Fraud_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c28e05c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ignore the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30129def",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, classification_report, plot_confusion_matrix, accuracy_score, auc,ConfusionMatrixDisplay, RocCurveDisplay\n",
    "from sklearn.metrics import roc_curve, auc,recall_score,precision_score, f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06ce65c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(y_test, y_prediction):\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_prediction)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.figure()\n",
    "\n",
    "    lw = 2\n",
    "    plt.plot(fpr, tpr, color='darkorange',\n",
    "             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([-0.02, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC curve')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b9415db",
   "metadata": {},
   "source": [
    "# Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dab328b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Data\n",
    "data1=pd.read_csv(\"transactions_obf.csv\")\n",
    "data2=pd.read_csv(\"labels_obf.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cac7fdce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transactionTime          0\n",
       "eventId                  0\n",
       "accountNumber            0\n",
       "merchantId               0\n",
       "mcc                      0\n",
       "merchantCountry          0\n",
       "merchantZip          23005\n",
       "posEntryMode             0\n",
       "transactionAmount        0\n",
       "availableCash            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef4c8baa",
   "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>transactionTime</th>\n",
       "      <th>eventId</th>\n",
       "      <th>accountNumber</th>\n",
       "      <th>merchantId</th>\n",
       "      <th>mcc</th>\n",
       "      <th>merchantCountry</th>\n",
       "      <th>merchantZip</th>\n",
       "      <th>posEntryMode</th>\n",
       "      <th>transactionAmount</th>\n",
       "      <th>availableCash</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01T00:00:00Z</td>\n",
       "      <td>18688431A1</td>\n",
       "      <td>94f9b4e7</td>\n",
       "      <td>b76d06</td>\n",
       "      <td>5968</td>\n",
       "      <td>826</td>\n",
       "      <td>CR0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.72</td>\n",
       "      <td>7500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01T00:49:03Z</td>\n",
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       "      <td>5499</td>\n",
       "      <td>826</td>\n",
       "      <td>DE14</td>\n",
       "      <td>81</td>\n",
       "      <td>21.19</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01T00:43:17Z</td>\n",
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       "      <td>c0ffab1b</td>\n",
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       "      <td>442</td>\n",
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       "      <td>5.04</td>\n",
       "      <td>9500</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01T00:15:07Z</td>\n",
       "      <td>11162049A1</td>\n",
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       "      <td>5499</td>\n",
       "      <td>826</td>\n",
       "      <td>NR1</td>\n",
       "      <td>81</td>\n",
       "      <td>21.00</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01T00:37:09Z</td>\n",
       "      <td>17067235A1</td>\n",
       "      <td>3130363b</td>\n",
       "      <td>12ca76</td>\n",
       "      <td>5411</td>\n",
       "      <td>826</td>\n",
       "      <td>M50</td>\n",
       "      <td>81</td>\n",
       "      <td>47.00</td>\n",
       "      <td>10500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        transactionTime     eventId accountNumber merchantId   mcc  \\\n",
       "0  2017-01-01T00:00:00Z  18688431A1      94f9b4e7     b76d06  5968   \n",
       "1  2017-01-01T00:49:03Z   2164986A1      648e19cf     718cc6  5499   \n",
       "2  2017-01-01T00:43:17Z  31294145A1      c0ffab1b     94cafc  5735   \n",
       "3  2017-01-01T00:15:07Z  11162049A1      038099dd     7d5803  5499   \n",
       "4  2017-01-01T00:37:09Z  17067235A1      3130363b     12ca76  5411   \n",
       "\n",
       "   merchantCountry merchantZip  posEntryMode  transactionAmount  availableCash  \n",
       "0              826         CR0             1              10.72           7500  \n",
       "1              826        DE14            81              21.19           4500  \n",
       "2              442         NaN            81               5.04           9500  \n",
       "3              826         NR1            81              21.00           7500  \n",
       "4              826         M50            81              47.00          10500  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26f0c060",
   "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>reportedTime</th>\n",
       "      <th>eventId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-04T00:00:00Z</td>\n",
       "      <td>101536329A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-04T00:00:00Z</td>\n",
       "      <td>101536425A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-04T00:00:00Z</td>\n",
       "      <td>101536473A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04T00:00:00Z</td>\n",
       "      <td>101536617A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-04T00:00:00Z</td>\n",
       "      <td>101536761A1</td>\n",
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      "text/plain": [
       "           reportedTime      eventId\n",
       "0  2017-01-04T00:00:00Z  101536329A1\n",
       "1  2017-01-04T00:00:00Z  101536425A1\n",
       "2  2017-01-04T00:00:00Z  101536473A1\n",
       "3  2017-01-04T00:00:00Z  101536617A1\n",
       "4  2017-01-04T00:00:00Z  101536761A1"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d2aedad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118621, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b31ae32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(875, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ad26b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a01961b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging two dataframes to flag fraud transcations \n",
    "values = set(data2['eventId'])\n",
    "\n",
    "data['fraudEvent'] = data['eventId'].isin(values).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a31667a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    117746\n",
       "1       875\n",
       "Name: fraudEvent, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fraudEvent'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f723d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118621, 11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Dimension\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "664aa4cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['transactionTime', 'eventId', 'accountNumber', 'merchantId', 'mcc',\n",
       "       'merchantCountry', 'merchantZip', 'posEntryMode', 'transactionAmount',\n",
       "       'availableCash', 'fraudEvent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b172ee",
   "metadata": {},
   "source": [
    "## Data cleaning and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed9b6af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for duplicate rows\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "482ba828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     5\n",
       "int64      4\n",
       "float64    1\n",
       "int32      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ec7f883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transactionTime      118105\n",
       "eventId              118621\n",
       "accountNumber           766\n",
       "merchantId            33327\n",
       "mcc                     361\n",
       "merchantCountry          82\n",
       "merchantZip            3260\n",
       "posEntryMode             10\n",
       "transactionAmount     16431\n",
       "availableCash            19\n",
       "fraudEvent                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "523c3fae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['merchantZip'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check out the object columns, number of uniques and number of NaNs\n",
    "data.columns[data.isna().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dc910fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN      19.393699\n",
       "0        12.201887\n",
       "E12       0.964416\n",
       "SL4       0.549650\n",
       "LS11      0.515086\n",
       "           ...    \n",
       "LL59      0.000843\n",
       "E79HL     0.000843\n",
       "B54US     0.000843\n",
       "LE35      0.000843\n",
       "W84PT     0.000843\n",
       "Name: merchantZip, Length: 3261, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['merchantZip'].value_counts(dropna=False, normalize=True)*100\n",
    "#Drop this column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "180ea3de",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('merchantZip', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f37fca4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-01-01 00:00:00+00:00\n",
       "1   2017-01-01 00:49:03+00:00\n",
       "2   2017-01-01 00:43:17+00:00\n",
       "3   2017-01-01 00:15:07+00:00\n",
       "4   2017-01-01 00:37:09+00:00\n",
       "Name: transactionTime, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['transactionTime']=pd.to_datetime(data['transactionTime'])\n",
    "data['transactionTime'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f77eb05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "          ..\n",
       "118616    23\n",
       "118617    23\n",
       "118618    23\n",
       "118619     0\n",
       "118620    23\n",
       "Name: time, Length: 118621, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df['transactionTime']\n",
    "data['year']=pd.DatetimeIndex(data['transactionTime']).year\n",
    "data['day']=pd.DatetimeIndex(data['transactionTime']).day\n",
    "data['time']=pd.DatetimeIndex(data['transactionTime']).hour\n",
    "\n",
    "data['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8d24d30",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventId</th>\n",
       "      <th>accountNumber</th>\n",
       "      <th>merchantId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18688431A1</td>\n",
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       "      <td>b76d06</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2164986A1</td>\n",
       "      <td>648e19cf</td>\n",
       "      <td>718cc6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31294145A1</td>\n",
       "      <td>c0ffab1b</td>\n",
       "      <td>94cafc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11162049A1</td>\n",
       "      <td>038099dd</td>\n",
       "      <td>7d5803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17067235A1</td>\n",
       "      <td>3130363b</td>\n",
       "      <td>12ca76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      eventId accountNumber merchantId\n",
       "0  18688431A1      94f9b4e7     b76d06\n",
       "1   2164986A1      648e19cf     718cc6\n",
       "2  31294145A1      c0ffab1b     94cafc\n",
       "3  11162049A1      038099dd     7d5803\n",
       "4  17067235A1      3130363b     12ca76"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat=data.select_dtypes(include=['object'])\n",
    "df_cat.head()\n",
