CreditCard_fraud_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CreditCard_fraud detection ",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "sMcXSZ4x0AfG"
      },
      "source": [
        "# Importing  the reuired packages and libraries\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "from sklearn.preprocessing import StandardScaler\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.linear_model import LogisticRegression\r\n",
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "from sklearn.tree import DecisionTreeClassifier \r\n",
        "from sklearn.neighbors import KNeighborsClassifier\r\n",
        "from sklearn.metrics import classification_report\r\n",
        "from sklearn.metrics import precision_score, recall_score,accuracy_score,confusion_matrix,f1_score\r\n",
        "from imblearn.over_sampling import SMOTE"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "685yjA6J0_Rr",
        "outputId": "ced1d03a-3074-4299-dd54-0d07ac5b6fff"
      },
      "source": [
        "# Importing the data\r\n",
        "data=pd.read_csv('/content/TRAIN.csv')\r\n",
        "data.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Index</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2.245295</td>\n",
              "      <td>-1.160960</td>\n",
              "      <td>-1.966682</td>\n",
              "      <td>-1.430190</td>\n",
              "      <td>-0.607246</td>\n",
              "      <td>-1.508696</td>\n",
              "      <td>-0.074415</td>\n",
              "      <td>-0.655096</td>\n",
              "      <td>-1.970141</td>\n",
              "      <td>1.607283</td>\n",
              "      <td>-0.780267</td>\n",
              "      <td>-0.294928</td>\n",
              "      <td>1.236719</td>\n",
              "      <td>-0.135565</td>\n",
              "      <td>-0.832677</td>\n",
              "      <td>-1.400205</td>\n",
              "      <td>0.807960</td>\n",
              "      <td>-0.670317</td>\n",
              "      <td>-0.044106</td>\n",
              "      <td>-0.292081</td>\n",
              "      <td>0.010490</td>\n",
              "      <td>0.521714</td>\n",
              "      <td>-0.064971</td>\n",
              "      <td>0.048849</td>\n",
              "      <td>0.383290</td>\n",
              "      <td>0.103970</td>\n",
              "      <td>-0.047350</td>\n",
              "      <td>-0.064800</td>\n",
              "      <td>74.75</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1.278609</td>\n",
              "      <td>0.102574</td>\n",
              "      <td>0.512079</td>\n",
              "      <td>0.003930</td>\n",
              "      <td>-0.723474</td>\n",
              "      <td>-1.334105</td>\n",
              "      <td>0.029958</td>\n",
              "      <td>-0.296595</td>\n",
              "      <td>0.128119</td>\n",
              "      <td>-0.209865</td>\n",
              "      <td>0.188511</td>\n",
              "      <td>0.674525</td>\n",
              "      <td>0.712608</td>\n",
              "      <td>0.122319</td>\n",
              "      <td>1.038024</td>\n",
              "      <td>0.128638</td>\n",
              "      <td>-0.222614</td>\n",
              "      <td>-0.687546</td>\n",
              "      <td>-0.056504</td>\n",
              "      <td>-0.040015</td>\n",
              "      <td>-0.081470</td>\n",
              "      <td>-0.182506</td>\n",
              "      <td>0.078986</td>\n",
              "      <td>0.789993</td>\n",
              "      <td>0.219794</td>\n",
              "      <td>0.938359</td>\n",
              "      <td>-0.078720</td>\n",
              "      <td>0.008119</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1.466457</td>\n",
              "      <td>0.026088</td>\n",
              "      <td>-0.499298</td>\n",
              "      <td>-0.674372</td>\n",
              "      <td>-0.144883</td>\n",
              "      <td>-1.178075</td>\n",
              "      <td>0.058089</td>\n",
              "      <td>-0.420145</td>\n",
              "      <td>-1.359651</td>\n",
              "      <td>0.210249</td>\n",
              "      <td>-0.033777</td>\n",
              "      <td>-0.001594</td>\n",
              "      <td>1.767471</td>\n",
              "      <td>-1.212943</td>\n",
              "      <td>0.427684</td>\n",
              "      <td>1.252714</td>\n",
