col19, #T_89f9e_row17_col20, #T_89f9e_row17_col25, #T_89f9e_row17_col26, #T_89f9e_row17_col27, #T_89f9e_row18_col3, #T_89f9e_row18_col10, #T_89f9e_row18_col19, #T_89f9e_row18_col20, #T_89f9e_row18_col25, #T_89f9e_row18_col26, #T_89f9e_row18_col27, #T_89f9e_row19_col3, #T_89f9e_row19_col10, #T_89f9e_row19_col20, #T_89f9e_row19_col25, #T_89f9e_row19_col26, #T_89f9e_row19_col27, #T_89f9e_row20_col3, #T_89f9e_row20_col10, #T_89f9e_row20_col19, #T_89f9e_row20_col25, #T_89f9e_row20_col26, #T_89f9e_row20_col27, #T_89f9e_row21_col3, #T_89f9e_row21_col10, #T_89f9e_row21_col19, #T_89f9e_row21_col20, #T_89f9e_row21_col25, #T_89f9e_row21_col26, #T_89f9e_row21_col27, #T_89f9e_row22_col3, #T_89f9e_row22_col10, #T_89f9e_row22_col19, #T_89f9e_row22_col20, #T_89f9e_row22_col25, #T_89f9e_row22_col26, #T_89f9e_row22_col27, #T_89f9e_row23_col3, #T_89f9e_row23_col10, #T_89f9e_row23_col19, #T_89f9e_row23_col20, #T_89f9e_row23_col25, #T_89f9e_row23_col26, #T_89f9e_row23_col27, #T_89f9e_row24_col3, #T_89f9e_row24_col10, #T_89f9e_row24_col19, #T_89f9e_row24_col20, #T_89f9e_row24_col25, #T_89f9e_row24_col26, #T_89f9e_row24_col27, #T_89f9e_row25_col3, #T_89f9e_row25_col10, #T_89f9e_row25_col19, #T_89f9e_row25_col20, #T_89f9e_row25_col26, #T_89f9e_row25_col27, #T_89f9e_row26_col3, #T_89f9e_row26_col10, #T_89f9e_row26_col19, #T_89f9e_row26_col20, #T_89f9e_row26_col25, #T_89f9e_row26_col27, #T_89f9e_row27_col3, #T_89f9e_row27_col10, #T_89f9e_row27_col19, #T_89f9e_row27_col20, #T_89f9e_row27_col25, #T_89f9e_row27_col26, #T_89f9e_row28_col5, #T_89f9e_row28_col6, #T_89f9e_row28_col8, #T_89f9e_row28_col9, #T_89f9e_row28_col11, #T_89f9e_row28_col12, #T_89f9e_row28_col13, #T_89f9e_row28_col14, #T_89f9e_row28_col15, #T_89f9e_row28_col16, #T_89f9e_row28_col17, #T_89f9e_row28_col23, #T_89f9e_row29_col0, #T_89f9e_row29_col1, #T_89f9e_row29_col2, #T_89f9e_row29_col4, #T_89f9e_row29_col7, #T_89f9e_row29_col10, #T_89f9e_row29_col14, #T_89f9e_row29_col18, #T_89f9e_row29_col21, #T_89f9e_row29_col22, #T_89f9e_row29_col24, #T_89f9e_row29_col25 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col4, #T_89f9e_row1_col4, #T_89f9e_row2_col4, #T_89f9e_row3_col4, #T_89f9e_row5_col4, #T_89f9e_row6_col4, #T_89f9e_row7_col4, #T_89f9e_row7_col29, #T_89f9e_row8_col4, #T_89f9e_row9_col4, #T_89f9e_row9_col29, #T_89f9e_row10_col4, #T_89f9e_row11_col4, #T_89f9e_row12_col4, #T_89f9e_row13_col4, #T_89f9e_row14_col4, #T_89f9e_row15_col4, #T_89f9e_row16_col4, #T_89f9e_row17_col4, #T_89f9e_row18_col4, #T_89f9e_row19_col4, #T_89f9e_row20_col4, #T_89f9e_row21_col4, #T_89f9e_row22_col4, #T_89f9e_row23_col4, #T_89f9e_row24_col4, #T_89f9e_row25_col4, #T_89f9e_row26_col4, #T_89f9e_row27_col4 {\n",
       "  background-color: #97b8ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row0_col5, #T_89f9e_row1_col5, #T_89f9e_row2_col5, #T_89f9e_row3_col5, #T_89f9e_row4_col5, #T_89f9e_row6_col5, #T_89f9e_row7_col5, #T_89f9e_row8_col5, #T_89f9e_row9_col5, #T_89f9e_row10_col5, #T_89f9e_row11_col5, #T_89f9e_row12_col5, #T_89f9e_row13_col5, #T_89f9e_row14_col5, #T_89f9e_row15_col5, #T_89f9e_row16_col5, #T_89f9e_row17_col5, #T_89f9e_row18_col5, #T_89f9e_row19_col5, #T_89f9e_row20_col5, #T_89f9e_row21_col5, #T_89f9e_row22_col5, #T_89f9e_row23_col5, #T_89f9e_row24_col5, #T_89f9e_row25_col5, #T_89f9e_row26_col5, #T_89f9e_row27_col5, #T_89f9e_row28_col20 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col6, #T_89f9e_row1_col6, #T_89f9e_row2_col6, #T_89f9e_row3_col6, #T_89f9e_row4_col6, #T_89f9e_row5_col6, #T_89f9e_row7_col6, #T_89f9e_row8_col6, #T_89f9e_row9_col6, #T_89f9e_row10_col6, #T_89f9e_row11_col6, #T_89f9e_row12_col6, #T_89f9e_row13_col6, #T_89f9e_row14_col6, #T_89f9e_row15_col6, #T_89f9e_row16_col6, #T_89f9e_row17_col6, #T_89f9e_row18_col6, #T_89f9e_row19_col6, #T_89f9e_row20_col6, #T_89f9e_row21_col6, #T_89f9e_row22_col6, #T_89f9e_row23_col6, #T_89f9e_row24_col6, #T_89f9e_row25_col6, #T_89f9e_row26_col6, #T_89f9e_row27_col6 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col7, #T_89f9e_row1_col7, #T_89f9e_row2_col7, #T_89f9e_row3_col7, #T_89f9e_row4_col7, #T_89f9e_row5_col7, #T_89f9e_row6_col7, #T_89f9e_row8_col7, #T_89f9e_row9_col7, #T_89f9e_row10_col7, #T_89f9e_row11_col7, #T_89f9e_row12_col7, #T_89f9e_row13_col7, #T_89f9e_row14_col7, #T_89f9e_row15_col7, #T_89f9e_row16_col7, #T_89f9e_row17_col7, #T_89f9e_row18_col7, #T_89f9e_row19_col7, #T_89f9e_row20_col7, #T_89f9e_row21_col7, #T_89f9e_row22_col7, #T_89f9e_row23_col7, #T_89f9e_row24_col7, #T_89f9e_row25_col7, #T_89f9e_row26_col7, #T_89f9e_row27_col7 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col8, #T_89f9e_row1_col8, #T_89f9e_row2_col8, #T_89f9e_row3_col8, #T_89f9e_row4_col8, #T_89f9e_row5_col8, #T_89f9e_row6_col8, #T_89f9e_row7_col8, #T_89f9e_row9_col8, #T_89f9e_row10_col8, #T_89f9e_row11_col8, #T_89f9e_row12_col8, #T_89f9e_row13_col8, #T_89f9e_row14_col8, #T_89f9e_row15_col8, #T_89f9e_row16_col8, #T_89f9e_row17_col8, #T_89f9e_row18_col8, #T_89f9e_row19_col8, #T_89f9e_row20_col8, #T_89f9e_row21_col8, #T_89f9e_row22_col8, #T_89f9e_row23_col8, #T_89f9e_row24_col8, #T_89f9e_row25_col8, #T_89f9e_row26_col8, #T_89f9e_row27_col8, #T_89f9e_row28_col18 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col9, #T_89f9e_row1_col9, #T_89f9e_row2_col9, #T_89f9e_row3_col9, #T_89f9e_row4_col9, #T_89f9e_row5_col9, #T_89f9e_row6_col9, #T_89f9e_row7_col9, #T_89f9e_row8_col9, #T_89f9e_row10_col9, #T_89f9e_row11_col9, #T_89f9e_row12_col9, #T_89f9e_row13_col9, #T_89f9e_row14_col9, #T_89f9e_row15_col9, #T_89f9e_row16_col9, #T_89f9e_row17_col9, #T_89f9e_row18_col9, #T_89f9e_row19_col9, #T_89f9e_row20_col9, #T_89f9e_row21_col9, #T_89f9e_row22_col9, #T_89f9e_row23_col9, #T_89f9e_row24_col9, #T_89f9e_row25_col9, #T_89f9e_row26_col9, #T_89f9e_row27_col9 {\n",
       "  background-color: #7396f5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col11, #T_89f9e_row1_col11, #T_89f9e_row2_col11, #T_89f9e_row3_col11, #T_89f9e_row4_col11, #T_89f9e_row5_col11, #T_89f9e_row6_col11, #T_89f9e_row7_col11, #T_89f9e_row8_col11, #T_89f9e_row9_col11, #T_89f9e_row10_col11, #T_89f9e_row12_col11, #T_89f9e_row13_col11, #T_89f9e_row14_col11, #T_89f9e_row15_col11, #T_89f9e_row16_col11, #T_89f9e_row17_col11, #T_89f9e_row18_col11, #T_89f9e_row19_col11, #T_89f9e_row20_col11, #T_89f9e_row21_col11, #T_89f9e_row22_col11, #T_89f9e_row23_col11, #T_89f9e_row24_col11, #T_89f9e_row25_col11, #T_89f9e_row26_col11, #T_89f9e_row27_col11 {\n",
       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col12, #T_89f9e_row0_col14, #T_89f9e_row0_col23, #T_89f9e_row1_col12, #T_89f9e_row1_col14, #T_89f9e_row1_col23, #T_89f9e_row2_col12, #T_89f9e_row2_col14, #T_89f9e_row2_col23, #T_89f9e_row3_col12, #T_89f9e_row3_col14, #T_89f9e_row3_col23, #T_89f9e_row4_col12, #T_89f9e_row4_col14, #T_89f9e_row4_col23, #T_89f9e_row5_col12, #T_89f9e_row5_col14, #T_89f9e_row5_col23, #T_89f9e_row6_col12, #T_89f9e_row6_col14, #T_89f9e_row6_col23, #T_89f9e_row7_col12, #T_89f9e_row7_col14, #T_89f9e_row7_col23, #T_89f9e_row8_col12, #T_89f9e_row8_col14, #T_89f9e_row8_col23, #T_89f9e_row9_col12, #T_89f9e_row9_col14, #T_89f9e_row9_col23, #T_89f9e_row10_col12, #T_89f9e_row10_col14, #T_89f9e_row10_col23, #T_89f9e_row11_col12, #T_89f9e_row11_col14, #T_89f9e_row11_col23, #T_89f9e_row12_col14, #T_89f9e_row12_col23, #T_89f9e_row13_col12, #T_89f9e_row13_col14, #T_89f9e_row13_col23, #T_89f9e_row14_col12, #T_89f9e_row14_col23, #T_89f9e_row15_col12, #T_89f9e_row15_col14, #T_89f9e_row15_col23, #T_89f9e_row16_col12, #T_89f9e_row16_col14, #T_89f9e_row16_col23, #T_89f9e_row17_col12, #T_89f9e_row17_col14, #T_89f9e_row17_col23, #T_89f9e_row18_col12, #T_89f9e_row18_col14, #T_89f9e_row18_col23, #T_89f9e_row19_col12, #T_89f9e_row19_col14, #T_89f9e_row19_col23, #T_89f9e_row20_col12, #T_89f9e_row20_col14, #T_89f9e_row20_col23, #T_89f9e_row21_col12, #T_89f9e_row21_col14, #T_89f9e_row21_col23, #T_89f9e_row22_col12, #T_89f9e_row22_col14, #T_89f9e_row22_col23, #T_89f9e_row23_col12, #T_89f9e_row23_col14, #T_89f9e_row24_col12, #T_89f9e_row24_col14, #T_89f9e_row24_col23, #T_89f9e_row25_col12, #T_89f9e_row25_col14, #T_89f9e_row25_col23, #T_89f9e_row26_col12, #T_89f9e_row26_col14, #T_89f9e_row26_col23, #T_89f9e_row27_col12, #T_89f9e_row27_col14, #T_89f9e_row27_col23, #T_89f9e_row28_col25 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col13, #T_89f9e_row1_col13, #T_89f9e_row2_col13, #T_89f9e_row3_col13, #T_89f9e_row4_col13, #T_89f9e_row5_col13, #T_89f9e_row6_col13, #T_89f9e_row7_col13, #T_89f9e_row8_col13, #T_89f9e_row9_col13, #T_89f9e_row10_col13, #T_89f9e_row11_col13, #T_89f9e_row12_col13, #T_89f9e_row14_col13, #T_89f9e_row15_col13, #T_89f9e_row16_col13, #T_89f9e_row17_col13, #T_89f9e_row18_col13, #T_89f9e_row19_col13, #T_89f9e_row20_col13, #T_89f9e_row21_col13, #T_89f9e_row22_col13, #T_89f9e_row23_col13, #T_89f9e_row24_col13, #T_89f9e_row25_col13, #T_89f9e_row26_col13, #T_89f9e_row27_col13 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col15, #T_89f9e_row1_col15, #T_89f9e_row2_col15, #T_89f9e_row3_col15, #T_89f9e_row4_col15, #T_89f9e_row5_col15, #T_89f9e_row6_col15, #T_89f9e_row7_col15, #T_89f9e_row8_col15, #T_89f9e_row9_col15, #T_89f9e_row10_col15, #T_89f9e_row11_col15, #T_89f9e_row12_col15, #T_89f9e_row13_col15, #T_89f9e_row14_col15, #T_89f9e_row16_col15, #T_89f9e_row17_col15, #T_89f9e_row18_col15, #T_89f9e_row19_col15, #T_89f9e_row20_col15, #T_89f9e_row21_col15, #T_89f9e_row22_col15, #T_89f9e_row23_col15, #T_89f9e_row24_col15, #T_89f9e_row25_col15, #T_89f9e_row26_col15, #T_89f9e_row27_col15 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col16, #T_89f9e_row1_col16, #T_89f9e_row2_col16, #T_89f9e_row3_col16, #T_89f9e_row4_col16, #T_89f9e_row5_col16, #T_89f9e_row6_col16, #T_89f9e_row7_col16, #T_89f9e_row8_col16, #T_89f9e_row9_col16, #T_89f9e_row10_col16, #T_89f9e_row11_col16, #T_89f9e_row12_col16, #T_89f9e_row13_col16, #T_89f9e_row14_col16, #T_89f9e_row15_col16, #T_89f9e_row17_col16, #T_89f9e_row18_col16, #T_89f9e_row19_col16, #T_89f9e_row20_col16, #T_89f9e_row21_col16, #T_89f9e_row21_col28, #T_89f9e_row22_col16, #T_89f9e_row23_col16, #T_89f9e_row24_col16, #T_89f9e_row24_col28, #T_89f9e_row25_col16, #T_89f9e_row25_col28, #T_89f9e_row26_col16, #T_89f9e_row27_col16, #T_89f9e_row29_col5 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row0_col17, #T_89f9e_row0_col22, #T_89f9e_row1_col17, #T_89f9e_row1_col22, #T_89f9e_row2_col17, #T_89f9e_row2_col22, #T_89f9e_row2_col28, #T_89f9e_row3_col17, #T_89f9e_row3_col22, #T_89f9e_row4_col17, #T_89f9e_row4_col22, #T_89f9e_row5_col17, #T_89f9e_row5_col22, #T_89f9e_row6_col17, #T_89f9e_row6_col22, #T_89f9e_row7_col17, #T_89f9e_row7_col22, #T_89f9e_row8_col17, #T_89f9e_row8_col22, #T_89f9e_row9_col17, #T_89f9e_row9_col22, #T_89f9e_row10_col17, #T_89f9e_row10_col22, #T_89f9e_row11_col17, #T_89f9e_row11_col22, #T_89f9e_row12_col17, #T_89f9e_row12_col22, #T_89f9e_row13_col17, #T_89f9e_row13_col22, #T_89f9e_row14_col17, #T_89f9e_row14_col22, #T_89f9e_row15_col17, #T_89f9e_row15_col22, #T_89f9e_row15_col28, #T_89f9e_row16_col17, #T_89f9e_row16_col22, #T_89f9e_row17_col22, #T_89f9e_row18_col17, #T_89f9e_row18_col22, #T_89f9e_row19_col17, #T_89f9e_row19_col22, #T_89f9e_row20_col17, #T_89f9e_row20_col22, #T_89f9e_row21_col17, #T_89f9e_row21_col22, #T_89f9e_row22_col17, #T_89f9e_row23_col17, #T_89f9e_row23_col22, #T_89f9e_row24_col17, #T_89f9e_row24_col22, #T_89f9e_row25_col17, #T_89f9e_row25_col22, #T_89f9e_row26_col17, #T_89f9e_row26_col22, #T_89f9e_row27_col17, #T_89f9e_row27_col22, #T_89f9e_row28_col22, #T_89f9e_row29_col3 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col18, #T_89f9e_row1_col18, #T_89f9e_row2_col18, #T_89f9e_row3_col18, #T_89f9e_row4_col18, #T_89f9e_row5_col18, #T_89f9e_row6_col18, #T_89f9e_row7_col18, #T_89f9e_row8_col18, #T_89f9e_row9_col18, #T_89f9e_row10_col18, #T_89f9e_row11_col18, #T_89f9e_row12_col18, #T_89f9e_row13_col18, #T_89f9e_row14_col18, #T_89f9e_row15_col18, #T_89f9e_row16_col18, #T_89f9e_row17_col18, #T_89f9e_row19_col18, #T_89f9e_row20_col18, #T_89f9e_row21_col18, #T_89f9e_row22_col18, #T_89f9e_row23_col18, #T_89f9e_row24_col18, #T_89f9e_row25_col18, #T_89f9e_row26_col18, #T_89f9e_row27_col18 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col21, #T_89f9e_row1_col21, #T_89f9e_row2_col21, #T_89f9e_row3_col21, #T_89f9e_row4_col21, #T_89f9e_row5_col21, #T_89f9e_row6_col21, #T_89f9e_row7_col21, #T_89f9e_row8_col21, #T_89f9e_row9_col21, #T_89f9e_row10_col21, #T_89f9e_row11_col21, #T_89f9e_row12_col21, #T_89f9e_row13_col21, #T_89f9e_row14_col21, #T_89f9e_row15_col21, #T_89f9e_row16_col21, #T_89f9e_row17_col21, #T_89f9e_row18_col21, #T_89f9e_row19_col21, #T_89f9e_row20_col21, #T_89f9e_row22_col21, #T_89f9e_row23_col21, #T_89f9e_row24_col21, #T_89f9e_row25_col21, #T_89f9e_row26_col21, #T_89f9e_row27_col21, #T_89f9e_row28_col21 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col24, #T_89f9e_row1_col24, #T_89f9e_row2_col24, #T_89f9e_row3_col24, #T_89f9e_row4_col24, #T_89f9e_row5_col24, #T_89f9e_row6_col24, #T_89f9e_row7_col24, #T_89f9e_row8_col24, #T_89f9e_row9_col24, #T_89f9e_row10_col24, #T_89f9e_row11_col24, #T_89f9e_row12_col24, #T_89f9e_row13_col24, #T_89f9e_row14_col24, #T_89f9e_row15_col24, #T_89f9e_row16_col24, #T_89f9e_row17_col24, #T_89f9e_row18_col24, #T_89f9e_row19_col24, #T_89f9e_row20_col24, #T_89f9e_row21_col24, #T_89f9e_row22_col24, #T_89f9e_row23_col24, #T_89f9e_row25_col24, #T_89f9e_row26_col24, #T_89f9e_row27_col24 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col28 {\n",
       "  background-color: #7093f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row0_col29, #T_89f9e_row29_col11 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row1_col0, #T_89f9e_row2_col0, #T_89f9e_row3_col0, #T_89f9e_row4_col0, #T_89f9e_row5_col0, #T_89f9e_row6_col0, #T_89f9e_row7_col0, #T_89f9e_row8_col0, #T_89f9e_row9_col0, #T_89f9e_row10_col0, #T_89f9e_row11_col0, #T_89f9e_row12_col0, #T_89f9e_row13_col0, #T_89f9e_row14_col0, #T_89f9e_row15_col0, #T_89f9e_row16_col0, #T_89f9e_row17_col0, #T_89f9e_row18_col0, #T_89f9e_row19_col0, #T_89f9e_row20_col0, #T_89f9e_row21_col0, #T_89f9e_row22_col0, #T_89f9e_row23_col0, #T_89f9e_row24_col0, #T_89f9e_row25_col0, #T_89f9e_row26_col0, #T_89f9e_row27_col0 {\n",
       "  background-color: #7699f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row1_col28, #T_89f9e_row24_col29 {\n",
       "  background-color: #a3c2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row2_col29, #T_89f9e_row28_col4 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row3_col29 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row4_col29, #T_89f9e_row29_col9 {\n",
       "  background-color: #5875e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row5_col28 {\n",
       "  background-color: #80a3fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row5_col29 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row6_col28, #T_89f9e_row28_col0 {\n",
       "  background-color: #5a78e4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row6_col29 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row7_col28, #T_89f9e_row19_col28, #T_89f9e_row26_col28, #T_89f9e_row29_col13 {\n",
       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row8_col29 {\n",
       "  background-color: #a5c3fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row9_col28 {\n",
       "  background-color: #5470de;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row10_col28 {\n",
       "  background-color: #b3cdfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row11_col28, #T_89f9e_row28_col24, #T_89f9e_row29_col8 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row11_col29 {\n",
       "  background-color: #adc9fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row12_col28, #T_89f9e_row14_col28, #T_89f9e_row22_col28 {\n",
       "  background-color: #8badfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row12_col29, #T_89f9e_row23_col29, #T_89f9e_row28_col29 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row13_col28, #T_89f9e_row28_col26 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row13_col29, #T_89f9e_row17_col29 {\n",
       "  background-color: #b6cefa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row16_col29, #T_89f9e_row27_col29 {\n",
       "  background-color: #b1cbfc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row17_col28, #T_89f9e_row29_col15 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row18_col28 {\n",
       "  background-color: #94b6ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row18_col29 {\n",
       "  background-color: #a2c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row19_col29 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row20_col28, #T_89f9e_row22_col29 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row20_col29 {\n",
       "  background-color: #c5d6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row21_col29 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row23_col28 {\n",
       "  background-color: #89acfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row26_col29 {\n",
       "  background-color: #b5cdfa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row27_col28, #T_89f9e_row29_col16, #T_89f9e_row29_col28 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row28_col1 {\n",
       "  background-color: #c3d5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row28_col2, #T_89f9e_row29_col23 {\n",
       "  background-color: #3e51c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row28_col3 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row28_col7 {\n",
       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row28_col10 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row28_col19 {\n",
       "  background-color: #4055c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row28_col27, #T_89f9e_row29_col12, #T_89f9e_row29_col27 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row29_col6 {\n",
