Creditcard_fraud_detection.ipynb

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  "metadata": {
    "colab": {
      "name": "Creditcard_fraud_detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOICajbnyZ6YYT6f4c052Uk",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kartikshastrakar/kartik/blob/main/Creditcard_fraud_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "qVcKv6cULSAu"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split \n",
        "\n",
        "from sklearn.metrics import classification_report, accuracy_score  \n",
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsnlVcs3L_E-",
        "outputId": "ac95366a-03d1-4f89-b5e7-7ce3ef1986f4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://www.dropbox.com/s/18vhxydhoe3g84n/creditcard%202.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MipOUSUxSQop",
        "outputId": "f0a4af92-d929-431d-ff99-8b9d57e8e8f7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-02-04 15:12:32--  https://www.dropbox.com/s/18vhxydhoe3g84n/creditcard%202.csv\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.69.18, 2620:100:6030:18::a27d:5012\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.69.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/18vhxydhoe3g84n/creditcard%202.csv [following]\n",
            "--2022-02-04 15:12:33--  https://www.dropbox.com/s/raw/18vhxydhoe3g84n/creditcard%202.csv\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uca84e64849ba0d365c45b6a13f9.dl.dropboxusercontent.com/cd/0/inline/BfEpphR1yW_EMu44jNt_Askuq43RLPChh-qOzbqbHbN903dd3glGV4AUdZ4dU--Wi_eaAwp2iXoFBqpuYPdMFUrE53aR_ucq4aS5P4pG7hPBuIxUfcjh_5WmgfjGY6P0DqHnjN3MavJpIGUH4DJ_XOPJ/file# [following]\n",
            "--2022-02-04 15:12:34--  https://uca84e64849ba0d365c45b6a13f9.dl.dropboxusercontent.com/cd/0/inline/BfEpphR1yW_EMu44jNt_Askuq43RLPChh-qOzbqbHbN903dd3glGV4AUdZ4dU--Wi_eaAwp2iXoFBqpuYPdMFUrE53aR_ucq4aS5P4pG7hPBuIxUfcjh_5WmgfjGY6P0DqHnjN3MavJpIGUH4DJ_XOPJ/file\n",
            "Resolving uca84e64849ba0d365c45b6a13f9.dl.dropboxusercontent.com (uca84e64849ba0d365c45b6a13f9.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f\n",
            "Connecting to uca84e64849ba0d365c45b6a13f9.dl.dropboxusercontent.com (uca84e64849ba0d365c45b6a13f9.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/plain]\n",
            "Saving to: ‘creditcard 2.csv.1’\n",
            "\n",
            "creditcard 2.csv.1      [             <=>    ] 143.84M  15.2MB/s    in 9.9s    \n",
            "\n",
            "2022-02-04 15:12:44 (14.5 MB/s) - ‘creditcard 2.csv.1’ saved [150828752]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "\n",
        "file_name = \"/content/creditcard.csv.zip\"\n",
        "\n",
        "with ZipFile(file_name, 'r') as zip:\n",
        "  zip.extractall()\n",
        "  print('done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIw3-7UgR9a2",
        "outputId": "027e95e4-cd2d-452b-a2eb-6ddcce22b10d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df=pd.read_csv('/content/creditcard.csv')"
      ],
      "metadata": {
        "id": "K5eXvIxiM081"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.head()"
      ],
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          "height": 270
        },
        "id": "tWEQpt5nM9hl",
        "outputId": "7737d252-61e6-49d7-e7ba-1e28c1d5d7bb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1</th>\n",
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              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
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              "      <td>-0.503198</td>\n",
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              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
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              "      <td>1.548718</td>\n",
