Untitled3.ipynb

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        "<a href=\"https://colab.research.google.com/github/Isoken00/Credit-Card-Fraud-Detection/blob/main/Untitled3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "CREDIT CARD FRAUD DETECTION USING ML"
      ],
      "metadata": {
        "id": "x_2alBJsZKLI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing the dependencies"
      ],
      "metadata": {
        "id": "DhWa-2aRZYbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "Cw6gbBmXZhLM"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the dataset to a Pandas DataFrame\n",
        "credit_card_data = pd.read_csv('/content/creditcard.csv')"
      ],
      "metadata": {
        "id": "Ta78Suf-aujI"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# first 5 rows of the dataset\n",
        "credit_card_data.head()"
      ],
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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      "source": [
        "# Last 5 rows of the dataset\n",
        "credit_card_data.tail()"
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              "            Time        V1         V2        V3        V4        V5        V6  \\\n",
              "188256  127893.0 -8.852104 -11.509369  0.449332  2.068166  5.423514 -4.506990   \n",
              "188257  127894.0  1.609287  -0.581437  0.008484  1.655629 -1.162795 -0.938786   \n",
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              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
              "188256 -3.722182 -0.343961 -1.255238  ... -1.390078  1.059268  5.788621   \n",
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              "188260 -0.174480  0.001335  1.259834  ...       NaN       NaN       NaN   \n",
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              "             V24       V25       V26       V27       V28  Amount  Class  \n",
              "188256  1.226642  1.186211  0.584517  1.783203 -0.400900    37.5    0.0  \n",
              "188257  0.858324 -0.281039 -0.628079  0.032977  0.003303   150.0    0.0  \n",
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              "      <th>V6</th>\n",
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              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>188256</th>\n",
              "      <td>127893.0</td>\n",
              "      <td>-8.852104</td>\n",
              "      <td>-11.509369</td>\n",
              "      <td>0.449332</td>\n",
              "      <td>2.068166</td>\n",
              "      <td>5.423514</td>\n",
              "      <td>-4.506990</td>\n",
              "      <td>-3.722182</td>\n",
              "      <td>-0.343961</td>\n",
              "      <td>-1.255238</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.390078</td>\n",
              "      <td>1.059268</td>\n",
              "      <td>5.788621</td>\n",
              "      <td>1.226642</td>\n",
              "      <td>1.186211</td>\n",
              "      <td>0.584517</td>\n",
              "      <td>1.783203</td>\n",
              "      <td>-0.400900</td>\n",
              "      <td>37.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188257</th>\n",
              "      <td>127894.0</td>\n",
              "      <td>1.609287</td>\n",
              "      <td>-0.581437</td>\n",
              "      <td>0.008484</td>\n",
              "      <td>1.655629</td>\n",
              "      <td>-1.162795</td>\n",
              "      <td>-0.938786</td>\n",
              "      <td>-0.287847</td>\n",
              "      <td>-0.124818</td>\n",
              "      <td>0.855258</td>\n",
              "      <td>...</td>\n",
              "      <td>0.373621</td>\n",
              "      <td>0.902301</td>\n",
              "      <td>0.129504</td>\n",
              "      <td>0.858324</td>\n",
              "      <td>-0.281039</td>\n",
              "      <td>-0.628079</td>\n",
              "      <td>0.032977</td>\n",
              "      <td>0.003303</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188258</th>\n",
              "      <td>127894.0</td>\n",
              "      <td>2.097886</td>\n",
              "      <td>-0.685229</td>\n",
              "      <td>-1.281921</td>\n",
              "      <td>-0.318993</td>\n",
              "      <td>-0.235987</td>\n",
              "      <td>-0.167462</td>\n",
              "      <td>-0.382762</td>\n",
              "      <td>-0.114099</td>\n",
              "      <td>-0.412410</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.136930</td>\n",
              "      <td>0.381340</td>\n",
              "      <td>0.056371</td>\n",
              "      <td>0.740105</td>\n",
              "      <td>0.272978</td>\n",
              "      <td>-0.107836</td>\n",
              "      <td>0.016936</td>\n",
              "      <td>-0.057214</td>\n",