       "  background-color: #dbdcde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row29_col17 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row29_col19 {\n",
       "  background-color: #abc8fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_89f9e_row29_col20 {\n",
       "  background-color: #5b7ae5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_89f9e_row29_col26 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_89f9e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_89f9e_level0_col0\" class=\"col_heading level0 col0\" >V1</th>\n",
       "      <th id=\"T_89f9e_level0_col1\" class=\"col_heading level0 col1\" >V2</th>\n",
       "      <th id=\"T_89f9e_level0_col2\" class=\"col_heading level0 col2\" >V3</th>\n",
       "      <th id=\"T_89f9e_level0_col3\" class=\"col_heading level0 col3\" >V4</th>\n",
       "      <th id=\"T_89f9e_level0_col4\" class=\"col_heading level0 col4\" >V5</th>\n",
       "      <th id=\"T_89f9e_level0_col5\" class=\"col_heading level0 col5\" >V6</th>\n",
       "      <th id=\"T_89f9e_level0_col6\" class=\"col_heading level0 col6\" >V7</th>\n",
       "      <th id=\"T_89f9e_level0_col7\" class=\"col_heading level0 col7\" >V8</th>\n",
       "      <th id=\"T_89f9e_level0_col8\" class=\"col_heading level0 col8\" >V9</th>\n",
       "      <th id=\"T_89f9e_level0_col9\" class=\"col_heading level0 col9\" >V10</th>\n",
       "      <th id=\"T_89f9e_level0_col10\" class=\"col_heading level0 col10\" >V11</th>\n",
       "      <th id=\"T_89f9e_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n",
       "      <th id=\"T_89f9e_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n",
       "      <th id=\"T_89f9e_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n",
       "      <th id=\"T_89f9e_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n",
       "      <th id=\"T_89f9e_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n",
       "      <th id=\"T_89f9e_level0_col16\" class=\"col_heading level0 col16\" >V17</th>\n",
       "      <th id=\"T_89f9e_level0_col17\" class=\"col_heading level0 col17\" >V18</th>\n",
       "      <th id=\"T_89f9e_level0_col18\" class=\"col_heading level0 col18\" >V19</th>\n",
       "      <th id=\"T_89f9e_level0_col19\" class=\"col_heading level0 col19\" >V20</th>\n",
       "      <th id=\"T_89f9e_level0_col20\" class=\"col_heading level0 col20\" >V21</th>\n",
       "      <th id=\"T_89f9e_level0_col21\" class=\"col_heading level0 col21\" >V22</th>\n",
       "      <th id=\"T_89f9e_level0_col22\" class=\"col_heading level0 col22\" >V23</th>\n",
       "      <th id=\"T_89f9e_level0_col23\" class=\"col_heading level0 col23\" >V24</th>\n",
       "      <th id=\"T_89f9e_level0_col24\" class=\"col_heading level0 col24\" >V25</th>\n",
       "      <th id=\"T_89f9e_level0_col25\" class=\"col_heading level0 col25\" >V26</th>\n",
       "      <th id=\"T_89f9e_level0_col26\" class=\"col_heading level0 col26\" >V27</th>\n",
       "      <th id=\"T_89f9e_level0_col27\" class=\"col_heading level0 col27\" >V28</th>\n",
       "      <th id=\"T_89f9e_level0_col28\" class=\"col_heading level0 col28\" >Class</th>\n",
       "      <th id=\"T_89f9e_level0_col29\" class=\"col_heading level0 col29\" >NormalizedAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row0\" class=\"row_heading level0 row0\" >V1</th>\n",
       "      <td id=\"T_89f9e_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col2\" class=\"data row0 col2\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col3\" class=\"data row0 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col4\" class=\"data row0 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col5\" class=\"data row0 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col6\" class=\"data row0 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col7\" class=\"data row0 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col8\" class=\"data row0 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col9\" class=\"data row0 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col10\" class=\"data row0 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col11\" class=\"data row0 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col12\" class=\"data row0 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col13\" class=\"data row0 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col14\" class=\"data row0 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col15\" class=\"data row0 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col16\" class=\"data row0 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col17\" class=\"data row0 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col18\" class=\"data row0 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col19\" class=\"data row0 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col20\" class=\"data row0 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col21\" class=\"data row0 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col22\" class=\"data row0 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col23\" class=\"data row0 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col24\" class=\"data row0 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col25\" class=\"data row0 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col26\" class=\"data row0 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col27\" class=\"data row0 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row0_col28\" class=\"data row0 col28\" >-0.10</td>\n",
       "      <td id=\"T_89f9e_row0_col29\" class=\"data row0 col29\" >-0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row1\" class=\"row_heading level0 row1\" >V2</th>\n",
       "      <td id=\"T_89f9e_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col13\" class=\"data row1 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col14\" class=\"data row1 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col15\" class=\"data row1 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col16\" class=\"data row1 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col17\" class=\"data row1 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col18\" class=\"data row1 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col19\" class=\"data row1 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col20\" class=\"data row1 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col21\" class=\"data row1 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col22\" class=\"data row1 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col24\" class=\"data row1 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col25\" class=\"data row1 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col26\" class=\"data row1 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col27\" class=\"data row1 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row1_col28\" class=\"data row1 col28\" >0.09</td>\n",
       "      <td id=\"T_89f9e_row1_col29\" class=\"data row1 col29\" >-0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row2\" class=\"row_heading level0 row2\" >V3</th>\n",
       "      <td id=\"T_89f9e_row2_col0\" class=\"data row2 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col4\" class=\"data row2 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col5\" class=\"data row2 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col6\" class=\"data row2 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col7\" class=\"data row2 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col8\" class=\"data row2 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col10\" class=\"data row2 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col11\" class=\"data row2 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col12\" class=\"data row2 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col13\" class=\"data row2 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col14\" class=\"data row2 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col15\" class=\"data row2 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col16\" class=\"data row2 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col17\" class=\"data row2 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col19\" class=\"data row2 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col20\" class=\"data row2 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col21\" class=\"data row2 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col22\" class=\"data row2 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col24\" class=\"data row2 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col25\" class=\"data row2 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col26\" class=\"data row2 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col27\" class=\"data row2 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row2_col28\" class=\"data row2 col28\" >-0.19</td>\n",
       "      <td id=\"T_89f9e_row2_col29\" class=\"data row2 col29\" >-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row3\" class=\"row_heading level0 row3\" >V4</th>\n",
       "      <td id=\"T_89f9e_row3_col0\" class=\"data row3 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col1\" class=\"data row3 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row3_col4\" class=\"data row3 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col5\" class=\"data row3 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col6\" class=\"data row3 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col7\" class=\"data row3 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col8\" class=\"data row3 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col9\" class=\"data row3 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col10\" class=\"data row3 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col11\" class=\"data row3 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col12\" class=\"data row3 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col13\" class=\"data row3 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col14\" class=\"data row3 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col15\" class=\"data row3 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col16\" class=\"data row3 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col17\" class=\"data row3 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col18\" class=\"data row3 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col19\" class=\"data row3 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col20\" class=\"data row3 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col21\" class=\"data row3 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col22\" class=\"data row3 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col24\" class=\"data row3 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col25\" class=\"data row3 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col26\" class=\"data row3 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col27\" class=\"data row3 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row3_col28\" class=\"data row3 col28\" >0.13</td>\n",
       "      <td id=\"T_89f9e_row3_col29\" class=\"data row3 col29\" >0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row4\" class=\"row_heading level0 row4\" >V5</th>\n",
       "      <td id=\"T_89f9e_row4_col0\" class=\"data row4 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col2\" class=\"data row4 col2\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col3\" class=\"data row4 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row4_col5\" class=\"data row4 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col6\" class=\"data row4 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col7\" class=\"data row4 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col8\" class=\"data row4 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col9\" class=\"data row4 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col10\" class=\"data row4 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col11\" class=\"data row4 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col13\" class=\"data row4 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col14\" class=\"data row4 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col15\" class=\"data row4 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col16\" class=\"data row4 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col17\" class=\"data row4 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col18\" class=\"data row4 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col19\" class=\"data row4 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col20\" class=\"data row4 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col21\" class=\"data row4 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col22\" class=\"data row4 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col23\" class=\"data row4 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col24\" class=\"data row4 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col25\" class=\"data row4 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col26\" class=\"data row4 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col27\" class=\"data row4 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row4_col28\" class=\"data row4 col28\" >-0.09</td>\n",
       "      <td id=\"T_89f9e_row4_col29\" class=\"data row4 col29\" >-0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row5\" class=\"row_heading level0 row5\" >V6</th>\n",
       "      <td id=\"T_89f9e_row5_col0\" class=\"data row5 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col3\" class=\"data row5 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col4\" class=\"data row5 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row5_col6\" class=\"data row5 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col7\" class=\"data row5 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col8\" class=\"data row5 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col10\" class=\"data row5 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col11\" class=\"data row5 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col12\" class=\"data row5 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col13\" class=\"data row5 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col14\" class=\"data row5 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col15\" class=\"data row5 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col16\" class=\"data row5 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col17\" class=\"data row5 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col18\" class=\"data row5 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col19\" class=\"data row5 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col20\" class=\"data row5 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col21\" class=\"data row5 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col22\" class=\"data row5 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col23\" class=\"data row5 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col24\" class=\"data row5 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col25\" class=\"data row5 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col26\" class=\"data row5 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col27\" class=\"data row5 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row5_col28\" class=\"data row5 col28\" >-0.04</td>\n",
       "      <td id=\"T_89f9e_row5_col29\" class=\"data row5 col29\" >0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row6\" class=\"row_heading level0 row6\" >V7</th>\n",
       "      <td id=\"T_89f9e_row6_col0\" class=\"data row6 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col1\" class=\"data row6 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col2\" class=\"data row6 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col3\" class=\"data row6 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col4\" class=\"data row6 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col5\" class=\"data row6 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row6_col7\" class=\"data row6 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col8\" class=\"data row6 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col9\" class=\"data row6 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col10\" class=\"data row6 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col11\" class=\"data row6 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col12\" class=\"data row6 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col13\" class=\"data row6 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col14\" class=\"data row6 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col15\" class=\"data row6 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col16\" class=\"data row6 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col17\" class=\"data row6 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col18\" class=\"data row6 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col19\" class=\"data row6 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col20\" class=\"data row6 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col21\" class=\"data row6 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col22\" class=\"data row6 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col24\" class=\"data row6 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col25\" class=\"data row6 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col26\" class=\"data row6 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col27\" class=\"data row6 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row6_col28\" class=\"data row6 col28\" >-0.19</td>\n",
