Credit_Card_Fraud.ipynb

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        "<a href=\"https://colab.research.google.com/github/Samtoosoon/Credit-Card-Fraud-Detection/blob/main/Credit_Card_Fraud.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": []
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      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlNfrSC1PGfZ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0CTsNioTKnJ"
      },
      "source": [
        "# loading the dataset to a Pandas DataFrame\n",
        "credit_card_data = pd.read_csv('/content/creditcard.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
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        "outputId": "d8956406-b542-47a2-b1a4-d5a472f70643"
      },
      "source": [
        "# first 5 rows of the dataset\n",
        "credit_card_data.head()"
      ],
      "execution_count": 4,
      "outputs": [
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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              "4     2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
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              "        V26       V27       V28  Amount  Class  \n",
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              "3 -0.221929  0.062723  0.061458  123.50    0.0  \n",
              "4  0.502292  0.219422  0.215153   69.99    0.0  \n",
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          "metadata": {},
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      "source": [
        "credit_card_data.tail()"
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      "execution_count": 5,
      "outputs": [
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          "data": {
            "text/plain": [
              "        Time        V1        V2        V3        V4        V5        V6  \\\n",
              "13949  24754  1.252924 -0.182189 -0.802716 -0.210981  1.916713  3.643624   \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KcCZquvEUD3A",
        "outputId": "9c2fba78-f0b0-45bc-8bdd-2bd0ebac8b35"
      },
      "source": [
        "# dataset informations\n",
        "credit_card_data.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13954 entries, 0 to 13953\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Time    13954 non-null  int64  \n",
            " 1   V1      13954 non-null  float64\n",
            " 2   V2      13954 non-null  float64\n",
            " 3   V3      13954 non-null  float64\n",
            " 4   V4      13954 non-null  float64\n",
            " 5   V5      13954 non-null  float64\n",
            " 6   V6      13954 non-null  float64\n",
            " 7   V7      13954 non-null  float64\n",
            " 8   V8      13954 non-null  float64\n",
            " 9   V9      13954 non-null  float64\n",
            " 10  V10     13954 non-null  float64\n",
            " 11  V11     13954 non-null  float64\n",
            " 12  V12     13954 non-null  float64\n",
            " 13  V13     13954 non-null  float64\n",
            " 14  V14     13954 non-null  float64\n",
            " 15  V15     13954 non-null  float64\n",
            " 16  V16     13954 non-null  float64\n",
            " 17  V17     13954 non-null  float64\n",
            " 18  V18     13954 non-null  float64\n",
            " 19  V19     13954 non-null  float64\n",
            " 20  V20     13954 non-null  float64\n",
            " 21  V21     13954 non-null  float64\n",
            " 22  V22     13954 non-null  float64\n",
            " 23  V23     13954 non-null  float64\n",
            " 24  V24     13954 non-null  float64\n",
            " 25  V25     13954 non-null  float64\n",
            " 26  V26     13954 non-null  float64\n",
            " 27  V27     13954 non-null  float64\n",
            " 28  V28     13954 non-null  float64\n",
            " 29  Amount  13953 non-null  float64\n",
            " 30  Class   13953 non-null  float64\n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 3.3 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxwasmfKUQiA",
        "outputId": "bee52971-089e-4efa-dad5-591210903967"
      },
      "source": [
        "# checking the number of missing values in each column\n",
        "credit_card_data.isnull().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
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              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    1\n",
              "Class     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EIpoKfp5Ugri",
        "outputId": "bad3878f-4fb9-4c30-ac15-43efc25d61fe"
      },
      "source": [
        "# distribution of legit transactions & fraudulent transactions\n",
        "credit_card_data['Class'].value_counts()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    13897\n",
              "1.0       56\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flz_InK7VGri"
      },
      "source": [
        "This Dataset is highly unblanced"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OlMhkHzVKMv"
      },
      "source": [
        "0 --> Normal Transaction\n",
        "\n",
        "1 --> fraudulent transaction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNn77bdbU2Lf"
      },
      "source": [
        "# separating the data for analysis\n",
        "legit = credit_card_data[credit_card_data.Class == 0]\n",
        "fraud = credit_card_data[credit_card_data.Class == 1]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsrMgOdoVnrV",
        "outputId": "708dc4dc-83a5-487f-fef6-7264f3f8e5f8"
      },
      "source": [
        "print(legit.shape)\n",
        "print(fraud.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(13897, 31)\n",
            "(56, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1s3KGshBVsTb",
        "outputId": "43400d26-602e-40da-bbfc-4f17e4723c0b"
      },
      "source": [
        "# statistical measures of the data\n",
        "legit.Amount.describe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    13897.000000\n",
              "mean        63.773909\n",
              "std        177.164503\n",
              "min          0.000000\n",
              "25%          5.770000\n",
              "50%         15.900000\n",