              "      <td>11.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188259</th>\n",
              "      <td>127894.0</td>\n",
              "      <td>1.851731</td>\n",
              "      <td>-0.368532</td>\n",
              "      <td>0.157316</td>\n",
              "      <td>1.422273</td>\n",
              "      <td>-0.917616</td>\n",
              "      <td>-0.315417</td>\n",
              "      <td>-0.696940</td>\n",
              "      <td>0.053708</td>\n",
              "      <td>1.206733</td>\n",
              "      <td>...</td>\n",
              "      <td>0.073982</td>\n",
              "      <td>0.288365</td>\n",
              "      <td>0.247731</td>\n",
              "      <td>-0.001908</td>\n",
              "      <td>-0.361825</td>\n",
              "      <td>-0.760861</td>\n",
              "      <td>0.069835</td>\n",
              "      <td>-0.013513</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188260</th>\n",
              "      <td>127894.0</td>\n",
              "      <td>1.759307</td>\n",
              "      <td>-0.459599</td>\n",
              "      <td>-0.379355</td>\n",
              "      <td>1.665059</td>\n",
              "      <td>-0.555768</td>\n",
              "      <td>-0.275380</td>\n",
              "      <td>-0.174480</td>\n",
              "      <td>0.001335</td>\n",
              "      <td>1.259834</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          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",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Information\n",
        "credit_card_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Az8B0Bc7fhjy",
        "outputId": "8c573188-7b16-49a6-b733-ef128880fc3e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 188261 entries, 0 to 188260\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    188261 non-null  float64\n",
            " 1   V1      188261 non-null  float64\n",
            " 2   V2      188261 non-null  float64\n",
            " 3   V3      188261 non-null  float64\n",
            " 4   V4      188261 non-null  float64\n",
            " 5   V5      188261 non-null  float64\n",
            " 6   V6      188261 non-null  float64\n",
            " 7   V7      188261 non-null  float64\n",
            " 8   V8      188261 non-null  float64\n",
            " 9   V9      188261 non-null  float64\n",
            " 10  V10     188261 non-null  float64\n",
            " 11  V11     188261 non-null  float64\n",
            " 12  V12     188261 non-null  float64\n",
            " 13  V13     188260 non-null  float64\n",
            " 14  V14     188260 non-null  float64\n",
            " 15  V15     188260 non-null  float64\n",
            " 16  V16     188260 non-null  float64\n",
            " 17  V17     188260 non-null  float64\n",
            " 18  V18     188260 non-null  float64\n",
            " 19  V19     188260 non-null  float64\n",
            " 20  V20     188260 non-null  float64\n",
            " 21  V21     188260 non-null  float64\n",
            " 22  V22     188260 non-null  float64\n",
            " 23  V23     188260 non-null  float64\n",
            " 24  V24     188260 non-null  float64\n",
            " 25  V25     188260 non-null  float64\n",
            " 26  V26     188260 non-null  float64\n",
            " 27  V27     188260 non-null  float64\n",
            " 28  V28     188260 non-null  float64\n",
            " 29  Amount  188260 non-null  float64\n",
            " 30  Class   188260 non-null  float64\n",
            "dtypes: float64(31)\n",
            "memory usage: 44.5 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values in each column\n",
        "credit_card_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6pZFFfuf7Si",
        "outputId": "50168c80-300c-4886-ac8f-b1b007f7de92"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       1\n",
              "V14       1\n",
              "V15       1\n",
              "V16       1\n",
              "V17       1\n",
              "V18       1\n",
              "V19       1\n",
              "V20       1\n",
              "V21       1\n",
              "V22       1\n",
              "V23       1\n",
              "V24       1\n",
              "V25       1\n",
              "V26       1\n",
              "V27       1\n",
              "V28       1\n",
              "Amount    1\n",
              "Class     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#   Distribution of real and fraudulent transaction\n",
        "credit_card_data['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jzpsZo0WgQzG",
        "outputId": "a95d72a7-78b6-467d-de63-106be71c61d4"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    187892\n",
              "1.0       368\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Dataset Is Highly Unbalanced\n",
        "\n",