       "      <td id=\"T_89f9e_row6_col29\" class=\"data row6 col29\" >0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row7\" class=\"row_heading level0 row7\" >V8</th>\n",
       "      <td id=\"T_89f9e_row7_col0\" class=\"data row7 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col1\" class=\"data row7 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col2\" class=\"data row7 col2\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col3\" class=\"data row7 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col4\" class=\"data row7 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col5\" class=\"data row7 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col6\" class=\"data row7 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row7_col8\" class=\"data row7 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col9\" class=\"data row7 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col10\" class=\"data row7 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col11\" class=\"data row7 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col12\" class=\"data row7 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col13\" class=\"data row7 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col14\" class=\"data row7 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col15\" class=\"data row7 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col16\" class=\"data row7 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col17\" class=\"data row7 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col18\" class=\"data row7 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col19\" class=\"data row7 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col20\" class=\"data row7 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col21\" class=\"data row7 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col22\" class=\"data row7 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col23\" class=\"data row7 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col24\" class=\"data row7 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col25\" class=\"data row7 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col26\" class=\"data row7 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col27\" class=\"data row7 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row7_col28\" class=\"data row7 col28\" >0.02</td>\n",
       "      <td id=\"T_89f9e_row7_col29\" class=\"data row7 col29\" >-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row8\" class=\"row_heading level0 row8\" >V9</th>\n",
       "      <td id=\"T_89f9e_row8_col0\" class=\"data row8 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col1\" class=\"data row8 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col2\" class=\"data row8 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col3\" class=\"data row8 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col4\" class=\"data row8 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col5\" class=\"data row8 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col6\" class=\"data row8 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col7\" class=\"data row8 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row8_col9\" class=\"data row8 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col10\" class=\"data row8 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col11\" class=\"data row8 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col12\" class=\"data row8 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col13\" class=\"data row8 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col14\" class=\"data row8 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col15\" class=\"data row8 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col16\" class=\"data row8 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col17\" class=\"data row8 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col18\" class=\"data row8 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col19\" class=\"data row8 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col20\" class=\"data row8 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col21\" class=\"data row8 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col22\" class=\"data row8 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col23\" class=\"data row8 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col24\" class=\"data row8 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col25\" class=\"data row8 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col26\" class=\"data row8 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col27\" class=\"data row8 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row8_col28\" class=\"data row8 col28\" >-0.10</td>\n",
       "      <td id=\"T_89f9e_row8_col29\" class=\"data row8 col29\" >-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row9\" class=\"row_heading level0 row9\" >V10</th>\n",
       "      <td id=\"T_89f9e_row9_col0\" class=\"data row9 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col1\" class=\"data row9 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col3\" class=\"data row9 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col4\" class=\"data row9 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col6\" class=\"data row9 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col7\" class=\"data row9 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col8\" class=\"data row9 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row9_col10\" class=\"data row9 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col11\" class=\"data row9 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col12\" class=\"data row9 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col13\" class=\"data row9 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col14\" class=\"data row9 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col15\" class=\"data row9 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col16\" class=\"data row9 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col17\" class=\"data row9 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col18\" class=\"data row9 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col19\" class=\"data row9 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col21\" class=\"data row9 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col22\" class=\"data row9 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col23\" class=\"data row9 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col24\" class=\"data row9 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col25\" class=\"data row9 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col26\" class=\"data row9 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col27\" class=\"data row9 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row9_col28\" class=\"data row9 col28\" >-0.22</td>\n",
       "      <td id=\"T_89f9e_row9_col29\" class=\"data row9 col29\" >-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row10\" class=\"row_heading level0 row10\" >V11</th>\n",
       "      <td id=\"T_89f9e_row10_col0\" class=\"data row10 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col1\" class=\"data row10 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col2\" class=\"data row10 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col3\" class=\"data row10 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col4\" class=\"data row10 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col5\" class=\"data row10 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col6\" class=\"data row10 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col7\" class=\"data row10 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col8\" class=\"data row10 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col9\" class=\"data row10 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row10_col11\" class=\"data row10 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col12\" class=\"data row10 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col13\" class=\"data row10 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col14\" class=\"data row10 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col15\" class=\"data row10 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col16\" class=\"data row10 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col17\" class=\"data row10 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col18\" class=\"data row10 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col19\" class=\"data row10 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col20\" class=\"data row10 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col21\" class=\"data row10 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col22\" class=\"data row10 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col24\" class=\"data row10 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col25\" class=\"data row10 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col26\" class=\"data row10 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col27\" class=\"data row10 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row10_col28\" class=\"data row10 col28\" >0.15</td>\n",
       "      <td id=\"T_89f9e_row10_col29\" class=\"data row10 col29\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row11\" class=\"row_heading level0 row11\" >V12</th>\n",
       "      <td id=\"T_89f9e_row11_col0\" class=\"data row11 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col1\" class=\"data row11 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col2\" class=\"data row11 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col3\" class=\"data row11 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col4\" class=\"data row11 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col5\" class=\"data row11 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col6\" class=\"data row11 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col7\" class=\"data row11 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col8\" class=\"data row11 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col9\" class=\"data row11 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col10\" class=\"data row11 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row11_col12\" class=\"data row11 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col13\" class=\"data row11 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col14\" class=\"data row11 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col15\" class=\"data row11 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col16\" class=\"data row11 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col17\" class=\"data row11 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col19\" class=\"data row11 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col20\" class=\"data row11 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col21\" class=\"data row11 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col22\" class=\"data row11 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col24\" class=\"data row11 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col25\" class=\"data row11 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col26\" class=\"data row11 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col27\" class=\"data row11 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row11_col28\" class=\"data row11 col28\" >-0.26</td>\n",
       "      <td id=\"T_89f9e_row11_col29\" class=\"data row11 col29\" >-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row12\" class=\"row_heading level0 row12\" >V13</th>\n",
       "      <td id=\"T_89f9e_row12_col0\" class=\"data row12 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col1\" class=\"data row12 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col2\" class=\"data row12 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col3\" class=\"data row12 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col5\" class=\"data row12 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col6\" class=\"data row12 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col7\" class=\"data row12 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col8\" class=\"data row12 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col9\" class=\"data row12 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col10\" class=\"data row12 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col11\" class=\"data row12 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row12_col13\" class=\"data row12 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col14\" class=\"data row12 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col15\" class=\"data row12 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col16\" class=\"data row12 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col17\" class=\"data row12 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col18\" class=\"data row12 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col19\" class=\"data row12 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col20\" class=\"data row12 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col21\" class=\"data row12 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col22\" class=\"data row12 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col23\" class=\"data row12 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col24\" class=\"data row12 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col25\" class=\"data row12 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col26\" class=\"data row12 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col27\" class=\"data row12 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col28\" class=\"data row12 col28\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row12_col29\" class=\"data row12 col29\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row13\" class=\"row_heading level0 row13\" >V14</th>\n",
       "      <td id=\"T_89f9e_row13_col0\" class=\"data row13 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col1\" class=\"data row13 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col2\" class=\"data row13 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col3\" class=\"data row13 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col4\" class=\"data row13 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col5\" class=\"data row13 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col6\" class=\"data row13 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col7\" class=\"data row13 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col8\" class=\"data row13 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col9\" class=\"data row13 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col10\" class=\"data row13 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col11\" class=\"data row13 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col12\" class=\"data row13 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row13_col14\" class=\"data row13 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col15\" class=\"data row13 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col16\" class=\"data row13 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col17\" class=\"data row13 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col18\" class=\"data row13 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col19\" class=\"data row13 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col20\" class=\"data row13 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col21\" class=\"data row13 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col22\" class=\"data row13 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col24\" class=\"data row13 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col25\" class=\"data row13 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col26\" class=\"data row13 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col27\" class=\"data row13 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row13_col28\" class=\"data row13 col28\" >-0.30</td>\n",
       "      <td id=\"T_89f9e_row13_col29\" class=\"data row13 col29\" >0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row14\" class=\"row_heading level0 row14\" >V15</th>\n",