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              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
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              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.shape"
      ],
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        "colab": {
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        },
        "id": "86_05Cm5OLUm",
        "outputId": "e88c69d3-847a-4e32-8f26-bae1330f0ed7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 17
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df[['Amount','Time','Class']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ElINAOn6OR4u",
        "outputId": "592679b0-52cc-429f-8de2-b2a51bb64131"
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      "execution_count": 18,
      "outputs": [
        {
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          "data": {
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              "\n",
              "  <div id=\"df-d7b643c4-57e2-48fe-b633-b44fd4581621\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>Amount</th>\n",
              "      <th>Time</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>88.349619</td>\n",
              "      <td>94813.859575</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>250.120109</td>\n",
              "      <td>47488.145955</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.600000</td>\n",
              "      <td>54201.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>84692.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>77.165000</td>\n",
              "      <td>139320.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>25691.160000</td>\n",
              "      <td>172792.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7b643c4-57e2-48fe-b633-b44fd4581621')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d7b643c4-57e2-48fe-b633-b44fd4581621');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              Amount           Time          Class\n",
              "count  284807.000000  284807.000000  284807.000000\n",
              "mean       88.349619   94813.859575       0.001727\n",
              "std       250.120109   47488.145955       0.041527\n",
              "min         0.000000       0.000000       0.000000\n",
              "25%         5.600000   54201.500000       0.000000\n",
              "50%        22.000000   84692.000000       0.000000\n",
              "75%        77.165000  139320.500000       0.000000\n",
              "max     25691.160000  172792.000000       1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
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      "cell_type": "code",
      "source": [
        "data_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ra0tI4nKOVZt",
        "outputId": "4139e3ae-10ad-461d-8f53-9500b70fff5a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
              "       'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_t3ZmFQLOYX6",
        "outputId": "f66d299e-5e6d-427b-949e-374274b4eefa"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      False\n",
              "V1        False\n",
              "V2        False\n",
              "V3        False\n",
              "V4        False\n",
              "V5        False\n",
              "V6        False\n",
              "V7        False\n",
              "V8        False\n",
              "V9        False\n",
              "V10       False\n",
              "V11       False\n",
              "V12       False\n",
              "V13       False\n",
              "V14       False\n",
              "V15       False\n",
              "V16       False\n",
              "V17       False\n",
              "V18       False\n",
              "V19       False\n",