              "      <td>0.843412</td>\n",
              "      <td>-1.534474</td>\n",
              "      <td>0.830734</td>\n",
              "      <td>0.207653</td>\n",
              "      <td>-0.234087</td>\n",
              "      <td>-0.710542</td>\n",
              "      <td>-0.001242</td>\n",
              "      <td>-0.174713</td>\n",
              "      <td>0.533719</td>\n",
              "      <td>-0.454779</td>\n",
              "      <td>0.001524</td>\n",
              "      <td>0.030935</td>\n",
              "      <td>10.95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>-0.922137</td>\n",
              "      <td>-0.371579</td>\n",
              "      <td>2.132018</td>\n",
              "      <td>-0.796997</td>\n",
              "      <td>0.324175</td>\n",
              "      <td>-1.162006</td>\n",
              "      <td>0.277208</td>\n",
              "      <td>-0.249586</td>\n",
              "      <td>-0.775648</td>\n",
              "      <td>-0.061757</td>\n",
              "      <td>-0.959725</td>\n",
              "      <td>0.408746</td>\n",
              "      <td>0.597641</td>\n",
              "      <td>-0.802430</td>\n",
              "      <td>-1.889520</td>\n",
              "      <td>-1.607785</td>\n",
              "      <td>-0.401475</td>\n",
              "      <td>0.729773</td>\n",
              "      <td>-2.445693</td>\n",
              "      <td>-0.461062</td>\n",
              "      <td>-0.302654</td>\n",
              "      <td>-0.246899</td>\n",
              "      <td>-0.045745</td>\n",
              "      <td>0.677110</td>\n",
              "      <td>0.016109</td>\n",
              "      <td>-0.734220</td>\n",
              "      <td>-0.034480</td>\n",
              "      <td>-0.064786</td>\n",
              "      <td>8.04</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>-1.166525</td>\n",
              "      <td>0.255439</td>\n",
              "      <td>2.108464</td>\n",
              "      <td>0.135019</td>\n",
              "      <td>-0.072979</td>\n",
              "      <td>0.910821</td>\n",
              "      <td>0.755918</td>\n",
              "      <td>0.355528</td>\n",
              "      <td>-0.422820</td>\n",
              "      <td>-0.842826</td>\n",
              "      <td>0.663538</td>\n",
              "      <td>0.624657</td>\n",
              "      <td>0.107262</td>\n",
              "      <td>-0.073654</td>\n",
              "      <td>-0.305506</td>\n",
              "      <td>0.618642</td>\n",
              "      <td>-0.952529</td>\n",
              "      <td>0.757618</td>\n",
              "      <td>-0.375461</td>\n",
              "      <td>0.353355</td>\n",
              "      <td>0.136470</td>\n",
              "      <td>0.017496</td>\n",
              "      <td>0.121017</td>\n",
              "      <td>0.636266</td>\n",
              "      <td>0.492943</td>\n",
              "      <td>-0.750242</td>\n",
              "      <td>0.029124</td>\n",
              "      <td>0.091303</td>\n",
              "      <td>175.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Index        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0      0  2.245295 -1.160960 -1.966682  ... -0.047350 -0.064800   74.75      0\n",
              "1      1  1.278609  0.102574  0.512079  ... -0.078720  0.008119    1.38      0\n",
              "2      2  1.466457  0.026088 -0.499298  ...  0.001524  0.030935   10.95      0\n",
              "3      3 -0.922137 -0.371579  2.132018  ... -0.034480 -0.064786    8.04      0\n",
              "4      4 -1.166525  0.255439  2.108464  ...  0.029124  0.091303  175.00      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgFmDhlOdjfQ",
        "outputId": "8fec3729-2832-4a94-b424-1898cc592dc6"
      },
      "source": [
        "# different columns in dataset\r\n",
        "data.columns"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Index', '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": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtSigxeUduc9",
        "outputId": "5e1419c8-dfd3-49ea-c45a-82b3e0624dc2"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284455 entries, 0 to 284454\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Index   284455 non-null  int64  \n",
            " 1   V1      284455 non-null  float64\n",
            " 2   V2      284455 non-null  float64\n",