       "      <td id=\"T_89f9e_row14_col0\" class=\"data row14 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col1\" class=\"data row14 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col2\" class=\"data row14 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col3\" class=\"data row14 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col4\" class=\"data row14 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col5\" class=\"data row14 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col6\" class=\"data row14 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col7\" class=\"data row14 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col8\" class=\"data row14 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col9\" class=\"data row14 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col10\" class=\"data row14 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col11\" class=\"data row14 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col12\" class=\"data row14 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col13\" class=\"data row14 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row14_col15\" class=\"data row14 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col16\" class=\"data row14 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col17\" class=\"data row14 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col18\" class=\"data row14 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col19\" class=\"data row14 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col20\" class=\"data row14 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col21\" class=\"data row14 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col22\" class=\"data row14 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col23\" class=\"data row14 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col24\" class=\"data row14 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col25\" class=\"data row14 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col26\" class=\"data row14 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col27\" class=\"data row14 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col28\" class=\"data row14 col28\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row14_col29\" class=\"data row14 col29\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row15\" class=\"row_heading level0 row15\" >V16</th>\n",
       "      <td id=\"T_89f9e_row15_col0\" class=\"data row15 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col1\" class=\"data row15 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col2\" class=\"data row15 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col3\" class=\"data row15 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col4\" class=\"data row15 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col5\" class=\"data row15 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col6\" class=\"data row15 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col7\" class=\"data row15 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col8\" class=\"data row15 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col9\" class=\"data row15 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col10\" class=\"data row15 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col11\" class=\"data row15 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col12\" class=\"data row15 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col13\" class=\"data row15 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col14\" class=\"data row15 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row15_col16\" class=\"data row15 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col17\" class=\"data row15 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col18\" class=\"data row15 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col19\" class=\"data row15 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col20\" class=\"data row15 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col21\" class=\"data row15 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col22\" class=\"data row15 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col23\" class=\"data row15 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col24\" class=\"data row15 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col25\" class=\"data row15 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col26\" class=\"data row15 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col27\" class=\"data row15 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row15_col28\" class=\"data row15 col28\" >-0.20</td>\n",
       "      <td id=\"T_89f9e_row15_col29\" class=\"data row15 col29\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row16\" class=\"row_heading level0 row16\" >V17</th>\n",
       "      <td id=\"T_89f9e_row16_col0\" class=\"data row16 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col1\" class=\"data row16 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col2\" class=\"data row16 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col3\" class=\"data row16 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col4\" class=\"data row16 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col5\" class=\"data row16 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col6\" class=\"data row16 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col7\" class=\"data row16 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col8\" class=\"data row16 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col9\" class=\"data row16 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col10\" class=\"data row16 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col11\" class=\"data row16 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col12\" class=\"data row16 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col13\" class=\"data row16 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col14\" class=\"data row16 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col15\" class=\"data row16 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row16_col17\" class=\"data row16 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col18\" class=\"data row16 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col19\" class=\"data row16 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col20\" class=\"data row16 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col21\" class=\"data row16 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col22\" class=\"data row16 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col23\" class=\"data row16 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col24\" class=\"data row16 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col25\" class=\"data row16 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col26\" class=\"data row16 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col27\" class=\"data row16 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row16_col28\" class=\"data row16 col28\" >-0.33</td>\n",
       "      <td id=\"T_89f9e_row16_col29\" class=\"data row16 col29\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row17\" class=\"row_heading level0 row17\" >V18</th>\n",
       "      <td id=\"T_89f9e_row17_col0\" class=\"data row17 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col1\" class=\"data row17 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col2\" class=\"data row17 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col3\" class=\"data row17 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col4\" class=\"data row17 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col5\" class=\"data row17 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col6\" class=\"data row17 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col7\" class=\"data row17 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col8\" class=\"data row17 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col9\" class=\"data row17 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col10\" class=\"data row17 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col11\" class=\"data row17 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col12\" class=\"data row17 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col13\" class=\"data row17 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col14\" class=\"data row17 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col15\" class=\"data row17 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col16\" class=\"data row17 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row17_col18\" class=\"data row17 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col19\" class=\"data row17 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col20\" class=\"data row17 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col21\" class=\"data row17 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col22\" class=\"data row17 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col23\" class=\"data row17 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col24\" class=\"data row17 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col25\" class=\"data row17 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col26\" class=\"data row17 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col27\" class=\"data row17 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row17_col28\" class=\"data row17 col28\" >-0.11</td>\n",
       "      <td id=\"T_89f9e_row17_col29\" class=\"data row17 col29\" >0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row18\" class=\"row_heading level0 row18\" >V19</th>\n",
       "      <td id=\"T_89f9e_row18_col0\" class=\"data row18 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col1\" class=\"data row18 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col3\" class=\"data row18 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col4\" class=\"data row18 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col5\" class=\"data row18 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col6\" class=\"data row18 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col7\" class=\"data row18 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col8\" class=\"data row18 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col9\" class=\"data row18 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col10\" class=\"data row18 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col12\" class=\"data row18 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col13\" class=\"data row18 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col14\" class=\"data row18 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col15\" class=\"data row18 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col16\" class=\"data row18 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col17\" class=\"data row18 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row18_col19\" class=\"data row18 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col21\" class=\"data row18 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col22\" class=\"data row18 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col24\" class=\"data row18 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col25\" class=\"data row18 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col26\" class=\"data row18 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col27\" class=\"data row18 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row18_col28\" class=\"data row18 col28\" >0.03</td>\n",
       "      <td id=\"T_89f9e_row18_col29\" class=\"data row18 col29\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row19\" class=\"row_heading level0 row19\" >V20</th>\n",
       "      <td id=\"T_89f9e_row19_col0\" class=\"data row19 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col1\" class=\"data row19 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col2\" class=\"data row19 col2\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col3\" class=\"data row19 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col4\" class=\"data row19 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col5\" class=\"data row19 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col6\" class=\"data row19 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col7\" class=\"data row19 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col8\" class=\"data row19 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col9\" class=\"data row19 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col10\" class=\"data row19 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col11\" class=\"data row19 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col12\" class=\"data row19 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col13\" class=\"data row19 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col14\" class=\"data row19 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col15\" class=\"data row19 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col16\" class=\"data row19 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col17\" class=\"data row19 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col18\" class=\"data row19 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row19_col20\" class=\"data row19 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col21\" class=\"data row19 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col22\" class=\"data row19 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col24\" class=\"data row19 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col25\" class=\"data row19 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col26\" class=\"data row19 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col27\" class=\"data row19 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row19_col28\" class=\"data row19 col28\" >0.02</td>\n",
       "      <td id=\"T_89f9e_row19_col29\" class=\"data row19 col29\" >0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row20\" class=\"row_heading level0 row20\" >V21</th>\n",
       "      <td id=\"T_89f9e_row20_col0\" class=\"data row20 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col1\" class=\"data row20 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col2\" class=\"data row20 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col3\" class=\"data row20 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col4\" class=\"data row20 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col5\" class=\"data row20 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col6\" class=\"data row20 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col7\" class=\"data row20 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col8\" class=\"data row20 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col10\" class=\"data row20 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col11\" class=\"data row20 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col12\" class=\"data row20 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col13\" class=\"data row20 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col14\" class=\"data row20 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col15\" class=\"data row20 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col16\" class=\"data row20 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col17\" class=\"data row20 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col19\" class=\"data row20 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row20_col21\" class=\"data row20 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col22\" class=\"data row20 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col24\" class=\"data row20 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col25\" class=\"data row20 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col26\" class=\"data row20 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col27\" class=\"data row20 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row20_col28\" class=\"data row20 col28\" >0.04</td>\n",
       "      <td id=\"T_89f9e_row20_col29\" class=\"data row20 col29\" >0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row21\" class=\"row_heading level0 row21\" >V22</th>\n",
       "      <td id=\"T_89f9e_row21_col0\" class=\"data row21 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col1\" class=\"data row21 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col2\" class=\"data row21 col2\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col3\" class=\"data row21 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col4\" class=\"data row21 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col5\" class=\"data row21 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col6\" class=\"data row21 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col7\" class=\"data row21 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col8\" class=\"data row21 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col9\" class=\"data row21 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col10\" class=\"data row21 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col11\" class=\"data row21 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col12\" class=\"data row21 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col13\" class=\"data row21 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col14\" class=\"data row21 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col15\" class=\"data row21 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col16\" class=\"data row21 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col17\" class=\"data row21 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col18\" class=\"data row21 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col19\" class=\"data row21 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col20\" class=\"data row21 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row21_col22\" class=\"data row21 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col24\" class=\"data row21 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col25\" class=\"data row21 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col26\" class=\"data row21 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col27\" class=\"data row21 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col28\" class=\"data row21 col28\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row21_col29\" class=\"data row21 col29\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row22\" class=\"row_heading level0 row22\" >V23</th>\n",