        "0--> Normal Transaction\n",
        "\n",
        "1--> Fraudulent Transaction"
      ],
      "metadata": {
        "id": "I67DfgKmmdBp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Seperating the data for analysis\n",
        "Legit = credit_card_data[credit_card_data.Class == 0]\n",
        "Fraud = credit_card_data[credit_card_data.Class == 1]"
      ],
      "metadata": {
        "id": "tpl4mQXRm4tJ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Legit.shape)\n",
        "print(Fraud.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlZopvsWnuxq",
        "outputId": "a7bdcb36-37e4-4536-9a9e-e4b74a4334aa"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(187892, 31)\n",
            "(368, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Statistical measures of the data\n",
        "Legit.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbjAejrQn_MS",
        "outputId": "0aa0ed4b-ccf8-440c-c568-72827b228e03"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    187892.000000\n",
              "mean         88.949661\n",
              "std         247.532742\n",
              "min           0.000000\n",
              "25%           5.960000\n",
              "50%          22.750000\n",
              "75%          79.000000\n",
              "max       19656.530000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Fraud.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VKDt1f8wqgPE",
        "outputId": "4a3e8144-6eeb-4998-d9c9-582f0034467f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     368.000000\n",
              "mean      115.659891\n",
              "std       248.444604\n",
              "min         0.000000\n",
              "25%         1.000000\n",
              "50%        11.395000\n",
              "75%       104.007500\n",
              "max      2125.870000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare the value for both transactions\n",
        "credit_card_data.groupby('Class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "QE1wzgBTqmUd",
        "outputId": "79f86bfe-5dd4-4f0b-a650-3fdababe06f1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0.0    66928.356710 -0.115473  0.005839  0.398688  0.079599 -0.127383   \n",
              "1.0    58116.711957 -5.926741  4.330987 -7.884388  4.836685 -4.286202   \n",
              "\n",
              "            V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                         ...                       \n",
              "0.0    0.05489 -0.048754  0.021358  0.018046  ...  0.023277 -0.020226   \n",
              "1.0   -1.42407 -6.893932  0.739547 -2.838131  ...  0.404833  0.790022   \n",
              "\n",
              "           V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                        \n",
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**UNDERSAMPLING**\n",
        "\n",
        "Build a sample dataset containing similar distribution of normal and fraudulent transactions\n",
        "\n",
        "a similar distribution of normal and fraudulent transactions\n",
        "\n",
        "Number of fraudulent transactions --> 368"
      ],
      "metadata": {
        "id": "o8RX_QWlyMI1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "legit_sample = Legit.sample(n=368)"
      ],
      "metadata": {
        "id": "qNiPGb4uy3MW"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concatenating two DataFrames"
      ],
      "metadata": {
        "id": "vYGK-sbezSuh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset = pd.concat([legit_sample, Fraud], axis=0)"
      ],
      "metadata": {
        "id": "r736HZDVzZm8"
      },
      "execution_count": 22,
      "outputs": []
    },
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        "outputId": "8f0302bc-8dc5-4014-e91f-f8e4beb25519"
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      "execution_count": 23,
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-d17723f4-5073-4008-be51-c5126bf94ba4 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-d17723f4-5073-4008-be51-c5126bf94ba4');\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",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c191bf43-dfe1-4dae-b328-013b88ca893b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
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        "id": "ojJU2ZAy0Bhe",
        "outputId": "07617cd1-d4ef-46ec-c02b-1dfcc491e8eb"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
              "178208  123525.0 -5.904921  4.439911 -8.631802  7.788684 -4.989580 -1.200144   \n",
              "181966  125200.0 -0.769172  1.342212 -2.171454 -0.151513 -0.648374 -0.973504   \n",
              "182992  125612.0  1.889618  1.073099 -1.678018  4.173268  1.015516 -0.009389   \n",
              "183106  125658.0  0.224414  2.994499 -3.432458  3.986519  3.760233  0.165640   \n",