            " 3   V3      284455 non-null  float64\n",
            " 4   V4      284455 non-null  float64\n",
            " 5   V5      284455 non-null  float64\n",
            " 6   V6      284455 non-null  float64\n",
            " 7   V7      284455 non-null  float64\n",
            " 8   V8      284455 non-null  float64\n",
            " 9   V9      284455 non-null  float64\n",
            " 10  V10     284455 non-null  float64\n",
            " 11  V11     284455 non-null  float64\n",
            " 12  V12     284455 non-null  float64\n",
            " 13  V13     284455 non-null  float64\n",
            " 14  V14     284455 non-null  float64\n",
            " 15  V15     284455 non-null  float64\n",
            " 16  V16     284455 non-null  float64\n",
            " 17  V17     284455 non-null  float64\n",
            " 18  V18     284455 non-null  float64\n",
            " 19  V19     284455 non-null  float64\n",
            " 20  V20     284455 non-null  float64\n",
            " 21  V21     284455 non-null  float64\n",
            " 22  V22     284455 non-null  float64\n",
            " 23  V23     284455 non-null  float64\n",
            " 24  V24     284455 non-null  float64\n",
            " 25  V25     284455 non-null  float64\n",
            " 26  V26     284455 non-null  float64\n",
            " 27  V27     284455 non-null  float64\n",
            " 28  V28     284455 non-null  float64\n",
            " 29  Amount  284455 non-null  float64\n",
            " 30  Class   284455 non-null  int64  \n",
            "dtypes: float64(29), int64(2)\n",
            "memory usage: 67.3 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VEAu4RpkNxT"
      },
      "source": [
        "# **Data Processing and EDA**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DcSGyi5XJn22",
        "outputId": "b4de26cb-033a-4bea-801e-bebc16f8c45c"
      },
      "source": [
        "# Total no. of cases present in each class\r\n",
        "Total_cases =len(data)\r\n",
        "nonfraud_count =len(data[data.Class == 0])\r\n",
        "fraud_count =len(data[data.Class == 1])\r\n",
        "print(f'Total no. of cases   {Total_cases}')\r\n",
        "print(f'Total no. of fraud cases    {fraud_count}')\r\n",
        "print(f'Total no. of Non_fraud cases    {nonfraud_count}')"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total no. of cases   284455\n",
            "Total no. of fraud cases    440\n",
            "Total no. of Non_fraud cases    284015\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Laa_uaG_NPuT",
        "outputId": "ebe5c69b-6dec-457e-c672-7bed5837a839"
      },
      "source": [
        "#Classifying the data as fraud and Non-fraud by assigning the class as 1 or 0\r\n",
        "nonfraud_cases = data[data.Class == 0]\r\n",
        "fraud_cases = data[data.Class == 1]\r\n",
        "print(\"NonFraud Case Amount Statistics\" )\r\n",
        "print(nonfraud_cases.Amount.describe())\r\n",
        "print(\"fraud_cases Amount Staticstics\")\r\n",
        "print(fraud_cases.Amount.describe())\r\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NonFraud Case Amount Statistics\n",
            "count    284015.000000\n",
            "mean         88.313070\n",
            "std         250.182582\n",
            "min           0.000000\n",
            "25%           5.650000\n",
            "50%          22.000000\n",
            "75%          77.050000\n",
            "max       25691.160000\n",
            "Name: Amount, dtype: float64\n",
            "fraud_cases Amount Staticstics\n",
            "count     440.000000\n",
            "mean      118.423773\n",
            "std       245.493158\n",
            "min         0.000000\n",
            "25%         1.000000\n",
            "50%         9.170000\n",
            "75%       106.130000\n",
            "max      2125.870000\n",
            "Name: Amount, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zT4FJ_egdSId",
        "outputId": "7309f88d-55fb-4f9b-8130-7e35f2f5470e"
      },
      "source": [
        "sc = StandardScaler()\r\n",
        "amount = data['Amount'].values\r\n",
        "data['Amount']= sc.fit_transform(amount.reshape(-1,1))\r\n",
        "print(data['Amount'].head(10))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0   -0.054400\n",
            "1   -0.347672\n",
            "2   -0.309419\n",
            "3   -0.321051\n",
            "4    0.346316\n",