       "      <td id=\"T_89f9e_row22_col0\" class=\"data row22 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col1\" class=\"data row22 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col2\" class=\"data row22 col2\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col3\" class=\"data row22 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col4\" class=\"data row22 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col5\" class=\"data row22 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col6\" class=\"data row22 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col7\" class=\"data row22 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col8\" class=\"data row22 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col9\" class=\"data row22 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col10\" class=\"data row22 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col11\" class=\"data row22 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col12\" class=\"data row22 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col13\" class=\"data row22 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col14\" class=\"data row22 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col15\" class=\"data row22 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col16\" class=\"data row22 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col17\" class=\"data row22 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col18\" class=\"data row22 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col19\" class=\"data row22 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col20\" class=\"data row22 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col21\" class=\"data row22 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col24\" class=\"data row22 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col25\" class=\"data row22 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col26\" class=\"data row22 col26\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col27\" class=\"data row22 col27\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col28\" class=\"data row22 col28\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row22_col29\" class=\"data row22 col29\" >-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row23\" class=\"row_heading level0 row23\" >V24</th>\n",
       "      <td id=\"T_89f9e_row23_col0\" class=\"data row23 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col4\" class=\"data row23 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col5\" class=\"data row23 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col7\" class=\"data row23 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col8\" class=\"data row23 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col9\" class=\"data row23 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col12\" class=\"data row23 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col14\" class=\"data row23 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col15\" class=\"data row23 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col16\" class=\"data row23 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col17\" class=\"data row23 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row23_col24\" class=\"data row23 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col25\" class=\"data row23 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col26\" class=\"data row23 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col27\" class=\"data row23 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row23_col28\" class=\"data row23 col28\" >-0.01</td>\n",
       "      <td id=\"T_89f9e_row23_col29\" class=\"data row23 col29\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row24\" class=\"row_heading level0 row24\" >V25</th>\n",
       "      <td id=\"T_89f9e_row24_col0\" class=\"data row24 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col1\" class=\"data row24 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col2\" class=\"data row24 col2\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col3\" class=\"data row24 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col4\" class=\"data row24 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col5\" class=\"data row24 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col6\" class=\"data row24 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col7\" class=\"data row24 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col8\" class=\"data row24 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col9\" class=\"data row24 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col10\" class=\"data row24 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col11\" class=\"data row24 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col12\" class=\"data row24 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col13\" class=\"data row24 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col14\" class=\"data row24 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col15\" class=\"data row24 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col16\" class=\"data row24 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col17\" class=\"data row24 col17\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col18\" class=\"data row24 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col19\" class=\"data row24 col19\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col20\" class=\"data row24 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col21\" class=\"data row24 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col22\" class=\"data row24 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col23\" class=\"data row24 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col24\" class=\"data row24 col24\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row24_col25\" class=\"data row24 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col26\" class=\"data row24 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col27\" class=\"data row24 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col28\" class=\"data row24 col28\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row24_col29\" class=\"data row24 col29\" >-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row25\" class=\"row_heading level0 row25\" >V26</th>\n",
       "      <td id=\"T_89f9e_row25_col0\" class=\"data row25 col0\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col1\" class=\"data row25 col1\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col2\" class=\"data row25 col2\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col3\" class=\"data row25 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col4\" class=\"data row25 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col5\" class=\"data row25 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col6\" class=\"data row25 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col7\" class=\"data row25 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col8\" class=\"data row25 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col9\" class=\"data row25 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col10\" class=\"data row25 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col11\" class=\"data row25 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col12\" class=\"data row25 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col13\" class=\"data row25 col13\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col14\" class=\"data row25 col14\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col15\" class=\"data row25 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col16\" class=\"data row25 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col17\" class=\"data row25 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col18\" class=\"data row25 col18\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col19\" class=\"data row25 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col20\" class=\"data row25 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col21\" class=\"data row25 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col22\" class=\"data row25 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col23\" class=\"data row25 col23\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col24\" class=\"data row25 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col25\" class=\"data row25 col25\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row25_col26\" class=\"data row25 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col27\" class=\"data row25 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col28\" class=\"data row25 col28\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row25_col29\" class=\"data row25 col29\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row26\" class=\"row_heading level0 row26\" >V27</th>\n",
       "      <td id=\"T_89f9e_row26_col0\" class=\"data row26 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col1\" class=\"data row26 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col2\" class=\"data row26 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col3\" class=\"data row26 col3\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col4\" class=\"data row26 col4\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col5\" class=\"data row26 col5\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col6\" class=\"data row26 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col7\" class=\"data row26 col7\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col8\" class=\"data row26 col8\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col9\" class=\"data row26 col9\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col10\" class=\"data row26 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col11\" class=\"data row26 col11\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col12\" class=\"data row26 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col13\" class=\"data row26 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col14\" class=\"data row26 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col15\" class=\"data row26 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col16\" class=\"data row26 col16\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col17\" class=\"data row26 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col18\" class=\"data row26 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col19\" class=\"data row26 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col20\" class=\"data row26 col20\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col21\" class=\"data row26 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col22\" class=\"data row26 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col23\" class=\"data row26 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col24\" class=\"data row26 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col25\" class=\"data row26 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col26\" class=\"data row26 col26\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row26_col27\" class=\"data row26 col27\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row26_col28\" class=\"data row26 col28\" >0.02</td>\n",
       "      <td id=\"T_89f9e_row26_col29\" class=\"data row26 col29\" >0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row27\" class=\"row_heading level0 row27\" >V28</th>\n",
       "      <td id=\"T_89f9e_row27_col0\" class=\"data row27 col0\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col1\" class=\"data row27 col1\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col2\" class=\"data row27 col2\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col3\" class=\"data row27 col3\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col4\" class=\"data row27 col4\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col5\" class=\"data row27 col5\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col6\" class=\"data row27 col6\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col7\" class=\"data row27 col7\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col8\" class=\"data row27 col8\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col9\" class=\"data row27 col9\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col10\" class=\"data row27 col10\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col11\" class=\"data row27 col11\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col12\" class=\"data row27 col12\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col13\" class=\"data row27 col13\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col14\" class=\"data row27 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col15\" class=\"data row27 col15\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col16\" class=\"data row27 col16\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col17\" class=\"data row27 col17\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col18\" class=\"data row27 col18\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col19\" class=\"data row27 col19\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col20\" class=\"data row27 col20\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col21\" class=\"data row27 col21\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col22\" class=\"data row27 col22\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col23\" class=\"data row27 col23\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col24\" class=\"data row27 col24\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col25\" class=\"data row27 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col26\" class=\"data row27 col26\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row27_col27\" class=\"data row27 col27\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row27_col28\" class=\"data row27 col28\" >0.01</td>\n",
       "      <td id=\"T_89f9e_row27_col29\" class=\"data row27 col29\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row28\" class=\"row_heading level0 row28\" >Class</th>\n",
       "      <td id=\"T_89f9e_row28_col0\" class=\"data row28 col0\" >-0.10</td>\n",
       "      <td id=\"T_89f9e_row28_col1\" class=\"data row28 col1\" >0.09</td>\n",
       "      <td id=\"T_89f9e_row28_col2\" class=\"data row28 col2\" >-0.19</td>\n",
       "      <td id=\"T_89f9e_row28_col3\" class=\"data row28 col3\" >0.13</td>\n",
       "      <td id=\"T_89f9e_row28_col4\" class=\"data row28 col4\" >-0.09</td>\n",
       "      <td id=\"T_89f9e_row28_col5\" class=\"data row28 col5\" >-0.04</td>\n",
       "      <td id=\"T_89f9e_row28_col6\" class=\"data row28 col6\" >-0.19</td>\n",
       "      <td id=\"T_89f9e_row28_col7\" class=\"data row28 col7\" >0.02</td>\n",
       "      <td id=\"T_89f9e_row28_col8\" class=\"data row28 col8\" >-0.10</td>\n",
       "      <td id=\"T_89f9e_row28_col9\" class=\"data row28 col9\" >-0.22</td>\n",
       "      <td id=\"T_89f9e_row28_col10\" class=\"data row28 col10\" >0.15</td>\n",
       "      <td id=\"T_89f9e_row28_col11\" class=\"data row28 col11\" >-0.26</td>\n",
       "      <td id=\"T_89f9e_row28_col12\" class=\"data row28 col12\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row28_col13\" class=\"data row28 col13\" >-0.30</td>\n",
       "      <td id=\"T_89f9e_row28_col14\" class=\"data row28 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row28_col15\" class=\"data row28 col15\" >-0.20</td>\n",
       "      <td id=\"T_89f9e_row28_col16\" class=\"data row28 col16\" >-0.33</td>\n",
       "      <td id=\"T_89f9e_row28_col17\" class=\"data row28 col17\" >-0.11</td>\n",
       "      <td id=\"T_89f9e_row28_col18\" class=\"data row28 col18\" >0.03</td>\n",
       "      <td id=\"T_89f9e_row28_col19\" class=\"data row28 col19\" >0.02</td>\n",
       "      <td id=\"T_89f9e_row28_col20\" class=\"data row28 col20\" >0.04</td>\n",
       "      <td id=\"T_89f9e_row28_col21\" class=\"data row28 col21\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row28_col22\" class=\"data row28 col22\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row28_col23\" class=\"data row28 col23\" >-0.01</td>\n",
       "      <td id=\"T_89f9e_row28_col24\" class=\"data row28 col24\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row28_col25\" class=\"data row28 col25\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row28_col26\" class=\"data row28 col26\" >0.02</td>\n",
       "      <td id=\"T_89f9e_row28_col27\" class=\"data row28 col27\" >0.01</td>\n",
       "      <td id=\"T_89f9e_row28_col28\" class=\"data row28 col28\" >1.00</td>\n",
       "      <td id=\"T_89f9e_row28_col29\" class=\"data row28 col29\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89f9e_level0_row29\" class=\"row_heading level0 row29\" >NormalizedAmount</th>\n",
       "      <td id=\"T_89f9e_row29_col0\" class=\"data row29 col0\" >-0.23</td>\n",
       "      <td id=\"T_89f9e_row29_col1\" class=\"data row29 col1\" >-0.53</td>\n",
       "      <td id=\"T_89f9e_row29_col2\" class=\"data row29 col2\" >-0.21</td>\n",
       "      <td id=\"T_89f9e_row29_col3\" class=\"data row29 col3\" >0.10</td>\n",
       "      <td id=\"T_89f9e_row29_col4\" class=\"data row29 col4\" >-0.39</td>\n",
       "      <td id=\"T_89f9e_row29_col5\" class=\"data row29 col5\" >0.22</td>\n",
       "      <td id=\"T_89f9e_row29_col6\" class=\"data row29 col6\" >0.40</td>\n",
       "      <td id=\"T_89f9e_row29_col7\" class=\"data row29 col7\" >-0.10</td>\n",
       "      <td id=\"T_89f9e_row29_col8\" class=\"data row29 col8\" >-0.04</td>\n",
       "      <td id=\"T_89f9e_row29_col9\" class=\"data row29 col9\" >-0.10</td>\n",
       "      <td id=\"T_89f9e_row29_col10\" class=\"data row29 col10\" >0.00</td>\n",