    "#Non of categorical columns are useful- no need to encode "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7fb0c83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "05f1d72f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>range</th>\n",
       "      <th>variation coefficient</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mcc</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>5834.407272</td>\n",
       "      <td>976.183515</td>\n",
       "      <td>742.00</td>\n",
       "      <td>5411.00</td>\n",
       "      <td>5691.00</td>\n",
       "      <td>5967.0</td>\n",
       "      <td>9405.0</td>\n",
       "      <td>8663.00</td>\n",
       "      <td>0.167315</td>\n",
       "      <td>0.800649</td>\n",
       "      <td>4.969162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchantCountry</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>759.124388</td>\n",
       "      <td>154.070084</td>\n",
       "      <td>31.00</td>\n",
       "      <td>826.00</td>\n",
       "      <td>826.00</td>\n",
       "      <td>826.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>809.00</td>\n",
       "      <td>0.202958</td>\n",
       "      <td>-2.020270</td>\n",
       "      <td>2.687052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posEntryMode</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>28.552651</td>\n",
       "      <td>35.647616</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>1.248487</td>\n",
       "      <td>0.804020</td>\n",
       "      <td>-1.344966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactionAmount</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>53.674774</td>\n",
       "      <td>183.665315</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>8.03</td>\n",
       "      <td>20.25</td>\n",
       "      <td>49.0</td>\n",
       "      <td>13348.0</td>\n",
       "      <td>13348.15</td>\n",
       "      <td>3.421818</td>\n",
       "      <td>28.405271</td>\n",
       "      <td>1503.445956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>availableCash</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>6625.508974</td>\n",
       "      <td>3410.289486</td>\n",
       "      <td>500.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>18500.0</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>0.514721</td>\n",
       "      <td>-0.182784</td>\n",
       "      <td>-0.541319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>12.026960</td>\n",
       "      <td>6.832199</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0.568074</td>\n",
       "      <td>-0.120168</td>\n",
       "      <td>-1.151825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>15.788790</td>\n",
       "      <td>8.717295</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.552119</td>\n",
       "      <td>0.004820</td>\n",
       "      <td>-1.173557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>2017.071918</td>\n",
       "      <td>0.258353</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>3.313981</td>\n",
       "      <td>8.982621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count         mean          std      min      25%  \\\n",
       "mcc                118621.0  5834.407272   976.183515   742.00  5411.00   \n",
       "merchantCountry    118621.0   759.124388   154.070084    31.00   826.00   \n",
       "posEntryMode       118621.0    28.552651    35.647616     0.00     5.00   \n",
       "transactionAmount  118621.0    53.674774   183.665315    -0.15     8.03   \n",
       "availableCash      118621.0  6625.508974  3410.289486   500.00  4500.00   \n",
       "time               118621.0    12.026960     6.832199     0.00     6.00   \n",
       "day                118621.0    15.788790     8.717295     1.00     8.00   \n",
       "year               118621.0  2017.071918     0.258353  2017.00  2017.00   \n",
       "\n",
       "                       50%     75%      max     range  variation coefficient  \\\n",
       "mcc                5691.00  5967.0   9405.0   8663.00               0.167315   \n",
       "merchantCountry     826.00   826.0    840.0    809.00               0.202958   \n",
       "posEntryMode          5.00    81.0     91.0     91.00               1.248487   \n",
       "transactionAmount    20.25    49.0  13348.0  13348.15               3.421818   \n",
       "availableCash      7500.00  8500.0  18500.0  18000.00               0.514721   \n",
       "time                 12.00    18.0     23.0     23.00               0.568074   \n",
       "day                  16.00    23.0     31.0     30.00               0.552119   \n",
       "year               2017.00  2017.0   2018.0      1.00               0.000128   \n",
       "\n",
       "                        skew     kurtosis  \n",
       "mcc                 0.800649     4.969162  \n",
       "merchantCountry    -2.020270     2.687052  \n",
       "posEntryMode        0.804020    -1.344966  \n",
       "transactionAmount  28.405271  1503.445956  \n",
       "availableCash      -0.182784    -0.541319  \n",
       "time               -0.120168    -1.151825  \n",
       "day                 0.004820    -1.173557  \n",
       "year                3.313981     8.982621  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#num_attributes=df.select_dtypes(exclude=['object'])\n",
    "num_attributes=data[['mcc','merchantCountry', 'posEntryMode', 'transactionAmount',\n",
    "       'availableCash','time','day', 'year']]\n",
    "\n",
    "describe = num_attributes.describe().T\n",
    "describe['range'] = (num_attributes.max() - num_attributes.min()).tolist()\n",
    "describe['variation coefficient'] = (num_attributes.std() / num_attributes.mean()).tolist()\n",
    "describe['skew'] = num_attributes.skew().tolist()\n",
    "describe['kurtosis'] = num_attributes.kurtosis().tolist()\n",
    "\n",
    "\n",
    "describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e1933cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "df = data.copy()\n",
    "df['transactionAmount'] = RobustScaler().fit_transform(df['transactionAmount'].to_numpy().reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "017ea1d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>range</th>\n",
       "      <th>variation coefficient</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mcc</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>5834.407272</td>\n",
       "      <td>976.183515</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>5411.000000</td>\n",
       "      <td>5691.0</td>\n",
       "      <td>5967.000000</td>\n",
       "      <td>9405.000000</td>\n",
       "      <td>8663.000000</td>\n",
       "      <td>0.167315</td>\n",
       "      <td>0.800649</td>\n",
       "      <td>4.969162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchantCountry</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>759.124388</td>\n",
       "      <td>154.070084</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>826.000000</td>\n",
       "      <td>826.0</td>\n",
       "      <td>826.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>809.000000</td>\n",
       "      <td>0.202958</td>\n",
       "      <td>-2.020270</td>\n",
       "      <td>2.687052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posEntryMode</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>28.552651</td>\n",
       "      <td>35.647616</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1.248487</td>\n",
       "      <td>0.804020</td>\n",
       "      <td>-1.344966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactionAmount</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>0.815835</td>\n",
       "      <td>4.482922</td>\n",
       "      <td>-0.497925</td>\n",
       "      <td>-0.298267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.701733</td>\n",
       "      <td>325.305101</td>\n",
       "      <td>325.803027</td>\n",
       "      <td>5.494886</td>\n",
       "      <td>28.405271</td>\n",
       "      <td>1503.445956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>availableCash</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>6625.508974</td>\n",
       "      <td>3410.289486</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>18500.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>0.514721</td>\n",
       "      <td>-0.182784</td>\n",
       "      <td>-0.541319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>12.026960</td>\n",
       "      <td>6.832199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.568074</td>\n",
       "      <td>-0.120168</td>\n",
       "      <td>-1.151825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>15.788790</td>\n",
       "      <td>8.717295</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.552119</td>\n",
       "      <td>0.004820</td>\n",
       "      <td>-1.173557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>118621.0</td>\n",
       "      <td>2017.071918</td>\n",
       "      <td>0.258353</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>3.313981</td>\n",
       "      <td>8.982621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count         mean          std          min  \\\n",
       "mcc                118621.0  5834.407272   976.183515   742.000000   \n",
       "merchantCountry    118621.0   759.124388   154.070084    31.000000   \n",
       "posEntryMode       118621.0    28.552651    35.647616     0.000000   \n",
       "transactionAmount  118621.0     0.815835     4.482922    -0.497925   \n",
       "availableCash      118621.0  6625.508974  3410.289486   500.000000   \n",
       "time               118621.0    12.026960     6.832199     0.000000   \n",
       "day                118621.0    15.788790     8.717295     1.000000   \n",
       "year               118621.0  2017.071918     0.258353  2017.000000   \n",
       "\n",
       "                           25%     50%          75%           max  \\\n",
       "mcc                5411.000000  5691.0  5967.000000   9405.000000   \n",
       "merchantCountry     826.000000   826.0   826.000000    840.000000   \n",
       "posEntryMode          5.000000     5.0    81.000000     91.000000   \n",
       "transactionAmount    -0.298267     0.0     0.701733    325.305101   \n",
       "availableCash      4500.000000  7500.0  8500.000000  18500.000000   \n",
       "time                  6.000000    12.0    18.000000     23.000000   \n",
       "day                   8.000000    16.0    23.000000     31.000000   \n",
       "year               2017.000000  2017.0  2017.000000   2018.000000   \n",
       "\n",
       "                          range  variation coefficient       skew     kurtosis  \n",
       "mcc                 8663.000000               0.167315   0.800649     4.969162  \n",
       "merchantCountry      809.000000               0.202958  -2.020270     2.687052  \n",
       "posEntryMode          91.000000               1.248487   0.804020    -1.344966  \n",
       "transactionAmount    325.803027               5.494886  28.405271  1503.445956  \n",
       "availableCash      18000.000000               0.514721  -0.182784    -0.541319  \n",
       "time                  23.000000               0.568074  -0.120168    -1.151825  \n",
       "day                   30.000000               0.552119   0.004820    -1.173557  \n",
       "year                   1.000000               0.000128   3.313981     8.982621  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attributes1=df[['mcc','merchantCountry', 'posEntryMode', 'transactionAmount',\n",
    "       'availableCash','time','day', 'year']]\n",
    "\n",
    "describe1 = num_attributes1.describe().T\n",
    "describe1['range'] = (num_attributes1.max() - num_attributes1.min()).tolist()\n",