              "V20       False\n",
              "V21       False\n",
              "V22       False\n",
              "V23       False\n",
              "V24       False\n",
              "V25       False\n",
              "V26       False\n",
              "V27       False\n",
              "V28       False\n",
              "Amount    False\n",
              "Class     False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "null_columns=pd.DataFrame({'Columns':data_df.isna().sum().index,'No. Null values':data_df.isna().sum().values,'Percentage':data_df.isna().sum().values/data_df.shape[0]})"
      ],
      "metadata": {
        "id": "UJVCmTDeOfoc"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "null_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "id": "CAurv0ozOleF",
        "outputId": "10acae1b-393d-491e-bf3c-d889c9fee9b5"
      },
      "execution_count": 22,
      "outputs": [
        {
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              "\n",
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              "      <th>5</th>\n",
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              "      <th>6</th>\n",
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              "      <th>7</th>\n",
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              "      <th>8</th>\n",
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              "      <th>9</th>\n",
              "      <td>V9</td>\n",
              "      <td>0</td>\n",
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              "      <th>10</th>\n",
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              "      <th>11</th>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
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              "      <td>0.0</td>\n",
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              "      <th>13</th>\n",
              "      <td>V13</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>V14</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>V15</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>16</th>\n",
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              "      <th>19</th>\n",
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              "      <th>20</th>\n",
              "      <td>V20</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>21</th>\n",
              "      <td>V21</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>V23</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>V24</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>26</th>\n",
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              "      <th>27</th>\n",
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              "      <th>28</th>\n",
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              "      <th>29</th>\n",
              "      <td>Amount</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b566aa5a-027f-46d1-8ca2-cde862905f9a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b566aa5a-027f-46d1-8ca2-cde862905f9a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Columns  No. Null values  Percentage\n",
              "0     Time                0         0.0\n",
              "1       V1                0         0.0\n",
              "2       V2                0         0.0\n",
              "3       V3                0         0.0\n",
              "4       V4                0         0.0\n",
              "5       V5                0         0.0\n",
              "6       V6                0         0.0\n",
              "7       V7                0         0.0\n",
              "8       V8                0         0.0\n",
              "9       V9                0         0.0\n",
              "10     V10                0         0.0\n",