       "      <td id=\"T_89f9e_row29_col11\" class=\"data row29 col11\" >-0.01</td>\n",
       "      <td id=\"T_89f9e_row29_col12\" class=\"data row29 col12\" >0.01</td>\n",
       "      <td id=\"T_89f9e_row29_col13\" class=\"data row29 col13\" >0.03</td>\n",
       "      <td id=\"T_89f9e_row29_col14\" class=\"data row29 col14\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row29_col15\" class=\"data row29 col15\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row29_col16\" class=\"data row29 col16\" >0.01</td>\n",
       "      <td id=\"T_89f9e_row29_col17\" class=\"data row29 col17\" >0.04</td>\n",
       "      <td id=\"T_89f9e_row29_col18\" class=\"data row29 col18\" >-0.06</td>\n",
       "      <td id=\"T_89f9e_row29_col19\" class=\"data row29 col19\" >0.34</td>\n",
       "      <td id=\"T_89f9e_row29_col20\" class=\"data row29 col20\" >0.11</td>\n",
       "      <td id=\"T_89f9e_row29_col21\" class=\"data row29 col21\" >-0.06</td>\n",
       "      <td id=\"T_89f9e_row29_col22\" class=\"data row29 col22\" >-0.11</td>\n",
       "      <td id=\"T_89f9e_row29_col23\" class=\"data row29 col23\" >0.01</td>\n",
       "      <td id=\"T_89f9e_row29_col24\" class=\"data row29 col24\" >-0.05</td>\n",
       "      <td id=\"T_89f9e_row29_col25\" class=\"data row29 col25\" >-0.00</td>\n",
       "      <td id=\"T_89f9e_row29_col26\" class=\"data row29 col26\" >0.03</td>\n",
       "      <td id=\"T_89f9e_row29_col27\" class=\"data row29 col27\" >0.01</td>\n",
       "      <td id=\"T_89f9e_row29_col28\" class=\"data row29 col28\" >0.01</td>\n",
       "      <td id=\"T_89f9e_row29_col29\" class=\"data row29 col29\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x28a25413d60>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlation table\n",
    "corr = df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm').set_precision(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fec711c",
   "metadata": {},
   "source": [
    "It is evident from the above correlation matrix that the column V11 has the highest correlation with the Class(a positive correlation of 0.15). Therefore our target variable (Class) is highly dependent on this column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d563b7",
   "metadata": {},
   "source": [
    "### Split data into separate training and test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "36feb3e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split X and y into training and testing sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b37b58d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((199364, 30), (85443, 30))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of X_train and X_test\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ad72688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((199364,), (85443,))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of y_train and y_test\n",
    "\n",
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ad791d",
   "metadata": {},
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6eb6a31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      float64\n",
       "V1        float64\n",
       "V2        float64\n",
       "V3        float64\n",
       "V4        float64\n",
       "V5        float64\n",
       "V6        float64\n",
       "V7        float64\n",
       "V8        float64\n",
       "V9        float64\n",
       "V10       float64\n",
       "V11       float64\n",
       "V12       float64\n",
       "V13       float64\n",
       "V14       float64\n",
       "V15       float64\n",
       "V16       float64\n",
       "V17       float64\n",
       "V18       float64\n",
       "V19       float64\n",
       "V20       float64\n",
       "V21       float64\n",
       "V22       float64\n",
       "V23       float64\n",
       "V24       float64\n",
       "V25       float64\n",
       "V26       float64\n",
       "V27       float64\n",
       "V28       float64\n",
       "Amount    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types in X_train\n",
    "\n",
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79158416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Time',\n",
       " 'V1',\n",
       " 'V2',\n",
       " 'V3',\n",
       " 'V4',\n",
       " 'V5',\n",
       " 'V6',\n",
       " 'V7',\n",
       " 'V8',\n",
       " 'V9',\n",
       " 'V10',\n",
       " 'V11',\n",
       " 'V12',\n",
       " 'V13',\n",
       " 'V14',\n",
       " 'V15',\n",
       " 'V16',\n",
       " 'V17',\n",
       " 'V18',\n",
       " 'V19',\n",
       " 'V20',\n",
       " 'V21',\n",
       " 'V22',\n",
       " 'V23',\n",
       " 'V24',\n",
       " 'V25',\n",
       " 'V26',\n",
       " 'V27',\n",
       " 'V28',\n",
       " 'Amount']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display numerical variables\n",
    "\n",
    "numerical = [col for col in X_train.columns if X_train[col].dtypes != 'O']\n",
    "\n",
    "numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80f2013a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values in X_train\n",
    "\n",
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3ee8e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values in X_test\n",
    "\n",
    "X_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b400d1d4",
   "metadata": {},
   "source": [
    "We can see that there are no missing values in X_train and X_test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0966474a",
   "metadata": {},
   "source": [
    "### Addressing the Class-Imbalance issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec0c6302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled shape of X:  (568630, 30)\n",
      "Resampled shape of y:  (568630,)\n",
      "Counter({0: 284315, 1: 284315})\n"
     ]
    }
   ],
   "source": [
    "# Address the Class-Imbalance issue using the SMOTE technique\n",
    "from imblearn.over_sampling import SMOTE\n",
    "X_resampled, y_resampled = SMOTE().fit_resample(X, y)\n",
    "print(\"Resampled shape of X: \", X_resampled.shape)\n",
    "print(\"Resampled shape of y: \", y_resampled.shape)\n",
    "value_counts = Counter(y_resampled)\n",
    "print(value_counts)\n",
    "(X_train, X_test, y_train, y_test) = train_test_split(X_resampled, y_resampled, test_size= 0.3, random_state= 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526ae284",
   "metadata": {},
   "source": [
    "We can see that the data is now balanced"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a26865b",
   "metadata": {},
   "source": [
    "### Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d1c425a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use feature scaling to ensure that that the values in the data are scaled equally\n",
    "cols = X_train.columns\n",
    "\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "deda2d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(X_train, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7ea3805b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(X_test, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "734fab21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.390721</td>\n",
       "      <td>0.464020</td>\n",
       "      <td>-0.314458</td>\n",
       "      <td>0.214233</td>\n",
       "      <td>0.005756</td>\n",
       "      <td>0.275011</td>\n",
       "      <td>0.550895</td>\n",
       "      <td>0.211969</td>\n",
       "      <td>-0.024775</td>\n",
       "      <td>0.568209</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038146</td>\n",
       "      <td>-0.592246</td>\n",
       "      <td>-0.997760</td>\n",
       "      <td>-0.245451</td>\n",
       "      <td>-1.280096</td>\n",
       "      <td>0.604051</td>\n",
       "      <td>-0.775151</td>\n",
       "      <td>-0.024963</td>\n",
       "      <td>0.091826</td>\n",
       "      <td>0.826834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.245453</td>\n",
       "      <td>-0.036577</td>\n",
       "      <td>0.273659</td>\n",
       "      <td>-0.335779</td>\n",
       "      <td>0.472329</td>\n",
       "      <td>-0.981580</td>\n",
       "      <td>0.254865</td>\n",
       "      <td>-0.664949</td>\n",
       "      <td>0.074810</td>\n",
       "      <td>-0.466313</td>\n",
       "      <td>...</td>\n",
       "      <td>1.343209</td>\n",
       "      <td>-0.050342</td>\n",
       "      <td>0.066639</td>\n",
       "      <td>2.070317</td>\n",
       "      <td>-0.187870</td>\n",
       "      <td>-1.496638</td>\n",
       "      <td>-0.670859</td>\n",
       "      <td>0.762218</td>\n",
       "      <td>0.398306</td>\n",
       "      <td>1.866254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.476795</td>\n",
       "      <td>-0.092292</td>\n",
       "      <td>-0.671105</td>\n",
       "      <td>0.283259</td>\n",
       "      <td>0.362502</td>\n",
       "      <td>1.449683</td>\n",
       "      <td>-0.131307</td>\n",
       "      <td>0.195921</td>\n",
       "      <td>-0.001338</td>\n",
       "      <td>-0.026166</td>\n",
       "      <td>...</td>\n",
       "      <td>0.303411</td>\n",
       "      <td>0.415367</td>\n",
       "      <td>0.819539</td>\n",
       "      <td>1.798333</td>\n",
       "      <td>0.869178</td>\n",
       "      <td>-2.398732</td>\n",
       "      <td>-0.652385</td>\n",
       "      <td>0.366159</td>\n",
       "      <td>1.356594</td>\n",
       "      <td>0.516513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.847805</td>\n",
       "      <td>0.101997</td>\n",
       "      <td>-0.178690</td>\n",
       "      <td>0.576055</td>\n",
       "      <td>-0.228047</td>\n",
       "      <td>0.160620</td>\n",
       "      <td>0.860640</td>\n",
       "      <td>0.326204</td>\n",
       "      <td>0.069232</td>\n",
       "      <td>0.422931</td>\n",
       "      <td>...</td>\n",
       "      <td>0.123268</td>\n",
       "      <td>-0.000737</td>\n",
       "      <td>0.347942</td>\n",
       "      <td>0.286732</td>\n",
       "      <td>0.590621</td>\n",
       "      <td>-0.703945</td>\n",
       "      <td>-1.262124</td>\n",
       "      <td>0.290439</td>\n",
       "      <td>0.602596</td>\n",
       "      <td>0.672740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.086171</td>\n",
       "      <td>-0.045079</td>\n",
       "      <td>-0.482793</td>\n",
       "      <td>0.814603</td>\n",
       "      <td>0.094707</td>\n",
       "      <td>-0.141949</td>\n",
       "      <td>1.205459</td>\n",
       "      <td>0.094373</td>\n",
       "      <td>0.344101</td>\n",
       "      <td>0.688917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.629976</td>\n",
       "      <td>-0.055624</td>\n",
       "      <td>0.495660</td>\n",
       "      <td>0.278928</td>\n",
       "      <td>0.189838</td>\n",
       "      <td>0.581437</td>\n",
       "      <td>-0.017982</td>\n",
       "      <td>0.017946</td>\n",
       "      <td>-0.051893</td>\n",
       "      <td>1.139145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0 -0.390721  0.464020 -0.314458  0.214233  0.005756  0.275011  0.550895   \n",
       "1 -0.245453 -0.036577  0.273659 -0.335779  0.472329 -0.981580  0.254865   \n",
       "2  0.476795 -0.092292 -0.671105  0.283259  0.362502  1.449683 -0.131307   \n",
       "3  0.847805  0.101997 -0.178690  0.576055 -0.228047  0.160620  0.860640   \n",
       "4 -0.086171 -0.045079 -0.482793  0.814603  0.094707 -0.141949  1.205459   \n",
       "\n",
       "         V7        V8        V9  ...       V20       V21       V22       V23  \\\n",
       "0  0.211969 -0.024775  0.568209  ... -0.038146 -0.592246 -0.997760 -0.245451   \n",
       "1 -0.664949  0.074810 -0.466313  ...  1.343209 -0.050342  0.066639  2.070317   \n",
       "2  0.195921 -0.001338 -0.026166  ...  0.303411  0.415367  0.819539  1.798333   \n",
       "3  0.326204  0.069232  0.422931  ...  0.123268 -0.000737  0.347942  0.286732   \n",
       "4  0.094373  0.344101  0.688917  ...  0.629976 -0.055624  0.495660  0.278928   \n",
       "\n",
       "        V24       V25       V26       V27       V28    Amount  \n",
       "0 -1.280096  0.604051 -0.775151 -0.024963  0.091826  0.826834  \n",
       "1 -0.187870 -1.496638 -0.670859  0.762218  0.398306  1.866254  \n",
       "2  0.869178 -2.398732 -0.652385  0.366159  1.356594  0.516513  \n",
       "3  0.590621 -0.703945 -1.262124  0.290439  0.602596  0.672740  \n",
       "4  0.189838  0.581437 -0.017982  0.017946 -0.051893  1.139145  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87318557",
   "metadata": {},
   "source": [
    "We now have X_train dataset ready to be fed into the classifiers. This will be done in the following section"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ce61e6",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a8aa1515",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98     85149\n",
      "           1       0.99      0.97      0.98     85440\n",
      "\n",
      "    accuracy                           0.98    170589\n",
      "   macro avg       0.98      0.98      0.98    170589\n",
      "weighted avg       0.98      0.98      0.98    170589\n",
      "\n",
      "98.04% Accurate\n"
     ]
    }
   ],
   "source": [
    "# train a logistic regression classifier on the training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "# instantiate the model\n",
    "LR = LogisticRegression(max_iter=100)\n",
    "\n",
    "\n",
    "# fit the model\n",
    "LR.fit(X_train, y_train)\n",
    "\n",
    "# predict values using the test data\n",
    "y_pred_1 = LR.predict(X_test)\n",
    "\n",
    "#  prediction summary for the model\n",
    "print(classification_report(y_test, y_pred_1))\n",
    "\n",
    "# Accuracy score\n",
    "LR_SC = accuracy_score(y_pred_1,y_test)\n",
    "print(f\"{round(LR_SC*100,2)}% Accurate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce1ac63",
   "metadata": {},
   "source": [
    "Here, y_test are the true class labels and y_pred are the predicted class labels in the test-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ce6e7c1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[84419   730]\n",
      " [ 2608 82832]]\n",
      "\n",
      "True Positives(TP) =  84419\n",
      "\n",
      "True Negatives(TN) =  82832\n",
      "\n",
      "False Positives(FP) =  730\n",
      "\n",
      "False Negatives(FN) =  2608\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_1)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ea80321e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8c771da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85149\n",
      "           1       1.00      1.00      1.00     85440\n",
      "\n",
      "    accuracy                           1.00    170589\n",
      "   macro avg       1.00      1.00      1.00    170589\n",
      "weighted avg       1.00      1.00      1.00    170589\n",
      "\n",
      "99.81% Accurate\n"
     ]
    }
   ],
   "source": [
    "# train a  decision tree classifier on the training set\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "# instantiate the model\n",
    "DT = DecisionTreeClassifier()\n",
    "\n",
    "\n",
    "# fit the model\n",
    "DT.fit(X_train, y_train)\n",
    "\n",
    "#predict values using the test data\n",
    "y_pred_2 = DT.predict(X_test)\n",
    "\n",
    "#  prediction Summary by model\n",
    "print(classification_report(y_test, y_pred_2))\n",
    "\n",
    "# Accuracy score\n",
    "DT_SC = accuracy_score(y_pred_2,y_test)\n",
    "print(f\"{round(DT_SC*100,2)}% Accurate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "20abfa1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[84912   237]\n",
      " [   87 85353]]\n",
      "\n",
      "True Positives(TP) =  84912\n",
      "\n",
      "True Negatives(TN) =  85353\n",
      "\n",
      "False Positives(FP) =  237\n",
      "\n",
      "False Negatives(FN) =  87\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_2)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5481fa08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b83a9585",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85149\n",
      "           1       1.00      1.00      1.00     85440\n",
      "\n",
      "    accuracy                           1.00    170589\n",
      "   macro avg       1.00      1.00      1.00    170589\n",
      "weighted avg       1.00      1.00      1.00    170589\n",
      "\n",
      "99.99% Accurate\n"
     ]
    }
   ],
   "source": [
    "# train a random forest classifier on the training set\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# instantiate the model\n",
    "RF = RandomForestClassifier()\n",
    "\n",
    "\n",
    "# fit the model\n",
    "RF.fit(X_train, y_train)\n",
    "\n",
    "# predict values using the test data\n",
    "y_pred_3 = RF.predict(X_test)\n",
    "\n",
    "#  prediction Summary by model\n",
    "print(classification_report(y_test, y_pred_3))\n",
    "\n",
    "# Accuracy score\n",
    "RF_SC = accuracy_score(y_pred_3,y_test)\n",
    "print(f\"{round(RF_SC*100,2)}% Accurate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ca3a538a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[85134    15]\n",
      " [    2 85438]]\n",
      "\n",
      "True Positives(TP) =  85134\n",
      "\n",
      "True Negatives(TN) =  85438\n",
      "\n",
      "False Positives(FP) =  15\n",
      "\n",
      "False Negatives(FN) =  2\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_3)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ff41f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "476afff0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85149\n",