    "describe1['variation coefficient'] = (num_attributes1.std() / num_attributes1.mean()).tolist()\n",
    "describe1['skew'] = num_attributes1.skew().tolist()\n",
    "describe1['kurtosis'] = num_attributes1.kurtosis().tolist()\n",
    "\n",
    "\n",
    "describe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ef2feaf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transactionTime</th>\n",
       "      <th>eventId</th>\n",
       "      <th>accountNumber</th>\n",
       "      <th>merchantId</th>\n",
       "      <th>mcc</th>\n",
       "      <th>merchantCountry</th>\n",
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       "      <td>2017-01-01 00:00:00+00:00</td>\n",
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       "      <td>826</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.232609</td>\n",
       "      <td>7500</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 00:49:03+00:00</td>\n",
       "      <td>2164986A1</td>\n",
       "      <td>648e19cf</td>\n",
       "      <td>718cc6</td>\n",
       "      <td>5499</td>\n",
       "      <td>826</td>\n",
       "      <td>81</td>\n",
       "      <td>0.022944</td>\n",
       "      <td>4500</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 00:43:17+00:00</td>\n",
       "      <td>31294145A1</td>\n",
       "      <td>c0ffab1b</td>\n",
       "      <td>94cafc</td>\n",
       "      <td>5735</td>\n",
       "      <td>442</td>\n",
       "      <td>81</td>\n",
       "      <td>-0.371247</td>\n",
       "      <td>9500</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 00:15:07+00:00</td>\n",
       "      <td>11162049A1</td>\n",
       "      <td>038099dd</td>\n",
       "      <td>7d5803</td>\n",
       "      <td>5499</td>\n",
       "      <td>826</td>\n",
       "      <td>81</td>\n",
       "      <td>0.018306</td>\n",
       "      <td>7500</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 00:37:09+00:00</td>\n",
       "      <td>17067235A1</td>\n",
       "      <td>3130363b</td>\n",
       "      <td>12ca76</td>\n",
       "      <td>5411</td>\n",
       "      <td>826</td>\n",
       "      <td>81</td>\n",
       "      <td>0.652917</td>\n",
       "      <td>10500</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            transactionTime     eventId accountNumber merchantId   mcc  \\\n",
       "0 2017-01-01 00:00:00+00:00  18688431A1      94f9b4e7     b76d06  5968   \n",
       "1 2017-01-01 00:49:03+00:00   2164986A1      648e19cf     718cc6  5499   \n",
       "2 2017-01-01 00:43:17+00:00  31294145A1      c0ffab1b     94cafc  5735   \n",
       "3 2017-01-01 00:15:07+00:00  11162049A1      038099dd     7d5803  5499   \n",
       "4 2017-01-01 00:37:09+00:00  17067235A1      3130363b     12ca76  5411   \n",
       "\n",
       "   merchantCountry  posEntryMode  transactionAmount  availableCash  \\\n",
       "0              826             1          -0.232609           7500   \n",
       "1              826            81           0.022944           4500   \n",
       "2              442            81          -0.371247           9500   \n",
       "3              826            81           0.018306           7500   \n",
       "4              826            81           0.652917          10500   \n",
       "\n",
       "   fraudEvent  year  day  time  \n",
       "0           0  2017    1     0  \n",
       "1           0  2017    1     0  \n",
       "2           0  2017    1     0  \n",
       "3           0  2017    1     0  \n",
       "4           0  2017    1     0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6a5acab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c99c18ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fraudEvent', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x=\"fraudEvent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff3ee37",
   "metadata": {},
   "source": [
    "Data is imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4989f81a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x2160 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=30, figsize=(30, 30))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd5d98ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9d6c5510",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_f=df[df['fraudEvent']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ae6fdd89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8b9c15ea    97\n",
       "3ec9dda7    59\n",
       "6bab7602    48\n",
       "39219923    45\n",
       "1602df4e    41\n",
       "            ..\n",
       "7158941b     1\n",
       "94c39106     1\n",
       "cddc7d2e     1\n",
       "1c8df5be     1\n",
       "cf56ce28     1\n",
       "Name: accountNumber, Length: 167, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f[\"accountNumber\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fc4a0dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test=df[df[\"accountNumber\"]==\"8b9c15ea\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "35f48f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "chart=sns.countplot(y='posEntryMode', data=test, hue='fraudEvent',palette='Set2') \n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9627856b",
   "metadata": {},
   "source": [
    "plt.figure(figsize=(20,25))\n",
    "chart=sns.countplot(y='merchantId', data=test, hue='fraudEvent',palette='Set2') \n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c58d08b1",
   "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>transactionTime</th>\n",
       "      <th>eventId</th>\n",
       "      <th>accountNumber</th>\n",
       "      <th>merchantId</th>\n",
       "      <th>mcc</th>\n",
       "      <th>merchantCountry</th>\n",
       "      <th>posEntryMode</th>\n",
       "      <th>transactionAmount</th>\n",
       "      <th>availableCash</th>\n",
       "      <th>fraudEvent</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-01-01 03:53:34+00:00</td>\n",
       "      <td>85602730A1</td>\n",
       "      <td>645c318d</td>\n",
       "      <td>66f14e</td>\n",
       "      <td>5541</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.226995</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2017-01-03 13:15:07+00:00</td>\n",
       "      <td>101536761A1</td>\n",
       "      <td>f42ba1f1</td>\n",
       "      <td>5f7cd4</td>\n",
       "      <td>5968</td>\n",
       "      <td>840</td>\n",
       "      <td>81</td>\n",
       "      <td>-0.053454</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2017-01-03 13:27:22+00:00</td>\n",
       "      <td>101536857A1</td>\n",
       "      <td>f42ba1f1</td>\n",
       "      <td>aebafc</td>\n",
       "      <td>4814</td>\n",
       "      <td>826</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2017-01-03 13:27:02+00:00</td>\n",
       "      <td>101536329A1</td>\n",
       "      <td>f42ba1f1</td>\n",
       "      <td>aebafc</td>\n",
       "      <td>4814</td>\n",
       "      <td>826</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.274591</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2017-01-03 13:38:15+00:00</td>\n",
       "      <td>101536953A1</td>\n",
       "      <td>f42ba1f1</td>\n",
       "      <td>7e57ae</td>\n",
       "      <td>4816</td>\n",
       "      <td>840</td>\n",
       "      <td>81</td>\n",
       "      <td>-0.494264</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              transactionTime      eventId accountNumber merchantId   mcc  \\\n",
       "14  2017-01-01 03:53:34+00:00   85602730A1      645c318d     66f14e  5541   \n",
       "740 2017-01-03 13:15:07+00:00  101536761A1      f42ba1f1     5f7cd4  5968   \n",
       "741 2017-01-03 13:27:22+00:00  101536857A1      f42ba1f1     aebafc  4814   \n",
       "743 2017-01-03 13:27:02+00:00  101536329A1      f42ba1f1     aebafc  4814   \n",
       "744 2017-01-03 13:38:15+00:00  101536953A1      f42ba1f1     7e57ae  4816   \n",
       "\n",
       "     merchantCountry  posEntryMode  transactionAmount  availableCash  \\\n",
       "14                36             5          -0.226995           1500   \n",
       "740              840            81          -0.053454           1500   \n",
       "741              826             1          -0.225775           1500   \n",
       "743              826             1          -0.274591           1500   \n",
       "744              840            81          -0.494264           1500   \n",
       "\n",
       "     fraudEvent  year  day  time  \n",
       "14            1  2017    1     3  \n",
       "740           1  2017    3    13  \n",
       "741           1  2017    3    13  \n",
       "743           1  2017    3    13  \n",
       "744           1  2017    3    13  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b89e3296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4fd0ff    49\n",
       "2be1cd    48\n",
       "707b6c    19\n",
       "6a5933    17\n",
       "e57467    14\n",
       "          ..\n",
       "2c3fc4     1\n",
       "cb180a     1\n",
       "45d79b     1\n",
       "da5585     1\n",
       "1a0510     1\n",
       "Name: merchantId, Length: 438, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f[\"merchantId\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e165a260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96878e    3294\n",
       "243074    2281\n",
       "7f7cdb    1784\n",
       "4fd0ff    1554\n",
       "7c3a49    1143\n",
       "          ... \n",
       "9e60e2       1\n",
       "309150       1\n",
       "d95c39       1\n",
       "261643       1\n",
       "506a9b       1\n",
       "Name: merchantId, Length: 33327, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"merchantId\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "303764cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "826    95616\n",
       "442    14929\n",
       "840     2873\n",
       "372     1414\n",
       "250      483\n",
       "       ...  \n",
       "703        1\n",
       "48         1\n",
       "566        1\n",
       "410        1\n",
       "32         1\n",
       "Name: merchantCountry, Length: 82, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"merchantCountry\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e4a317d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "chart=sns.countplot(y='availableCash', data=df, hue='fraudEvent',palette='Set2') \n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9b4fbdd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(20,10))\n",