              "11     V11                0         0.0\n",
              "12     V12                0         0.0\n",
              "13     V13                0         0.0\n",
              "14     V14                0         0.0\n",
              "15     V15                0         0.0\n",
              "16     V16                0         0.0\n",
              "17     V17                0         0.0\n",
              "18     V18                0         0.0\n",
              "19     V19                0         0.0\n",
              "20     V20                0         0.0\n",
              "21     V21                0         0.0\n",
              "22     V22                0         0.0\n",
              "23     V23                0         0.0\n",
              "24     V24                0         0.0\n",
              "25     V25                0         0.0\n",
              "26     V26                0         0.0\n",
              "27     V27                0         0.0\n",
              "28     V28                0         0.0\n",
              "29  Amount                0         0.0\n",
              "30   Class                0         0.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['Class'].tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZzb4YQQOohZ",
        "outputId": "8a2eea28-4f9c-429a-f2e5-73162111fc66"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "284797    0\n",
              "284798    0\n",
              "284799    0\n",
              "284800    0\n",
              "284801    0\n",
              "284802    0\n",
              "284803    0\n",
              "284804    0\n",
              "284805    0\n",
              "284806    0\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <b> Percentage of total not fraud transaction"
      ],
      "metadata": {
        "id": "s459RCrHOs5s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fcount=0\n",
        "Fraud=data_df['Class']\n",
        "for i in range(len(Fraud)):\n",
        "  if Fraud[i]==1:\n",
        "    fcount=fcount+1\n",
        "\n",
        "fcount    \n",
        "per_f=(fcount/len(Fraud))*100\n",
        "print('percentage of total fraud transaction in the dataset: ',per_f)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UO21gZb5T6Ic",
        "outputId": "e80ae962-79ae-4a44-93aa-e7832462fcc9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "percentage of total fraud transaction in the dataset:  0.1727485630620034\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nfcount=0\n",
        "notFraud=data_df['Class']\n",
        "for i in range(len(notFraud)):\n",
        "  if notFraud[i]==0:\n",
        "    nfcount=nfcount+1\n",
        "\n",
        "nfcount    \n",
        "per_nf=(nfcount/len(notFraud))*100\n",
        "print('percentage of total not fraud transaction in the dataset: ',per_nf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0YDIKqZPI97",
        "outputId": "15d9d0b0-1fb4-4ff2-eb9e-1d3ddd7e570b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "percentage of total not fraud transaction in the dataset:  99.82725143693798\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_data=pd.DataFrame()\n",
        "plot_data['Fraud Transaction']=Fraud\n",
        "plot_data['Genuine Transaction']=notFraud\n",
        "plot_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "dYTIkq6PPOYV",
        "outputId": "ab860bd3-49b4-4ed8-803b-3bcf567af5cf"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<p>284807 rows × 2 columns</p>\n",
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              "          const element = document.querySelector('#df-d022b002-feed-4425-9e23-e3dd6b0a0580');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Fraud Transaction  Genuine Transaction\n",
              "0                       0                    0\n",
