Online Payments Fraud Detection with Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2337d511",
   "metadata": {},
   "source": [
    "# Online Payments Fraud Detection with Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0847787",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#from pandas_profiling import ProfileReport\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ca3afe8",
   "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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.00</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.00</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.00</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7817.71</td>\n",
       "      <td>C90045638</td>\n",
       "      <td>53860.00</td>\n",
       "      <td>46042.29</td>\n",
       "      <td>M573487274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7107.77</td>\n",
       "      <td>C154988899</td>\n",
       "      <td>183195.00</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>M408069119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7861.64</td>\n",
       "      <td>C1912850431</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>168225.59</td>\n",
       "      <td>M633326333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4024.36</td>\n",
       "      <td>C1265012928</td>\n",
       "      <td>2671.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>M1176932104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>5337.77</td>\n",
       "      <td>C712410124</td>\n",
       "      <td>41720.00</td>\n",
       "      <td>36382.23</td>\n",
       "      <td>C195600860</td>\n",
       "      <td>41898.0</td>\n",
       "      <td>40348.79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815      170136.00       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295       21249.00        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145         181.00            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671         181.00            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720       41554.00        29885.86   \n",
       "5     1   PAYMENT   7817.71    C90045638       53860.00        46042.29   \n",
       "6     1   PAYMENT   7107.77   C154988899      183195.00       176087.23   \n",
       "7     1   PAYMENT   7861.64  C1912850431      176087.23       168225.59   \n",
       "8     1   PAYMENT   4024.36  C1265012928        2671.00            0.00   \n",
       "9     1     DEBIT   5337.77   C712410124       41720.00        36382.23   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0            0.00        0               0  \n",
       "1  M2044282225             0.0            0.00        0               0  \n",
       "2   C553264065             0.0            0.00        1               0  \n",
       "3    C38997010         21182.0            0.00        1               0  \n",
       "4  M1230701703             0.0            0.00        0               0  \n",
       "5   M573487274             0.0            0.00        0               0  \n",
       "6   M408069119             0.0            0.00        0               0  \n",
       "7   M633326333             0.0            0.00        0               0  \n",
       "8  M1176932104             0.0            0.00        0               0  \n",
       "9   C195600860         41898.0        40348.79        0               0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"C:\\\\Users\\\\pc513\\\\Downloads\\\\archive\\\\payment.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45bee620",
   "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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6362610</th>\n",
       "      <td>742</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>C778071008</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1812552860</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362611</th>\n",
       "      <td>742</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>C994950684</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1662241365</td>\n",
       "      <td>276433.18</td>\n",
       "      <td>339850.17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362612</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>C1531301470</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1470998563</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362613</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>C1436118706</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1240760502</td>\n",
       "      <td>503464.50</td>\n",
       "      <td>1762283.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362614</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>C2013999242</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1850423904</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362615</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>C786484425</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C776919290</td>\n",
       "      <td>0.00</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362616</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>C1529008245</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1881841831</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362617</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>C1162922333</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1365125890</td>\n",
       "      <td>68488.84</td>\n",
       "      <td>6379898.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362618</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>C1685995037</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2080388513</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362619</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>C1280323807</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C873221189</td>\n",
       "      <td>6510099.11</td>\n",
       "      <td>7360101.63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type      amount     nameOrig  oldbalanceOrg  \\\n",
       "6362610   742  TRANSFER    63416.99   C778071008       63416.99   \n",
       "6362611   742  CASH_OUT    63416.99   C994950684       63416.99   \n",
       "6362612   743  TRANSFER  1258818.82  C1531301470     1258818.82   \n",
       "6362613   743  CASH_OUT  1258818.82  C1436118706     1258818.82   \n",
       "6362614   743  TRANSFER   339682.13  C2013999242      339682.13   \n",
       "6362615   743  CASH_OUT   339682.13   C786484425      339682.13   \n",