              "184379  126219.0 -1.141559  1.927650 -3.905356 -0.073943 -0.044858 -1.756999   \n",
              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
              "178208 -7.674060  4.125761 -5.315778  ...  1.775030  1.266441 -0.199410   \n",
              "181966 -1.706658  0.313745 -1.982302  ... -0.036122 -0.753591 -0.047113   \n",
              "182992 -0.079706  0.064071 -0.714517  ...  0.203728  0.733796 -0.036560   \n",
              "183106  1.099378 -0.654557 -2.435416  ...  0.491337 -0.984223 -0.421979   \n",
              "184379 -1.217416  0.364563 -2.770148  ...  0.102081 -0.531496 -0.328741   \n",
              "\n",
              "             V24       V25       V26       V27       V28  Amount  Class  \n",
              "178208  0.014960 -0.000019  0.544210 -1.167566 -1.206354   45.51    1.0  \n",
              "181966  0.358493 -0.287407  0.476506  0.250531  0.250987   40.00    1.0  \n",
              "182992  0.334306  0.147171  0.279556  0.031669  0.035883    3.22    1.0  \n",
              "183106 -1.048058  0.726412  0.268625  0.283689  0.419102    0.00    1.0  \n",
              "184379  0.393100  0.568435  0.786605 -0.146102  0.076211   25.00    1.0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
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              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
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              "      <th>178208</th>\n",
              "      <td>123525.0</td>\n",
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              "      <td>-7.674060</td>\n",
              "      <td>4.125761</td>\n",
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              "      <td>1.266441</td>\n",
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              "      <td>0.544210</td>\n",
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              "      <th>181966</th>\n",
              "      <td>125200.0</td>\n",
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              "      <td>1.342212</td>\n",
              "      <td>-2.171454</td>\n",
              "      <td>-0.151513</td>\n",
              "      <td>-0.648374</td>\n",
              "      <td>-0.973504</td>\n",
              "      <td>-1.706658</td>\n",
              "      <td>0.313745</td>\n",
              "      <td>-1.982302</td>\n",
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              "      <td>-0.036122</td>\n",
              "      <td>-0.753591</td>\n",
              "      <td>-0.047113</td>\n",
              "      <td>0.358493</td>\n",
              "      <td>-0.287407</td>\n",
              "      <td>0.476506</td>\n",
              "      <td>0.250531</td>\n",
              "      <td>0.250987</td>\n",
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              "      <td>125612.0</td>\n",
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              "      <td>1.073099</td>\n",
              "      <td>-1.678018</td>\n",
              "      <td>4.173268</td>\n",
              "      <td>1.015516</td>\n",
              "      <td>-0.009389</td>\n",
              "      <td>-0.079706</td>\n",
              "      <td>0.064071</td>\n",
              "      <td>-0.714517</td>\n",
              "      <td>...</td>\n",
              "      <td>0.203728</td>\n",
              "      <td>0.733796</td>\n",
              "      <td>-0.036560</td>\n",
              "      <td>0.334306</td>\n",
              "      <td>0.147171</td>\n",
              "      <td>0.279556</td>\n",
              "      <td>0.031669</td>\n",
              "      <td>0.035883</td>\n",
              "      <td>3.22</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>183106</th>\n",
              "      <td>125658.0</td>\n",
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              "      <td>-3.432458</td>\n",
              "      <td>3.986519</td>\n",
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              "      <td>0.726412</td>\n",
              "      <td>0.268625</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f03eb7c5-b5aa-47f9-a774-9cc711cecd6f 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-f03eb7c5-b5aa-47f9-a774-9cc711cecd6f');\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",
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              "  </button>\n",
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              "  .colab-df-quickchart {\n",
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              "    fill: #1967D2;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3f232767-8169-4c8b-9f28-7e47095ec828 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuJCSe7F0077",
        "outputId": "06986eff-a4c0-4f65-a178-9527dbd49ea0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    368\n",