    "chart=sns.countplot(y='availableCash', data=df_f, hue='fraudEvent',palette='Set2') \n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9a1c82df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u9qVJ9kxyKJ10tOoJvh9JkiRpGPYG1jXf6L+ezs8UAHwfOKz5jLs3nSFF0LnJ0KFJntnMn9Dj61wA/C7wK8BYSPgS8LbmC3uSPCvJjK308wCdmxz1ZGtJ4y+APwNozhRc1BSysFn2my3bHklnh13XnCqh6esDwPIkbwZup3NxNVV1Q5LlwI10EtlJVbWp1zciSZIkDdEZwGeTHA9cRnMWoqruaD7jXgvcAny7af9Jc63uvyS5G/g68Nyu/ra8puHtVfX/gC8Df0dnaNTYHU3PpjPs6Ormi/sNPHazoYmcBXwxybqqeunW3lw6Q6gmWJhcX1XPnWDZdVX1n7f2Av20cOHCWr169UBfc/369Zz51f8A4K2/+kwOPPDAgb6+JEmSejbeNbPaBlu7mHlay7LpO7IQSZIkSZPT1kLDVUnesmVjM7ToW/0pSZIkSdJksrVrGk4GPpfkdTwWEhYCewCv7mNdkiRJkiaJ1tBQVXcBL07yUh67MONfquorfa9MkiRJ0qTQ031aq+oyOleBS5IkSXqS6fUXoSVJkiQ9SRkaJEmSpB0oU6auTVI77DFl6tqtvmZyTpKRJNf34z1t089IS5IkSZrA6KaDfu5PP//+HdXd9z/4yvf2sNq5wF/T+eG3Hc4zDZIkSdJOrqquAH7Yr/4NDZIkSZJaGRokSZIktTI0SJIkSWplaJAkSZLUyrsnSZIkSTvSblN+0OMdj3rub2urJPkUcBSwf5K1wHur6pM7qgRDgyRJkrQD1aaN8wb+mlUn9LN/hydJkiRJamVokCRJktTK0CBJkiSplaFBkiRJUitDgyRJkqRWhgZJkiRJrQwNkiRJkloZGiRJkiS1MjRIkiRJamVokCRJktTK0CBJkiSplaFBkiRJUitDgyRJkqRWfQsNSc5JMpLk+q629yX5QZJrmsexXctOTbImyc1JjulXXZIkSZKemH6eaTgXWDxO+0erakHz+AJAksOApcDhzTZnJJnSx9okSZIk9ahvoaGqrgB+2OPqS4ALquqRqroVWAMs6ldtkiRJkno3jGsa3pHk2mb40qym7SDgjq511jZtj5PkxCSrk6zesGFDv2uVJEmSnvQGHRo+BjwTWACsA05v2jPOujVeB1V1VlUtrKqFs2fP7kuRkiRJkh4z0NBQVXdV1aaqGgU+wWNDkNYCB3etOg+4c5C1SZIkSRrfQENDkrlds68Gxu6stAJYmmTPJIcC84FVg6xNkiRJ0vim9qvjJJ8CjgL2T7IWeC9wVJIFdIYe3Qa8FaCqbkiyHLgR2AicVFWb+lWbJEmSpN71LTRU1QnjNH+yZf3TgNP6VY8kSZKkbeMvQkuSJElqZWiQJEmS1MrQIEmSJKmVoUGSJElSK0ODJEmSpFaGBkmSJEmtDA2SJEmSWhkaJEmSJLUyNEiSJElqZWiQJEmS1MrQIEmSJKmVoUGSJElSK0ODJEmSpFaGBkmSJEmtDA2SJEmSWhkaJEmSJLUyNEiSJElqZWiQJEmS1MrQIEmSJKmVoUGSJElSK0ODJEmSpFaGBkmSJEmtDA2SJEmSWhkaJEmSJLUyNEiSJElqZWiQJEmS1MrQIEmSJKlV30JDknOSjCS5vqtt3ySXJrmleZ7VtezUJGuS3JzkmH7VtaOMjo4yMjLC+vXrGR0dHXY5kiRJUt/080zDucDiLdpOAVZW1XxgZTNPksOApcDhzTZnJJnSx9q220P338uZK2/i9ItXMTIyMuxyJEmSpL7pW2ioqiuAH27RvAQ4r5k+Dziuq/2Cqnqkqm4F1gCL+lXbjjJj71nM3Ge/YZchSZIk9dWgr2k4oKrWATTPc5r2g4A7utZb27RJkiRJGrLJciF0xmmrcVdMTkyyOsnqDRs29LksSZIkSYMODXclmQvQPI9dDLAWOLhrvXnAneN1UFVnVdXCqlo4e/bsvhYrSZIkafChYQWwrJleBlzc1b40yZ5JDgXmA6sGXJskSZKkcUztV8dJPgUcBeyfZC3wXuADwPIkbwZuB44HqKobkiwHbgQ2AidV1aZ+1SZJkiSpd30LDVV1wgSLXjbB+qcBp/WrHkmSJEnbZrJcCC1JkiRpkjI0SJIkSWplaJAkSZLUytAgSZIkqZWhQZIkSVIrQ4MkSZKkVoYGSZIkSa0MDZIkSZJaGRokSZIktTI0SJIkSWplaJAkSZLUytAgSZIkqZWhQZIkSVIrQ4MkSZKkVoaG7TQ6OsrIyAijo6PDLkWSJEnqC0PDdnro/nv5yEXfYGRkZNilSJIkSX1haNgB9nrqPsMuQZIkSeobQ4MkSZKkVoYGSZIkSa0MDZIkSZJaGRokSZIktTI0SJIkSWplaJAkSZLUytAgSZIkqZWhQZIkSVIrQ4MkSZKkVoYGSZIkSa0MDZIkSZJaTR3Giya5DXgA2ARsrKqFSfYFLgQOAW4Dfruq7h1GfZIkSZIeM8wzDS+tqgVVtbCZPwVYWVXzgZXNvCRJkqQhm0zDk5YA5zXT5wHHDa8USZIkSWOGFRoK+HKSbyU5sWk7oKrWATTPc8bbMMmJSVYnWb1hw4YBlStJkiQ9eQ3lmgbgyKq6M8kc4NIk3+11w6o6CzgLYOHChdWvAiVJkiR1DOVMQ1Xd2TyPAJ8DFgF3JZkL0DyPDKM2SZIkSZsbeGhIMiPJU8amgV8HrgdWAMua1ZYBFw+6NkmSJEmPN4zhSQcAn0sy9vr/WFWXJLkKWJ7kzcDtwPFDqE2SJEnSFgYeGqrqe8Dzxmm/B3jZoOuRJEmS1G5YF0LvUmp0lJGRziUYc+bMYbfdJtOdbCVJkqTt46fbHeDHD97PmStv4vSLVz0aHiRJkqRdhWcadpAZe89i+vTpnnGQJEnSLsdPtTvQQ/ff6xkHSZIk7XI807CDzdh7FjNmzBh2GZIkSdIO45kGSZIkSa0MDZIkSZJaGRokSZIktfKahj4a9fcbJEmStAvwU2wfjYyMcPrFq7ybkiRJknZqnmnos5n77DfsEiRJkqTt4pkGSZIkSa0MDZIkSZJaGRokSZIktfKahj7ovmtSVZFkyBVJkiRJ287Q0AcP3X8vZ668k/rZT5i5/1xmzJgx7JIkSZKkbWZo6JMZe89i9Gc/GXYZkiRJ0nbzmgZJkiRJrQwNAzQ6Osr69esZHR0ddimSJElSzwwNAzQyMsL7z7vEX4eWJEnSTsVrGgag+25Kez11n+EWI0mSJD1BhoYB6L6bElP3GHY5kiRJ0hNiaBiQsbspPfzQQ+Mu7z4bMWfOHHbbzZFjkiRJmhz8ZDpg1YSDLS+IHhkZ4fSLV3H6xau85kGSJEmTiqFhwH784P2cufKmccPBzH32Y6+nzho3VEiSJEnD4vCkIZix96wJfyV67PqHvfa6jXcvWcSBBx444dClJzKkabx1HRIlSZKkXhgaJqEtQ8XY0CXg0SDR3V6jo7zhyGcwZ84c9t9/f+6+++5Hz1LstttuzJkzZ9w+JupXkiRJ6mZo2EnM3Ge/Cdsf+NE9nLnyJqZP+x7H/vxT+cK/P8jD999Ldp/GXntN591LFk3Yx0T9PlFjZy22DCuevZAkSdr5TbrQkGQx8JfAFODsqvrAkEvqiy0/ZN99991U1bjLR0ZGqCqqasJtxu7OdOYXVvFzh72AvfaexW67T2P69OmPDkHaso8t+4X2YUpjv2gNjw8FY2ctHrpv87DSffZiW4ZDtW3j8CpJkqTBmFShIckU4G+AXwPWAlclWVFVNw63sh2v+7cbsvs06mc/Yeb+c8ddfs/IOn7usBd0QkHLNgDTZ+494evM3H/uZn1s2e/0ad/jDUc+g/3333+zPrqHOJ16xqeZud+Bj4aCsfaRkRFm7L0vBey2+7Rxr9nYcjjU2LbAo8OqgHHDyNgQrLHaxq7J+Og/r360v+0ZXtVLOHkiZ1GeDIFmV3uPg3w/OzpAdy/fWY7F9u7vfh6vnW1fSoO0vddTauc1qUIDsAhYU1XfA0hyAbAEmFSh4cEf3cPDzTfq9bOf8OMH7+Oh++7d7MP8RM9brtttvD7GW9a2zUS1TNRHd9vDD9zHRy76BrXxp2TqHo8+T58+nbe94gWP2w9jYeFjX7yahx+4j5n7HbjZ646MPOVx64+3LcDxRzydT191OwBve8ULmDNnzmbbbFnb9OnTOf6Ip4/b97borqX79buXPfzAfZvtj+51nkh/u4pd7T0O8v1sy2ttbZuRkRE+fOFXeM9rj94pjsX27u9+Hq+dbV9Kg/RE/t/rXve9yxZ77eROLt1DYoYtyWuAxVX1e83864Ffqqp3dK1zInBiM/vzwM0DL/Qx+wN3D/H15TGYDDwGw+cxGD6PwfB5DIZvMh6Du6tq8bCL2BVMtjMNGadts1RTVWcBZw2mnHZJVlfVwmHX8WTmMRg+j8HweQyGz2MwfB6D4fMY7Nom2+CytcDBXfPzgDuHVIskSZIkJl9ouAqYn+TQJHsAS4EVQ65JkiRJelKbVMOTqmpjkncAX6Jzy9VzquqGIZfVZlIMk3qS8xgMn8dg+DwGw+cxGD6PwfB5DHZhk+pCaEmSJEmTz2QbniRJkiRpkjE0SJIkSWplaNhGSRYnuTnJmiSnDLueJ4MkBye5LMlNSW5I8q6mfd8klya5pXmeNexad2VJpiT5dpLPN/Pu/wFKsk+SzyT5bvP/wos8BoOV5A+bf4OuT/KpJNM8Bv2V5JwkI0mu72qbcJ8nObX5+3xzkmOGU/WuZYJj8KHm36Jrk3wuyT5dyzwGuxhDwzZIMgX4G+AVwGHACUkOG25VTwobgXdX1XOAFwInNfv9FGBlVc0HVjbz6p93ATd1zbv/B+svgUuq6tnA8+gcC4/BgCQ5CHgnsLCqnkvnph1L8Rj027nAlj/QNe4+b/4uLAUOb7Y5o/m7re1zLo8/BpcCz62qXwD+HTgVPAa7KkPDtlkErKmq71XVT4ELgCVDrmmXV1XrqurqZvoBOh+WDqKz789rVjsPOG4oBT4JJJkH/AZwdlez+39AkjwVeAnwSYCq+mlV/QiPwaBNBaYnmQrsRef3hDwGfVRVVwA/3KJ5on2+BLigqh6pqluBNXT+bms7jHcMqurLVbWxmf0mnd/XAo/BLsnQsG0OAu7oml/btGlAkhwCPB+4EjigqtZBJ1gAc4ZY2q7uL4A/AUa72tz/g/MMYAPwt80QsbOTzMBjMDBV9QPgw8DtwDrgvqr6Mh6DYZhon/s3ejjeBHyxmfYY7IIMDdsm47R579oBSTIT+CxwclXdP+x6niySvBIYqapvDbuWJ7GpwAuAj1XV84GHcBjMQDXj5pcAhwJPA2Yk+Z3hVqUt+Dd6wJL8OZ0hxOePNY2zmsdgJ2do2DZrgYO75ufROT2tPkuyO53AcH5VXdQ035VkbrN8LjAyrPp2cUcCr0pyG50heUcn+Qfc/4O0FlhbVVc285+hEyI8BoPzcuDWqtpQVT8DLgJejMdgGCba5/6NHqAky4BXAq+rx378y2OwCzI0bJurgPlJDk2yB52LfVYMuaZdXpLQGct9U1V9pGvRCmBZM70MuHjQtT0ZVNWpVTWvqg6h89/8V6rqd3D/D0xVrQfuSPLzTdPLgBvxGAzS7cALk+zV/Jv0MjrXV3kMBm+ifb4CWJpkzySHAvOBVUOob5eXZDHwp8CrqurhrkUeg12Qvwi9jZIcS2d89xTgnKo6bbgV7fqS/DLwNeA6HhtT/2d0rmtYDjydzh/046tqywvmtAMlOQp4T1W9Msl+uP8HJskCOhei7wF8D/hdOl8AeQwGJMn7gdfSGY7xbeD3gJl4DPomyaeAo4D9gbuA9wL/xAT7vBku8yY6x+jkqvri43vVEzHBMTgV2BO4p1ntm1X1+836HoNdjKFBkiRJUiuHJ0mSJElqZWiQJEmS1MrQIEmSJKmVoUGSJElSK0ODJEmSpFaGBknqkmSfJG8fdh3dkrwxydO65s9Octh29vmd5haKQ5NkQXP7aknSJGdokKTN7QM8LjQkmTL4Uh71RuDR0FBVv1dVN25rZ0meQ+ff/5ckmbH95W2zBYChQZJ2AoYGSdrcB4BnJrkmyVVJLkvyj3R+VJAk/5TkW0luSHLi2EZJHkxyWvMN/jeTHNC0H5/k+qb9iqbtkCRfS3J183hxVz9/kuS6Zv0PJHkNsBA4v6lpepLLkyxs1j+hWf/6JB/cWj2N/wr8PfBl4FVd21ye5KNJrkhyU5IjklyU5JYk/6trvT9qXu/6JCd3vafru9Z5T5L3dfX7wSSrkvx7kl9JsgfwP4DXNu/rtdt32CRJ/WRokKTNnQL8R1UtAP4YWAT8eVWNDQd6U1X9Ip0P8u9sfhEbYAadX0N9HnAF8Jam/b8DxzTtYx/QR4Bfq6oX0Pll4b8CSPIK4Djgl5r1/09VfQZYDbyuqhZU1Y/HCm2GLH0QOJrOt/ZHJDluK/XQvOaFwKeAE7Z4/z+tqpcAHwcuBk4Cngu8Mcl+SX6Rzq9Q/xLwQuAtSZ6/1b0KU6tqEXAy8N6q+mmzby5s3teFPfQhSRoSQ4MktVtVVbd2zb8zyXeAbwIHA/Ob9p8Cn2+mvwUc0kx/Azg3yVuAsSFOuwOfSHId8GlgLJC8HPjbqnoYoKp+uJXajgAur6oNVbUROB94SVs9SY4ANlTV94GVwAuSzOrqc0XzfB1wQ1Wtq6pHgO817/eXgc9V1UNV9SBwEfArW6mTZr3NapEk7TwMDZLU7qGxiSRH0flg/6LmG/xvA9OaxT+rqmqmNwFTAarq94H/RucD9zXNmYk/BO4CnkfnjMUeYy8BjPXRi7QsG7ceOmcWnp3kNuA/gKcCv9W13SPN82jX9Nj81JbX3Mjmf1OmbbF8rK/uWiRJOwlDgyRt7gHgKRMs2xu4t6oeTvJsOsNzWiV5ZlVdWVX/HbibTnjYG1hXVaPA63nsDMSXgTcl2avZdt+t1HQl8KtJ9m8u1D4B+GpLLbsBxwO/UFWHVNUhwBIeP0SpzRXAcUn2ai6ifjXwNTohaE4zhGlP4JU99NW2ryVJk4ihQZK6VNU9wDeai3o/tMXiS4CpSa4F/iedIUpb86GxC5XpfOD+DnAGsCzJN4Fn0ZzNqKpL6AwPWp3kGuA9TR/nAh8fuxC6q9Z1wKnAZU2/V1fVxS21vAT4QVX9oKvtCuCwJHN7eC9U1dVNPavohJazq+rbVfUzOhc2X0lnWNR3e+jusua1vRBakia5PHb2WpIkSZIezzMNkiRJkloZGiRJkiS1MjRIkiRJamVokCRJktTK0CBJkiSplaFBkiRJUitDgyRJkqRW/x+/Dol8sCmYwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 783.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='transactionAmount', data=df_f, hue='fraudEvent', aspect=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bb1e0b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outliers\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(data=df_f, x='transactionAmount')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "360d3433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x540 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 495.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=90)\n",
    "sns.displot(x='time', data=df_f, hue='fraudEvent',palette='Set2', aspect=1.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ea822702",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x540 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 495.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=90)\n",
    "sns.displot(x='day', data=df_f, hue='fraudEvent',palette='Set2', aspect=1.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "614ede1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x540 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 495.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=90)\n",
    "sns.displot(x='day', data=df, hue='fraudEvent',palette='Set2', aspect=1.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "790cb77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x540 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 783.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=90)\n",
    "sns.displot(x='time', data=df, hue='fraudEvent', aspect=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ce647b59",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81    630\n",
      "1     210\n",
      "5      18\n",
      "90     13\n",
      "0       2\n",
      "2       2\n",
      "Name: posEntryMode, dtype: int64 5     70210\n",
      "81    35202\n",
      "1     10358\n",
      "90     1189\n",
      "7       637\n",
      "80       92\n",
      "79       31\n",
      "2        14\n",
      "0         9\n",
      "91        4\n",
      "Name: posEntryMode, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "list_fe=df[df[\"fraudEvent\"]==1][\"posEntryMode\"].value_counts()\n",
    "list_nfe= df[df[\"fraudEvent\"]==0][\"posEntryMode\"].value_counts()\n",
    "print(list_fe, list_nfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7edb2265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "chart=sns.countplot(y='posEntryMode', data=df, hue='fraudEvent',palette='Set2') \n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e411924b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='posEntryMode', ylabel='transactionAmount'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, y=\"transactionAmount\", x=\"posEntryMode\", hue='fraudEvent')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "85951508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='availableCash', ylabel='transactionAmount'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, y=\"transactionAmount\", x=\"availableCash\", hue='fraudEvent')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d79ce78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5), dpi=100)\n",
    "sns.heatmap(df.corr(), annot=True, fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "32b37d4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['transactionTime', 'eventId', 'accountNumber', 'merchantId', 'mcc',\n",
       "       'merchantCountry', 'posEntryMode', 'transactionAmount', 'availableCash',\n",
       "       'fraudEvent', 'year', 'day', 'time'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "be7b48de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final=df[['mcc','merchantCountry', 'posEntryMode', 'transactionAmount',\n",
    "       'availableCash', 'fraudEvent']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ffd272bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 118621 entries, 0 to 118620\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   mcc                118621 non-null  int64  \n",
      " 1   merchantCountry    118621 non-null  int64  \n",
      " 2   posEntryMode       118621 non-null  int64  \n",
      " 3   transactionAmount  118621 non-null  float64\n",
      " 4   availableCash      118621 non-null  int64  \n",
      " 5   fraudEvent         118621 non-null  int32  \n",
      "dtypes: float64(1), int32(1), int64(4)\n",
      "memory usage: 5.0 MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c506ae79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating X and y variables\n",
    "X =df_final.drop(\"fraudEvent\", axis=1)\n",
    "y = df_final.loc[:,\"fraudEvent\"].values.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d2ef68",
   "metadata": {},
   "source": [
    "### ExtraTreesClassifier for feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a6e696fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=ExtraTreesClassifier()\n",
    "m.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1b84c89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imp_featrs= pd.Series(m.feature_importances_, index=X.columns)\n",
    "# imp_featrs.nlargest(10).plot(kind='barh')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fee4d0a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sampling\n",
    "random_stat=20\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4a310f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"training data :\", y_train[y_train['fraudEvent']==1],y_train[y_train['fraudEvent']==0])\n",