            "5   -0.237070\n",
            "6   -0.315974\n",
            "7   -0.229276\n",
            "8   -0.287555\n",
            "9   -0.241947\n",
            "Name: Amount, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etgvsUD9Kei8"
      },
      "source": [
        "# **Feature Selection and Data split**\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUAYc39he6Cd",
        "outputId": "603fc42d-9bb1-4ecb-ef96-ad3a3b0264bf"
      },
      "source": [
        "x = data.drop('Class',axis = 1).values\r\n",
        "y = data['Class'].values\r\n",
        "#Train_test_split\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(x,y , test_size =0.2,random_state =0)\r\n",
        "\r\n",
        "print(f'X_train_Sample  :   {X_train}') \r\n",
        "print(f'X_test_Sample   :   {y_test}')\r\n",
        "print(f'Y_train_sample  :   {y_train}')\r\n",
        "print(f'Y_test_sample   :   {y_test}')\r\n",
        "\r\n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train_Sample  :   [[ 6.75390000e+04  2.03290251e+00  8.72104658e-02 ... -2.68770997e-03\n",
            "  -5.63309355e-02 -3.49190894e-01]\n",
            " [ 7.43990000e+04  1.42471913e+00 -6.40710760e-01 ... -2.80971140e-02\n",
            "  -1.72212258e-02 -2.73244764e-01]\n",
            " [ 8.84200000e+04 -2.12968987e+00  1.32652086e+00 ...  6.81433878e-01\n",
            "   4.51243274e-01  4.64884409e-02]\n",
            " ...\n",
            " [ 1.22579000e+05 -1.31066539e+00  1.62559754e+00 ... -6.24072031e-01\n",
            "   4.29722435e-02 -3.53188058e-01]\n",
            " [ 1.52315000e+05 -1.01555239e+00 -7.96095303e-01 ...  4.91034029e-03\n",
            "   5.18016397e-02 -2.92710956e-01]\n",
            " [ 1.17952000e+05  6.87872620e-01 -1.36790890e+00 ... -9.36404094e-02\n",
            "   4.31728942e-02  8.64907915e-01]]\n",
            "X_test_Sample   :   [0 0 0 ... 0 0 0]\n",
            "Y_train_sample  :   [0 0 0 ... 0 0 0]\n",
            "Y_test_sample   :   [0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TApADvvijk_L"
      },
      "source": [
        "# **Data Modeling & Evaluation of matrix**\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdkZ1iY8hVDx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e324e2c-2089-4d4d-c625-2ffb4378713e"
      },
      "source": [
        "#Random forest Classifier\r\n",
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "clf_rf = RandomForestClassifier(random_state=55)\r\n",
        "clf_rf.fit(X_train, y_train)\r\n",
        "print(clf_rf)\r\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=None, max_features='auto',\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                       n_jobs=None, oob_score=False, random_state=55, verbose=0,\n",
            "                       warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GAr_YZspVMDJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62c280ce-5c0a-4078-a8d9-9df147f5c4f2"
      },
      "source": [
        "y_pred=clf_rf.predict(X_test)\r\n",
        "y_pred"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "Z1UIWRJ-USWI",
        "outputId": "a48a8d0d-b6d6-4de0-a817-5856a8bde0b0"
      },
      "source": [
        "print(\"Confusion Matrix\")\r\n",
        "print(confusion_matrix(y_test,y_pred))\r\n",
        "print(\"Accuracy of rf Model:\")\r\n",
        "print(accuracy_score(y_test, y_pred))\r\n",
        "print(\"Precision of rf Model:\")\r\n",
        "print (precision_score(y_test, y_pred))\r\n",
        "print (\"Recall of rf Model:\" )\r\n",
        "print( recall_score(y_test, y_pred))\r\n",
        "print (\"F1 Score of rf Model:\")\r\n",
        "print (f1_score(y_test, y_pred))\r\n",
        "confusion_matrix_data=pd.crosstab(y_test,y_pred,rownames=['Actual_Case'],colnames=['Predicted_Case'])\r\n",
        "sns.heatmap(confusion_matrix_data,annot=True)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[56793     2]\n",
            " [   21    75]]\n",
            "Accuracy of rf Model:\n",
            "0.9995957181276476\n",
            "Precision of rf Model:\n",
            "0.974025974025974\n",
            "Recall of rf Model:\n",
            "0.78125\n",
            "F1 Score of rf Model:\n",
            "0.8670520231213873\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e9086d6a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sjvc0tQGVIsx",
        "outputId": "3ce446d3-0771-42a3-b9b2-0089a23f03a5"