       "6362616   743  TRANSFER  6311409.28  C1529008245     6311409.28   \n",
       "6362617   743  CASH_OUT  6311409.28  C1162922333     6311409.28   \n",
       "6362618   743  TRANSFER   850002.52  C1685995037      850002.52   \n",
       "6362619   743  CASH_OUT   850002.52  C1280323807      850002.52   \n",
       "\n",
       "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
       "6362610             0.0  C1812552860            0.00            0.00        1   \n",
       "6362611             0.0  C1662241365       276433.18       339850.17        1   \n",
       "6362612             0.0  C1470998563            0.00            0.00        1   \n",
       "6362613             0.0  C1240760502       503464.50      1762283.33        1   \n",
       "6362614             0.0  C1850423904            0.00            0.00        1   \n",
       "6362615             0.0   C776919290            0.00       339682.13        1   \n",
       "6362616             0.0  C1881841831            0.00            0.00        1   \n",
       "6362617             0.0  C1365125890        68488.84      6379898.11        1   \n",
       "6362618             0.0  C2080388513            0.00            0.00        1   \n",
       "6362619             0.0   C873221189      6510099.11      7360101.63        1   \n",
       "\n",
       "         isFlaggedFraud  \n",
       "6362610               0  \n",
       "6362611               0  \n",
       "6362612               0  \n",
       "6362613               0  \n",
       "6362614               0  \n",
       "6362615               0  \n",
       "6362616               0  \n",
       "6362617               0  \n",
       "6362618               0  \n",
       "6362619               0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9d75146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6362620, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5c8eead",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "CASH_OUT    2237500\n",
      "PAYMENT     2151495\n",
      "CASH_IN     1399284\n",
      "TRANSFER     532909\n",
      "DEBIT         41432\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.type.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c16f9a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hole": 0.5,
         "hovertemplate": "label=%{label}<br>value=%{value}<extra></extra>",
         "labels": [
          "CASH_OUT",
          "PAYMENT",
          "CASH_IN",
          "TRANSFER",
          "DEBIT"
         ],
         "legendgroup": "",
         "name": "",
         "showlegend": true,
         "type": "pie",
         "values": [
          2237500,
          2151495,
          1399284,
          532909,
          41432
         ]
        }
       ],
       "layout": {
        "autosize": true,
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Distribution of Transaction Type"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"d327a93f-4ae8-413c-9065-9b5378f517c0\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"d327a93f-4ae8-413c-9065-9b5378f517c0\")) {                    Plotly.newPlot(                        \"d327a93f-4ae8-413c-9065-9b5378f517c0\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.5,\"hovertemplate\":\"label=%{label}\\u003cbr\\u003evalue=%{value}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"labels\":[\"CASH_OUT\",\"PAYMENT\",\"CASH_IN\",\"TRANSFER\",\"DEBIT\"],\"legendgroup\":\"\",\"name\":\"\",\"showlegend\":true,\"values\":[2237500,2151495,1399284,532909,41432],\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Distribution of Transaction Type\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('d327a93f-4ae8-413c-9065-9b5378f517c0');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type = data[\"type\"].value_counts()\n",
    "transactions = type.index\n",
    "quantity = type.values\n",
    "\n",
    "import plotly.express as px\n",
    "figure = px.pie(data, \n",
    "             values=quantity, \n",
    "             names=transactions,hole = 0.5, \n",
    "             title=\"Distribution of Transaction Type\")\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d7595ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step              0\n",
       "type              0\n",
       "amount            0\n",
       "nameOrig          0\n",
       "oldbalanceOrg     0\n",
       "newbalanceOrig    0\n",
       "nameDest          0\n",
       "oldbalanceDest    0\n",
       "newbalanceDest    0\n",
       "isFraud           0\n",
       "isFlaggedFraud    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9459795",
   "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>step</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.433972e+02</td>\n",
       "      <td>1.798619e+05</td>\n",
       "      <td>8.338831e+05</td>\n",
       "      <td>8.551137e+05</td>\n",
       "      <td>1.100702e+06</td>\n",
       "      <td>1.224996e+06</td>\n",
       "      <td>1.290820e-03</td>\n",
       "      <td>2.514687e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.423320e+02</td>\n",
       "      <td>6.038582e+05</td>\n",
       "      <td>2.888243e+06</td>\n",
       "      <td>2.924049e+06</td>\n",
       "      <td>3.399180e+06</td>\n",
       "      <td>3.674129e+06</td>\n",
       "      <td>3.590480e-02</td>\n",
       "      <td>1.585775e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.560000e+02</td>\n",
       "      <td>1.338957e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.390000e+02</td>\n",
       "      <td>7.487194e+04</td>\n",
       "      <td>1.420800e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.327057e+05</td>\n",
       "      <td>2.146614e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.350000e+02</td>\n",
       "      <td>2.087215e+05</td>\n",
       "      <td>1.073152e+05</td>\n",
       "      <td>1.442584e+05</td>\n",
       "      <td>9.430367e+05</td>\n",
       "      <td>1.111909e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.430000e+02</td>\n",
       "      <td>9.244552e+07</td>\n",
       "      <td>5.958504e+07</td>\n",
       "      <td>4.958504e+07</td>\n",
       "      <td>3.560159e+08</td>\n",
       "      <td>3.561793e+08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
       "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
       "mean   2.433972e+02  1.798619e+05   8.338831e+05    8.551137e+05   \n",
       "std    1.423320e+02  6.038582e+05   2.888243e+06    2.924049e+06   \n",