              "1                       0                    0\n",
              "2                       0                    0\n",
              "3                       0                    0\n",
              "4                       0                    0\n",
              "...                   ...                  ...\n",
              "284802                  0                    0\n",
              "284803                  0                    0\n",
              "284804                  0                    0\n",
              "284805                  0                    0\n",
              "284806                  0                    0\n",
              "\n",
              "[284807 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Bar plot for Fraud VS Genuine transactions\")\n",
        "sns.barplot(x = 'Fraud Transaction', y = 'Genuine Transaction', data = plot_data, palette = 'Blues', edgecolor = 'w')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "yy5Ir1FtPhA_",
        "outputId": "4cd1fe90-b1c3-42d4-8520-6516d420d3a0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fefefc8fed0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As per the graph we can say the ratio of genuine transactions are higher than fraud transactions.\n"
      ],
      "metadata": {
        "id": "BXcqM4T0PokT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=data_df['Amount']\n",
        "y=data_df['Time']\n",
        "plt.plot(x, y) \n",
        "plt.title('Time Vs amount') \n",
        "#sns.barplot(x = x, y = y, data = data, palette = 'Blues', edgecolor = 'w')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Cid7MFGRPqiq",
        "outputId": "21ce7f13-5d01-4942-d55a-966789c40857"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Time Vs amount')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+cPaBHLzH7pFgW5oDu19piaY0FZF2aTVRuPsj4UohXvZgbHUZcGaqY5jZcKC/uy8L6wuA04gSxQxgSqg6H3iYKFHMABZ49Im3zMzKzWy4u29o9ay6oS8sfKrNdf/wpaMZPaRf6xVFREjPVKifIvrAbzDKzJ4ysz+Z2ZGhrApYF6uzLpQBVMY+/N8AKmP7rG1hnybMbLaZ1ZtZ/ebNrTfkdkdV5b2pKu/Nxw4aSdXA3tkOR0TySKcas83sK8AO4FehaANQ4+5vhTaJu81s37YeL7RZJL9nknq/ucBcgLq6unbvX6i+evLefPrI0dkOQ0TyXIcThZmdR9TIfVy4PYS7bwe2h+XlZvYKMBZYD4yM7T4ylAFsbLilFG5RbQrl64HqFvaRNrj23lVce++qtBzrhWumacRXkW6qQ7eezGwa8GXgo+7+r1j5EDMrDsujiRqiXw23lt4zs8mhN9O5wD1ht8XArLA8K6H8XItMBt5V+0R2HD12CCVFbZ8vW0QKS1u6xy4kamyuMLN1wDeIejmVAkvDCKEN3WCPAq42sw+BXcBn3X1LONTn2d099n52t2tcB9xhZhcArwNnhfL7iLrGribqHnt+Z05Udpv/qUM5euyQbIchInnCWupGma/q6uq8vr6+3fvVzrk3A9FIIbv+4xP42MEjW68okgfMbLm71yXbpsF5RDqob6nabKR70BAe3ZQap0WkrZQoctRlU8dx3N5Dm5QZyRuU+/cuYfgAPRshIpmhRJGj9q8awPhh/bMdhoiIEkVXKikyehQXMXZYGZ86vLaxPJr7IZrvoWEeiI3vfcDS5zdy/N5Dm8w9LSLS1ZQoutCOXc6OXTt5eu07fHHRijbt88OzD+TUCSMyHJmISMuUKLKksn8ph9QOwixqeTAjfA8tEQbDB/Ri6r7DshuoiHR7ShRdaPywMorMMIP/+ei+1NVqPgYRyX1KFF3ohTe2Ni6f+dO/pf34Zx9aw97Dy5pNGARtmJwIGFtZxuGamlREEihRFJCFj/+908f402VT2GNw3zREIyKFQokij00ePYg9wwREDR2jGp61SNZRKrEosTfVASMHKEmISDNKFHnsC8eO4TDdKhKRDNNYT3nqZ588WElCRLqEEkWeqh7YJ9shiEg3oVtPaTSxurzJerxn0b4j+vOxg0Y2ljY8gd24HHoleViPlndXcmBIWSljK8syFb6ISFJKFGm0Yu07TdavOW2/xofpAF58Y2us0TkqP+WAEfQt1dsgIrlLn1AZ9LW7n2u1zkOrNjH33KRzhYiI5AQliix78PmNGZ1d78IjR/GVk/fJ2PFFpPCpMbvArd3yfrZDEJE816YrCjObB5wCbHL3/ULZIOB2oBZYA5zl7m9b9BTXjcB04F/Aee7+ZNhnFvDVcNhr3X1+KD8YuBXoDdwHfNHdvaXX6NQZd5GPThjBD84+MNthiIh0miWOAZS0ktlRwDZgQSxRfAfY4u7XmdkcYKC7X25m04H/IkoUk4Ab3X1S+NCvB+qIOvEsBw4OyeVx4AvAY0SJ4gfufn9Lr5Eq1rq6Oq+vr2/3DyKTt3/y1W8vPoL9Rw7Idhgi0gXMbLm7J20wbdOtJ3d/BNiSUDwDmB+W5wOnxcoXeGQZUG5mw4GpwFJ33xKuCpYC08K2/u6+zKOstSDhWMleQ7rAinXvtF5JRApeZxqzK919Q1h+A6gMy1XA2li9daEsVfm6JOWpXqOgvXDNNHr1KM52GCIiQJp6PYX2hNbvYWXoNcxsNjAboKamJpNhtGr0kL6ccWBV0qG+oelDdrvLdq9MGjVYSUJEckpnEsVGMxvu7hvC7aNNoXw9UB2rNzKUrQemJJQ/HMpHJqmf6jWacPe5wFyI2ig6cU6d9urmf/K9B19qUjausoxFsyczsG/PLEUlItJxnUkUi4FZwHXh+z2x8ovNbBFRY/a74YN+CfBNMxsY6p0IXOHuW8zsPTObTNSYfS7ww1ZeI6+8uHErB16ztFn5lHFD+NknD6a0RFcQIpK72to9diHR1UCFma0DvkH04X2HmV0AvA6cFarfR9TjaTVR99jzAUJCuAZ4ItS72t0bGsg/z+7usfeHL1K8RkF4+MXN/HP7TiUKEclpbeoem0/UPVYkP5WWFPHitSdlO4xuq9PdY0VEMu2Q2kHZDkFaoLGeusi0fYdx4VGjgabTlPbvVcJeQzV0uIjkLiWKLvLAyjd4YOUbSbf973mHcMz4oV0ckYhI2+jWUw6oqx3YeiURkSxRosgBz657N9shiIi0SLeeMug7HzuA/r17AN586tNQNqSslMmjB2czTBGRlJQoMujLv3mmcfn4vYcSTYDavDvyzx95tVnZ1aftR1V57wxGJyLSNkoUXeT3q5KOPtKiAUte5IZPTMxQNCIibadEkSF7De2HsbsrrBEtNK6HhZpBvTnz4N1DYzX0nD1ybEUXRSoikpoSRYas3rStTfVWbXiPJSs3Zjia1IqLjD9/+RhG6FaXiCShXk/Czl3Ohnc1t7aIJKcriizo36skatJuZZgtD3U/XldNcZE12Xbs+KHsV6VpSkUk85QosuC9D3a0ue627Tu48aGXm5XfsPSlJLV3u+Kk8Xzm6D3bHZuISCLdeipQdRpgTUTSRFcUeezsQ6v51hkHZDsMESlwShQ57Jun709ZrxLMou61A3r3oLxPDyDqZjt+WP8sRygi3YESRQ678q5nk5ZfPWNfxlWWUb9mS2PZHoP7MmxAr64KTUS6ESWKPPT1e1Zm/DVOPmA4N35iIiXFasYS6e70KSBJ3fvMBt7/cGe2wxCRHKAriiwqLjJe+eb0bIchIpJSh68ozGycma2Ifb1nZpeY2VVmtj5WPj22zxVmttrMXjSzqbHyaaFstZnNiZWPMrPHQvntZtaz46eae3buco749h/4wsKnuGrxSj7QX/AikoPMvZXHg9tyELNiYD0wCTgf2Obu30uosw+wEDgUGAH8HhgbNr8EnACsA54Aznb3583sDuBOd19kZj8Fnnb3m1LFUldX5/X19e0+h9o597Z7n0y79MTox2NmSbfXDu7L9P2HtbhdRKStzGy5u9cl25auW0/HAa+4++spPrRmAIvcfTvwmpmtJkoaAKvd/dUQ7CJghpmtAo4Fzgl15gNXASkTRSH53oOpn75uj4p+Pan/6glpO56IdB/pasyeSXS10OBiM3vGzOaZWcOE0FXA2liddaGspfLBwDvuviOhvBkzm21m9WZWv3nz5s6fTQGaWK15uUWkYzp9RRHaDT4KXBGKbgKuIRrT7hrgeuBTnX2dVNx9LjAXoltPmXytXHPZ1HFcdMxe2Q5DRApYOm49nQQ86e4bARq+A5jZz4HfhdX1QHVsv5GhjBbK3wLKzawkXFXE60vw6uZ/MidMudowF/fk0YM5YkxF42RJDfr3LqG0pDgbYYpIHktHojib2G0nMxvu7hvC6unAc2F5MXCbmd1A1Jg9BnicaFK3MWY2iigRzATOcXc3sz8CZwKLgFnAPWmIt6D85sl1zcruqG9e1mDNdSdnMhwRKUCdShRm1peot9JnYsXfMbOJRLee1jRsc/eVoRfT88AO4CJ33xmOczGwBCgG5rl7w6PHlwOLzOxa4Cngls7E251U9Gvek/hzU3SLSkTaLy3dY3NJIXWP/dThozgqzJ2d2JssvpbY0Sx+y6n5tuQrE0aW07dUz1+KdFdd0T1WMmDeX19j3l9f65LXGl3Rlz9cOqVLXktE8osShQAwbEAvvrvkhcb1xIbw+JXJqRNGMLayrKtCE5EsU6IQAB595S0efeWtNtX94R9Wq1FcpBtRoigQNYP6cPahNThN25xaa4JKbKNKVj+xaPr+wzoQoYjkKyWKPDKkrDThgzxaGdinJwsuOJThA3pnJS4RKWyajyKPbN66nTe3xb/+zZvb/s3Lm7bxkW/9gZc3bs12iCJSgHRFUUBO+P4jGTv2+GFl3PeFIykq0ki1It2NriikTV54Y2uztgoR6R50RVHAFl44mY/sOTjbYYhInlOiyAOlJUVUD+rTrIcSRE9sl/Vq/jZe//EJjB7SryvCE5ECp0SRB7bv2MXqTdsa148bP5Sf/OdBGglWRLqEEkUeeuiFTYz76gNpPeaE6nLuuejwtB5TRAqDGrMFgA937Mp2CCKSo3RFUWA0452IpJsSRR475YDh/Oicg7IdhogUOCWKPPa7Zzbwu2dybx6NTDl67BBunlVHj2LdMRXpSvofJ3njTy9tZrvaUkS6nK4oCsT3Pj6BkiJrHD02/shFk+XGst2FVeW9OWyvii6IUkTykRJFgbj010+3Wueuzx9Grx7Jn71YteG9JusjBvRmQJ8eaYlNRPKbEkU3cvpPHm1XfU1OJCKQhkRhZmuArcBOYIe715nZIOB2oBZYA5zl7m+bmQE3AtOBfwHnufuT4TizgK+Gw17r7vND+cHArUBv4D7gi55sLAtpl3GVZU2mN030n5P36LpgRCSnpeuK4hh3fzO2Pgd4yN2vM7M5Yf1y4CRgTPiaBNwETAqJ5RtAHdFt9OVmttjd3w51LgQeI0oU04D70xR3t/ViwtwVQ8pK+dIJY/l4XTXFGkpcRGIy1etpBjA/LM8HTouVL/DIMqDczIYDU4Gl7r4lJIelwLSwrb+7LwtXEQtix5I02rx1O3PufJb5j67JdigikmPScUXhwINm5sDP3H0uUOnuG8L2N4DKsFwFrI3tuy6UpSpfl6S8CTObDcwGqKmp6ez5dFvjKsuoKCvlgefeSLK1bXf7kvWwaq1eyuMlOUpb5vVu+XXbfx6t1m3h1ROP0fZzTrEtdpA2h9javOlJesp14nCNx2rp59JS/bY4fu9Khg3o1ebjSnqkI1Ec4e7rzWwosNTMXohvdHcPSSRjQnKaC1BXV6f2iw56ceNWvrDwqWyHIdKib923ipVXT8t2GN1OpxOFu68P3zeZ2V3AocBGMxvu7hvC7aNNofp6oDq2+8hQth6YklD+cCgfmaS+5LnSkiKO23tom+oaSdpM2laU/HgttOInlqZq7G/raye+VpsPmaJi/OfR1hhbq9ZwnKQ/6xT1W6/T9h9iW4557kfUySIbOpUozKwvUOTuW8PyicDVwGJgFnBd+H5P2GUxcLGZLSJqzH43JJMlwDfNbGCodyJwhbtvMbP3zGwyUWP2ucAPOxOz5IbtO3Zx37PJbnHll8P2HMxtF07OdhgiGdXZK4pK4K7wV1MJcJu7P2BmTwB3mNkFwOvAWaH+fURdY1cTdY89HyAkhGuAJ0K9q919S1j+PLu7x96PejxJDtmlntrSDXQqUbj7q8CEJOVvAcclKXfgohaONQ+Yl6S8HtivM3F2J0UGV07fG4hue8QbP+O3QZI16saLaiv6csI+lc3qiEj3oyezC8wuh2vvXdWlr7nq6mn07qlpWUUKlRKFdNreX0/vtKyFzAxe+5aGRpH8omHGRbrQfx8/NtshiLSbrigkLU7cp5K559ZlOwwRyQAligJ3xF4V/PLTk7IdhojkMSWKAte3tJg76qPRUVI9z9TWh9Ca7pO8fFj/XpoISaSAKFEUuCUrN7Jk5cYuf93bLpzEYXsqWYgUAiWKbuDMg0emHHStxcHbUu7TstEVfZk0anCbYhOR3KdE0Q187+PNnokUEWkzJYoCUVpSxIqvn6gH30Qk7ZQo8tDpB1bx/U9MzHYYItJNKFHkobueWs9dT7U82vqa6/Tkr4ikjxJFAaqdc2/K7f1KS7h5Vh0lsbmxnYTZ6RJav5ttT2zO9uYN3IkN6GMr+zG0v2YnE8k3ShTd0LbtO5g5d1lWXltXOyL5