      },
      "source": [
        "#Logistic Regression\r\n",
        "from sklearn.linear_model import LogisticRegression\r\n",
        "lr_model=LogisticRegression(random_state=55)\r\n",
        "lr_model.fit(X_train, y_train)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=55, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwJ4faNJZhhZ",
        "outputId": "ad07ea07-c9df-4b5f-a075-b46877c8b838"
      },
      "source": [
        "y_pred = lr_model.predict(X_test)\r\n",
        "y_pred"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "U8vfNBEkeY0F",
        "outputId": "0f135c32-a4d0-4809-b6f6-4b6a2c7e5c5f"
      },
      "source": [
        "print(\"Confusion Matrix\")\r\n",
        "print(confusion_matrix(y_test,y_pred))\r\n",
        "print(\"Accuracy of rf Model:\")\r\n",
        "print(accuracy_score(y_test, y_pred))\r\n",
        "print(\"Precision of rf Model:\")\r\n",
        "print (precision_score(y_test, y_pred))\r\n",
        "print (\"Recall of rf Model:\" )\r\n",
        "print( recall_score(y_test, y_pred))\r\n",
        "print (\"F1 Score of rf Model:\")\r\n",
        "print (f1_score(y_test, y_pred))\r\n",
        "confusion_matrix_data=pd.crosstab(y_test,y_pred,rownames=['Actual_Case'],colnames=['Predicted_Case'])\r\n",
        "sns.heatmap(confusion_matrix_data,annot=True)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[56784    11]\n",
            " [   17    79]]\n",
            "Accuracy of rf Model:\n",
            "0.9995078307640928\n",
            "Precision of rf Model:\n",
            "0.8777777777777778\n",
            "Recall of rf Model:\n",
            "0.8229166666666666\n",
            "F1 Score of rf Model:\n",
            "0.8494623655913979\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e90869828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gfH_kvdlefHl",
        "outputId": "4dbbf314-2dc7-416b-b561-e6914de4c522"
      },
      "source": [
        "# Decision Tree Algorithim\r\n",
        "tree_model = DecisionTreeClassifier(max_depth = 4, criterion = 'entropy')\r\n",
        "tree_model.fit(X_train, y_train)\r\n",
        "tree_yhat = tree_model.predict(X_test)\r\n",
        "y_pred_tree = tree_model.predict(X_test)\r\n",
        "y_pred_tree"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "4CyhMZUSi796",
        "outputId": "71ad1c3c-74b1-47fa-fbc0-d35d49da272e"
      },
      "source": [
        "print(\"Confusion Matrix\")\r\n",
        "print(confusion_matrix(y_test,y_pred_tree))\r\n",
        "print(\"Accuracy of rf Model:\")\r\n",
        "print(accuracy_score(y_test, y_pred_tree))\r\n",
        "print(\"Precision of rf Model:\")\r\n",
        "print (precision_score(y_test, y_pred_tree))\r\n",
        "print (\"Recall of rf Model:\" )\r\n",
        "print( recall_score(y_test, y_pred_tree))\r\n",
        "print (\"F1 Score of rf Model:\")\r\n",
        "print (f1_score(y_test, y_pred_tree))\r\n",
        "confusion_matrix_data=pd.crosstab(y_test,y_pred_tree,rownames=['Actual_Case'],colnames=['Predicted_Case'])\r\n",
        "sns.heatmap(confusion_matrix_data,annot=True)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[56789     6]\n",
            " [   26    70]]\n",
            "Accuracy of rf Model:\n",
            "0.9994375208732489\n",
            "Precision of rf Model:\n",
            "0.9210526315789473\n",
            "Recall of rf Model:\n",
            "0.7291666666666666\n",
            "F1 Score of rf Model:\n",
            "0.8139534883720931\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e9076fcc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StEqK_ebicqa",
        "outputId": "35d8eaa2-7a5f-41af-8d35-66df3d99a3ca"
      },
      "source": [
        "# KNN Algorithim\r\n",
        "n = 5\r\n",
        "knn = KNeighborsClassifier(n_neighbors = n)\r\n",
        "knn.fit(X_train, y_train)\r\n",
        "knn_yhat = knn.predict(X_test)\r\n",
        "y_pred_KN = knn.predict(X_test)\r\n",
        "y_pred_KN"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "nlbBdSJ0jAYr",
        "outputId": "76bca849-4b37-4806-9efe-bed6141c512a"
      },
      "source": [
        "print(\"Confusion Matrix\")\r\n",
        "print(confusion_matrix(y_test,y_pred_KN))\r\n",
        "print(\"Accuracy of rf Model:\")\r\n",
        "print(accuracy_score(y_test, y_pred_KN))\r\n",
        "print(\"Precision of rf Model:\")\r\n",