    "# print(\"testing data :\",y_test[y_test['fraudEvent']==1], y_test[y_test['fraudEvent']==0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a19a5ae",
   "metadata": {},
   "source": [
    "# Random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f93b207e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', max_depth=2)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_depth=2,class_weight=\"balanced\")\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f832e05a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8509420839656044\n",
      "0.8504952581664911\n"
     ]
    }
   ],
   "source": [
    "print(rf.score(X_train, y_train)) # train accu\n",
    "print(rf.score(X_test, y_test)) # test accu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ac198efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_predictions = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1961c8ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[20057  3501]\n",
      " [   46   121]] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.85      0.92     23558\n",
      "           1       0.03      0.72      0.06       167\n",
      "\n",
      "    accuracy                           0.85     23725\n",
      "   macro avg       0.52      0.79      0.49     23725\n",
      "weighted avg       0.99      0.85      0.91     23725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation \n",
    "cm = confusion_matrix(y_test, rf_predictions)\n",
    "cr = classification_report(y_test, rf_predictions)\n",
    "print(cm,\"\\n\")\n",
    "print(cr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a59fbfa0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting confusion metrix\n",
    "plt.figure(figsize=(6,6), dpi=90)\n",
    "sns.heatmap(cm, annot=True, fmt=\".1f\")\n",
    "plt.title(\"Confusion Metrix\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"predict Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0f89b1f8",
   "metadata": {
    "scrolled": false
   },
   "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": [
    "plot_roc_curve(y_test, rf_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2480f6f",
   "metadata": {},
   "source": [
    "# XG Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "28ad5e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134.56685714285715"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['fraudEvent'].value_counts()[0]/df_final['fraudEvent'].value_counts()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7c732448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              eta=0.05, eval_metric='mlogloss', gamma=2, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.05, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=8, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=135,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(eta=0.05, gamma=2,learning_rate=0.05, scale_pos_weight=135,  eval_metric='mlogloss')\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fbb7b0c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8957384926656551\n",
      "0.893277133825079\n"
     ]
    }
   ],
   "source": [
    "print(xgb.score(X_train, y_train)) # train accu\n",
    "print(xgb.score(X_test, y_test)) # test accu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "178f2f67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "xgb_predictions = xgb.predict(X_test)\n",
    "print(y_test,xgb_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "712ecdab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[21045  2513]\n",
      " [   19   148]] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.89      0.94     23558\n",
      "           1       0.06      0.89      0.10       167\n",
      "\n",
      "    accuracy                           0.89     23725\n",
      "   macro avg       0.53      0.89      0.52     23725\n",
      "weighted avg       0.99      0.89      0.94     23725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation \n",
    "xgb_cm = confusion_matrix(y_test, xgb_predictions)\n",
    "xgb_cr = classification_report(y_test, xgb_predictions)\n",
    "print(xgb_cm,\"\\n\")\n",
    "print(xgb_cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7a750f47",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4Xx/yZcZfPoH5cxcwf+4Cxo+5kYO/+eXyX1Alq438qUoUHpQldZE0UtKrJGtXzQAWSXpN0oi6BQo7ouXLVzDtuefZcfttmiy7+O0lzJk7nw/tsN2afR/aYVtmz5nLkqXLAFi1ajU1PxvDiNNPpnv37g0e564/PMAnP38YXzvqeCbccie1GS2Qcs5n6+rRswcf+ejOvPjsS2v2feWwL/DX5+5n0iM3cfQJRyLlm01y2PBv8+hLf+aRZ+5jx5235+br7miwXJ9+fRi0+Sb8d9qLa/Y9P+0FNttiU3r32aB5L6iStPFq1h1Ze3RfXA7sB/yQZJXXRSRrWH0U+DHwAdYuUthhRATnXHQ5W22xOQd+ep8my7/zbtJi6tN77R9Y3fayd96hT+8NuPHWO9lxu23Y6392419TnnrfMY467GucfvJx9Ovbh2nPvcDp51xIF3Xh6CEHN+t8tq6aS3/M9Fdn8Od7Hwbg5mtv57KfjmXxwrfZdfCHueSaUdTWBr+55tayj3n92F9z/dhfs80OW/Olb3yOBXMXNFhu/Q3WA2DJ22uXdVvydrL82wa912fpkg7+QVpFLd+82qNV+k3g8xExMSJeioj5EfFyREwEvgR8q6kD1F/V9qcXj2nzSjcmIvjpJWN57fWZXHHROXTp0vSvdf31egGs00pduuwdADZYf31en/kGt0y6hzOGfyfzGDvvtD0b9d+Qrl27svuuH+Y73zqcPzzwSLPOZ+sa+bMz+eD2W3Hq0B9RtxDEc0+/wMIFi6itreWpKc9w/dhf8/mvfaZZx3/1xek8/+xLnD/mJw3mv7PsXQB69+m9Zl/d9rKl7zTrnJUkamtzp2rRHi1lAVkfk5HmNypdxbam7nF7rjwSEYy69CqmPfc81465sOwWZ7++fdjkAxvz/IuvsNUWmwHw3xdfZtAmA+nTewMeeOQfLFy4mK99K7mIt3LlSpYue4dPf+WbjP3ZuXxk553ed8zGvko3dT5ba8RFZ/CRj+7Mdw77fqMt0toWtva6d+vGVttu0WDeksVLeHPWHD606w5r+rg/tOsOzJ75ZsdvJYNbyo1oj5byLcAfJB0saVtJG0naRtLBJEt939QOdWq2Cy4bxxNPP8v4y0fTr++61ytXrVrN8uUrWL16NbW1tSxfvmKd4VQHf/GzXPOrW5m/4C3mL3iL8b+6jUPS4XSfP/BT3D/xBu6cMJY7J4zlvLNOZYP11+POCWP50I5Jv/AfHvgLS5ctIyKY9twLXPeb2zlw/30z69rY+Sxx9oVnMHjP3fjeET9gyeJ1V4Q/6KufYYPeybeKnXf/EMO+/+01XRsAXbt2pUfPHnTt2pUuXbrQo2cPuqXDFddbfz2+NuRL9OmbtHZ3+NB2fPe0ofzjoX9m1mXybffy3VOGMmDgRgwYuBHf+cExTLr57lZ+xVZp2qOlfCrwE2AMsAVrW8czSVaBPb8d6tQsb7w5h1sn3UOPHt357CHHrNn/5YMO4Nwzv88vb7yFq69f+xnzsQO+xh4f/QgTxl4MwPHHfpNFby/hq0cdD8CXPvu/fPfoIQD06tmTXj17rnluv759kMTGAzZas++WO+/mvIuvYNXq1Wyy8QCGfOPLDD3yG2vyz7v4SgDOPfP7TZ7PYNMtBjHk2ENY/t5y7n/8t2v23zPxfkb96GKOPPYQzrnkR3Tr1pW5s+dx+4Q7ufHqm9eU+95pQznxjLXdTY9Pf4R//2MKx33jZCD44sEHcfo536dHz+68NX8hf773YcZdMn5N+ZE/OxOAUT9K/n9cc9n1bNi/H7/76y0A3Dvpj1w75sa2/BUUp4ou3OXVrgunStoQ6A0sjYhFzT2OF07tuLxwasfW3IVTl/30qNx/sxucc5MXTm1raSBe1J51MLN2UEUX7vLyHX1mVjxf6MvUYW/UMLMOrI1vHpE0XNLjkpZLmlwvb4KkFZKWlqS9S/K7Sxor6a00XSmpW2vlN8VB2cyK1/a3Wb8BjALGZ+SPi4jeJenRkryRwL7ALmnaDzi7FfMb5aBsZoVr65tHImJSREwG3j8TWNOGAaMiYnZEzAYuAI5rxfxGOSibWfHaf0Kio9OuhWcknV43546k/iRDdaeWlJ0KbCWpX0vzy6mYg7KZFa8ZQbn+9AqSapp59iuAnYCBJC3YU1g7307dfe2LSsrXbfdphfwmOSibWfGacaEvImoiQiWpplmnjpgSEfMiYnVEPAZcBNTNjbs0/Vnaqq3bXtIK+U1yUDaz4rV/98U6tanbiIiFJHcXDy7JHwzMiIjFLc0vpzIOymZWuKiN3CkPSd0k9SK5F6OLpF6SeqR5h0vqq8QewFnAnSVPvwEYIWmQpEEkIyeubcX8RvnmETMrXtvfPDISOLfk8bvAI8D+wHDgGpL4NwsYB1xaUvZ8YADwXPr4JmB0K+Y3ql3nvmgtnvui4/LcFx1bc+e+WDL8i7n/ZvuM/b3nvjAzaxO+zTqTg7KZFc9BOZMv9JmZVRC3lM2scJ3hWlZbcVA2s+K5+yKTg7KZFc9BOZODspkVLu/NINXEQdnMiuegnMlB2cyK5yX6Mjkom1nh3H2RLVdQlrQlsCQiFknqCZwErAKujohVbVFBM+uEHJQz5W0pTyKZFHoRyRykBwIrgR2B77dqzcys83L3Raa8QXl74Ol0+whgb5JJnafhoGxmZXL3Rba8QbkW6CFpJ+DtiJguSaxdAsXMrGluKWfKG5QfAW4nmSv0t+m+7YG5rVkpM+vc3FLOljcoHwecQdKPfEm6b0eShQjNzMrjlnKmXEE5XX9qRL1997Zqjcys0wsH5Uy5pu6U1F3SeZJekrQ43fd5SSe3TfXMrFOqbUaqEnnnU74Y2Bc4AajrFHoOOL41K2VmnVvU5k/VIm+f8mHARyJioaRagHQExlatXzUz67SqKMjmlbelLOCddXZIvYElrVYjM7MqljcoP0SyfHapHwF/ap3qmFk1cPdFtrxB+TRgP0nzgL6SZpHcan1mq9fMzDqttg7KkoZLelzSckmTS/b3lDRe0quSlkj6r6Rh9Z47QdIKSUtL0t4l+d0ljZX0VpqulNSt3Pym5B0SNw/YW9KewNbADODfEdX0OWZmLVVAxHgDGEXSaNyiZH83YHa6/xXg48B9kmZGxB9Lyo2LiFMzjj2SZMDDLunj+4CzgZ+Wmd+oZq1mHRH/joiJEfFPB2Qzyy2UP+U5fMSkiJgMzK+3f1lEnBMRL0fiMZJu2X1zHH4YMCoiZkfEbOACkhvrys1vVN6pOx9i7VC4dUTEAXmOZWbVq1KacpJ6AXsBN9fLOlrS0SSt6uuBn0dEraT+JC3vqSVlpwJbSepH0tDNzI+IxU3VKW9LeTLwu5L0L2An4NGcxzGzKha1yp0k1UiKklTTkjqkk6ldC7xIMi1xnStI4tpAkhbuKWmCtZOvLSopX7fdp4z8JuXtUx5Tf5+kW4EL8xzHzKpbc1rKEVED1LTG+dOAfDVJ8D2wtBs2IqaUFH1M0kXA0cDPSaYqBujH2q6RfunPJaxt6GblN6lZfcr1PA3s0wrHMbMqEaHcqbWkAfkqkm6Lg8roUigN2AuBmcDgkvzBwIyIWNxUfjn1y9unvFu9XesD3wZeznMcM6tubd2nnA5Bq0td0r7j2ohYAYwlaUgekAbR+s89HPgDScv2Y8BZJEG8zg3ACEl/Tx+fTdINUm5+o/LeZj2V5EJf3cfWMmAKcEzO45hZFYva1mv5ZhgJnFvy+F3gEUnHkKwtuhyYnjSaAfhNRJyQbg8HriGJj7OAccClJcc6n2RO+efSxzcBo3PkN0oRHX+y6ZXzX+n4L6JKfWzXo9q7CtYCT735aLOi6+t7fCb33+xWjz/Q5pG8EuRtKZuZtVgBLeUOq8mgLOkJMsYml4qI/2mVGplZp+egnK2clvLlbV0JM6sunaDXtM00GZQj4sYiKmJm1cMt5Wy5+5QlbQDsBmzM2lEYRMRdrVgvM+vEWnPccWeTd5zy/sBEkptO+gJvk9w6OANwUDYza6G8LeVLgAsj4lJJCyNiI0nnkIxXNjMrS6VMSFSJ8gblHVl74a/u+8fPgJdYd3C1mVmmWndfZMoblJcC66U/50raDniLtRNumJk1yX3K2fIG5QeBQ4AbgTuAPwIr8Bp9ZpaDR19kKysoSzoXuD4ivl2yeyTJvd19SIK0mVlZPE45W7lTdx4EvCLpfkmHSeqeLqXym4i4OiLeactKmlnn0pxJ7qtFWUE5IvYBdgWeILnQN1vS5ZJ2bcO6mVknVRvKnapF2ZPcR8TzEXEWsCUwlGQdqv9I+pek77VR/cysE2rPSe4rXe6VRyKiNiLuiYhDgU+S3Nl3davXzMw6rYj8qVrkDsqSeko6UtIfgceA10lazmZmZXH3Rbayh8RJ2gM4FjiSZBb/G4GTIuKlNqqbmXVS1dQdkVe5Q+KeJrmb7/ckq7r+vnT1VzOzPKqpOyKvclvKvwYmRMTctqyMmVWHauqOyKusoBwRF7d1RVpivc32a+8qWDP5T7M6ufsim9foM7PCuaWczUHZzArnLuVsuYfEmZlVOknDJT0uabmkyfXyuksaK+mtNF0pqVtR+U1pMihL6ltOKveEZmYFjFN+AxgFjG8gbySwL7BLmvYDzi4wv1HltJQXAQsbSXX5ZmZlaevbrCNiUkRMBuY3kD0MGBURsyNiNnABcFyB+Y0qp0m9TbkHMzMrR3vd5CCpP8m8PVNLdk8FtpLUj6Sh2mb5EbG4qTo2GZQjYnpTZczM8ohmDIaUVAOcW7LrvIioyXmY3unPRSX76rb7sHaUZlvltzwo1ydpb2B/komI1vxmI+L/8h7LzKpTbTOGX6QBuKaFp16a/uzH2q6NuuXslrC2S7et8puUa/SFpJOBB4C9gJNJujaOBwblOY6ZVbdalDu1hohYCMwEBpfsHgzMiIjFbZ1fTh3zDok7FfhCRBwMvJv+PAxYnvM4ZlbFAuVOeUjqJqkXSW9AF0m9JPVIs28ARkgaJGkQyciIa0ue3tb5jcrbfbFJRDySbockAfeRzI1hZlaWAi70jWTd/ud3gUdIul7PBwaQrDEKcBMwuqRsW+c3SpFjuiZJLwD7R8Qbkv4DnAHMAx6KiIFlH6iVdeuxuW8Q6qB8s23HtnLFrGa9hX/cZEjuv9mD5txaFf9d8nZfXA18PN2+DPgjybp9V7Vmpcysc6ttRqoWubovIuLnJds3SfoL0DsinmvkaWZm66imIJtXiyYkiogZrVURM6sezRmnXC1yBWVJr5IxwVNEbNsqNTKzTq/WMTlT3pbyqfUebwacRLJen5lZWVpr3HFnlLdP+Xf190m6H/gN8P9aq1Jm1rl5uFS21pjkfhbJ9HRmZmXxhb5sefuUv1pv1/rAkcCTrVYjM7MqlrelPKbe46XAFGB461THzKpBrdynnCVvn7LnVjazFnOfcra8s8Q9nbF/aqvUxsyqgu/oy5a3++KDGfu3amE9zKyKeJxytrKCsqTL0s3uJdt1tgVeadVamVmn5nHK2cptKfdPf3Yp2YbkW8WzgFcdMbOyuU85W1lBOSKOBZA0JSKubNsqmVln5+6LbHmn7nxe0k6lOyR9SNJnW7FOZtbJ+UJftrxB+UpgWb19y9L9ZmZliWakapF39MWmETGzdEdEzJC0eSvWycw6OXdfZMvbUp4laXDpDkm7A2+0Wo3MrNNz90W2vC3lq4GJks4FXgK2J1mc8OrWrpiZdV7VFGTzyhuU6/qORwBbA9OBqyKi/pwYZmaZwt0XmfLOfRHAFWlaQ9L+EfFwK9bLzDoxt5SzNXs+ZUmbAkOBYSQrkGzQSnUys07OQTlb3gmJukr6uqS7gdeBUcDNeO4LM8uhLYfESVpaL62U9FRJ/gRJK+qV2bskv7uksZLeStOVkrqVm99SZQVlSTtJuphklMWvgYXAF4B5JH3KC1qrQmZmLRERvUsT8Bxwa71i4+qVe7QkbySwL8mKSrsA+wFn58hvkXJbys8BxwI/BgZFxNER8Weqa0y3mbWSWuVPzSFpL2BnYEKOpw0DRkXE7IiYDVwAHJcjv0XKDcoTgF7pyWsk7dpaFTCz6lPgOOXjgPsiov69FEenXQ/PSDpdUhcASf2BLYCpJWWnAltJ6tdUfvOruVZZQTkihgGDgJ8A+wBPSpoC9AFapSJmVj2aE5Ql1UiKklTT2DkkrQ8MAa6tl3UFsBMwkCRon5ImgN7pz0Ul5eu2+5SR32JlX+iLiGURcW1EfBL4CPAQybwXT0qq/6LNzDI150JfRNREhEpSTROnORx4B7h3nXNHTImIeRGxOiIeAy4Cjkizl6Y/SxubddtLyshvsby3WQMQEc9GxOnA5sC3gU1bozJmVh0K6lP+DnBjRKxqqjp1GxGxEJgJDC7JHwzMiIjFTeU3q5b1NCso14mIVRFxZ0R8qTUqY2bVoa37lNMphj8JXN9A3uGS+iqxB3AWcGdJkRuAEZIGSRpEMrLi2hz5LdJqY+vMzMpVwLCt44C/RsQLDeQNB64hiX+zgHHApSX55wMDSEadAdwEjM6R3yJK7pzu2Lr12Lzjv4gq5SkQOraVK2Y16y28YOujcv/Njph+U1X8d3FL2cwK59usszkom1nh/NU2m4OymRXOLeVsDspmVjgvB5XNQdnMClfrDoxMDspmVjiH5GwOymZWOPcpZ3NQNrPCufsim4OymRXOITlbi+a+MDOz1uWWspkVzn3K2RyUzaxw7lPO5qBsZoVzSM7moGxmhXP3RTYHZTMrXLitnMlB2cwK55ZyNgdlMyucL/Rl8zjlgp104lAee/T3LFvyCndOvG6dvG233Zp77vo18+Y8w/RXH+eM009s9FjdunVjzOWjmPvmNOa+OY3Lf34+Xbt2bcvqV5W692rpkleYWO+9qtOrVy+ee/ZvzJv77Dr7N9tsEBMnXsebs6cx+42nueWWX/KBD2ycea6693LOm9OYUwXvZXNWs64WDsoFe2P2HEZfOIZrr7t5nf1dunTht5Nu4Imp09h089357OeO4KQTj2XIkK9nHmvE2aewzz57sdvgA9ht8AHsu+/H+fFZ32/jV1A96t6r6+q9V6Vqzj2DWbNmv2//lVcmS7Ztt/1e7LDjJ+jZsweXXfbTzOOcnb6Xuw8+gN3T9/KsTvxe1hK5U7VwUC7Y5Mn3cddd97NgwVvr7N9pp+3Yacft+On5l7Fq1SpeeOFlbphwC9897qjMYw09ZgijLxzDm2/O5c0353LhRVdw7NAj2/olVI2692p+vfeqzkcH78rnv3AAP7t47PvytvnglkyceDfLlr3D0qXLuOOOu9hll50yz1Vt72Vbr2bdkTkoV4guXZK3QtI6+z7ykQ83WH7DDfux5Zab8eSTz6zZ9+STz7D11lvQt2+ftq2s0bVrV37xi0v4wQ9GsHz5ivflX375eA455Mv07duHfv36csThX+e++x5o8FjV+F5GM/5Vi4oKypK6S3qwvevRHp5//mVefXUG59WcQY8ePdh55x0ZesyQzD/K3r03AGDRosVr9