R4miQOxfNYAio/EpuJZma7MmZc1nX4sXNZvtzBL3T9xszcti6584RPOZi+QjJYoC8ez6d7MdQqv+uvqtpHNyP3zpFGor+mYhIhFpC40eK1lXXNT2eZVFpOvpikIoKTJeuvYkivSBLSJJKFHkmJMPGM4FR4wC4r2GmnYf6lFcxKiKvk1Kmw3R4U17JjXtsdRQtrvw7X/9u9WeTYYxpKy0zeciIoWhw4nCzKqBBUAl0WfPXHe/0cyuAi4ENoeqV7r7fWGfK4ALgJ3AF9x9SSifBtwIFAM3u/t1oXwUsAgYDCwHPunu/+5ozPng3tBDhU8AAAnoSURBVGc2cO8zG7IdRosumzqOi47ZK9thiEgX6swVxQ7gS+7+pJmVAcvNbGnY9n13/168spntA8wE9gVGAL83s4bpvn4MnACsA54ws8Xu/jzw7XCsRWb2U6Ikc1MnYi54Zb2itzTqwZTQq6lJj6aGstQ9n+L9nHr3LOKUA4anLVYRyQ8dThTuvgHYEJa3mtkqoCrFLjOARe6+HXjNzFYDh4Ztq939VQAzWwTMCMc7Fjgn1JkPXEWeJ4qHvnQ0vXpE4zE13PoZWtaLniXqVyAiuSktbRRmVgscCDwGHA5cbGbnAvVEVx1vEyWR+FNe69idWNYmlE8iut30jrvvSFI/8fVnA7MBampyt6/+gN49GF3Rt8VJgkREclGn/4w1s37Ab4BL3P09or/49wQmEl1xXN/Z12iNu8919zp3rxsyZEimX65dJlSXc2BNOceOH8rDl05RkhCRvNOpKwoz60GUJH7l7ncCuPvG2PafA78Lq+uB6tjuI0MZLZS/BZSbWUm4qojXzxv3XHR4tkMQEemUDl9RWPSn8S3AKne/IVYeb+08HXguLC8GZppZaejNNAZ4HHgCGGNmo8ysJ1GD92KPbuD/ETgz7D8LuKej8Xa1w/YczKNzjs12GCIindaZK4rDgU8Cz5rZilB2JXC2mU0k6jK7BvgMgLuvNLM7gOeJekxd5O47AczsYmAJUffYee6+MhzvcmCRmV0LPEWUmHLWXy4/hpED+2Q7DBGRtLLE4aTzXV1dndfX17d7v9aG5o4b0LsHl08bT21Fn8YeTBB1JI3/NONdUA+oGqAnn0UkZ5nZcnevS7ZNT2Z3wLvvf8iVdz3brn1mHzWaK6fvnaGIREQyR4kizSZUlze7sigpMmYdVpuliEREOkeJIo00KY+IFCIlig66fNp4agf3YVcYfG9idXm2QxIRyQglig46bu+hjK0sy3YYIiIZp0TRQSd+/5Gk5SMG9OLQUYPY5bDLHQ/fG5Ynjx7M+YfX6gltEckbShRp9o93P+DuFf9ocfuDz2/kpP2HMXxA7y6MSkSk45QoOulbZ+xPkUXPShhQZEZRUfSdsF5cZI3Dfo+q6KskISJ5RYmik66481nu/PxhHFQzMNuhiIhkhBJFGpzxk0dbrfPrz36EQ2oHdUE0IiLppUTRRT7+079l/DWWXHIU44apJ5aIpJemVSsQvXsUM6hvz2yHISIFSImiQLz/4U4++HBntsMQkQKkRFFANm39INshiEgBUhtFGlxz2n58cvIe2Q5DRCQjlCjS4Gt3P8fX7n6uSVm/0hKWXXkc/Ur1IxaR/KZbTxmybfsOfrXsdf7+1r+yHYqISKcoUWTQt+5/gaO++8dshyEi0ilKFBlUVlrC7bMnZzsMEZFO0Q30DKkqj8Zz+n93PN1q3dFD+lIzqE/j+uB+pVx8zF70LFEeF5Hsy/lEYWbTgBuBYuBmd78uyyG1yfp33u9U3R889HKr+40fVsaBNS1NmNR0GPPEUc0TBzlvvt3w2ISu1myPthyz5aHUqwf14fzDaikq0nDrIrkupxOFmRUDPwZOANYBT5jZYnd/PruR5YYX3tjKC29szXYYHTZ6SF+OGTc022GISCtyOlEAhwKr3f1VADNbBMwAlChizj60hv2rBjSux68EALzpasLW5hXia5asfrJjpjhGsvqV/XsxZeyQJEcWkVyT64miClgbW18HTEqsZGazgdkANTU1HXqhG2dO5IuLVnRo32yqGdSHy6aO0zhPIpIxuZ4o2sTd5wJzAerq6pL9AdyqGROrmDGxKq1xiYgUglzvVrMeqI6tjwxlIiLSRXI9UTwBjDGzUWbWE5gJLM5yTCIi3UpO33py9x1mdjGwhKh77Dx3X5nlsEREupWcThQA7n4fcF+24xAR6a5y/daTiIhkmRKFiIikpEQhIiIpKVGIiEhKljj0Qr4zs83A6x3cvQJ4M43h5LrudL4618Kkc02fPdw96bg6BZcoOsPM6t29LttxdJXudL4618Kkc+0auvUkIiIpKVGIiEhKShRNzc12AF2sO52vzrUw6Vy7gNooREQkJV1RiIhISkoUIiKSkhJFYGbTzOxFM1ttZnOyHU9HmdkaM3vWzFaYWX0oG2RmS83s5fB9YCg3M/tBOOdnzOyg2HFmhfovm9msbJ1PnJnNM7NNZvZcrCxt52ZmB4ef3eqwr3XtGe7WwrleZWbrw3u7wsymx7ZdEeJ+0cymxsqT/l6HofsfC+W3h2H8s8LMqs3sj2b2vJmtNLMvhvKCe29TnGtuv7fu3u2/iIYwfwUYDfQEngb2yXZcHTyXNUBFQtl3gDlheQ7w7bA8HbifaGrsycBjoXwQ8Gr4PjAsD8yBczsKOAh4LhPnBjwe6lrY96QcO9ergEuT1N0n/M6WAqPC73Jxqt9r4A5gZlj+KfC5LJ7rcOCgsFwGvBTOqeDe2xTnmtPvra4oIocCq939VXf/N7AImJHlmNJpBjA/LM8HTouVL/DIMqDczIYDU4Gl7r7F3d8GlgLTujroRO7+CLAloTgt5xa29Xf3ZR79D1sQO1aXa+FcWzIDWOTu2939NWA10e900t/r8Nf0scD/hf3jP7cu5+4b3P3JsLwVWAVUUYDvbYpzbUlOvLdKFJEqYG1sfR2p37xc5sCDZrbczGaHskp33xCW3wAqw3JL551PP490nVtVWE4szzUXh9st8xpuxdD+cx0MvOPuOxLKs87MaoEDgcco8Pc24Vwhh99bJYrCc4S7HwScBFxkZkfFN4a/qAqyT3Qhn1twE7AnMBHYAFyf3XDSy8z6Ab8BLnH39+LbCu29TXKuOf3eKlFE1gPVsfWRoSzvuPv68H0TcBfRJerGcPlN+L4pVG/pvPPp55Guc1sflhPLc4a7b3T3ne6+C/g50XsL7T/Xt4hu15QklGeNmfUg+uD8lbvfGYoL8r1Ndq65/t4qUUSeAMaE3gI9gZnA4izH1G5m1tfMyhqWgROB54jOpaEHyCzgnrC8GDg39CKZDLwbLvWXACea2cBwCXxiKMtFaTm3sO09M5sc7vOeGztWTmj40AxOJ3pvITrXmWZWamajgDFEjbdJf6/DX+d/BM4M+8d/bl0u/LxvAVa5+w2xTQX33rZ0rjn/3nZFS38+fBH1pHiJqCfBV7IdTwfPYTRR74engZUN50F03/Ih4GXg98CgUG7Aj8M5PwvUxY71KaKGs9XA+dk+txDTQqLL8g+J7r1ekM5zA+qI/oO+AvyIMHJBDp3rL8K5PEP0ATI8Vv8rIe4XifXoaen3OvyuPB5+Br8GSrN4rkcQ3VZ6BlgRvqYX4nub4lxz+r3VEB4iIpKSbj2JiEhKShQiIpKSEoWIiKSkRCEiIikpUYiISEpKFCIikpIShYiIpPT/AVaM6cscl0/WAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this graph we try to plot the relation between Time and the amount."