       "min    1.000000e+00  0.000000e+00   0.000000e+00    0.000000e+00   \n",
       "25%    1.560000e+02  1.338957e+04   0.000000e+00    0.000000e+00   \n",
       "50%    2.390000e+02  7.487194e+04   1.420800e+04    0.000000e+00   \n",
       "75%    3.350000e+02  2.087215e+05   1.073152e+05    1.442584e+05   \n",
       "max    7.430000e+02  9.244552e+07   5.958504e+07    4.958504e+07   \n",
       "\n",
       "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
       "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
       "mean     1.100702e+06    1.224996e+06  1.290820e-03    2.514687e-06  \n",
       "std      3.399180e+06    3.674129e+06  3.590480e-02    1.585775e-03  \n",
       "min      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
       "25%      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
       "50%      1.327057e+05    2.146614e+05  0.000000e+00    0.000000e+00  \n",
       "75%      9.430367e+05    1.111909e+06  0.000000e+00    0.000000e+00  \n",
       "max      3.560159e+08    3.561793e+08  1.000000e+00    1.000000e+00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e7278258",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n",
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n",
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='type', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data, x='type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5976a3ea-224a-4c30-b198-972b5e46aad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n",
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n",
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='type', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data[data['isFraud']==1], x='type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "342dfb53-5954-48cc-8fc7-5bebe1926404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'amount')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data[data['isFraud']==1][\"amount\"].values, orient='h')\n",
    "plt.title('Fraud Transaction Amount Distribution')\n",
    "plt.xlabel('amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b384197c-dacb-4015-bbb4-09e1b6c8e429",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n",
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n",
      "C:\\Users\\pc513\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning:\n",
      "\n",
      "is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hour', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['hour'] = (data['step'] - 1) % 24\n",
    "sns.countplot(data, x='hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d9327ca8-646f-40e6-a3bd-0c90b472354e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['nameOrig', 'nameDest', 'isFlaggedFraud'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[35], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Dropping columns that are not needed\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnameOrig\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnameDest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43misFlaggedFraud\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Lib\\site-packages\\pandas\\core\\frame.py:5347\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5199\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   5200\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   5201\u001b[0m     labels: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5208\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5209\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5210\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5211\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5212\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5345\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5346\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5347\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   5348\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5349\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5350\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5351\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5352\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5353\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5354\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5355\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Lib\\site-packages\\pandas\\core\\generic.py:4711\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4709\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4710\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4711\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4713\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4714\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32m~\\Lib\\site-packages\\pandas\\core\\generic.py:4753\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4751\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4752\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4753\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4754\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4756\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4757\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32m~\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6992\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6990\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   6991\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 6992\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask]\u001b[38;5;241m.\u001b[39mtolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6993\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   6994\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['nameOrig', 'nameDest', 'isFlaggedFraud'] not found in axis\""
     ]
    }
   ],
   "source": [
    "# Dropping columns that are not needed\n",
    "data.drop(['nameOrig','nameDest','isFlaggedFraud'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e542e650-3cb7-45e9-af77-4f9e960b768a",
   "metadata": {
    "scrolled": true
   },
   "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>step</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>hour</th>\n",
       "      <th>type_CASH_IN</th>\n",
       "      <th>type_CASH_OUT</th>\n",
       "      <th>type_DEBIT</th>\n",
       "      <th>type_PAYMENT</th>\n",
       "      <th>type_TRANSFER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.00</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.00</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.00</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362615</th>\n",