tVt9+nTu+0rXOVOO+14nn76Of7yl0cbzP/Ho//mAwM3Zt7cZ5k75xk22mhDRo8e02DZanwv3VLOVlFBmaQ+n26qkKQaSVGXale/XUDV2taqVas4+JCh7L7bLkx/9XF+deNYbvzVbSxYsLDB8kuXLgOgX7++a/bVbS9ZsrTtK1zFtt12a048YShn/uj8BvMlcd/vb+Efj/6bDfvvwIb9d+Dv//gXv7+34b7panwv27KlLGmCpBWSlpakvUvyu0saK+mtNF0pqVtr5bdU4UFZ0qSsBNxWzjEioiYiVJe6dO3b9JM6gP/+9yW++OWj2HTz3dhjz4Po2bMHf/nrYw2WXbRoMTNmvMHuu++yZt/uu+/C66/P4u23lxRV5aq07z4fZ+DAjXhy6oPMeP0J7rh9PH379mHG60+w5x6D2WijDfngB7dk7Njreffd93j33fe46qob2HvvPRgwoP/7jleN72UBLeVxEdG7JJV+pRkJ7Avskqb9gLNbMb9F2qOl/EVgJvBkA2laO9SnUF27dqVnz55069aNLl260LNnT7p37w7ARz7yYdZffz26d+/O17/+hTUXf7Lc+Kvb+PFZP2CTTQayySYDOetH3+f6G7JHClg+We/V7XfcxQ477s0eex7EHnsexPEn/JAlS5ayx54H8cTUaSxYsJAXX3yVE088hp49e9KzZ09OPPEYZsx4I/ObT7W9l7URuVMrGgaMiojZETEbuAA4rhXzW6Q9bh55GvhzRNxVP0NSL1rxE6cSjTj7FM75yelrHi9b8gqPPPIPPvPZwzjs0K9wwvFH07NnT5566lkOOXQYTz/93JqyV429CICTh58FwKgLLmejjfoz7amHAbj5lt9y4UVXFvdiOrmz671XS9P36sDPHsZ77723Zv9bby0iIpgzZ96afYcceiz/75Iapr/2H7p0EVOnPsM3Dhm6Jr/+e3nBBZczYKP+PJ2+l7fc8lsu6sTvZQGX7Y6WdDQwG7ge+HlE1ErqD2wBTC0pOxXYSlI/koZqs/MjYjEtpGjdT6CmTyidDMyKiMkN5HUFRkbEeXmO2a3H5tVzabaT8UrzHdvKFbOa9RZ+a+tv5P6bven1354HnFuy67yIqKlfTtL/ADOAt4A9gdtJgvLPJW0JvA4MjIj5afmBwFxgS5L/ks3Oj4iZeV9XfYW3lCPiqkbyVgO5ArKZdTzNuRkkDcA1ZZSbUvLwMUkXAUcDPwfqrpz2A+aXbAMsYW2XbnPzW6zSRl+YWRUoeJzymuuEEbGQ5JrW4JL8wcCMiFjc0vyWVLKOg7KZFa4tR19IOlxSXyX2AM4C7iwpcgMwQtIgSYNIrmNd24r5LeJZ4syscG08l8Vw4BqS+DYLGAdcWpJ/PjAAqLuKfhMwuhXzW6TwC31twRf6Oi5f6OvYmnuh79Ctv5r7b3bi9Luq4r+LW8pmVrhqum06LwdlMytcZ/iG3lYclM2scNU0P3JeDspmVjh3X2RzUDazwlXT/Mh5OSibWeHcfZHNQdnMCucLfdkclM2scO5TzuagbGaFc59yNgdlMyuc+5SzOSibWeHcp5zNs8SZmVUQt5TNrHDuvsjmoGxmhfOFvmwOymZWuFZenbpTcVA2s8I5JGdzUDazwrlPOZuDspkVzkE5m4OymRXO45SzOSibWeHcUs7moGxmhfOQuGwOymZWOHdfZPNt1mZWuFoidyqXpJ6Sxkt6VdISSf+VNKwkf4KkFZKWlqS9S/K7Sxor6a00XSmpW7n5LeWgbGaFi4jcKYduwGzgQKAvMBS4VNJBJWXGRUTvkvRoSd5IYF9glzTtB5ydI79FHJTNrHBt2VKOiGURcU5EvByJx4CHSAJpOYYBoyJidkTMBi4AjsuR3yIOymZWuGjGv+aS1AvYC3iqZPfRadfDM5JOl9QlLdsf2AKYWlJ2KrCVpH5N5Te7kiUclM2scLURuZOkGklRkmqaOo8kAdcCLwKT0t1XADsBA0lauKekCaB3+nNRyWHqtvuUkd9iDspmVrjmtJQjoiYiVJJqGjtHGpCvJgnAX4+IWoCImBIR8yJiddq1cRFwRPq0penP0lZv3faSMvJbzEHZzDqdNCBfRdJtcVBELG6k+Jp1XCNiITATGFySPxiYERGLm8pvjbo7KJtZ4ZrTfZHTWGAf4LNpIF1D0uGS+iqxB3AWcGdJkRuAEZIGSRpEMrLi2hz5LeKbR8yscG15R5+krYGTgOXA9KTRDMBvIuIEYDhwDUn8mwWMAy4tOcT5wADgufTxTcDoHPktq39nuLOmW4/NO/6LqFJquohVsJUrZjXrLdxx4B65/2ZfmPd4Vfx3cUvZzArnuS+yOSibWeG8HFQ2B2UzK5xbytkclM2scOmQYWuAg7KZFc6T3GdzUDazwnWGUV9txUHZzArnlnI2B2UzK5xbytkclM2scB4Sl81B2cwK5yFx2RyUzaxw7r7I5qBsZoXzhb5sDspmVji3lLN5PmUzswrilrKZFc6jL7I5KJtZ4dx9kc1B2cwK5wt92RyUzaxwbilnc1A2s8K5Tzmbg7KZFc539GVzUDazwrmlnM1B2cwK5z7lbA7KZlY4d19kc1A2s8K5pZzNQdnMCuegnM1B2cwK55CcTf7EqnySaiKipr3rYfn5vbO8HJQ7AEkREWrvelh+fu8sL0/daWZWQRyUzcwqiINyx3Bee1fAms3vneXiPmUzswrilrKZWQVxUDYzqyAOymZmFcRB2cysgjgoVyhJ3SWNlfRWmq6U5NviOwBJwyU9Lmm5pMntXR/rWByUK9dIYF9glzTtB5zdrjWycr0BjALGt3dFrONxUK5cw4BRETE7ImYDFwDHtXOdrAwRMSkiJgPz27su1vE4KFcgSf2BLYCpJbunAltJ6tcedTKzYjgoV6be6c9FJfvqtvsUWhMzK5SDcmVamv4sbRXXbS8puC5mViAH5QoUEQuBmcDgkt2DgRkRsbg96mRmxXBQrlw3ACMkDZI0iGTkxbXtXCcrg6RuknqRrOzTRVIvST3au17WMXjca+U6HxgAPJc+vgkY3X7VsRxGAueWPH4XeATYv11qYx2KZ4kzM6sg7r4wM6sgDspmZhXEQdnMrII4KJuZVRAHZTOzCuKgbGZWQRyUzcwqiIOyNYukqZKGpttHSfpHO1cJAEkPSzq1veth1lwOytZiEXFTRHyynLKSXpP09Ubyn5V0RgP7d5BUK2m7FlTVrOI5KFc5SV0lqb3rUeI64NgG9g8DHo6Ilwuuj1mhHJQ7obQ1OkLSFElvS7pf0mYl+ZGuIzcNeAfoLWk7SXdLmidpuqSRkrqUPGe4pBmSFki6oN75hkqaWvK4b7q+4Ovp+f8taUtJdwBbAbdIWirpFw1U/1fADpI+XnK8rsDRwHWSPirpb+m6hfMk3SJpQMbvYZ16pfvWdLukjw+U9C9JiyQ9I+mrJXmflfSUpCWS5ki6utFfvFkrcFDuvL4DfBMYBLxJMqFRqW8CBwF9gVrgAeBBYHOS9QCHkLZYJR1AshzV4cCm6fN3beTcE4DtgU8AGwLfA96NiMOA14EjI6J3RJxQ/4kRMQ+4m6RlXOdzwPrApLSuZwGbpHXYHLiokbpkkrQbcEd6vI2A44FfS9opLXIjcElE9AG2BX7dnPOY5eGg3HldHRH/jYh3gDOB/SVtUZJ/cUS8ERHLgS8CCyPi5xGxIiJeB8aQBG6Ao4CbIuLRiFgB1ADLGjqppE2Ag4HvpcevjYgnIiLPenXXAUMkrZc+HgbcHBHvRsSTEfG3iFgZEXOAy2j+7GvHAxMi4sG0nn8D7iH58AFYCWwvaWBELIuIiriYaZ2bp+7svKbXbUTEHEnLSVqVM9Pdr5eU/SCwq6RFJfu6ADPS7c2Ah0uOt1LS7Izzbg0sTwN7c91PssLKIZLuA74C7A0gaXvgUmBPkmWzupAEz+b4IHCApNI+7G7A2+n2wcAI4HlJ04ELI+L2Zp7LrCwOyp3X1nUbkj4A9ARmleTXlmzPAP4TEZ/IONYb9Y7XnbXdGPVNB3pK2jIiZjSQX9vAvnVExGpJE0hayAOAZyNiSpr9C+AF4JiIWJSO5JiQcailJN0epQaVbM8AxkTEWRn1mELywdAF+Dpwu6RH0ha6WZtw90XndbykndIugJ8Bf4mImRll7wE2kXRSukpG1/S5+6f5twBHSfp4uoLGOcAGDR0oDVi/A34haVNJXdKLc3UX4+YA5Qxrux74NHAaSXdGnb4krei3JW0J/LCRY0wFtpW0X7oayJkkQb7OL4FjJf1v+pp7Stpb0ocl9ZD0bUn9I6KWtQvXriqj7mbN5qDceV1PEkznkHRbHJVVMCKWAgcCnwFeAxYAN5O2KiPiz8BPgDuB2ST/b6Y1cu5jSFqhj5MEs18Adf3Do4HhkhZKGtdInV4hWa1jE9a9SPl/wJdJuhh+l9Yp6xgvkfSnT0zr3RN4piT/CeBIYBQwj+SbxPlpOUj61F+StAS4EvhmRCxo5HWbtZhXHumEJL0GnBoRk9u5KmaWk1vKZmYVxEHZzKyCuPvCzKyCuKVsZlZBHJTNzCqIg7KZWQVxUDYzqyAOymZmFcRB2cysgjgom5lVEAdlM7MK8v8BqN00+2SDOhEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting confusion metrix\n",
    "plt.figure(figsize=(4,4), dpi=90)\n",
    "sns.heatmap(xgb_cm, annot=True, fmt=\".1f\")\n",
    "plt.title(\"Confusion Metrix\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"predict Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a2b9ebc0",
   "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": [
    "plot_roc_curve(y_test, xgb_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a8db4fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Cross Validation\n",
    "# score=cross_val_score(xgb,X,y,cv=10)\n",
    "# score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b2795c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cross Validation\n",
    "# score=cross_val_score(rf,X,y,cv=10)\n",
    "# score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "764a54d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "945e5bc0",
   "metadata": {},
   "source": [
    "# IsolationForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fd54c2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.007431250318482157"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud=df_final[df_final['fraudEvent']==1]\n",
    "valid=df_final[df_final['fraudEvent']==0]\n",
    "outlier_fraction = len(fraud)/float(len(valid))\n",
    "outlier_fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8cd540ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IsolationForest(contamination=0.1, n_estimators=70, n_jobs=2)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isoFor=IsolationForest(n_estimators=70, max_samples='auto', contamination=float(0.1),max_features=1.0, n_jobs=2)\n",
    "isoFor.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "536fe85b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>mcc</th>\n",
       "      <th>merchantCountry</th>\n",
       "      <th>posEntryMode</th>\n",
       "      <th>transactionAmount</th>\n",
       "      <th>availableCash</th>\n",
       "      <th>score</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98059</th>\n",
       "      <td>5969</td>\n",
       "      <td>442</td>\n",
       "      <td>81</td>\n",
       "      <td>0.091530</td>\n",
       "      <td>7500</td>\n",
       "      <td>0.101128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48317</th>\n",
       "      <td>5942</td>\n",
       "      <td>442</td>\n",
       "      <td>81</td>\n",
       "      <td>0.114230</td>\n",
       "      <td>4500</td>\n",
       "      <td>0.084750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105638</th>\n",
       "      <td>5411</td>\n",
       "      <td>826</td>\n",
       "      <td>81</td>\n",
       "      <td>-0.421040</td>\n",
       "      <td>12500</td>\n",
       "      <td>0.064513</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100628</th>\n",
       "      <td>5541</td>\n",
       "      <td>826</td>\n",
       "      <td>5</td>\n",
       "      <td>1.579204</td>\n",
       "      <td>7500</td>\n",
       "      <td>0.166139</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59046</th>\n",
       "      <td>5735</td>\n",
       "      <td>442</td>\n",
       "      <td>81</td>\n",
       "      <td>-0.295338</td>\n",
       "      <td>3500</td>\n",
       "      <td>0.078427</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mcc  merchantCountry  posEntryMode  transactionAmount  availableCash  \\\n",
       "98059   5969              442            81           0.091530           7500   \n",
       "48317   5942              442            81           0.114230           4500   \n",
       "105638  5411              826            81          -0.421040          12500   \n",
       "100628  5541              826             5           1.579204           7500   \n",
       "59046   5735              442            81          -0.295338           3500   \n",
       "\n",
       "           score  anomaly  \n",
       "98059   0.101128        1  \n",
       "48317   0.084750        1  \n",
       "105638  0.064513        1  \n",
       "100628  0.166139        1  \n",
       "59046   0.078427        1  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=X_test.copy()\n",
    "df1['score']=isoFor.decision_function(X_test)\n",
    "\n",
    "df1['anomaly']=isoFor.predict(X_test)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "044ef706",
   "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>score</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98059</th>\n",
       "      <td>0.101128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48317</th>\n",
       "      <td>0.084750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105638</th>\n",
       "      <td>0.064513</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100628</th>\n",
       "      <td>0.166139</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59046</th>\n",
       "      <td>0.078427</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           score  anomaly\n",
       "98059   0.101128        1\n",
       "48317   0.084750        1\n",
       "105638  0.064513        1\n",
       "100628  0.166139        1\n",
       "59046   0.078427        1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[['score','anomaly']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ecc3239c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = df1['anomaly'].copy()\n",
    "\n",
    "y_pred.values[y_pred.values == 1]=0\n",
    "y_pred.values[y_pred.values == -1]=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1fea9e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_cmb=confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9ecae846",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[21104  2454]\n",
      " [  102    65]] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.90      0.94     23558\n",
      "           1       0.03      0.39      0.05       167\n",
      "\n",
      "    accuracy                           0.89     23725\n",
      "   macro avg       0.51      0.64      0.50     23725\n",
      "weighted avg       0.99      0.89      0.94     23725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation \n",
    "iso_cmb = confusion_matrix(y_test, y_pred)\n",
    "iso_crb = classification_report(y_test, y_pred)\n",
    "print(iso_cmb,\"\\n\")\n",
    "print(iso_crb)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93880506",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4d6743c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting confusion metrix\n",
    "plt.figure(figsize=(4,4), dpi=90)\n",
    "sns.heatmap(iso_cmb, annot=True, fmt=\".1f\")\n",
    "plt.title(\"Confusion Metrix\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"predict Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85743d8e",
   "metadata": {},
   "source": [
    "# Balanced data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8ed5cba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    117746\n",
       " Name: fraudEvent, dtype: int64,\n",
       " 1    875\n",
       " Name: fraudEvent, dtype: int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_frauds = df_final.query('fraudEvent == 0')\n",