        "print (precision_score(y_test, y_pred_KN))\r\n",
        "print (\"Recall of rf Model:\" )\r\n",
        "print( recall_score(y_test, y_pred_KN))\r\n",
        "print (\"F1 Score of rf Model:\")\r\n",
        "print (f1_score(y_test, y_pred_KN))\r\n",
        "confusion_matrix_data=pd.crosstab(y_test,y_pred,rownames=['Actual_Case'],colnames=['Predicted_Case'])\r\n",
        "sns.heatmap(confusion_matrix_data,annot=True)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[56795     0]\n",
            " [   96     0]]\n",
            "Accuracy of rf Model:\n",
            "0.9983125626197465\n",
            "Precision of rf Model:\n",
            "0.0\n",
            "Recall of rf Model:\n",
            "0.0\n",
            "F1 Score of rf Model:\n",
            "0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e90db7668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvQrtJcLBahW",
        "outputId": "ff7e9270-4d13-4b63-ea97-164bbe761cfe"
      },
      "source": [
        "#Resampling of data as data was imbalanced using SMOTE\r\n",
        "print(\"Label 1 before Resampling,: \", sum(y_train == 1)) \r\n",
        "print(\"Label 0 before Resampling: \", sum(y_train == 0)) \r\n",
        "\r\n",
        "sm = SMOTE(random_state = 3) \r\n",
        "X_train_sm, Y_train_sm = sm.fit_sample(X_train, y_train.ravel()) \r\n",
        "  \r\n",
        "print('Shape of train_X after Resampling : ', X_train_sm.shape) \r\n",
        "print('Shape of train_Y after Reampling,: \\n', Y_train_sm.shape) \r\n",
        "  \r\n",
        "print(\"Label 1 after Resampling,: \", sum(Y_train_sm == 1)) \r\n",
        "print(\"Label 0 after Resampling: \", sum(Y_train_sm == 0)) "
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label 1 before Resampling,:  344\n",
            "Label 0 before Resampling:  227220\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Shape of train_X after Resampling :  (454440, 30)\n",
            "Shape of train_Y after Reampling,: \n",
            " (454440,)\n",
            "Label 1 after Resampling,:  227220\n",
            "Label 0 after Resampling:  227220\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "OIOaRcjPBxCj",
        "outputId": "d5a28988-a70b-4988-f38f-989d79f74656"
      },
      "source": [
        "#Classification using Logistic Regression after Resampling\r\n",
        "logistic_regression = LogisticRegression()\r\n",
        "logistic_regression.fit(X_train_sm,Y_train_sm)\r\n",
        "pred_lr_sm = logistic_regression.predict(X_test)\r\n",
        "confusionmat_lr_sm = pd.crosstab(y_test,pred_lr_sm,rownames=['Actual'],colnames=['Predicted'])\r\n",
        "sns.heatmap(confusionmat_lr_sm, annot=True)\r\n",
        "print('Accuracy:',accuracy_score(y_test,pred_lr_sm))\r\n",
        "plt.show()\r\n",
        "print(classification_report(y_test, pred_lr_sm)) "
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9503787945369215\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.95      0.97     56795\n",
            "           1       0.03      0.89      0.06        96\n",
            "\n",
            "    accuracy                           0.95     56891\n",
            "   macro avg       0.51      0.92      0.52     56891\n",
            "weighted avg       1.00      0.95      0.97     56891\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "sbLuAfYLB5kL",
        "outputId": "c19bd116-818f-4535-e0f6-0422086e9a6d"
      },
      "source": [
        "#Classification using RandomForest Classifier after Resampling\r\n",
        "Randomforest = RandomForestClassifier()\r\n",
        "Randomforest.fit(X_train_sm,Y_train_sm)\r\n",
        "pred_rf_sm = Randomforest.predict(X_test)\r\n",
        "confusionmat_rf_sm = pd.crosstab(y_test,pred_rf_sm,rownames=['Actual'],colnames=['Predicted'])\r\n",
        "sns.heatmap(confusionmat_rf_sm,annot=True)\r\n",
        "print('Accuracy:',accuracy_score(y_test, pred_rf_sm))\r\n",
        "plt.show()\r\n",
        "print(classification_report(y_test, pred_rf_sm))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9995254082368037\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56795\n",
            "           1       0.88      0.83      0.86        96\n",
            "\n",
            "    accuracy                           1.00     56891\n",
            "   macro avg       0.94      0.92      0.93     56891\n",