      "           1       1.00      1.00      1.00     85440\n",
      "\n",
      "    accuracy                           1.00    170589\n",
      "   macro avg       1.00      1.00      1.00    170589\n",
      "weighted avg       1.00      1.00      1.00    170589\n",
      "\n",
      "99.84% Accurate\n"
     ]
    }
   ],
   "source": [
    "# train a KNN classifier on the training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# instantiate the model\n",
    "KNN = KNeighborsClassifier()\n",
    "\n",
    "\n",
    "# fit the model\n",
    "KNN.fit(X_train, y_train)\n",
    "\n",
    "# predict values using the test data\n",
    "y_pred_4 = KNN.predict(X_test)\n",
    "\n",
    "#  prediction Summary by model\n",
    "print(classification_report(y_test, y_pred_4))\n",
    "\n",
    "# Accuracy score\n",
    "KNN_SC = accuracy_score(y_pred_4,y_test)\n",
    "print(f\"{round(KNN_SC*100,2)}% Accurate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "665b2df7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[84884   265]\n",
      " [    0 85440]]\n",
      "\n",
      "True Positives(TP) =  84884\n",
      "\n",
      "True Negatives(TN) =  85440\n",
      "\n",
      "False Positives(FP) =  265\n",
      "\n",
      "False Negatives(FN) =  0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_4)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "01ed6278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xxZDUHNifaPFdzOw7M/ucaGrqVPfxOOCY8Lo/MMHMVpnZPGAu0EtSG6C5mb0UVlq/p0qeVFkPAH1SrfC4y8vkxoRpzjmXe7WYeyT96e2wDU0rqTPwCXC3pP9IukPSpkBrM1sMEH62Cue3BRam5a8IaW3D66rp6+UxszVET6C3jLu8Gm9ESko9VLONpAvTDjUHSqvP5ZxzOVaLOUXMrBwor+FwGdFU1Oea2TRJNxC6Qmp65+reIiY9Lk+N4lrajYHNiCq+edq2AhgQV6hzzuVM/a3GXgFUmNm0sP8AURBfEro8CD+Xpp3fPi1/O2BRSG9XTfp6eSSVAS2AZXGVihvy9zzwvKSxZvZhpqtzzrl8YPU0TtvMPpa0UNJOZvYO0Ad4M2yDgdHh50Mhy2Tgr5L+BGxHdMNxuplVSlopqTcwDTiVdV3Mk0MZLxE1hp+xmp54DJIsgnCHpIGhA54wvGWCmfWNz+acczlQvw/XnAvcJ6kx8AFwGlEPxSRJQ4AFwEAAM5sjaRJRUF8DDDOz1NoDZwFjgabA42GD6CbnvZLmErWwB2WqUJKgvXUqYIeKLZfUKuZ855zLnXqcJ9vMXgX2rOZQnxrOHwWMqiZ9JtCjmvRvCUE/qSSjR9ZK6pDakdSRDB3lzjmXM/U05C9fJWlpXwa8GFatgWjc4tCY851zLncKdEWapDIGbTN7QtIeQG+i4SkXmNmnGbI551xuFOjiBknFjdPe2czeDgEb1g1R6SCpg5nNyn71nHOudhI+nl6w4lraFwGnA9dWc8yAg7JSI+ec2xDF3dCOHad9evh54MarjnPObaCG2tKWdFxcRjP7e/1XxznnNlBDXQQBOCr8bEU0B0lqIcQDgecAD9rOufzTUIO2mZ0GIOkRoFtqVqvwrP3NG6d6zjlXO5Z5TpGClmScdqdUwA6WADtmqT7OObdhGmqfdprnJD0JjCcaNTIIeDartXLOubpqqN0jKWZ2jqRjiZ6EBCg3swezWy3nnKuj4o7ZiVraALOAlWb2tKRmkjY3s5XZrJhzztVFSZGP006yRuTpRJN/3x6S2gL/yGKdnHOuzkpKkm+FKEm1hwH7Eq1Yg5m9x7o10ZxzLq9ISrwVoiTdI6vM7LvUBYYlcXxqVudcXirQWJxYkpb285IuBZpKOgS4H3g4u9Vyzrm6kZJvmcvSfEmzJb0qaWZI20rSFEnvhZ9bpp0/UtJcSe9I6puW3jOUM1fSGIVWsKQmkiaG9GmSOmWqU5KgfQnRMvKzgTOAx4DLE+RzzrmNTiXJt4QONLPdzCy1gs0IYKqZdQWmhn0kdSMaEt0d6AfcIqk05LmVaB2CrmHrF9KHAMvNrAtwHXB1psrEdo9IKgFeN7MewJ8TX6JzzuXIRuge6Q8cEF6PI5rW45KQPsHMVgHzwrqPvSTNB5qb2UtR/XQPcAzROpH9gV+Fsh4AbpKkuMV9Y//WmNla4LX05caccy6flZYk3xIw4ClJr0hKrdjVOvWUePiZGpjRFliYlrcipLUNr6umr5fHzNYAXwAt4yqU5EZkG2COpOnAV99fidnRCfI659xGVZuWdgjE6csnlptZedr+vma2KCxmPkXS23HFVZNmMelxeWqUJGj/OsE5zjmXF2ozlC8E6PKY44vCz6WSHgR6AUsktTGzxWECvaXh9AqgfVr2dkQrflWE11XT0/NUhJF5LYBlcXWu8QuCpE0knU+0vPvOwL/M7PnUFleoc87lSn3diJS0qaTNU6+BQ4E3gMnA4HDaYOCh8HoyMCiMCNme6Ibj9NCFslJS7zBq5NQqeVJlDQCeievPhviW9jhgNfBP4DCgG3Be/GU651xu1eONyNbAg6HlXgb8NSx0PgOYJGkIsICoYYuZzZE0CXgTWAMMM7PKUNZZwFigKdENyMdD+p3AveGm5TKi0Sex4oJ2NzP7MYCkO4Hpya/VOedyo74eTzezD4Bdq0n/DOhTQ55RwKhq0mcCPapJ/5YQ9JOKC9qr0wpeU6iPfDrnGpYin5k1NmjvKmlFeC2iJyJXhNdmZs2zXjvnnKulYm9fxi03VlrTMeecy1cNNmg751whUpH3j3jQds4VFW9pO+dcASnUxQ2S8qDtnCsqRd474kHbOVdcvHvEOecKSC3myS5IHrSdc0XFW9rOOVdAiv3pbQ/azrmi4qNHnHOugBR5Qzv7QfvrD6/M9lu4AtS0g38u3A99s2D8BpfhQ/6cc66AeNB2zrkCUqLYhV8Kngdt51xRKSvylnaR32d1zjU0JbLEWxKSSiX9R9IjYX8rSVMkvRd+bpl27khJcyW9I6lvWnpPSbPDsTFhrUjCepITQ/o0SZ0yXl9t/4c451w+K1HyLaHzgLfS9kcAU82sKzA17COpG9Eaj92BfsAtklLrEtwKDCVa7LdrOA4wBFhuZl2A64CrM15f4mo751wBKKnFlomkdsARwB1pyf2JFj4n/DwmLX2Cma0ys3nAXKCXpDZAczN7Kay0fk+VPKmyHgD6KMPTQR60nXNFpTYtbUlDJc1M24ZWKe564GJgbVpaazNbDBB+tgrpbYGFaedVhLS24XXV9PXymNka4AugZdz1+Y1I51xRUS1Gj5hZOVBefTk6ElhqZq9IOiDJW1f3FjHpcXlq5EHbOVdU6nH0yL7A0ZIOBzYBmkv6C7BEUhszWxy6PpaG8yuA9mn52wGLQnq7atLT81RIKgNaAMviKhXbPaLIXpKOk3RseF3kA2qcc4WsvkaPmNlIM2tnZp2IbjA+Y2YnA5OBweG0wcBD4fVkYFAYEbI90Q3H6aELZaWk3iF+nlolT6qsAeE96tbSlnQocAvwHvBRSG4HdJF0tpk9FXvFzjmXAxvhicjRwCRJQ4AFwEAAM5sjaRLwJrAGGGZmlSHPWcBYoCnweNgA7gTulTSXqIU9KNObx3WP3AAcbGbz0xPDX5DHgB8luDjnnNuosjG6wsyeA54Lrz8D+tRw3ihgVDXpM4Ee1aR/Swj6ScUF7TLWv+OZ8hHQqDZv4pxzG0tDnnvkLmCGpAmsG8bSnqj5fme2K+acc3XRYOceMbOrJP2DaPD33kRDUyqAn5nZmxunes45VzvFPvdI7JA/M3uL9R/fdM65vFbsLe1EffaSfhW375xz+SILc4/klaQP17ySYd855/JCoQbjpBIFbTN7OG7fOefyRbFPqJTx+iTtKGmqpDfC/i6SLs9+1ZxzrvbKSizxVoiS/FH6MzASWA1gZq+T4Kkd55zLhfqcmjUfJekeaWZm06tMObImS/VxzrkN4n3a8KmkHQjTBUoaACzOaq2cc66OajM1ayFKErSHEc03u7Okj4B5wM+yWivnnKsjb2nDh2Z2sKRNgRIzW5ntSjnnXF0Val91UkmC9jxJTwATgWeyXB/nnNsghToqJKkkf5R2Ap4m6iaZJ+kmSftlt1rOOVc3xf5EZMagbWbfmNkkMzsO2B1oDjyf9Zo551wdlNZiK0RJ5x75b0m3ALOI1ko7Iau1cs65Oqqv5cYkbSJpuqTXJM2R9OuQvpWkKZLeCz+3TMszUtJcSe9I6puW3lPS7HBsTGrZxrA02cSQPk1Sp4zXl+kESfOA84F/Aj3M7AQz+1umfM45lwv12D2yCjjIzHYFdgP6SeoNjACmmllXYGrYR1I3ogcPuwP9gFskpRr0twJDidaN7BqOAwwBlptZF+A64OqM15fg/8GuZnasmY03s68SnO+cczlTX0HbIl+G3UZhM6I1BsaF9HHAMeF1f2CCma0ys3nAXKBXWLG9uZm9FBbtvadKnlRZDwB9Mi2eHrew78Vmdg0wStWMVjez4XEFO+dcLjSqxZg/SUOJWsAp5WZWnna8lGhW0y7AzWY2TVLrsMI6ZrZYUqtwelvg5bSyKkLaatZfujGVnsqzMJS1RtIXQEvg05rqHDfkL7X4wcyYc5xzLq/UZhGEEKDLY45XArtJ2gJ4UNIPFudNU10L2WLS4/LUKG65sdT0q1+b2f3r1Uyq1erBzjm3sWRjKJ+ZfS7pOaK+6CWS2oRWdhtgaTitgmgd3ZR2wKKQ3q6a9PQ8FZLKgBbAsri6JPkiMTJhmnPO5Vx9DfmTtE1oYSOpKXAw8DYwGRgcThsMPBReTwYGhREh2xPdcJweulJWSuod+qtPrZInVdYA4JnQ712juD7tw4DDgbaSxqQdao7P8uecy1P12NJuA4wL/dolwCQze0TSS8AkSUOABcBAADObI2kS8CZRjBwWulcAzgLGAk2Bx8MGcCdwr6S5RC3sjNNex/VpLyLqzz6a9ZcXWwlckPFynXMuBxrV02PsYe2A3atJ/wzoU0OeUcCoatJnAj/oDzezbwlBP6m4Pu3XgNck/dXMVtemUOecy5VCfTw9qSQTRnWSdBXQjehpSADMrHPWauWcc3VU7EE7yY3Iu4me5lkDHEg0MPzebFbKOefqqsFPGAU0NbOpgMzsQzP7FXBQdqvlnHN1UypLvBWiJN0j30oqAd6TdA7wEdAqQx7nnMsJXwQhmiyqGTAc+C1RK3twXAbnnMuVsiKP2hmDtpnNCC+/BE7LbnWcc27DFGq3R1IZg7akh/nhs/BfEI3hvj2MM3TOubxQqDcYk0ryReIDolb2n8O2AlgC7Bj2nXMubxT76JEkfdq7m9n+afsPS3rBzPaXNCdbFXPOuboo1GCcVJKgvY2kDma2AEBSB2DrcOy7rNXMOefqoL4eY89XSYL2RcCLkt4nmvt1e+BsSZuybsUF55zLC0U+eCTRauyPEU0xeH7YdjKzR83sKzO7Pqu1KyIvvPAKffueySGHDKW8/P7MGVzBOXfIYbzy9B+YOeUaxt14Lk2aNOKyC47n/ek38/LjV/Hy41fR98Dd1svTfruWfPLW3Zw/9IgflHf/nb9k5pRrvt9v3LiMe28ezhsvXMcLD/2WDu22/kEe533aSGoGXAh0NLPTJXWVtJOZPZL96hWHyspKfvOb27j77t/SunVLBgy4kIMO2osuXTrkumqunmzXekvOPq0fu/f5Jd+uWs1fbjmPgUftDcCNdzzG9eWPVpvvmitO4annXv1Bev9+P+Grr9YfmPXzEw9k+Rdf0WP/Cxh41N6MGvlTThk25gd5G7rSAg3GSSWde+Q7YO+wXwH8Lms1KkKvv/4eHTu2oX37bWncuBFHHLE/U6dOy3W1XD0rKyul6SaNKS0toWnTxixesjz2/KMO3ZN5C5by5rsV66Vv2qwJw08/nNE3Prhe+pGH9uS+B14A4O+PTeOAfeNWvmq4SmSJt0KUJGjvEBb4XQ1gZt9Q/bpmrgZLlnzGttuu+yrbunVLliz5LIc1cvVt0ZLlXF/+CO++fBPzZt7KihVfM/WfswE4c3Bfpj95Nbf94Qy2aLEpAM2aNuGis45i1PV/+0FZV/7yBG4of5Svv1m1Xvp2225FxaLoc1NZuZYVK7+m5ZabZ/nKCk+xd48kCdrfhaV2DEDSDsCquAyShkqaKWlmefnEeqhmYatu9aBo1SFXLLZosSlHHrInP9p3OJ1/cjabNmvCoGP348/3Pk23/zqPvfqN4OOlyxl9+ckA/N+FA7jxzsf56uv1f5V26daRzp1aM/nJH66nXd1nJsPKVA1SmZJvhSjJ6JErgSeA9pLuA/YFfh6XYf0Vjt9t8J+qbbfdmo8//vT7/SVLPqNVq61yWCNX3w7arwfzFy7l02UrAfjHEzPo3XNHJjz44vfn3DX+Gf5+98UA/GT3Lhx7+F6MGvlTWjRvxlozvl21msrKtezx4868/a8xlJWVsE3LFjw58f/oe+Jv+WjxZ7TbriUffbyM0tISmm/ejGWff5mT681n9dUektSeaCrqbYG1QLmZ3SBpK2Ai0AmYD5xgZstDnpHAEKASGG5mT4b0nqxbbuwx4DwzM0lNwnv0BD4DTjSz+XH1SjL3yBRJs4DeRN0i55nZpxmyuTQ//nFX5s9fxMKFH9O6dUseffQFrr32l7mulqtHCz/6lF57dKXpJo355tvvOHDfHsx6/QO2bbUFHy/9HID+fX/Cm+8sBODgAb/+Pu9lFxzPV199y23jngLgz395GoAO7bbm73dfTN8TfwvAo1Ne4WcD9mfarPc47vC9eP7f/mxbdeqxAb0GuMjMZknaHHhF0hSiRutUMxstaQQwArhEUjeiNR67A9sBT0vaMawTeSswFHiZKGj3I1oncgiw3My6SBoEXA2cGFepuIV9qw5tmB1+Nkt/2MZlVlZWyhVXnMkvfnEllZVrOf74g+natWOuq+Xq0YxX3+fBx6bx0mO/Z03lWl6bM587/zqVW68Zyi7dOmIGH1Z8wrkj76jze4yd+Bx3XX82b7xwHcs//5JTzrmxHq+geNRXSzusor44vF4p6S2gLdAfOCCcNg54DrgkpE8ws1XAvLBYby9J84HmZvZSVD/dAxxDFLT7A78KZT0A3CRJcSuyq6ZjkmYT9WOn/y8wYBuglZllWoE+8O4R90NNO1yZ6yq4PPTNgvEbHHJnffpo4pjTc5sjzyBqAaeUh+7d9UjqBLxAtDjvAjPbIu3YcjPbUtJNwMtm9peQfidRYJ4PjDazg0P6fwGXmNmRkt4A+plZRTj2PrBXXG9G3MK+P66m0pcABwO/rymfc87lkmoxlG/9+281lafNgL8B55vZiphBBNUdqNrwTU+Py1OjjKNHwsM0Y4n+YrwCdDMz/17mnMtL9TnkT1IjooB9n5n9PSQvkdQmHG8DLA3pFUD7tOztgEUhvV016evlkVQGtACWxV5fTGV7SBofKvw00MPM7jCz1Rmu0znncka12GLLiZrUdwJvmdmf0g5NZt3qXYOBh9LSB0lqIml7ouk/poe+8ZWSeocyT62SJ1XWAOCZuP5siB898hqwEHgU6EXUof79QTMbHlewc87lQj0+NLMvcAowW9KrIe1SYDQwSdIQYAEwEMDM5kiaBLxJNPJkWBg5AnAW64b8PR42iP4o3BtuWi4jGn0SKy5o/0/SK3POuXxRXzHbzF6MKa5PDXlGAaOqSZ9JdBOzavq3hKCfVNyNSJ921TlXcIr9YeMkT0Q651zBKPb5tD1oO+eKSqFOBJVUkiF/+yZJc865fFBfo0fyVZJvEtWNyfZx2s65vCRZ4q0Qxc09sjewD9HCvhemHWoOJHyE3TnnNq5CbUEnFden3RjYLJyTPtP6CqJB4M45l3ca7OgRM3seeF7SWDP7cCPWyTnn6szXiIQ7JG2R2pG0paQns1cl55yru2K/EZlkyN/WZvZ5asfMlktqlb0qOedc3RV790iSlvba9AURJHUkw9SBzjmXK97ShsuAFyU9H/b3Z/1Jw51zLm8U+8M1SdaIfELSHqxbI/ICXyPSOZevijxmx47T3tnM3g4BG9ZN2t0hrBE5K/vVc8652ikp0IdmkopraV8EnA5cW80xAw7KSo2cc24DFPuNyLhx2qeHnwduvOo459yGKfKYHds9clxcxrT10pxzLm/U59Ssku4CjgSWmlmPkLYVMBHoRLTS+glmtjwcGwkMASqB4Wb2ZEjvybqVax4DzjMzk9QEuAfoCXwGnGhm8+t6fUeFbQjRkjg/C9sdwMm1unLnnNtIpORbAmOBflXSRgBTzawrMDXsI6kb0XJh3UOeWySl5mm6lWjUXdewpcocAiw3sy7AdcDVmSpUY9A2s9PM7DSi/utuZna8mR0fKuScc3lJlCTeMjGzF/jh6uj9gdTKXuOAY9LSJ5jZKjObB8wlWlu3DdDczF4Ki/beUyVPqqwHgD5S/J+TJN8kOoXVhFOWADsmyOeccxudVFKLTUMlzUzbkjyD0joVE8PP1BPibYkWQ0+pCGltw+uq6evlMbM1wBdAy7g3T/JwzXNhrpHxRK3uQcCzCfI551wOJL8VaWblQHkW39hi0uPy1CjJwzXnSDqW6ElIgHIzezBTPuecywVlf/zIEkltzGxx6PpYGtIrgPZp57Ujer6lIryump6ep0JSGdCCH3bHrCfpjdZZwKNmdgHwpKTNM2VwzrncyPrsI5OBweH1YOChtPRBkppI2p7ohuP00IWyUlLv0F99apU8qbIGAM+Efu8aZWxpSzqd6K7nVsAORH0wtwF9kl2fc85tPFL9DfqTNB44ANhaUgVwJTAamCRpCLAAGAhgZnMkTQLeBNYAw8ysMhR1FuuG/D0eNohG5t0raS5RC3tQxjplCOpIehXoBUwzs91D2mwz+3Giq+bd4n6m1NVJ0w5X5roKLg99s2D8BvdtrFw9NXHM2bxRn4J7FifJjchVZvZdahRK6HfxQOycy0sboU87p5J8j3he0qVAU0mHAPcDD2e3Ws45V1cltdgKT5JaXwJ8AswGziB6BPPybFbKOefqSlLirRDFdo8o6tF/PTxz/+eNUyXnnNsQhRmMk4ptaZvZWuC19OXGnHMun6kW/xWiJDci2wBzJE0HvkolmtnRWauVc87VkSjNfFIBSxK0f531WjjnXD0p1L7qpOLm094EOBPoQnQT8s4woYlzzuWxBhq0iaYLXA38EzgM6AactzEq5ZxzdZVkytVCFhe0u6WeepR0JzB941TJOec2RMNtaa9OvTCzNcXeT+ScKw71OfdIPooL2rtKWhFei+iJyBXhtZlZ86zXzjnnaqnBdo+YWXGPm3HOFani7hVIMuTPOecKRqE+NJOUB23nXFEp9vtvHrSdc0WmgfZpO+dcISr2G5HFfXXOuQanPqdmldRP0juS5koasRGqn5EHbedckamfRRAklQI3s+6J8JMkdctatRPyoO2cKyr1ODVrL2CumX1gZt8BE4D+Wb+ADDZCn/aOxX0rtxYkDTWz8lzXIx98s2B8rquQN/xzUd+SxxxJQ4GhaUnlaf8WbYGFaccqgL02vH4bxlvaG9fQzKe4Bsg/FzliZuVmtmfalv7Hs7rgn/NFzT1oO+dc9SqA9mn77YBFOarL9zxoO+dc9WYAXSVtL6kxMAiYnOM6+Tjtjcz7LV11/HORh8LspucATwKlwF1mNifH1UJmOe+icc45l5B3jzjnXAHxoO2ccwWk6IK2pGMlmaSdE5x7vqRmG/BeP5d0Uw3pn0h6VdKbkk6vQ9lnSjo1rbzt0o7dUR9PZkkaKGmOpLWS9tzQ8vJNHn0W1kraJS3tDUmd6vpeNbz/bpIOT9s/ur4eu5Y0MjzG/Y6kvvVRpqu7ogvawEnAi0R3ejM5H6jzL2oGE81sN+AA4PeSWtcms5ndZmb3hN2fA9ulHfuFmb1ZD3V8AzgOeKEeyspH+fJZqAAuy1LZKbsB3wdtM5tsZqM3tNDQOBgEdAf6AbeEx7tdjhRV0Ja0GbAvMIS0X1RJpZL+KGm2pNclnStpOFEgfFbSs+G8L9PyDJA0Nrw+StI0Sf+R9HRtArCZLQXeBzpK6hPKmC3pLklNQvmjQ4v8dUl/DGm/kvRLSQOAPYH7Qsu9qaTnJO0p6SxJ16TV+eeSbgyvT5Y0PeS5vbpfNDN7y8zeSXothSTPPguPAN0l7VRNPQ+V9JKkWZLuD/VG0uGS3pb0oqQxkh4J6b0k/Tu8/78l7RSGo/0GODH8e5+YavlLaiFpvsLCiZKaSVooqZGkHSQ9IekVSf+s4RtJf2CCma0ys3nAXKLHu12OFFXQBo4BnjCzd4FlkvYI6UOB7YHdzWwX4D4zG0M0UP5AMzswQ7kvAr3NbHei+QcuTlohSZ2BzkStrbHAiWGV+zLgLElbAccC3UPdfpee38weAGYCPzOz3czsm7TDDxC1lFNOBCZK+lF4vW9o7VcCPwv1uaMYu0KqcQz581lYC1wDXJqeKGlr4HLgYDPbg+jf+UJJmwC3A4eZ2X7ANmnZ3gb2D+9/BfD7MC/GFYRvd2Y2MXWymX0BvAb8d0g6CnjSzFYTDTU818x6Ar8Ebgn1OlrSb8L51T3K3TbBNbssKbZx2icB14fXE8L+LOBg4DYzWwNgZstqWW47omDYBmgMzEuQ50RJ+wGrgDOIfvHmhSACMA4YBtwEfAvcIelRolZZImb2iaQPJPUG3gN2Av4Vyu0JzFA0/WRTYGnI84uk5Re4fPosAPwVuEzS9mlpvYlmj/tX+HdqDLwE7Ax8EFq2AONZ96h7C2CcpK5Ej1Q3SvDeE4n+iD9L9K3jltCi3we4X+umKG0CUdcK6x4iyctHuRuyognakloCBwE9JBnRYHiTdDFhBfkExaSfs0na6xuBP5nZZEkHAL9KUNZEMzsnrX67VfuG0QD+XkAfol+oc8J1JDUROIGoBfagmZmi38JxZjayFuUUjTz8LKT+na8FLkmvKjDFzE6qUv/dY4r6LfCsmR2r6GbmcwnefjJwVfhW1xN4BtgU+Dx8E4uTl49yN2TF1D0yALjHzDqaWScza0/UCtoPeAo4U1IZQPjwAqwENk8rY4mkH4X+v2PT0lsAH4XXg+tYv7eBTpK6hP1TgOdDi6eFmT1GdDNst2ryVq1nur8TdQWcRBTAAaYCAyS1guh6JXWsY70LUb5+FsYStfRT3R0vA/umPhOhv3lHos9KZ60bYXJiDe//87T0Gj8jZvYlMB24AXjEzCrNbAUwT9LA8N6StGs12ScDgyQ1Cd8SuoayXI4UU9A+CXiwStrfgJ8CdwALgNclvRbSIOrTezx18wkYQdQ98QywOK2cXxF9jfwn8GldKmdm3wKnhXJmE/Vz3kb0i/aIpNeB54ELqsk+Frgt3GRqWqXc5cCbQEczmx7S3iTqK30qlDsFaAPr92krGhJXAewNPCrpybpcWx7Ky89C6HseA7QK+58QBd7x4d/pZWDncN/ibOAJSS8CS4AvQjHXELWa/0X0DSLlWaBb6kZkNW8/ETiZdX/YIbrPMST8f5hDmCs6vU87PLY9iegz9gQwzMwqa3Pdrn75Y+zO5SFJm5nZl6Gr62bgPTO7Ltf1crlXTC1t54rJ6ZJeJWoBtyAaTeKct7Sdc66QeEvbOecKiAdt55wrIB60nXOugHjQds65AuJB2znnCsj/A+TmF4Yi/+ZrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "81bb416c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.98     85149\n",
      "           1       0.99      0.96      0.98     85440\n",
      "\n",
      "    accuracy                           0.98    170589\n",
      "   macro avg       0.98      0.98      0.98    170589\n",
      "weighted avg       0.98      0.98      0.98    170589\n",
      "\n",
      "97.58% Accurate\n"
     ]
    }
   ],
   "source": [
    "# train a Linear SVM classifier on the training set\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# instantiate the model\n",
    "LSVC = LinearSVC(C=1, max_iter=100)\n",
    "\n",
    "\n",
    "# fit the model\n",
    "LSVC.fit(X_train, y_train)\n",
    "# predict values using the test data\n",
    "y_pred_5 = LSVC.predict(X_test)\n",
    "\n",
    "#  prediction Summary by model\n",
    "print(classification_report(y_test, y_pred_5))\n",
    "\n",
    "# Accuracy score\n",
    "LSVC_SC = accuracy_score(y_pred_5,y_test)\n",
    "print(f\"{round(LSVC_SC*100,2)}% Accurate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0f88c7ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[84172   977]\n",
      " [ 3153 82287]]\n",
      "\n",
      "True Positives(TP) =  84172\n",
      "\n",
      "True Negatives(TN) =  82287\n",
      "\n",
      "False Positives(FP) =  977\n",
      "\n",
      "False Negatives(FN) =  3153\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_5)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce61de8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "df79a4a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Musa Masango\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.98      0.93     85149\n",
      "           1       0.97      0.87      0.92     85440\n",
      "\n",
      "    accuracy                           0.92    170589\n",
      "   macro avg       0.93      0.92      0.92    170589\n",
      "weighted avg       0.93      0.92      0.92    170589\n",
      "\n",
      "92.34% Accurate\n"
     ]
    }
   ],
   "source": [
    "# train a GaussianNB classifier on the training set\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# instantiate the model\n",
    "GNB = GaussianNB()\n",
    "\n",
    "\n",
    "# fit the model\n",
    "GNB.fit(X_train, y_train)\n",
    "# predict values using the test data\n",
    "y_pred_6 = GNB.predict(X_test)\n",
    "\n",
    "#  prediction Summary by model\n",
    "print(classification_report(y_test, y_pred_6))\n",
    "\n",
    "# Accuracy score\n",
    "GNB_SC = accuracy_score(y_pred_6,y_test)\n",
    "print(f\"{round(GNB_SC*100,2)}% Accurate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "071282e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[83183  1966]\n",
      " [11103 74337]]\n",
      "\n",
      "True Positives(TP) =  83183\n",
      "\n",
      "True Negatives(TN) =  74337\n",
      "\n",
      "False Positives(FP) =  1966\n",
      "\n",
      "False Negatives(FN) =  11103\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_6)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2c5ebfc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a68a102f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gaussian NB</td>\n",
       "      <td>92.338896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Linear SVM</td>\n",
       "      <td>97.578976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>98.043250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>99.810070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN</td>\n",
       "      <td>99.844656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>99.990035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Models      Score\n",
       "5          Gaussian NB  92.338896\n",
       "4           Linear SVM  97.578976\n",
       "0  Logistic Regression  98.043250\n",
       "1        Decision Tree  99.810070\n",
       "3                  KNN  99.844656\n",
       "2        Random Forest  99.990035"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = [LR_SC*100,DT_SC*100,RF_SC*100,KNN_SC*100,LSVC_SC*100,GNB_SC*100]\n",
    "Models = pd.DataFrame({\n",
    "    'Models': [\"Logistic Regression\",\"Decision Tree\",\"Random Forest\", \"KNN\", \"Linear SVM\", \"Gaussian NB\"],\n",
    "    'Score': score})\n",
    "Models.sort_values('Score', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b550db2",
   "metadata": {},
   "source": [
    "We can see that the best performing algorithm is the RandomForest with an accuracy score of 99%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a45d724",
   "metadata": {},
   "source": [
    "### ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ba937595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC Curve for the logistic regression classifier\n",
    "\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "RocCurveDisplay.from_predictions(y_test, y_pred_1)\n",
    "\n",
    "plt.title('ROC curve for Logistic regression Classifier for Predicting Fraud')\n",
    "\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4ef5d309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC Curve for the decision tree classifier\n",
    "\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "RocCurveDisplay.from_predictions(y_test, y_pred_2)\n",
    "\n",