    "frauds = df_final.query('fraudEvent == 1')\n",
    "not_frauds['fraudEvent'].value_counts(), frauds['fraudEvent'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d4ea2944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    875\n",
       "0    875\n",
       "Name: fraudEvent, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df = pd.concat([frauds, not_frauds.sample(len(frauds), random_state=20)])\n",
    "balanced_df['fraudEvent'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "afdb5bca",
   "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>mcc</th>\n",
       "      <th>merchantCountry</th>\n",
       "      <th>posEntryMode</th>\n",
       "      <th>transactionAmount</th>\n",
       "      <th>availableCash</th>\n",
       "      <th>fraudEvent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37865</th>\n",
       "      <td>5411</td>\n",
       "      <td>826</td>\n",
       "      <td>5</td>\n",
       "      <td>0.632902</td>\n",
       "      <td>7500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70712</th>\n",
       "      <td>4814</td>\n",
       "      <td>826</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.445448</td>\n",
       "      <td>7500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15051</th>\n",
       "      <td>5735</td>\n",
       "      <td>442</td>\n",
       "      <td>81</td>\n",
       "      <td>-0.284598</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mcc  merchantCountry  posEntryMode  transactionAmount  availableCash  \\\n",
       "37865  5411              826             5           0.632902           7500   \n",
       "70712  4814              826             1          -0.445448           7500   \n",
       "15051  5735              442            81          -0.284598           1500   \n",
       "\n",
       "       fraudEvent  \n",
       "37865           0  \n",
       "70712           0  \n",
       "15051           1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df = balanced_df.sample(frac=1, random_state=1)\n",
    "balanced_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3fb32812",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating X and y variables\n",
    "X_b =balanced_df.drop(\"fraudEvent\", axis=1)\n",
    "y_b = balanced_df.loc[:,\"fraudEvent\"].values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "55cbb11e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sampling\n",
    "random_stat=20\n",
    "Xb_train, Xb_test, yb_train, yb_test = train_test_split(X_b, y_b, test_size = 0.2, random_state=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "135e60b5",
   "metadata": {},
   "source": [
    "## Random Forest - balanced data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "01bffb3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7878571428571428\n",
      "0.8\n"
     ]
    }
   ],
   "source": [
    "rf_b= RandomForestClassifier(max_depth=2,class_weight=\"balanced\")\n",
    "rf_b.fit(Xb_train, yb_train)\n",
    "print(rf_b.score(Xb_train, yb_train)) # train accu\n",
    "print(rf_b.score(Xb_test, yb_test))  #test accu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e6e26ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_b_predictions = rf_b.predict(Xb_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e5e0ce50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[167  31]\n",
      " [ 39 113]] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.84      0.83       198\n",
      "           1       0.78      0.74      0.76       152\n",
      "\n",
      "    accuracy                           0.80       350\n",
      "   macro avg       0.80      0.79      0.80       350\n",
      "weighted avg       0.80      0.80      0.80       350\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation \n",
    "cmb = confusion_matrix(yb_test, rf_b_predictions)\n",
    "crb = classification_report(yb_test, rf_b_predictions)\n",
    "print(cmb,\"\\n\")\n",
    "print(crb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6e84dc5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting confusion metrix\n",
    "plt.figure(figsize=(4,4), dpi=90)\n",
    "sns.heatmap(cmb, annot=True, fmt=\".1f\")\n",
    "plt.title(\"Confusion Metrix\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"predict Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a7592d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1ru9jNkFnxYqdbNiwl7Zta5OU1JfzzjvD3yEFNG8milTA9bcTi+PIwdXtwAuqqkCKiGwEWgG/eTEuUxYKTr+Zezu4uXDb3Ok38120ZtNvmrKjqmzYsJcWLRxXxw8ZcjaRkRW47bY4IiJsXK1T5c1EsRhoLiKNgW3ADcCNBdpsAS4G5otIXaAl8JcXYzKloTmwL8UlISy16TdNufHnn3sZNOhrFi1KZc2aIcTGViM8PIyBAzv5O7Sg4bVEoapZIjIU+A7H6bHvqepqERnkfH488AwwUURW4ihVPayqRXz6GJ+x6TdNgMjMzObllxfy9NM/cfRoFjVqRLJ+/R4bm8kLvHraiKrOAmYVeGy8y/2/gV7ejMG4YdNvmgD166+pJCbOYOXKXQDcckt7Xn65F7Vr2xhc3mDnF4aKvOk3XcpHNv2mCUCvvfYr99//HarQpEl1xo+/gp49m/o7rKBmiSLYqEL61vyjotr0myaIXHTRmUREhDNs2Lk88cRFVKligzZ6myWKQGbTb5oQkJp6kP/+dxUPPHAeAPHx9dmyZRh16liZyVcsUQSKvOk3C3QyFzX9ZmStwqUjm37TBJjs7BzeemsJjz32I+npx2nevCZXXum4qtqShG9ZoiiPso/D9kX5T0W16TdNCFmxYieJiTNYtGgbAFdf3Spvxjnje5YoyqOpV8CW2QUedJ1+M/fCtTibftMElSNHMhk58idGj15IVlYOp58ezdixl3HNNa39HVpIs0RR3hzY5EgSFSpD65tt+k0TUkaPXsALL/yCCAwdejbPPnsx1apZP5q/WaIob9Z/4fjZ9Cro9bZ/YzHGB1wnExo2rAuLFm1j+PALOffcWD9HZnJ5PO+jiNjXWV9Y/7njZ4t/+jcOY7xMVZk4MZlOnZLIyHBMQRMVFcHMmTdakihnSkwUInKeiKwB1jqXO4jIm16PLBQd2AQ7fnOUmBpf5u9ojPGaDRv2cPHFH3L77V+ybNkOPvxwub9DMm54ckTxKo7hwPcAqOpy4EJvBhWycstOTfpAxSr+jcUYLzh+PJtnn53HWWe9xZw5m6hVqwqTJl3DXXcl+Ds044ZHfRSqulXyn25ZxAz25pSt/8zxs+X1/o3DGC9YtCiVO+74itWr0wAYMCCOl17qSa1a9qWovPMkUWwVkfMAFZEI4B6cZShThg5sgh2LHWWnRlZ2MsFn585DrF6dRrNmNZgwoQ89ejT2d0jGQ56UngYBQ3BMbZoKxAGDvRhTaMrtxG7SFypG+jcWY8qAqrJy5c685SuvbMlHH13DihWDLEkEGE8SRUtVvUlV66pqHVW9GbCrX8pabqJoaWc7mcC3desBrrpqMnFxE1i6dHve4zfd1J7ISBvEL9B4kije8PAxU1oHNlrZyQSF7OwcXn/9V1q3HseMGeuJiopg06b9/g7LnKJi+yhEpAtwHlBbRO53eaoajhnrTFnJO9vJyk4mcCUn72DgwBksWfI3AP/4R2vGjLmM00+3YesDnbvO7AggytnG9Td9ELjOm0GFnHW5ZztZ2ckEpo8+WsGAAdPJzlZiY6sxbtzleSO9msBXbKJQ1Z+An0Rkoqpu9mFMoeXARti5xMpOJqB169aI6OhK3HZbB555pjvR0TY+UzDx5PTYwyLyEtAWqJz7oKr28FpUocTKTiYA7dyZwdixvzFiRDfCw8OIja3Gn3/eQ40a9jccjDzpzP4Y+ANoDDwNbAIWezGm0LLOLrIzgUNVeffdpbRuPY5Ro+YzbtyJjwJLEsHLkyOKmqr6rojc61KO+snbgYWEvLJTFDTq7e9ojHFr3brd3HnnTH76yVGJvvTSpvTt28LPURlf8CRRZDp/bheRK4C/ARvasSysc1470dTKTqb8OnYsixdf/IVnn53P8ePZ1K5dhddf780NN7RDbCbFkOBJohglIjHAAziun6gG3OfNoEKGDSluAsAnn6zkqafmAnDHHfH85z89rcwUYkpMFKo603n3ANAdQETO92ZQIWH/X1Z2MuVWTo4SFuY4Wrj11g7Mnr2RgQM70q1bI/8GZvyi2M5sEQkXkf4i8qCItHM+1kdEFgBjfRZhsMqbyc7KTqb8UFX++99VtGo1lm3bDgIQHh7Gxx9fa0kihLk76+ld4P+AmsAYEXkfGA38R1XjfRFcUMsdUryFne1kyofNm/fTp8+n3HDDFDZs2Mv48Uv8HZIpJ9yVnhKA9qqaIyKVgd1AM1Xd4ZvQgtj+v2Dn786y06X+jsaEuKysHMaMWcQTT8zh8OFMYmIq8dJLPbnjjo7+Ds2UE+4SxXFVzQFQ1aMist6SRBlZb2c7mfJh5cqdDBjwZd4Ir/36teW113pTr16UnyMz5Ym7RNFKRFY47wvQ1LksgKpqe69HF6zyznayspPxr8zMHJKTd9CwYQxvvnk5V1xh10WYwtwlCptzwhus7GT8bMmSv0lIOB2Ajh3rM316P7p3b0xUVISfIzPlVbGd2aq62d3Nl0EGlbyy05VWdjI+tWNHBv36fcHZZ7/NV1+ty3u8b9+WliSMW56M9VRqItJbRNaJSIqIPFJMm24ikiwiq0NiaBC7yM74WE6OkpT0O61ajeWzz1ZTpUpFdu8+7O+wTADx5MrsUhGRcGAc0BPHXNuLReQrVV3j0uY04E2gt6puEZE63oqnXHAtOzW2i+yM961Zk0Zi4gx++WUrAJdf3pxx4y6nUaPT/BuYCSgeJQoRiQQaquq6Ehuf0BlIUdW/nOuYDFwFrHFpcyMwVVW3AKjqrpNYf+BxLTtVqOy+rTGn6NtvU7jyyk/JzMyhbt2qjBlzGf/8Zxsbn8mctBJLTyLSF0gGvnUux4nIVx6suwGw1WU51fmYqxZAdRGZKyK/i8itHkUdqGxIceNDF1zQkNNPjyYxsSNr1w7h+uvbWpIwpeJJH8UIHEcH+wFUNRlo5MHrivqL1ALLFYBOwBXApcATIlLo/DwRSRSRJSKyJC0tzYNNl0P7/4RdSyEi2s52Ml6xd+8RHnzwezIyjgMQFRXBihV3MWFCX6pXtxMnTOl5UnrKUtUDpfgmkgqc4bIci2OI8oJtdqvqIeCQiMwDOgDrXRupahKQBJCQkFAw2QSGdVZ2Mt6hqnz66Sruu+9b0tIOo6q8/LLjy0i1ajYlqTl1nhxRrBKRG4FwEWkuIm8ACzx43WKguYg0FpEI4AagYMnqS6CriFQQkSrAOcDak4g/cNjZTsYL/vprH717f8xNN00lLe0wF110JomJnfwdlgkynhxR3A08DhwDPgG+A0aV9CJVzRKRoc724cB7qrpaRAY5nx+vqmtF5FtgBZADvKOqq0r3VsoxKzuZMpaZmc2rr/7KiBFzOXIki+rVKzN6dC9uvz3O+iFMmfMkUbRU1cdxJIuToqqzgFkFHhtfYPkl4KWTXXdAsbKTKWNz5mzi4YdnA9C/fzteffVS6ta18ZmMd3iSKF4RkfrA58BkVV3t5ZiCj5WdTBnIysqhQgVHtbhXr6bce+859O7djN69m/k5MhPsSuyjUNXuQDcgDUgSkZUiMtzbgQUNKzuZMvDVV+to3vwNli3bnvfYa6/1tiRhfMKjITxUdYeqjgEG4bim4klvBhVUrOxkTsG2bQf5xz8+46qrJrNp037GjVvs75BMCCqx9CQirYF+wHXAHmAy8ICX4woeNpOdKYWcHGX8+CU88shs0tOPU7VqRZ577mKGDDnb36GZEORJH8X7wKdAL1UteB2EcWdfCuxa5iw79fJ3NCZAbNiwh1tvnc6vv6YC0LdvC8aOvZyGDWP8HJkJVSUmClU91xeBBKW8sZ2usrKT8VhkZEVWr95F/fpRvPHGZVx7bWs75dX4VbGJQkQ+U9XrRWQl+YfesBnuPGVnOxkPLVy4lc6dGxAeHkZsbDW++qo/8fH1iImxLxjG/9wdUdzr/NnHF4EEHSs7GQ/s3n2YBx/8ng8+WM6YMb25++5zAOjWrZF/AzPGhbsZ7nLPwxtcxOx2g30TXgCzspNxQ1X58MPltGo1lg8+WE6lSuFkZeX4OyxjiuTJ6bE9i3jssrIOJOjkDiluZSdTQErKXnr2nMRtt01nz54jdO/eiJUr72LYsC7+Ds2YIrnro7gLx5FDExFZ4fJUNPCLtwMLaPtSIC0ZIqpZ2cnks2TJ33Tt+j5Hj2ZRs2YkL7/ci1tv7WCd1aZcc9dH8QnwDfA84Drfdbqq7vVqVIHOZrIzxYiPr8dZZ9WhVatavPxyL2rXrurvkIwpkbtEoaq6SUSGFHxCRGpYsnDDZrIzTgcPHmPEiLk88EAXGjSoRnh4GHPnDqBKlYr+Ds0Yj5V0RNEH+B3H6bGux8YKNPFiXIFr34YTZaczrewUyqZNW8vQod/w99/pbN16kM8/d/RXWZIwgabYRKGqfZw/G/sunCCQW3ZqdhVUsNnFQlFq6kGGDp3Fl1+uA+CccxrwxBMX+jkqY0rPk7GezgeSVfWQiNwMdAReU9UtXo8uEK2zi+xCVXZ2DuPGLebxx/9HRsZxoqMjeP75ixk0KIHwcI/G3zSmXPLkr/ct4LCIdAAeAjYDk7waVaCyslNIW7t2N8OGfUdGxnGuuaYVa9cOYciQzpYkTMDzZFDALFVVEbkKeF1V3xWR27wdWECyslPIOXYsi0qVHP9G7drV4bnnetCyZS2uvrqVnyMzpux48lUnXUQeBW4BvhaRcMB644piF9mFlO++S6F163HMmLEu77GHH77AkoQJOp4kin7AMeBfqroDaECwz3FdGnvXQ9pyKzuFgF27DnHTTVPp3ftjNm7cz4QJv/s7JGO8ypOpUHcAHwMxItIHOKqqH3o9skBjZaegp6q8//4yWrUayyefrKRy5Qq88MLFTJvWz9+hGeNVnpz1dD2OI4i5OK6leENE/q2qX3g5tsCSN6S4XWQXjLZtO8jNN09j7txNAPTs2YS33rqCpk1r+DcwY3zAk87sx4GzVXUXgIjUBmYDlihy5Ss7FTWGogl0MTGV+euvfdSqVYXXXruUG288y8ZnMiHDk0QRlpsknPbgWd9G6MgrO11tZacgsmDBVtq3r0tUVARRURFMm9aPM8+MoWbNKv4OzRif8uQD/1sR+U5EBojIAOBrYJZ3wwowNpNdUNm//yiDBs3k/PPf48kn5+Q93rFjfUsSJiR5Mmf2v0XkWuACHH0USao6zeuRBYrcslOlGCs7BThV5Ysv1nDPPd+yY0cGFSuGUa2aHSEa424+iubAaKApsBJ4UFW3+SqwgJFvJjv7UAlUmzfvZ8iQWXz99QYAzjvvDJKS+tC2bR0/R2aM/7k7ongP+BCYB/QF3gCu9UVQAWW9XWQX6DZt2k+7dm9y6FAmMTGVePHFSxg4sBNhYdZZbQy4TxTRqvq28/46EVnqi4ACyt51kLbCyk4BrlGj07j00maEhwuvv96b+vWj/R2SMeWKu0RRWUTiOTEPRaTrsqpa4rCyU0A6dOg4I0bM5cYbzyI+vj4An3xybd6YTcaY/Nz9Z2wHXnFZ3uGyrEAPbwUVMHIThc1kFzBmzdrA4MFfs3nzAebN28Kvv96BiFiSMMYNdxMXdfdlIAHHyk4BZceODO6771v++9/VAMTF1WPcuMvtojljPGBfo0rL9SK78Ai/hmKKl5OjvPvuUh56aDb79x+lSpWKjBzZjXvvPZcKFey6UWM84dX/FBHpLSLrRCRFRB5x0+5sEckWkeu8GU+ZsovsAsKuXYd44IHv2b//KL17N2PVqrt44IHzLEkYcxK8dkThnLdiHNATSAUWi8hXqrqmiHYvAt95K5YyZ2Wncu3YsSwqVAgjPDyMevWiGDPmMipXrkC/fm2t1GRMKZT4tUocbhaRJ53LDUWkswfr7gykqOpfqnocmAxcVUS7u4EpwK4iniufrOxUbs2bt5m4uAmMG7c477EBA+K44YZ2liSMKSVPjr/fBLoA/Z3L6TiOFErSANjqspzqfCyPiDQArgHGu1uRiCSKyBIRWZKWlubBpr0sbyY7O9upvNi37wgDB37FRRdN5I8/dvPRRyvIyVF/h2VMUPAkUZyjqkOAowCqug/w5Gt0UV/fCv7nvgY8rKrZ7lakqkmqmqCqCbVr1/Zg01605w/YvRIqnQZnXuLfWAyqyuTJq2jVahzvvLOMiIhwRoy4iPnzb7crq40pI570UWQ6+xEU8uajyPHgdanAGS7LscDfBdokAJOdJYFawOUikqWq0z1Yv3+4zmRnZSe/2rfvCDfeOJVvv00BoGvXhkyY0IfWrf38ZcKYIONJohgDTAPqiMizwHXAcA9etxhoLiKNgW3ADcCNrg1UtXHufRGZCMws10kCbCa7cqRatUrs3n2Y006rzEsv9eRf/4q3owhjvMCTYcY/FpHfgYtxlJOuVtW1HrwuS0SG4jibKRx4T1VXi8gg5/Nu+yXKJSs7+d2SJX9Tv34UDRpUIzw8jE8+uZbo6ErUqxfl79CMCVqezJndEDgMzHB9TFW3lPRaVZ1FgUmOiksQqjqgpPX5nZ3t5DcZGcd54on/MWbMb1x1VUumTu0HQPPmNf0cmTHBz5PS09c4+icEqAw0BtYBbb0YV/lkQ4r7xYwZ6xgyZBZbtx4kLExo0qQ62dk5hIfbRXPG+IInpaezXJdFpCNwp9ciKq/2/AG7V1nZyYe2b0/nnnu+5YsvHNdodupUn6SkvnTsWN/PkRkTWk76ymxVXSoiZ3sjmHLNyk4+deDAUdq1e4u9e49QtWpFRo3qwdChnW3oDWP8wJM+ivtdFsOAjkA5uOrNx3LLTjakuE/ExFTm9tvj+OOP3Ywbdzlnnnmav0MyJmR5ckThOt1XFo4+iyneCaec2rP2RNmp4cX+jiYoHT2axbPPzuOcc2Lp06cFAC+8cAnh4WJDbxjjZ24ThfNCuyhV/beP4imfrOzkVXPmbOTOO2eyYcNeGjaMoVevpkREhFuZyZhyothEISIVnNdCdPRlQOWSzWTnFXv2HObf//6B999PBqB161okJfUlIiLcv4EZY/Jxd0TxG47+iGQR+Qr4HDiU+6SqTvVybOWDlZ3KnKry8ccrGTbsO3bvPkxERDjDh3floYfOtylJjSmHPPmvrAHswTFHdu71FAqERqLIKztdY2WnMnL4cCaPPfYju3cfplu3RkyY0IcWLezCOWPKK3eJoo7zjKdVnEgQuUJn/ObcIcVb2kV2pyIzM5usrBwiIytStWoESUl92b49nQED4qyz2phyzl2iCAei8Gy48OC0Zw3sWQ2Vq1vZ6RT89ts2Bg6cwcUXN+aVVy4FoHfvZn6OyhjjKXeJYruqjvRZJOXROmfZqenVVnYqhYMHjzF8+P8YO/Y3VOHIkUyefbYHkZEV/R2aMeYkuDv/0OoBdrZTqU2f/gdt2ozjjTd+IyxMeOih80hOHmRJwpgA5O6IIrRrLVZ2KpVjx7