              "1.0    368\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset.groupby('Class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "Ub7b4wxG1A0E",
        "outputId": "61241f40-f96f-43b8-d303-620c92899f45"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0.0    68601.755435 -0.360396  0.023011  0.149244 -0.044053 -0.205188   \n",
              "1.0    58116.711957 -5.926741  4.330987 -7.884388  4.836685 -4.286202   \n",
              "\n",
              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                          ...                       \n",
              "0.0    0.155191  0.118541  0.035064  0.032612  ... -0.015238  0.043683   \n",
              "1.0   -1.424070 -6.893932  0.739547 -2.838131  ...  0.404833  0.790022   \n",
              "\n",
              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
              "0.0   -0.092735 -0.031255  0.021681  0.061018  0.019217  0.020831 -0.033169   \n",
              "1.0   -0.023060 -0.043940 -0.071941  0.080214  0.040915  0.177371  0.050569   \n",
              "\n",
              "           Amount  \n",
              "Class              \n",
              "0.0    115.859565  \n",
              "1.0    115.659891  \n",
              "\n",
              "[2 rows x 30 columns]"
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              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
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              "      <td>68601.755435</td>\n",
              "      <td>-0.360396</td>\n",
              "      <td>0.023011</td>\n",
              "      <td>0.149244</td>\n",
              "      <td>-0.044053</td>\n",
              "      <td>-0.205188</td>\n",
              "      <td>0.155191</td>\n",
              "      <td>0.118541</td>\n",
              "      <td>0.035064</td>\n",
              "      <td>0.032612</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.015238</td>\n",
              "      <td>0.043683</td>\n",
              "      <td>-0.092735</td>\n",
              "      <td>-0.031255</td>\n",
              "      <td>0.021681</td>\n",
              "      <td>0.061018</td>\n",
              "      <td>0.019217</td>\n",
              "      <td>0.020831</td>\n",
              "      <td>-0.033169</td>\n",
              "      <td>115.859565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>58116.711957</td>\n",
              "      <td>-5.926741</td>\n",
              "      <td>4.330987</td>\n",
              "      <td>-7.884388</td>\n",
              "      <td>4.836685</td>\n",
              "      <td>-4.286202</td>\n",
              "      <td>-1.424070</td>\n",
              "      <td>-6.893932</td>\n",
              "      <td>0.739547</td>\n",
              "      <td>-2.838131</td>\n",
              "      <td>...</td>\n",
              "      <td>0.404833</td>\n",
              "      <td>0.790022</td>\n",
              "      <td>-0.023060</td>\n",
              "      <td>-0.043940</td>\n",
              "      <td>-0.071941</td>\n",
              "      <td>0.080214</td>\n",
              "      <td>0.040915</td>\n",
              "      <td>0.177371</td>\n",
              "      <td>0.050569</td>\n",
              "      <td>115.659891</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-ce37cfb2-af2d-44da-bb95-e6f02f845c35');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Splitting the data into features and targets*"
      ],
      "metadata": {
        "id": "ZtGNge6P4E-u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = new_dataset.drop(columns='Class', axis=1)\n",
        "Y = new_dataset['Class']"
      ],
      "metadata": {
        "id": "KvV88LI-4eHV"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h0qQ6FZx46vE",
        "outputId": "754eb207-72aa-4dc5-dcb1-16880569fc35"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Time        V1        V2        V3        V4        V5        V6  \\\n",
            "165529  117491.0 -0.340362  0.814983  0.485907 -0.365913  0.698083 -1.039745   \n",
            "112132   72526.0  1.331381 -0.407347  0.583222 -0.501637 -1.186158 -1.196555   \n",
            "26578    34149.0 -2.830384 -1.437631  1.192068 -0.459457 -0.778949  0.102314   \n",
            "106953   70209.0  0.766512 -1.095266  0.875239  0.375112 -1.258366  0.140694   \n",
            "45730    42473.0  1.314249 -0.933226 -0.261099 -0.969678 -0.164030  0.982521   \n",
            "...          ...       ...       ...       ...       ...       ...       ...   \n",
            "178208  123525.0 -5.904921  4.439911 -8.631802  7.788684 -4.989580 -1.200144   \n",
            "181966  125200.0 -0.769172  1.342212 -2.171454 -0.151513 -0.648374 -0.973504   \n",
            "182992  125612.0  1.889618  1.073099 -1.678018  4.173268  1.015516 -0.009389   \n",
            "183106  125658.0  0.224414  2.994499 -3.432458  3.986519  3.760233  0.165640   \n",