       "      <td>743</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362616</th>\n",
       "      <td>743</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362617</th>\n",
       "      <td>743</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68488.84</td>\n",
       "      <td>6379898.11</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362618</th>\n",
       "      <td>743</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362619</th>\n",
       "      <td>743</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6510099.11</td>\n",
       "      <td>7360101.63</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6362620 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0           1     9839.64      170136.00       160296.36            0.00   \n",
       "1           1     1864.28       21249.00        19384.72            0.00   \n",
       "2           1      181.00         181.00            0.00            0.00   \n",
       "3           1      181.00         181.00            0.00        21182.00   \n",
       "4           1    11668.14       41554.00        29885.86            0.00   \n",
       "...       ...         ...            ...             ...             ...   \n",
       "6362615   743   339682.13      339682.13            0.00            0.00   \n",
       "6362616   743  6311409.28     6311409.28            0.00            0.00   \n",
       "6362617   743  6311409.28     6311409.28            0.00        68488.84   \n",
       "6362618   743   850002.52      850002.52            0.00            0.00   \n",
       "6362619   743   850002.52      850002.52            0.00      6510099.11   \n",
       "\n",
       "         newbalanceDest  isFraud  hour  type_CASH_IN  type_CASH_OUT  \\\n",
       "0                  0.00        0     0         False          False   \n",
       "1                  0.00        0     0         False          False   \n",
       "2                  0.00        1     0         False          False   \n",
       "3                  0.00        1     0         False           True   \n",
       "4                  0.00        0     0         False          False   \n",
       "...                 ...      ...   ...           ...            ...   \n",
       "6362615       339682.13        1    22         False           True   \n",
       "6362616            0.00        1    22         False          False   \n",
       "6362617      6379898.11        1    22         False           True   \n",
       "6362618            0.00        1    22         False          False   \n",
       "6362619      7360101.63        1    22         False           True   \n",
       "\n",
       "         type_DEBIT  type_PAYMENT  type_TRANSFER  \n",
       "0             False          True          False  \n",
       "1             False          True          False  \n",
       "2             False         False           True  \n",
       "3             False         False          False  \n",
       "4             False          True          False  \n",
       "...             ...           ...            ...  \n",
       "6362615       False         False          False  \n",
       "6362616       False         False           True  \n",
       "6362617       False         False          False  \n",
       "6362618       False         False           True  \n",
       "6362619       False         False          False  \n",
       "\n",
       "[6362620 rows x 13 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying encoding on type column\n",
    "df_enc = pd.get_dummies(data, columns = ['type'])\n",
    "df_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "931ad6c3-24ce-4c84-bd4a-cca0347f21c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_enc.drop('isFraud', axis = 1)\n",
    "y = df_enc['isFraud']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4409a808-0a59-4c50-ae30-4e4e2a3a2799",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e03af534-3b3d-4ef8-bd06-899032a67d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   4448115\n",
      "           1       0.77      0.49      0.60      5719\n",
      "\n",
      "    accuracy                           1.00   4453834\n",
      "   macro avg       0.89      0.74      0.80   4453834\n",
      "weighted avg       1.00      1.00      1.00   4453834\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(max_iter=1000)\n",
    "lr.fit(X_train, y_train)\n",
    "y_lr_pred = lr.predict(X_test)\n",
    "print('LogisticRegression')\n",
    "print(classification_report(y_test, y_lr_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "33a66f6a-ad15-4a7d-a470-44c0ff0d34a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   4448115\n",
      "           1       0.87      0.86      0.86      5719\n",
      "\n",
      "    accuracy                           1.00   4453834\n",
      "   macro avg       0.94      0.93      0.93   4453834\n",
      "weighted avg       1.00      1.00      1.00   4453834\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(random_state=0)\n",
    "dt.fit(X_train, y_train)\n",
    "y_dt_pred = dt.predict(X_test)\n",
    "print('DecisionTreeClassifier')\n",
    "print(classification_report(y_test, y_dt_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e66f85fb-4fb9-496a-a24e-28cebb200b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(random_state=0)\n",
    "rf.fit(X_train, y_train)\n",
    "y_rf_pred = rf.predict(X_test)\n",
    "print('RandomForestClassifier')\n",
    "print(classification_report(y_test, y_rf_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9513456-af3e-41b6-902c-04ffd6911078",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(2,2,1)\n",
    "sns.heatmap(confusion_matrix(y_test, y_lr_pred), annot=True, annot_kws={\"fontsize\":10}, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('LogisticRegression')\n",
    "plt.subplot(2,2,2)\n",
    "sns.heatmap(confusion_matrix(y_test, y_dt_pred), annot=True, annot_kws={\"fontsize\":10}, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('DecisionTreeClassifier')\n",
    "plt.subplot(2,2,3)\n",
    "sns.heatmap(confusion_matrix(y_test, y_rf_pred), annot=True, annot_kws={\"fontsize\":10}, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('RandomForestClassifier')\n",
    "plt.subplots_adjust(hspace=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b7d364a-cf8c-4a28-95af-921d4eb822f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}