    "plt.title('ROC curve for Decision tree Classifier for Predicting Fraud')\n",
    "\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cc15fc",
   "metadata": {},
   "source": [
    "ROC curve help us to choose a threshold level that balances sensitivity and specificity for a particular context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "af5a5829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC Curve for the random forest classifier\n",
    "\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "RocCurveDisplay.from_predictions(y_test, y_pred_3)\n",
    "\n",
    "plt.title('ROC curve for Random forest Classifier for Predicting Fraud')\n",
    "\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d180f8a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC Curve for the KNN classifier\n",
    "\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "RocCurveDisplay.from_predictions(y_test, y_pred_4)\n",
    "\n",
    "plt.title('ROC curve for KNN Classifier for Predicting Fraud')\n",
    "\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c2d47869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC Curve for the Linear SVM classifier\n",
    "\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "RocCurveDisplay.from_predictions(y_test, y_pred_5)\n",
    "\n",
    "plt.title('ROC curve for Linear SVM Classifier for Predicting Fraud')\n",
    "\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "63ba8460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WNM9bnQB8Vl2fcz+V3bsxpG8lg/v1ZHC/SkZv1J8h/SsZ0jdMD+7Xk8F9KxnSrycb9q2ksnt52xV1ZAIxj5bjow6n5Ti2zjlXNjX1jS2f7le2fNpfFP8vWVXL0lV11Deu+awqwQZ9Khnct5LB/Sr5/LABLRKAIf16MqRfJYNjAtCvZ3ek9hiNtDQ6MoF4ADhH0hRgV+Azr39wzrWXpiZjWXU9i1fWhsg+UZSzuEWxTijnX1nbkHM/vXp0ixF7TzYZ1IvtNh3Y/HSfjOyH9OvJBn160L1i/Xl7oGQJhKTbgX2AIZLmEQZm7wFgZtcAjxDGQJ4DVAEnlyoszrn1Q1Vdw+qn+0QlbnI6LK9j6apamnIUSHcTbNg3Ru79Ktl++CCGND/ht4zwB/erpE9lp+vTtN2UshXTCUWWG/CNUh3fObfua2hsYmlVXXOZ/epWO6uLeRYninmq6xtz7qd/z+7NT/UjNuzDjiM2iJF95kk/kyD0ZFDvHnTrtu4W66xLum7S6Jxrd5kmmplIfnHiqX5JVmS/eGUty6rryTWoZfduSjzF92TLIX3XqLRNTvfqUVH+k+0CPIFwzhVU19DE0lUtK20Xr1xdlJOdANTlaaI5sHePEOn37clWG/Vj1y02bE4AWlTk9u3JgN7rduVtV+EJhHNdjJnxWXV9zoraJatqWbwiWZZfy/Ka3JW3ld27MTRRdr/N5/o3R/DJMvxQeVv+Jpqu7TyBcG49UFPfuEbFbctWO6sj/KWr6mjIUXub3URzzCYDQiTfoplm52mi6drOEwjn1kGNTcayqro1Ivc1KnLj27f5mmj2qawIZfV9WzbRTD7dZ5avb000Xdt5AuFcGZgZVXWNObtXWLyyZcudzItYuZpoVnQTG/atbK6o3WzDPolmmSGiH9K/Z3MuoCs30XRt53ePc2sp00Qzu8y+ZV87qyt1a+pzV94mm2iOHNKHnUdu0KLSdnBfb6LpOoYnEM5FZsaKTC+aK/O9gLU6Afi0Knf/Oj0q1PxUn2yimWmxk6zI3dCbaLp1mCcQbr1W29DI0kQvmcnIflHW9JKVddQ15n7KH9SnR3Nl7dYb92PwFoNXd7fQN9HtQr+eDOjllbdu/eAJhOtUmpqM5TX1iXL7Nd++TXa/sCJFE82N+vfi858bkIjks/vX8SaarmvyBMJ1uDWaaK7IVZEbPhdqorlhn9WR+9hEE83Vlbarn/L7Vlb4U75zRXgC4dpdY5PxadXqdvjZ3Stkv327qi53/zp9Kiuam2FuOqg3OwwfuMbTfWb5Bn0qqfDKW+falScQrqhME80WzTFbvH3bMgFYWlWXs3+d7Caam4/os0al7eB+3kTTuXWF/wK7qPrGJj5dtbovnRZ96+TofiFvE81e3ZuLcrKbaGZ3oTzQm2g616l4ArGeMDOW1zSsGbk3N81s+fS/rEATzeTbtVtu1G/N7hb69mRI/9BEs2d3b6Lp3PrKE4h1WG1D4xpj22b3rZPsXC3XEIjQsonmNp/rHyP8xNN9IgHwJprOuQxPIMqoqSn0ornGKFhZFbmZBCBfE82e3bs1D4CS3UQz+fQ/pF8lG/StpIf3r+OcWwueQLRRday8bdm9wppdJi9ZVcfSVXU0FmiimYncx206MFbktny6zyQAfbyJpnOuDDyByNLYZOHN22TknjXWbbJStypPE82+lRXNZfbDN+jDjiMGteh+IdnXjjfRdM6tizyBAD5cUsXZt73KwmU1BZtoJp/kN9+wzxqVts0JQN+e9K70ylvnXOdWMIGQNBw4Htgb2ASoBmYCDwOPmlnuto+dzBvzP2Pm/OUcvsMmjBrSt8WgKJlinQG9vImmc65ryZtASLoR2BR4CPg58AnQC9gaOBi4UNIFZvZcOQJaStX1oZjoewduw4jBfTo4NM45t24olIP4XzObmWP+TOAeSZXAiNIEq7yq60JrIS8Wcs651fK2f8wkDpImSlpjPTOrM7M5pQxcuWRyEJ5AOOfcamkayB8PvCvpF5I+X+oAdYRMS6TePnCLc841K5pAmNl/AjsC/wJulPQPSWdI6l/y0JVJdX0jld27eVNT55xLSPWKrZktB+4GpgDDgEnAa5K+WcKwlU11XSN9vHjJOedaKJpASDpC0r3A00APYBczOwTYATivxOEri+q6Ri9ecs65LGlelDsGuDK7OauZVUk6pTTBKq+q+kavoHbOuSxpipgWZicOkn4OYGZ/K0moyqzGcxDOObeGNAnEATnmHdLeAelIVV4H4Zxza8ibQEj6uqQ3gG0lvZ74ex94Pc3OJR0s6R1JcyRdkGP5QEkPSpoh6U1JJ6/9qay96vpGenkOwjnnWihUB3Eb8ChwOZCM3FeY2dJiO5ZUAVxFyIHMA16R9ICZzUqs9g1glpkdLmko8I6kW82srrUn0hbVdY1sPKBnOQ/pnHPrvEJFTGZmcwmR+IrEH5I2TLHvXYA5ZvZejPCnAEdmHwPorzC4QT9gKZB7lJwSqq73OgjnnMtWLAcxEXiVEJEn3yIzYIsi+94U+CgxPQ/YNWud3wMPAAuA/sBxuXqIlXQGcAbAiBHt3/1Ttbdics65NeRNIMxsYvw/ai33neu15OyRFg4CpgP7AVsCT0p6Pr6YlwzLtcC1ABMmTMg98HIbhPcgfGgM55xLSvOi3P2STpDU2n6w5wGbJaaHE3IKSScD91gwB3gf2LaVx2kTM4s5CB+32TnnktLEir8iDBj0lqQ7JR0jqVeK7V4BRksaFbsGP55QnJT0IfBlAEkbA9sA76UOfTuoa2yiscnoU+k5COecSyoaK5rZs8CzsVXSfsDpwA3AgCLbNUg6B3gcqABuMLM3JZ0Vl18DXArcFJvTCjjfzBa35YRaq6YuVHl4M1fnnGsp1WOzpN7A4cBxwE7AzWm2M7NHgEey5l2T+LwAODBtYEuhqj40mvIX5ZxzrqWiCYSkvxJaHz1GeK9h6voyFjWECmrwsSCccy5bmhzEjcBXzayx1IHpCM2DBXkOwjnnWsibQEjaz8yeBvoAR4Z32VYzs3tKHLayqKn3HIRzzuVSKAfxJcIYEIfnWGbAepFAZHIQXgfhnHMtFXpR7kfx4yVm9n5ymaS1fXlunVMdcxDeisk551pK8x7E3Tnm3dXeAeko1Z6DcM65nArVQWwLjAUGSvqPxKIBQJoX5TqFTA7CK6mdc66lQnUQ2xA66xtEy3qIFYSX5dYL3szVOedyK1QHcT9wv6TdzewfZQxTWXkOwjnncitUxPTfZvYL4KuSTshebmbnljRkZVJd10g3QWWFd9bnnHNJhYqY3or/p5UjIB0ljEfdnez3PJxzrqsrVMT0YPzf3O+SpG5Av+zxGjozH4/aOedySzMexG2SBkjqC8wijBv9vdIHrTyq6xq8iatzzuWQpuB9TMwxHEXomXUEcGIpA1VOPh61c87lliaB6CGpByGBuN/M6llz6NBOq6rOx6N2zrlc0iQQfwTmAn2B5yRtDqw3dRA1noNwzrmciiYQZvZbM9vUzA6NY0d/AOxbhrCVRWjF5AmEc85lSzNgUE/gaGBk1vqXlChMZVVd30gvTyCcc24NaQYMuh/4DHgVqC1tcMqvuq6RPl7E5Jxza0iTQAw3s4NLHpIOUl3vldTOOZdLmkrqv0varuQh6SDVdV5J7ZxzuaTJQewFTJb0PqGISYCZ2fYlDVkZNDYZtQ1NnoNwzrkc0iQQh5Q8FB3Ex6N2zrn80jRz/QDYDNgvfq5Ks11n4ONRO+dcfmn6YvoRcD7wP3FWD+CWUgaqXGp8PGrnnMsrTU5gEnAEsArAzBYA/UsZqHJZnYNIU9LmnHNdS5oEos7MjNj/UuzVdb2wejS59aLEzDnn2lWamPEOSX8EBkk6HXgKuK60wSqPqroGAHr38ByEc85lKxozmtkVkg4gdNC3NXCRmT1Z8pCVQY2PR+2cc3mlenQ2syclvQZ8EVha2iCVj7dics65/PIWMUl6SNK4+HkYMBM4BfiLpG+n2bmkgyW9I2mOpAvyrLOPpOmS3pT0bOtPYe1V1/l7EM45l0+hHMQoM5sZP58MPGlmJ0nqD7wI/LrQjiVVAFcBBwDzgFckPWBmsxLrDAKuBg42sw8lbbTWZ7IWqr2Zq3PO5VWokro+8fnLhOFGMbMVQFOKfe8CzDGz98ysDpgCHJm1zleBe8zsw7jvT9IGvD1UexGTc87lVSiB+EjSNyVNAnYCHgOQ1JvwslwxmwIfJabnxXlJWwMbSJoq6VVJJ+XakaQzJE2TNG3RokUpDp2O5yCccy6/QgnEqcBYYDJwnJkti/N3A25MsW/lmJc9lnV3YGfgMOAg4IeStl5jI7NrzWyCmU0YOnRoikOnU13XSM/u3ajoliuozjnXteWtg4jFPWflmP8M8EyKfc8j9OGUMRxYkGOdxWa2Clgl6TlgB2B2iv23mY8F4Zxz+RVqxXRtphVTjmV9JZ0i6WsF9v0KMFrSKEmVwPHAA1nr3A/sLam7pD7ArsBbrTuFtVflo8k551xehVoxXQ1cFAcLmgksAnoBo4EBwA3Arfk2NrMGSecAjwMVwA1m9qaks+Lya8zsLUmPAa8TKr6vT7ScKjkfj9o55/IrVMQ0HThWUj9gAjAMqAbeMrN30uzczB4htn5KzLsma/qXwC9bF+z2UV3X6C2YnHMujzRdbawEppY+KOXnw40651x+Xbob06r6Rnp7V9/OOZdTl04gauoa6d2jS18C55zLK3XsuD6NA5FRVd/ggwU551weaYYc3UPSLGLzU0k7SLq65CErg+q6Jn+L2jnn8kiTg7iS8JbzEgAzm0Ho9rvTq6n3SmrnnMsnVRGTmX2UNauxBGEpKzOjqq7Bm7k651weaQrgP5K0B2DxjehzKePbzqVS19hEk/locs45l0+aHMRZwDcIPbHOA8YDZ5cwTGXhgwU551xhaXIQ25hZiz6XJO1JGDSo06r28aidc66gNDmI36Wc16n4eNTOOVdY3hyEpN2BPYChkv5fYtEAQud7nVqmiMmbuTrnXG6FipgqgX5xnf6J+cuBY0oZqHLIFDF5DsI553Ir1Jvrs8Czkm4ysw/KGKay8Epq55wrLE0ldZWkXxKGH+2VmWlm+5UsVGWQqYPwSmrnnMstTSX1rcDbwCjgx8BcwmhxnVpNvecgnHOukDQJxGAz+xNQb2bPmtkpwG4lDlfJrW7F5J31OedcLmlix/r4f6Gkw4AFwPDSBak8qj0H4ZxzBaVJIC6TNBD4LuH9hwHAt0sZqHLIFDH1qvTxIJxzLpc0Q44+FD9+BuwLzW9Sd2pVdQ1UdBOVFZ5AOOdcLoVelKsAjiX0wfSYmc2UNBH4PtAb2LE8QSyN6romeveoQFJHB8U559ZJhXIQfwI2A14GfivpA2B34AIzu68MYSup6voGb+LqnHMFFEogJgDbm1mTpF7AYmArM/u4PEErreo6HyzIOecKKVQAX2dmTQBmVgPMXl8SBwjNXL2bDeecy69QDmJbSa/HzwK2jNMCzMy2L3noSqi6vtE76nPOuQIKJRCfL1soOkC15yCcc66gQp31rXcd9CVV1zcysHePjg6Gc86ts7rsSwDVdY3eisk55wrouglEvbdics65QlIlEJJ6S9qm1IEpJ2/F5JxzhRVNICQdDkwHHovT4yU9UOJwlVx1fSO9PIFwzrm80uQgLgZ2AZYBmNl0YGSanUs6WNI7kuZIuqDAel+Q1CipLEOZNjYZdQ1NXsTknHMFpEkgGszss9buOPbldBVwCDAGOEHSmDzr/Rx4vLXHWFs+HrVzzhWXJoGYKemrQIWk0ZJ+B/w9xXa7AHPM7D0zqwOmAEfmWO+bwN3AJ2kD3VY+HrVzzhWXJoH4JmE86lrgNkK3399Osd2mwEeJ6XlxXjNJmwKTgGsK7UjSGZKmSZq2aNGiFIcurDmB8NHknHMurzQx5DZmdiFwYSv3nasfbcua/jVwvpk1Fup228yuBa4FmDBhQvY+Ws1Hk3POueLSJBC/kjQMuBOYYmZvptz3PEJ34RnDCcOVJk0ApsTEYQhwqKSGUncnXlXXAHgdhHPOFVK0iMnM9gX2ARYB10p6Q9IPUuz7FWC0pFGSKoHjgRbNY81slJmNNLORwF3A2eUYayKTg/DO+pxzLr9UL8qZ2cdm9lvgLMI7ERel2KYBOIfQOukt4A4ze1PSWZLOWvsgt12mDsJzEM45l1/RIiZJnweOA44BlhBaI303zc7N7BHgkax5OSukzWxymn22h+Y6CE8gnHMurzR1EDcCtwMHmll2HUKnVOXNXJ1zrqiiCYSZ7VaOgJRTjecgnHOuqLwJhKQ7zOxYSW/Qsnlqpx9RrsrrIJxzrqhCOYhvxf8TyxGQcspUUvfq7gmEc87lk7cVk5ktjB/PNrMPkn/A2eUJXmnU1DfSs3s3unXL/3Kec851dWmauR6QY94h7R2QcvKxIJxzrrhCdRBfJ+QUtpD0emJRf+DFUgeslHw0OeecK65QHcRtwKPA5UByLIcVZra0pKEqMR+P2jnniiuUQJiZzZX0jewFkjbszIlEdb0nEM45V0yxHMRE4FVCM9dkja4BW5QwXCVVVddAnx7e1bdzzhWSN5Y0s4nx/6jyBac8quubGNi7R0cHwznn1mlFWzFJ2lNS3/j5PyX9StKI0getdKrrGujjldTOOVdQmmaufwCqJO0A/DfwAfCXkoaqxLwOwjnnikuTQDSYmRHGk/6Nmf2G0NS10/JWTM45V1yamtoVkv4HOBHYW1IF0KkL8Kvr/D0I55wrJk0O4jigFjjFzD4GNgV+WdJQlZCZUVXvb1I751wxaYYc/Ri4FRgoaSJQY2Z/LnnISqS2oQkzH27UOeeKSdOK6VjgZeArwLHAPyUdU+qAlUrzWBCeQDjnXEFp6iAuBL5gZp8ASBoKPAXcVcqAlYqPBeGcc+mkqYPolkkcoiUpt1sn+XjUzjmXTpocxGOSHieMSw2h0vqR0gWptKp9PGrnnEslzZjU35P0H8BehP6YrjWze0seshLxHIRzzqVTaDyI0cAVwJbAG8B5Zja/XAErFa+DcM65dArVJdwAPAQcTejR9XdlCVGJNY9H7UVMzjlXUKEipv5mdl38/I6k18oRoFKrrm8AoE+ld/ftnHOFFIole0nakdXjQPROTptZp0wwquuaAK+kds65YgolEAuBXyWmP05MG7BfqQJVSlV1IQfhldTOOVdYoQGD9i1nQMrF36R2zrl0Ou0Lb2urqq6Rim6iR4WKr+ycc11Yl0sgqusb6dOjAskTCOecK6SkCYSkgyW9I2mOpAtyLP+apNfj39/jqHUlVVPfSC+vf3DOuaLS9OaqOBb1RXF6hKRdUmxXAVwFHAKMAU6QNCZrtfeBL5nZ9sClwLWtPYHWqqrzsSCccy6NNDmIq4HdgRPi9ApCxF/MLsAcM3vPzOqAKYRhS5uZ2d/N7NM4+RIwPFWo28BHk3POuXTSJBC7mtk3gBqAGKFXpthuU+CjxPS8OC+fU4FHcy2QdIakaZKmLVq0KMWh86uu9/GonXMujTQJRH0sLjJoHg+iKcV2uWqBLeeK0r6EBOL8XMvN7Fozm2BmE4YOHZri0Pl5DsI559JJk0D8FrgX2EjST4AXgJ+m2G4esFliejiwIHslSdsD1wNHmtmSFPttE6+DcM65dNJ0932rpFeBLxNyBUeZ2Vsp9v0KMFrSKGA+cDzw1eQKkkYA9wAnmtns1gZ+bdTUN3pHfc45l0LRBCJG4lXAg8l5ZvZhoe3MrEHSOcDjQAVwg5m9KemsuPwa4CJgMHB1fC+hwcwmrO3JpOE5COecSydNl6YPE+oOBPQCRgHvAGOLbWhmj5A1+lxMGDKfTwNOa0V426y63usgnHMujTRFTNslpyXtBJxZshCVWHVdI729q2/nnCuq1W9Sx26+v1CCsJRcQ2MTdY1NnoNwzrkU0tRB/L/EZDdgJ6BtLyN0kNXjUXe5Lqicc67V0pS19E98biDUSdxdmuCU1uoEwouYnHOumIIxZXxBrp+Zfa9M4SmpGh9NzjnnUstb1iKpu5k1EoqU1gtVzeNRewLhnHPFFMpBvExIHKZLegC4E1iVWWhm95Q4bO2uus5Hk3POubTSFMZvCCwhjEGdeR/CCG9AdyrNCYTnIJxzrqhCCcRGsQXTTFYnDBk5O91b11X7eNTOOZdaoQSiAuhHK3plXddVxRyE10E451xxhRKIhWZ2SdlCUgaZHIR31uecc8UVemMsV86hU6v2HIRzzqVWKIH4ctlCUSarX5TzBMI554rJm0CY2dJyBqQcMnUQvbp7AuGcc8V0qU6JwmBB3ejWbb0rPXPOuXbXpRKIqroGb+LqnHMpdakEorquiT7eUZ9zzqXSpRKITBGTc8654rpUbFlV1+A5COecS6lLJRA+HrVzzqXXtRKIukZ/B8I551LqUuUt1fWNDPMchFtH1NfXM2/ePGpqajo6KG490qtXL4YPH06PHj3avK8ulUBU1TV6NxtunTFv3jz69+/PyJEjkfzdHNd2ZsaSJUuYN28eo0aNavP+ulQRU019I708gXDriJqaGgYPHuyJg2s3khg8eHC75Uq7VAJRVddIHy9icusQTxxce2vPe6rLJBBmFloxeQ7COedS6TIJRG1DE2bek6tzSR9//DHHH388W265JWPGjOHQQw9l9uzZzJ07l3HjxrXbcS666CKeeuopAJ5//nnGjh3L+PHjmT9/Psccc0yb9m1m7Lfffixfvrx53r333osk3n777eZ5U6dOZeLEiS22nTx5MnfddRcQGg1ccMEFjB49mnHjxrHLLrvw6KOPtilsAJdffjlbbbUV22yzDY8//njOdWbMmMHuu+/Odtttx+GHH958Lk8++SQ777wz2223HTvvvDNPP/108zb7778/n376aZvDV0iXSSCax6P2IibngBCxTpo0iX322Yd//etfzJo1i5/+9Kf8+9//bvdjXXLJJey///4A3HrrrZx33nlMnz6dTTfdtDmCTqOxsXGNeY888gg77LADAwYMaJ53++23s9deezFlypTU+/7hD3/IwoULmTlzJjNnzuTBBx9kxYoVqbfPZdasWUyZMoU333yTxx57jLPPPjvnOZx22mn87Gc/44033mDSpEn88pe/BGDIkCE8+OCDvPHGG9x8882ceOKJzduceOKJXH311W0KXzFdphVTlY9H7dZhP37wTWYtWF58xVYYs8kAfnT42LzLn3nmGXr06MFZZ53VPG/8+PEAzJ07t3ne3LlzOfHEE1m1ahUAv//979ljjz1YuHAhxx13HMuXL6ehoYE//OEP7LHHHpx66qlMmzYNSZxyyil85zvfYfLkyUycOJFly5Zxxx138Pjjj/PUU0/xk5/8hIkTJzJz5kwaGxu54IILmDp1KrW1tXzjG9/gzDPPZOrUqfz4xz9m2LBhTJ8+nVmzZrU4j1tvvZUzzjijeXrlypW8+OKLPPPMMxxxxBFcfPHFRa9VVVUV1113He+//z49e/YEYOONN+bYY48tum0h999/P8cffzw9e/Zk1KhRbLXVVrz88svsvvvuLdZ75513+OIXvwjAAQccwEEHHcSll17Kjjvu2LzO2LFjqampoba2lp49e3LEEUew9957c+GFF7YpjIV0mQSiOQfhRUzOATBz5kx23nnnoutttNFGPPnkk/Tq1Yt3332XE044gWnTpnHbbbdx0EEHceGFF9LY2EhVVRXTp09n/vz5zJw5E4Bly5a12Ndpp53GCy+8wMSJEznmmGNaJER/+tOfGDhwIK+88gq1tbXsueeeHHjggQC8/PLLzJw5M2fTzRdffJE//vGPzdP33XcfBx98MFtvvTUbbrghr732GjvttFPBc5wzZw4jRoxokQvJ5zvf+Q7PPPPMGvOPP/54Lrjgghbz5s+fz2677dY8PXz4cObPn7/GtuPGjeOBBx7gyCOP5M477+Sjjz5aY527776bHXfcsTkB22CDDaitrWXJkiUMHjy4aLjXRpdJIGo8B+HWYYWe9DtafX0955xzDtOnT6eiooLZs2cD8IUvfIFTTjmF+vp6jjrqKMaPH88WW2zBe++9xze/+U0OO+yw5gg+jSeeeILXX3+9ucjps88+491336WyspJddtklb7v+pUuX0r9//+bp22+/nW9/+9tAiLRvv/12dtppp7yte1rb6ufKK69Mva6ZpTreDTfcwLnnnssll1zCEUccQWVlZYvlb775Jueffz5PPPFEi/kbbbQRCxYs6JwJhKSDgd8AFcD1ZvazrOWKyw8FqoDJZvZaKcJS1TwedZdJE50raOzYsanK/6+88ko23nhjZsyYQVNTE7169QLgi1/8Is899xwPP/wwJ554It/73vc46aSTmDFjBo8//jhXXXUVd9xxBzfccEOq8JgZv/vd7zjooINazJ86dSp9+/bNu1337t1pamqiW7duLFmyhKeffpqZM2ciicbGRiTxi1/8gsGDB69Rqbt06VKGDBnCVlttxYcffsiKFStaJDa5tCYHMXz48Ba5gXnz5rHJJpusse22227bHPnPnj2bhx9+uMU2kyZN4s9//jNbbrlli+1qamro3bt3wfC2RckqqSVVAFcBhwBjgBMkjcla7RBgdPw7A/hDqcKzejzqLlMv71xB++23H7W1tVx33XXN81555RWeffbZFut99tlnDBs2jG7duvGXv/yluZL1gw8+YKONNuL000/n1FNP5bXXXmPx4sU0NTVx9NFHc+mll/Laa+mf9w466CD+8Ic/UF9fD4SIMlPvUcg222zDe++9B8Bdd93FSSedxAcffMDcuXP56KOPGDVqFC+88AKjR49mwYIFvPXWW83hnzFjBuPHj6dPnz6ceuqpnHvuudTV1QGwcOFCbrnlljWOd+WVVzJ9+vQ1/rITB4AjjjiCKVOmUFtby/vvv8+7777LLrvsssZ6n3zyCQBNTU1cdtllzfVCy5Yt47DDDuPyyy9nzz33bLGNmfHxxx8zcuTIotdobZUyttwFmGNm75lZHTAFODJrnSOBP1vwEjBI0rBSBKa6rgGA3j08B+EchKKOe++9lyeffJItt9ySsWPHcvHFF6/xhHv22Wdz8803s9tuuzF79uzmp/mpU6cyfvx4dtxxR+6++26+9a1vMX/+fPbZZx/Gjx/P5MmTufzyy1OH57TTTmPMmDHstNNOjBs3jjPPPJOGhoai2x122GFMnToVCMVLkyZNarH86KOP5rbbbqNnz57ccsstnHzyyYwfP55jjjmG66+/noEDBwJw2WWXMXToUMaMGcO4ceM46qijGDp0aOrw5zJ27FiOPfZYxowZw8EHH8xVV11FRUVF8/lOmzatOdxbb7012267LZtssgknn3wyEBoEzJkzh0svvZTx48czfvz45sTk1VdfZbfddqN79xLGaWZWkj/gGEKxUmb6ROD3Wes8BOyVmP4bMCHHvs4ApgHTRowYYWtj2twl9vVbptnCZdVrtb1z7W3WrFkdHYT1woIFC2z//ffv6GCU3bnnnmtPPfVUzmW57i1gmrUyHi/l43Sump/sGps062Bm1wLXAkyYMGHNWp8Udt58Q3befMO12dQ5tw4bNmwYp59+OsuXL0/VCml9MW7cOL785S+X9BilTCDmAZslpocDC9ZiHeecK6it7yt0RqeffnrJj1HKOohXgNGSRkmqBI4HHsha5wHgJAW7AZ+Z2cIShsm5dYrlaAbpXFu05z1VshyEmTVIOgd4nNDM9QYze1PSWXH5NcAjhCaucwjNXE8uVXicW9f06tWr+SUn79XVtQeL40FkmiK3lTrbE8yECRMsU/PvXGfmI8q5Usg3opykV81sQmv25W0+nesgPXr0aJdRv5wrFX9rzDnnXE6eQDjnnMvJEwjnnHM5dbpKakmLgA/WcvMhwOJ2DE5n4OfcNfg5dw1tOefNzaxVfYd0ugSiLSRNa20tfmfn59w1+Dl3DeU+Zy9ics45l5MnEM4553LqagnEtR0dgA7g59w1+Dl3DWU95y5VB+Gccy69rpaDcM45l5InEM4553JaLxMISQdLekfSHElrDBQbuxf/bVz+uqSdOiKc7SnFOX8tnuvrkv4uaYeOCGd7KnbOifW+IKlR0jHlDF8ppDlnSftImi7pTUnP5lqnM0lxbw+U9KCkGfGcO3Wv0JJukPSJpJl5lpcv/mrtEHTr+h+ha/F/AVsAlcAMYEzWOocCjxJGtNsN+GdHh7sM57wHsEH8fEhXOOfEek8TupY/pqPDXYbveRAwCxgRpzfq6HCX4Zy/D/w8fh4KLAUqOzrsbTjnLwI7ATPzLC9b/LU+5iB2AeaY2XtmVgdMAY7MWudI4M8WvAQMkjSs3AFtR0XP2cz+bmafxsmXCKP3dWZpvmeAbwJ3A5+UM3AlkuacvwrcY2YfAphZZz/vNOdsQH+FQTX6ERKIhvIGs/2Y2XOEc8inbPHX+phAbAp8lJieF+e1dp3OpLXncyrhCaQzK3rOkjYFJgHXlDFcpZTme94a2EDSVEmvSjqpbKErjTTn/Hvg84Thit8AvmVmTeUJXocoW/y1Po4HkWtoruy2vGnW6UxSn4+kfQkJxF4lDVHppTnnXwPnm1njejJiW5pz7g7sDHwZ6A38Q9JLZja71IErkTTnfBAwHdgP2BJ4UtLzZra8xGHrKGWLv9bHBGIesFliejjhyaK163Qmqc5H0vbA9cAhZrakTGErlTTnPAGYEhOHIcChkhrM7L6yhLD9pb23F5vZKmCVpOeAHYDOmkCkOeeTgZ9ZKKCfI+l9YFvg5fIEsezKFn+tj0VMrwCjJY2SVAkcDzyQtc4DwEmxNcBuwGdmtrDcAW1HRc9Z0gjgHuDETvw0mVT0nM1slJmNNLORwF3A2Z04cYB09/b9wN6SukvqA+wKvFXmcLanNOf8ISHHhKSNgW2A98oayvIqW/y13uUgzKxB0jnA44QWEDeY2ZuSzorLryG0aDkUmANUEZ5AOq2U53wRMBi4Oj5RN1gn7gkz5TmvV9Kcs5m9Jekx4HWgCbjezHI2l+wMUn7PlwI3SXqDUPxyvpl12m7AJd0O7AMMkTQP+BHQA8off3lXG84553JaH4uYnHPOtQNPIJxzzuXkCYRzzrmcPIFwzjmXkycQzjnncvIEoguLPZxOT/yNLLDuynY43k2S3o/Hek3S7muxj+sljYmfv5+17O9tDWPcT+a6zIy9hA4qsv54SYeuxXGGSXoofh4s6RlJKyX9fi3DfWHszfT1GP5d12Y/Bfb/SOZaSDpX0luSbpV0RL6eZRPb/j3+HynpqymONVHSj9sl4G6teTPXLkzSSjPr197rFtjHTcBDZnaXpAOBK8xs+zbsr81hKrZfSTcDs83sJwXWnwxMMLNzWnmcXwIvmNn9kvoCOwLjgHFrsa/dgV8B+5hZraQhhB5NS/KGraS3CW/kv9/K7fYBzjOziUXWE/AasKeZVa1tOF3beA7CNZPUT9Lf4tP9G5LW6B01PvU+l3jC3jvOP1DSP+K2d0oqFnE/B2wVt/1/cV8zJX07zusr6WGFPv5nSjouzp8qaYKknwG9YzhujctWxv9/TT7Rx5zL0ZIqJP1S0ivxKfvMFJflH8SO0CTtojCWxv/F/9sovN17CXBcDMtxMew3xOP8X67rGB0NPAZgZqvM7AWgJkWYchlG6GKjNu5vcSZxkDRX0s8lvRz/Mtd9qKS7YzhfkbRnnN9P0o3xHnhd0tGJ/QyRdA2h++0HJH1H0uRMrkfSxpLujd/bDEl7xPmZHOjPCG96T4/bPi9pfOYkJL0oafvYbcZUoGBC4kqsVP2I+9+6/wc0Ejo5mw7cS3izfkBcNoTwpmYml7ky/v8ucGH8XAH0j+s+B/SN888HLspxvJuIYzIAXwH+SehY7g2gL6Gr5jcJT9JHA9clth0Y/08lPK03hymxTiaMk4Cb4+dKQs+XvYEzgB/E+T2BacCoHOFcmTi/O4GD4/QAoHv8vD9wd/w8Gfh9YvufAv8ZPw8i9IPUN+sYo4BXcxy7xb5a8V32i9/jbOBq4EuJZXMT39lJhFwcwG3AXvHzCOCt+PnnwK8T22+Q2M+QHJ+bwwz8Ffh24vplvrfMNd0nc/w4/V+ZYxF6op2WWPY14Hcd/Tvpyn/rXVcbrlWqzWx8ZkJSD+Cnkr5I6KZhU2Bj4OPENq8AN8R17zOz6ZK+BIwBXgwlA1QSnrxz+aWkHwCLCL3Kfhm410Lncki6B9ib8GR9haSfEyKU51txXo8Cv5XUEzgYeM7MqmOx1vZaPbLcQGA0kF1M0lvSdGAk8CrwZGL9myWNJvSe2SPP8Q8EjpB0XpzuRYyAE+sMi9egXZjZSkk7E67dvsBfJV1gZjfFVW5P/L8yft4fGKPVPd0OkNQ/zj8+se/MOCJp7EdIhDCzRuCzIuvfCfxQ0veAUwgPERmfAJu04tiunXkC4ZK+RhiRa2czq5c0lxC5NTOz52ICchjwl1iO/inwpJmdkOIY3zOzuzITkvbPtZKZzY4R3qHA5ZKeMLNL0pyEmdVImkroBvo4VkeOAr5pZo8X2UW1mY2XNBB4CPgG8FtCnz/PmNkkhQr9qXm2F3C0mb1T6BhkXdtiFCqd/xgnLzKz7M4JG2OYpir0S/RfrI5wk5WNmc/dgN3NrDrrOKJM3d+bWZWkJwmD4BxL6IE3oxfhOrkO4nUQLmkg8ElMHPYFNs9eQdLmcZ3rgD8RhkZ8CdgzUbbdR9LWKY/5HHBU3KYvoXjoeUmbAFVmdgtwRTxOtvqYk8llCqETs70JHb0R/389s42kreMxczKzz4BzgfPiNgOB+XHx5MSqKwhFbRmPA9+MES2Sdsyx+9mEHEpqZvZPMxsf/7J7690m5mwyxgMfJKaPS/zP5O6eAJorwxN1AdnzN2hFMP8GfD1uVyFpQNby7GsFoQv63wKvmFlyJLWtgU7b0eD6wBMIl3QrMEHSNEJu4u0c6+wDTJf0f4R6gt+Y2SJChHm7pNcJCca2aQ5oZq8RnnJfJtRJXG9m/wdsB7wci3ouBC7Lsfm1wOuZSuosTxDG9n3KwlCVECKiWcBrCgPC/5EiuegYlhmEIpdfEHIzLxLK1zOeIRTVTFeoTL+UUPz0ejzOpTn2uwr4VyZRhVAJTGiJNFnSPMXmvCn1IxR/zYrfwRjg4sTynpL+CXwL+E6cdy7h+35d0izgrDj/MsKodDMlzSAUWaX1LWDfmIN5FRibtfx1oCFWYH8HwMxeBZYDN2atuy/wcCuO7dqZN3N1roNImkQozvtBiY8zl1Cxv052gR1zi1OBbS0OFaowrsNtZvbljgxbV+c5COc6iJndS2gN1GUpjJn9T0Irq+Q40iMILeZcB/IchHPOuZw8B+Gccy4nTyCcc87l5AmEc865nDyBcM45l5MnEM4553L6/1MFkzyWMaE3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC Curve for the gaussian naive bayes classifier\n",
    "\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "RocCurveDisplay.from_predictions(y_test, y_pred_6)\n",
    "\n",
    "plt.title('ROC curve for Gaussian Naive Bayes Classifier for Predicting Fraud')\n",
    "\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9b51bf",
   "metadata": {},
   "source": [
    "Interpretation :\n",
    "1. ROC AUC is a single number summary of classifier performance. The higher the value, the better the classifier.\n",
    "2. OC AUC of our model approaches towards 1. So, we can conclude that our classifiers does a good job in predicting whether a transcation is genuine or fraudulent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac4c4413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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