            "184379  126219.0 -1.141559  1.927650 -3.905356 -0.073943 -0.044858 -1.756999   \n",
            "\n",
            "              V7        V8        V9  ...       V20       V21       V22  \\\n",
            "165529  1.203193 -0.624393  0.002697  ... -0.071883 -0.152249 -0.265194   \n",
            "112132 -0.332429 -0.329411 -1.063399  ... -0.360108 -0.439921 -0.787674   \n",
            "26578   0.712889  0.130140  0.476592  ...  0.081335 -0.026995  0.091646   \n",
            "106953 -0.481270  0.160416  1.239595  ...  0.286659 -0.084968 -0.440206   \n",
            "45730  -0.762154  0.259775 -0.477700  ...  0.157407 -0.047657 -0.203255   \n",
            "...          ...       ...       ...  ...       ...       ...       ...   \n",
            "178208 -7.674060  4.125761 -5.315778  ... -0.505608  1.775030  1.266441   \n",
            "181966 -1.706658  0.313745 -1.982302  ... -0.102294 -0.036122 -0.753591   \n",
            "182992 -0.079706  0.064071 -0.714517  ... -0.153570  0.203728  0.733796   \n",
            "183106  1.099378 -0.654557 -2.435416  ... -0.200846  0.491337 -0.984223   \n",
            "184379 -1.217416  0.364563 -2.770148  ... -0.250583  0.102081 -0.531496   \n",
            "\n",
            "             V23       V24       V25       V26       V27       V28  Amount  \n",
            "165529  0.148873  0.063198 -1.060213  0.068564 -0.429565  0.058281    1.79  \n",
            "112132  0.151131  0.767439  0.089158  0.877304 -0.049853  0.020803   27.51  \n",
            "26578  -0.768046 -0.291327  0.283213  1.062419  0.281932 -0.756640  382.50  \n",
            "106953 -0.068612  0.147630 -0.027093  0.951633 -0.059073  0.046170  216.20  \n",
            "45730  -0.254032 -1.702853  0.607508 -0.154716  0.011298 -0.008002   67.55  \n",
            "...          ...       ...       ...       ...       ...       ...     ...  \n",
            "178208 -0.199410  0.014960 -0.000019  0.544210 -1.167566 -1.206354   45.51  \n",
            "181966 -0.047113  0.358493 -0.287407  0.476506  0.250531  0.250987   40.00  \n",
            "182992 -0.036560  0.334306  0.147171  0.279556  0.031669  0.035883    3.22  \n",
            "183106 -0.421979 -1.048058  0.726412  0.268625  0.283689  0.419102    0.00  \n",
            "184379 -0.328741  0.393100  0.568435  0.786605 -0.146102  0.076211   25.00  \n",
            "\n",
            "[736 rows x 30 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QP9DA-PL4_Yd",
        "outputId": "66c41892-3a78-4dd7-d1a8-22cbcb68aa88"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "165529    0.0\n",
            "112132    0.0\n",
            "26578     0.0\n",
            "106953    0.0\n",
            "45730     0.0\n",
            "         ... \n",
            "178208    1.0\n",
            "181966    1.0\n",
            "182992    1.0\n",
            "183106    1.0\n",
            "184379    1.0\n",
            "Name: Class, Length: 736, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split the data into training & testing data"
      ],
      "metadata": {
        "id": "-9d6pBNq556q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
      ],
      "metadata": {
        "id": "_qWh7LmA5P-7"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Cvz0PmCFGvS",
        "outputId": "fb79ab85-32f4-4d63-f67a-acc4e0b7e551"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(736, 30) (588, 30) (148, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MODEL TRAINING**\n",
        "\n",
        "*Logistic Regression*"
      ],
      "metadata": {
        "id": "iaK74_IfFn2z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "ZAOC2wg9Fh6W"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the Logistic Regression Model with Training Data\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "xpf4CsnfF_DT",
        "outputId": "9c963b7c-cb46-427a-b284-c8b1ef846331"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MODEL EVALUATION**\n",
        "\n",
        "*Accuracy Score*"
      ],
      "metadata": {
        "id": "ift2JJaFHo5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "metadata": {
        "id": "0hKMF-9kGgKs"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on training data : ', training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxJvyx7EIlJy",
        "outputId": "98c0c94f-c439-497f-86bb-4bc74e5fb627"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data :  0.9438775510204082\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test )"
      ],
      "metadata": {
        "id": "Vdv_VzQxI9CN"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on test data : ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v00ivn4iUH9m",
        "outputId": "af902656-2bee-48b0-8e09-99b50bc1a141"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data :  0.9324324324324325\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XZoWOvGAWEj0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}