      ],
      "metadata": {
        "id": "EMqbWl9DP7X0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8), )\n",
        "plt.title('Amount Distribution')\n",
        "\n",
        "sns.distplot(data_df['Amount'],color='red');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "UcgBxTxrP3p9",
        "outputId": "c362c345-b5d9-4bc5-d7de-2bc07a6db801"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From this amount distribution curve  it is shown that the number high amount transactions are very low. So there is a high probability for huge transactions to be fraudulent ."
      ],
      "metadata": {
        "id": "M5Ab6g_vQJDX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outliers**"
      ],
      "metadata": {
        "id": "7oBkZUuLQSh8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(16,8))\n",
        "ax.scatter(data_df['Amount'], data_df['Time'])\n",
        "ax.set_xlabel('Amount')\n",
        "ax.set_ylabel('Time')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "E7qsFzSTQDML",
        "outputId": "264e4d1b-e05f-4bb5-e4f2-89b68089c975"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrix \n",
        "correlation_metrics = data_df.corr() \n",
        "fig = plt.figure(figsize = (14, 9)) \n",
        "sns.heatmap(correlation_metrics, vmax = .9, square = True) \n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "6bFRHMRtQvF9",
        "outputId": "ed419c41-5894-4254-b0d5-f4c642c4450a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation metrics help us to understand the core relation between two attributes."
      ],
      "metadata": {
        "id": "KN2x28e-Q12s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>To start with modelling First we need to split the dataset<br>\n",
        "80% → 80% of the data will use to train the model<br>\n",
        "20% → 20% to validate the model<br>"
      ],
      "metadata": {
        "id": "ULzOXmGyQ75s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=data_df.drop(['Class'], axis = 1)#drop the target variable\n",
        "y=data_df['Class']\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.2, random_state = 42) "
      ],
      "metadata": {
        "id": "LplmEkq5Q6QU"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logisticreg = LogisticRegression()\n",
        "logisticreg.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p75zlVL9RQg-",
        "outputId": "97e22952-9059-428f-956b-bafff4a9840d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = logisticreg.predict(xtest)\n",
        "accuracy= logisticreg.score(xtest,ytest)"
      ],
      "metadata": {
        "id": "1_fDZR39RUwS"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <B> Confusion Matrix</b>\n",
        "-- Positive (P) : Observation is positive (for example: The transaction genuine).<br>\n",
        "-- Negative (N) : Observation is not positive (for example:The transaction is not genuine).<br>\n",
        "-- True Positive (TP) : Observation is positive, and is predicted to be positive.<br>\n",
        "-- False Negative (FN) : Observation is positive, but is predicted negative.<br>\n",
        "-- True Negative (TN) : Observation is negative, and is predicted to be negative.<br>\n",
        "-- False Positive (FP) : Observation is negative, but is predicted positive.<br>\n",
        "\n",
        "\n",
        "<a href=\"https://imgbb.com/\"><img src=\"https://i.ibb.co/41Vjvvh/Confusion-Matrix1-1.png\" alt=\"Confusion-Matrix1-1\" border=\"0\"></a><br /><a target='_blank' href='https://poetandpoem.com/Richard-Brautigan/poems'></a><br />\n",
        "\n",
        "<a href=\"https://imgbb.com/\"><img src=\"https://i.ibb.co/MnfKcWZ/Confusion-Matrix2-2.png\" alt=\"Confusion-Matrix2-2\" border=\"0\"></a><br /><a target='_blank' href='https://poetandpoem.com/pain'></a><br />"
      ],
      "metadata": {
        "id": "xpRU_X0vRb-E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = metrics.confusion_matrix(ytest, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rB1-H-mBRebR",
        "outputId": "645a9cfe-9045-4875-b75c-b912cc420ee6"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[56831    33]\n",
            " [   42    56]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <b>Accuracy calculation"
      ],
      "metadata": {
        "id": "lyKJBx6aRmrm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the Logistic regression model: ', accuracy*100,'%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ptu5OULBRnyL",
        "outputId": "9f62d10f-1869-4e2c-c26f-2f1d1bc6b011"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the Logistic regression model:  99.86833327481479 %\n"
          ]
        }
      ]
    }
  ]
}