            "weighted avg       1.00      1.00      1.00     56891\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "FI1t5i_bE_E3",
        "outputId": "c55a64a8-6097-4c8c-cdee-d4ee071ee6a7"
      },
      "source": [
        "#Classification using DecisionTree Classifier after Resampling\r\n",
        "dt = DecisionTreeClassifier()\r\n",
        "dt.fit(X_train_sm,Y_train_sm)\r\n",
        "pred_dt_sm = dt.predict(X_test)\r\n",
        "confusionmat_dt_sm = pd.crosstab(y_test,pred_dt_sm,rownames=['Actual'],colnames=['Predicted'])\r\n",
        "sns.heatmap(confusionmat_dt_sm,annot=True)\r\n",
        "print('Accuracy:',accuracy_score(y_test, pred_dt_sm))\r\n",
        "plt.show()\r\n",
        "print(classification_report(y_test, pred_dt_sm)) "
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9978731258019722\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56795\n",
            "           1       0.43      0.81      0.56        96\n",
            "\n",
            "    accuracy                           1.00     56891\n",
            "   macro avg       0.72      0.91      0.78     56891\n",
            "weighted avg       1.00      1.00      1.00     56891\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "8hk9HNa6n2NL",
        "outputId": "6eaaeb0a-c052-4a0d-8860-50f6bab6693a"
      },
      "source": [
        "#Classification using KNN Classifier after Resampling\r\n",
        "n = 5\r\n",
        "knn = KNeighborsClassifier(n_neighbors = n)\r\n",
        "knn.fit(X_train_sm,Y_train_sm)\r\n",
        "pred_dt_sm = dt.predict(X_test)\r\n",
        "confusionmat_dt_sm = pd.crosstab(y_test,pred_dt_sm,rownames=['Actual'],colnames=['Predicted'])\r\n",
        "sns.heatmap(confusionmat_dt_sm,annot=True)\r\n",
        "print('Accuracy:',accuracy_score(y_test, pred_dt_sm))\r\n",
        "plt.show()\r\n",
        "print(classification_report(y_test, pred_dt_sm)) "
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9978731258019722\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56795\n",
            "           1       0.43      0.81      0.56        96\n",
            "\n",
            "    accuracy                           1.00     56891\n",
            "   macro avg       0.72      0.91      0.78     56891\n",
            "weighted avg       1.00      1.00      1.00     56891\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sE3VBeLftVSN"
      },
      "source": [
        "# Test Dataset\r\n",
        "fraud_test = pd.read_csv('/content/TEST[1].csv')"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "2xhP1Ganqdxk",
        "outputId": "d3450f49-6e7b-45c3-ec20-9fa82c550a3a"
      },
      "source": [
        "#prediction of test data on the trained model i.e. on random forest as its accuracy is more among all the trained model\r\n",
        "final_predictions = clf_rf.predict(fraud_test)\r\n",
        "predictions_data = pd.DataFrame(data=final_predictions, columns=[\"Class\"])\r\n",
        "predictions_data.head()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class\n",
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      1\n",
              "4      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "jpb4YGsmsNKF",
        "outputId": "11202c9f-8651-462e-fa48-51f2536afde4"
      },
      "source": [
        "fraud_test_index = fraud_test[[\"Index\"]].copy()\r\n",
        "fraud_test_index.head()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Index\n",
              "0      0\n",
              "1      1\n",
              "2      2\n",
              "3      3\n",
              "4      4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "z01bzOart-qQ",
        "outputId": "eb0049b9-c843-466b-eeed-d3f3ab7f6419"
      },
      "source": [
        "final_data=pd.concat([fraud_test_index, predictions_data], axis=1, sort=True)\r\n",
        "final_data.head()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Index</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Index  Class\n",
              "0      0      0\n",
              "1      1      0\n",
              "2      2      0\n",
              "3      3      1\n",
              "4      4      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4XnvWdtsT3Y"
      },
      "source": [
        "#conversion of final predicted data into csv file\r\n",
        "final_data.to_csv('/content/target_prediction.csv', index=False)"
      ],
      "execution_count": 78,
      "outputs": []
    }
  ]
}