Lo338K06b9AUDnzg1ISupDhw71/ByZMaa0ik0UqrrXl4GUO+tcz3ayb8GeqlSpAiJCVFQEzz3Xg8GDz7ZRXo0JcHbSenHyZrKzs51KsnLlTkSEdu3qADBu3OVkZmZzxhkxfo7MGFMW7KteUazs5JEjRxzXQ3TsmMTtt39JdrZjKvV69aIsSRgTROyIoihWdirR7Nl/MWjQTP78cx8i0Lnz6Rw7lk2VKvbdw5hgY4miKDaTXbHS0g7xwAPfM2nSCgDatatDUlIfunQ5w8+RGWO8xRJFQbtXO0pPVnYqJDMzm3POeYeNG/dTqVI4Tz11EQ88cJ4N4mdMkLNEUdB6KzsVp2LFcO6/vwvTp//B+PF9aNashr9DMsb4gCWKguwiuzzHj2czevQCataM5M47EwAYPPhshgw528ZnMiaEWKJwlVd2qgFn9PB3NH61YMFWEhNnsHp1GtHREVx/fVuqV48kLMwShDGhxhKFKys7ceDAUR599EfGj1+CKjRtWp3x4/tQvXqkv0MzxviJJQpXeWWn0DvbSVWZOnUtd9/9Ddu3Z1ChQhgPPXQew4dfaOMzGRPiLFHkCvGyU06O8sILv7B9ewbnnhtLUlIfzjqrrr/DMsaUA5YocoVg2Sk7O4eMjOPExFQmPDyMt9/uy4IFW7nzzk42PpMxJo8lilwhNpNdcvIOEhNncMYZMUyZ4jjDKy6uHnFxNsqrMSY/SxTgKDvtXRsSZafDhzMZMWIur7yykOxs5e+/09m16xB16lT1d2jGmHLKEgWcOJoI8rLTd9+lcNddX7Nx435E4J57OjNqVA+ioyv5OzRjTDlmiUI16C+yU1Vuu2163vhMHTrUJSmpL507N/BzZMaYQGCJYo9r2am7v6PxChGhVq0qREZWYMSIbgwbdi4VK9r4TMYYz3j11BYR6S0i60QkRUQeKeL5m0RkhfO2QEQ6eDOeIuUOKd782qAqO61bt5uff96StzxyZHdWrRrMQw+db0nCGHNSvJYoRCQcGAdcBrQB+otImwLNNgIXqWp74BkgyVvxFEk16IYUP3Ysi5Ejf6J9+/HceOMUMjKOAxAVFUGTJtX9HJ0xJhB5s/TUGUhR1b8ARGQycBWwJreBqi5waf8rEOvFeArbsxr2/gGVawZF2ennn7eQmDiDtWt3A3DJJU3Iysrxc1TGmEDnzUTRANjqspwKnOOm/R3AN0U9ISKJQCJAw4YNyyo+l7JTYJ/ttH//UR5++AeSkpYC0Lx5DSZM6EP37o39HJkxJhh4M1EUNcyoFtlQpDuORHFBUc+rahLOslRCQkKR6zhpQVJ2UlUuvfQjfvttGxUrhvHIIxfw2GNdqVzZzlMwxpQNb36apAKu82PGAn8XbCQi7YF3gMtUdY8X48kvSMpOIsLw4V158cVfSErqS5s2tf0dkjEmyHgzUSwGmotIY2AbcANwo2sDEWkITAVuUdX1XoylsNyL7AKs7JSVlcOYMYvYv/8oI0c6Elzfvi3p06eFTSZkjPEKryUKVc0SkaHAd0A48J6qrhaRQc7nxwNPAjWBN50fclmqmuCtmFyCO3GRXYvAuchu6dLtDBw4g6VLtxMWJgwYEJd3JpMlCWOMt3i1kK2qs4BZBR4b73L//4D/82YMRdq96kTZqWH5LztlZBznqafm8Npri8jJUc44oxpvvnmFne5qjPGJ0OzxXO9ykV1Y+d4Fs2ZtYPDgr9m8+QBhYcKwYecycmR3oqIi/B2aMSZElO9PSW9QPdE/EQBnO73zzlI2bz5AfHw93n67L506ne7vkIwxISb0EsXuVbBvXbktO+XkKHv2HKZ2bcew32+8cRlduzbk7rvPoUIFm0zIGON7offJk3vtRDksO61dm8ZFF03k8ss/ITvbcUV1gwbVGDasiyUJY4zfhNanj+qJq7HL0ZDiR49m8dRTc+jQYTw//7yFLVsOkJKy199hGWMMEGqlp9yyU2QtOKObv6MB4KefNnHnnTNZt85xreHAgR158cVLqF490s+RGWOMQ2glivUuM9mVg7LTsGHf8tpriwBo1aoWSUl96Nr1TD9HZYwx+fn/09JXymHZqXHj6kREhPPYYxfwyCMXUKlS6Pw6jDGBI3Q+mXav9HvZaePGfaxenUafPi0AGDLkbC6/vDnNmtXwSzzGGOOJ0OnM9uNFdllZObz00i+0bfsmN944hdTUgwCEh4dZkjDGlHuhcUThx4vsFi/eRmLiTJKTdwBwww3tiIiwqUiNMYEjNBLF7pWwb71Py07p6cd44ok5vPHGb+TkKGeeGcNbb13BZZc198n2jTGmrIRGoljn+4vsbrllGl9+uY7wcOHBB7swYkQ3qla18ZmMMYEn+BOFn4YUf+qpi9ixI4O33rqC+Pj6PtuuKT8yMzNJTU3l6NGj/g7FhJDKlSsTGxtLxYplN89O8CeKtBUuZaeLvLKJnBxlwoQlLFnyN+++exUA8fH1WbjwDpsnIoSlpqYSHR1No0aN7O/A+ISqsmfPHlJTU2ncuHGZrTf4z3ry8tlOq1btomvX9xk8eBbvvZfMggVb856zD4fQdvToUWrWrGl/B8ZnRISaNWuW+VFscB9ReLHsdORIJqNGzeM//1lAVlYO9epF8cYbl9GlS2yZbscENksSxte88TcX3InCS2Wn//1vI3feOTNv4L5Bgzrx/POXcNpplctsG8YYU14Ed+kpr+z0jzItO82cuZ6UlL20bVubX375F2+91ceShCmXwsPDiYuLo127dvTt25f9+/fnPbd69Wp69OhBixYtaN68Oc888wyqmvf8N998Q0JCAq1bt6ZVq1Y8+OCDfngH7i1btoz/+z/fz6bsqWPHjtGvXz+aNWvGOeecw6ZNmwq1SU9PJy4uLu9Wq1Yt7rvvPgA2b97MxRdfTPv27enWrRupqakApKWl0bt3b9+9EVUNqFunTp3UIzk5qu82Vx2N6qbZnr2m2FXlaGrqgbzl9PRj+sorC/TYsaxTWq8JbmvWrPF3CFq1atW8+7feequOGjVKVVUPHz6sTZo00e+++05VVQ8dOqS9e/fWsWPHqqrqypUrtUmTJrp27VpVVc3MzNRx48aVaWyZmZmnvI7rrrtOk5OTfbrNkzFu3Di98847VVX1008/1euvv77E13Ts2FF/+uknVXW8v4kTJ6qq6o8//qg333xzXrsBAwbozz//XOQ6ivrbA5ZoKT93/f7Bf7I3jxPFzmRHkhhXWzW79H8cKSl7tGfPD/WMM17R9PRjpV6PCT35/llH451bCVwTxVtvvaV33XWXqqq+8847esstt+Rrm5KSorGxsaqqesstt+i7775b4vrT09N1wIAB2q5dOz3rrLP0iy++KLTdzz//XG+77TZVVb3tttt02LBh2q1bN73vvvv0zDPP1H379uW1bdq0qe7YsUN37dql1157rSYkJGhCQkKRH4gHDx7UFi1a5C0vWrRIu3TponFxcdqlSxf9448/VFX1/fff1+uuu0779Omj3bt314yMDL399ts1ISFB4+LidPr06aqqunHjRr3gggs0Pj5e4+Pj9Zdffinx/ZekV69eumDBAlV1JKmaNWtqTk5Ose3Xr1+vsbGxeW3atGmjW7duVVXHF9bo6Oi8ttOnT8/7fRZU1okiePsoTnEmu8zMbF5+eSFPP/0TR49mUaNGJKtW7eLcc62z2gSe7OxsfvzxR+644w7AUXbq1KlTvjZNmzYlIyODgwcPsmrVKh544IES1/vMM88QExPDypUrAdi3b1+Jr1m/fj2zZ88mPDycnJwcpk2bxu23386iRYto1KgRdevW5cYbb2TYsGFccMEFbNmyhUsvvZS1a9fmW8+SJUto165d3nKrVq2YN28eFSpUYPbs2Tz22GNMmTIFgIULF7JixQpq1KjBY489Ro8ePXjvvffYv38/nTt35pJLLqFOnTr88MMPVK5cmQ0bNtC/f3+WLFlSKP6uXbuSnp5e6PHRo0dzySWX5Hts27ZtnHHGGQBUqFCBmJgY9uzZQ61atYrcN59++in9+vXL65Du0KEDU6ZM4d5772XatGmkp6ezZ88eatasSUJCAsOHDy9xf5eF4EwUrmc7lWJI8V9/TSUxcQYrV+4C4Oab2/PKK73y5rE25qQ9oCW38YIjR44QFxfHpk2b6NSpEz179gQclYTizo45mbNmZs+ezeTJk/OWq1evXuJr/vnPfxIe7hjvrF+/fowcOZLbb7+dyZMn069fv7z1rlmzJu81Bw8eJD09nejo6LzHtm/fTu3atfOWDxw4wG233caGDRsQETIzM/Oe69mzJzVqOAbg/P777/nqq68YPXo04DiNecuWLZx++ukMHTqU5ORkwsPDWb9+fZHxz58/v8T3mEu18O/d3f6dPHkykyZNylsePXo0Q4cOZeLEiVx44YU0aNCAChUcH9t16tTh77//9jiWUxGciSJtOezbAJG1IfbCk3rpqFHzePLJOahCkybVGT/+Cnr2bOqlQI3xrsjISJKTkzlw4AB9+vRh3Lhx3HPPPbRt25Z58+bla/vXX38RFRVFdHQ0bdu25ffff6dDhw5u119cwnF9rOA5/VWrnvjC1aVLF1JSUkhLS2P69Ol535BzcnJYuHAhkZHFz/QYGRmZb91PPPEE3bt3Z9q0aWzatIlu3boVuU1VZcqUKbRs2TLf+kaMGEHdunVZvnw5OTk5VK5c9AkqJ3NEERsby9atW4mNjSUrK4sDBw7kJayCli9fTlZWVr4jvdNPP52pU6cCkJGRwZQpU4iJiQEc+9Xd/ilLwXnWU961Eyd/tlOHDnUJDw/jkUfOZ+XKuyxJmKAQExPDmDFjGD16NJmZmdx00038/PPPzJ49G3Acedxzzz089NBDAPz73//mueeey/tWnZOTwyuvvFJovb169WLs2LF5y7mlp7p167J27dq80lJxRIRrrrmG+++/n9atW1OzZs0i15ucnFzota1btyYlJSVv+cCBAzRo0ACAiRMnFrvNSy+9lDfeeCPv2/6yZcvyXl+/fn3CwsKYNGkS2dnZRb5+/vz5JCcnF7oVTBIAV155JR988AEAX3zxBT169Cj2iOLTTz+lf//++R7bvXs3OTk5ADz//PP861//yntu/fr1+Upv3hR8iUJPbkjx1NSDTJq0PG+5b9+WbNhwN88/fwlVqpTdWCnG+Ft8fDwdOnRg8uTJREZG8uWXXzJq1ChatmzJWWedxdlnn83QoUMBaN++Pa+99hr9+/endevWtGvXju3btxda5/Dhw9m3bx/t2rWjQ4cOzJkzB4AXXniBPn360KNHD+rXdz/WWb9+/fjoo4/yyk4AY8aMYcmSJbRv3542bdowfvz4Qq9r1aoVBw4cyPt2/9BDD/Hoo49y/vnnF/shD44jj8zMTNq3b0+7du144oknABg8eDAffPAB5557LuvXr893FFJad9xxB3v27KFZs2a88sorvPDCC3nPxcXF5Wv72WefFUoUc+fOpWXLlrRo0YKdO3fy+OOP5z03Z84crrjiilOO0RNSVA2tPEtISNCiOpjy7EqGSfGOstOgv4s9osjOzuGtt5bw2GM/cuhQJosXD6RjRxu8z5SdtWvX0rp1a3+HEdReffVVoqOjy/W1FN5y4YUX8uWXXxbZL1TU356I/K6qCaXZVvAdUXhQdlqxYifnn/8ed9/9Denpx+nbtwV16lhHtTGB5q677qJSpUr+DsPn0tLSuP/++z06eaAsBFdndgllp8OHMxk58idefnkhWVk5nH56NGPHXsY119i3PmMCUeXKlbnlllv8HYbP1a5dm6uvvtpn2wuuRJG2HPanQJU6RZ7t9O9/f8+bby5BBIYOPZtnn72YatVC79uI8R13p6Ea4w3e6E4IrkRRwkx2jz3WlRUrdvHSSz3twjnjdZUrV867OMqShfEFVcd8FMWd2ltawZMoNP+Q4qrKxInJfPbZGmbO7E94eBgNGlRj/vzb/RunCRmxsbGkpqaSlpbm71BMCMmd4a4sBU+i2JWcV3Zaf7gtgy7+kDlzNgEwdepa/vnPtn4Nz4SeihUrluksY8b4i1fPehKR3iKyTkRSROSRIp4XERnjfH6FiHQs9cbWf87xrHBG/XYr7eOSmDNnE7VqVWHSpGu47ro2p/Q+jDEmlHntiEJEwoFxQE8gFVgsIl+p6hqXZpcBzZ23c4C3nD9PjioLZs1h4Pt3smZnFJDNgAFxvPRST2rVqnKqb8UYY0KaN0tPnYEUVf0LQEQmA1cBroniKuBD5xC4v4rIaSJSX1ULXwLqzq5kfv8jhzU769CsWXUmTOhLjx52yG+MMWXBm4miAbDVZTmVwkcLRbVpAORLFCKSCCQ6FzNEZF3Rm/ytVkoKuy+++N7SRx34agG7/R1EOWD7wcH2g+2DXC1LblI0byaKos4HLHiCrydtUNUkIKnEDYosKe0l6sHC9oGD7QcH2w+2D3KJiJuxj9zzZmd2KnCGy3IsUHDwdE/aGGOM8SNvJorFQHMRaSwiEcANwFcF2nwF3Oo8++lc4MBJ908YY4zxKq+VnlQ1S0SGAt8B4cB7qrpaRAY5nx8PzAIuB1KAw8CpXg1XYnkqBNg+cLD94GD7wfZBrlLvh4AbZtwYY4xvBd8w48YYY8qUJQpjjDFuBVyi8OmwIOWYB/vhJuf7XyEiC0Skgz/i9LaS9oNLu7NFJFtErvNlfL7gyT4QkW4ikiwiq0XkJ1/H6Ase/E/EiMgMEVnu3A9BN0KoiLwnIrtEZFUxz5fu81FVA+aGo1P8T6AJEAEsB9oUaHM58A2OazTOBRb5O24/7YfzgOrO+5eF6n5wafc/HCdPXOfvuP3wt3AajhERGjqX6/g7bj/th8eAF533awN7gQh/x17G++FCoCOwqpjnS/X5GGhHFHnDgqjqcSB3WBBXecOCqOqvwGkiEmyTYZe4H1R1garucy7+iuMalWDjyd8DwN3AFGCXL4PzEU/2wY3AVFXdAqCqobofFIgWx+QgUTgSRZZvw/QuVZ2H430Vp1Sfj4GWKIob8uNk2wS6k32Pd+D4FhFsStwPItIAuAYY78O4fMmTv4UWQHURmSsiv4vIrT6Lznc82Q9jgdY4LupdCdyrqjm+Ca/cKNXnY6DNR1Fmw4IEOI/fo4h0x5EoLvBqRP7hyX54DXhYVbODdJY5T/ZBBaATcDEQCSwUkV9Vdb23g/MhT/bDpUAy0ANoCvwgIvNV9aCXYytPSvX5GGiJwoYFcfDoPYpIe+Ad4DJV3eOj2HzJk/2QAEx2JolawOUikqWq030Sofd5+j+xW1UPAYdEZB7QAQimROHJfrgdeEEdxfoUEdkItAJ+802I5UKpPh8DrfRkw4I4lLgfRKQhMBW4Jci+OboqcT+oamNVbaSqjYAvgMFBlCTAs/+JL4GuIlJBRKrgGMV5rY/j9DZP9sMWHEdViEhdHKOp/uXTKP2vVJ+PAXVEof4ZFqTc8XA/PAnUBN50fpvO0iAbQdPD/RDUPNkHqrpWRL4FVgA5wDuqWuTpk4HKw7+FZ4CJIrISRwnmYVUNquHHReRToBtQS0RSgaeAinBqn482hIcxxhi3Aq30ZIwxxscsURhjjHHLEoUxxhi3LFEYY4xxyxKFMcYYtyxRmHLJOdJrssutkZu2GWWwvYkistG5raUi0qUU63hHRNo47z9W4LkFpxqjcz25+2WVcyTU00poHycil5fFtk3ostNjTbkkIhmqGlXWbd2sYyIwU1W/EJFewGhVbX8K6zvlmEpar4h8AKxX1WfdtB8AJKjq0LKOxYQOO6IwAUFEokTkR+e3/ZUiUmiUWBGpLyLzXL5xd3U+3ktEFjpf+7mIlPQBPg9o5nzt/c51rRKR+5yPVRWRr53zGqwSkX7Ox+eKSIKIvABEOuP42PlchvPnf12/4TuPZP4hIuEi8pKILBbHPAF3erBbFuIc0E1EOotj3pFlzp8tnVcojwT6OWPp54z9Ped2lhW1H40pxN/jp9vNbkXdgGwcA7glA9NwjCJQzflcLRxXluYeEWc4fz4APO68Hw5EO9vOA6o6H38YeLKI7U3EOVcF8E9gEY6B9FYCVXEMS70aiAf+Abzt8toY58+5OL6958Xk0iY3xmuAD5z3I3CM5BkJJALDnY9XApYAjYuIM8Pl/X0O9HYuVwMqOO9fAkxx3h8AjHV5/XPAzc77p+EY76mqv3/fdivft4AawsOElCOqGpe7ICIVgedE5EIcw1A0AOoCO1xesxh4z9l2uqomi8hFQBvgF+dQJhE4vokX5SURGQ6k4Rhx92JgmjoG00NEpgJdgW+B0SLyIo5y1fyTeF/fAGNEpBLQG5inqkec5a72cmIGvhigObCxwOsjRSQZaAT8Dvzg0v4DEWmOYzTQisVsvxdwpYg86FyuDDQk+MZ+MmXIEoUJFDfhmJWsk6pmisgmHB9yeVR1njORXAFMEpGXgH3AD6ra34Nt/FtVv8hdEJFLimqkqutFpBOOMXOeF5HvVXWkJ29CVY+KyFwcQ173Az7N3Rxwt6p+V8IqjqhqnIjEADOBIcAYHOMYzVHVa5wd/3OLeb0A/1DVdZ7EawxYH4UJHDHALmeS6A6cWbCBiJzpbPM28C6OKSF/Bc4Xkdw+hyoi0sLDbc4Drna+piqOstF8ETkdOKyqHwGjndspKNN5ZFOUyTgGY+uKYxA7nD/vyn2NiLRwbrNIqnoAuAd40PmaGGCb8+kBLk3TcZTgcn0H3C3OwysRiS9uG8bkskRhAsXHQIKILMFxdPFHEW26AckisgxHP8LrqpqG44PzUxFZgSNxtPJkg6q6FEffxW84+izeUdVlwFnAb84S0OPAqCJengSsyO3MLuB7HHMbz1bHtJ3gmDdkDbBURFYBEyjhiN8Zy3IcQ2r/B8fRzS84+i9yzQHa5HZm4zjyqOiMbZVz2Ri37PRYY4wxbtkRhTHGGLcsURhjjHHLEoUxxhi3LFEYY4xxyxKFMcYYtyxRGGOMccsShTHGGLf+H8MncuwgO+U3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(yb_test, rf_b_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feddc3cb",
   "metadata": {},
   "source": [
    "## XG Boost - balanced data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "caabadee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              eta=0.5, eval_metric='mlogloss', gamma=2, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.01, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=8, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_b = XGBClassifier (gamma=2,learning_rate=0.01, eta=0.5,  eval_metric='mlogloss', max_depth=3)\n",
    "xgb_b.fit(Xb_train, yb_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "18a35edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8378571428571429\n",
      "0.8142857142857143\n"
     ]
    }
   ],
   "source": [
    "print(xgb_b.score(Xb_train, yb_train)) # train accu\n",
    "print(xgb_b.score(Xb_test, yb_test)) # test accu\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2e74e42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_b_predictions = xgb_b.predict(Xb_test)\n",
    "#print(yb_test,xgb_b_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7b76354a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[155  43]\n",
      " [ 22 130]] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.78      0.83       198\n",
      "           1       0.75      0.86      0.80       152\n",
      "\n",
      "    accuracy                           0.81       350\n",
      "   macro avg       0.81      0.82      0.81       350\n",
      "weighted avg       0.82      0.81      0.82       350\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation \n",
    "xgb_b_cm = confusion_matrix(yb_test, xgb_b_predictions)\n",
    "xgb_b_cr = classification_report(yb_test, xgb_b_predictions)\n",
    "print(xgb_b_cm,\"\\n\")\n",
    "print(xgb_b_cr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b54d08f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting confusion metrix\n",
    "plt.figure(figsize=(4,4), dpi=90)\n",
    "sns.heatmap(xgb_b_cm, annot=True, fmt=\".1f\")\n",
    "plt.title(\"Confusion Metrix\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"Predict Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b3b8c7af",
   "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": [
    "plot_roc_curve(yb_test, xgb_b_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "11f43d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84      , 0.81142857, 0.85142857, 0.82857143, 0.80571429,\n",
       "       0.81142857, 0.83428571, 0.81142857, 0.81142857, 0.86285714])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cross Validation\n",
    "score=cross_val_score(xgb_b,X_b,y_b,cv=10)\n",
    "score"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}