              "75%         52.370000\n",
              "max       7712.430000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KkU3Mzr5V7fR",
        "outputId": "671135a7-563c-4957-bb96-3540bd666658"
      },
      "source": [
        "fraud.Amount.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      56.000000\n",
              "mean       90.815893\n",
              "std       310.308450\n",
              "min         0.000000\n",
              "25%         1.000000\n",
              "50%         1.000000\n",
              "75%         1.025000\n",
              "max      1809.680000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "aFUMMHwYWMvp",
        "outputId": "aa18d041-55c6-43b7-95e2-e503ad16b024"
      },
      "source": [
        "# compare the values for both transactions\n",
        "credit_card_data.groupby('Class').mean()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0.0    10111.481255 -0.217093  0.266252  0.887227  0.276789 -0.107510   \n",
              "1.0    12262.107143 -4.727948  4.660436 -9.328536  6.783464 -2.890388   \n",
              "\n",
              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                          ...                       \n",
              "0.0    0.136745 -0.131462 -0.020385  1.005460  ...  0.020758 -0.068686   \n",
              "1.0   -2.030360 -6.578337  1.028374 -2.981468  ...  0.453283  0.116896   \n",
              "\n",
              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
              "0.0   -0.158230 -0.034834  0.013065  0.117928  0.034349  0.009565  0.002329   \n",
              "1.0    0.048132 -0.225526 -0.452514 -0.124184  0.333325  0.793484 -0.046177   \n",
              "\n",
              "          Amount  \n",
              "Class             \n",
              "0.0    63.773909  \n",
              "1.0    90.815893  \n",
              "\n",
              "[2 rows x 30 columns]"
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              "      <td>-2.981468</td>\n",
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              "      <td>-0.452514</td>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybHNYPpEW0N6"
      },
      "source": [
        "Under-Sampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MlXkIGEIW3KM"
      },
      "source": [
        "Build a sample dataset containing similar distribution of normal transactions and Fraudulent Transactions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCj3Dee7XB0F"
      },
      "source": [
        "Number of Fraudulent Transactions --> 492"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtWT13mKWjJ_"
      },
      "source": [
        "legit_sample = legit.sample(n=492)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNiYI_SmXeim"
      },
      "source": [
        "Concatenating two DataFrames"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yiXrYiRXcnE"
      },
      "source": [
        "new_dataset = pd.concat([legit_sample, fraud], axis=0)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "UuLw43AXX0bq",
        "outputId": "53e5eb99-26b4-4e20-db6e-2cb3ebfbeb4a"
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      "execution_count": 16,
      "outputs": [
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              "        Time        V1        V2        V3        V4        V5        V6  \\\n",
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          "metadata": {},
          "execution_count": 16
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 255
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        "id": "n_Rjt1qDX3AQ",
        "outputId": "56208f6c-e2b7-43ed-88f3-b337967d4ce9"
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      "source": [
        "new_dataset.tail()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Time         V1         V2         V3         V4         V5        V6  \\\n",
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              "\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</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>...</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>11880</th>\n",
              "      <td>20451</td>\n",
              "      <td>-15.819179</td>\n",
              "      <td>8.775997</td>\n",
              "      <td>-22.804686</td>\n",
              "      <td>11.864868</td>\n",
              "      <td>-9.092361</td>\n",
              "      <td>-2.386893</td>\n",
              "      <td>-16.560368</td>\n",
              "      <td>0.948349</td>\n",
              "      <td>-6.310658</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.350634</td>\n",
              "      <td>1.036362</td>\n",
              "      <td>1.136051</td>\n",
              "      <td>-1.043414</td>\n",
              "      <td>-0.108923</td>\n",
              "      <td>0.657437</td>\n",
              "      <td>2.136424</td>\n",
              "      <td>-1.411945</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12070</th>\n",
              "      <td>20931</td>\n",
              "      <td>-16.367923</td>\n",
              "      <td>9.223692</td>\n",
              "      <td>-23.270631</td>\n",
              "      <td>11.844777</td>\n",
              "      <td>-9.462037</td>\n",
              "      <td>-2.450444</td>\n",
              "      <td>-16.925152</td>\n",
              "      <td>1.384208</td>\n",
              "      <td>-6.287736</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.343674</td>\n",
              "      <td>1.004602</td>\n",
              "      <td>1.188212</td>\n",
              "      <td>-1.047184</td>\n",
              "      <td>-0.035573</td>\n",
              "      <td>0.664900</td>\n",
              "      <td>2.122796</td>\n",
              "      <td>-1.416741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12108</th>\n",
              "      <td>21046</td>\n",
              "      <td>-16.917468</td>\n",
              "      <td>9.669900</td>\n",
              "      <td>-23.736443</td>\n",
              "      <td>11.824990</td>\n",
              "      <td>-9.830548</td>\n",
              "      <td>-2.514829</td>\n",
              "      <td>-17.290657</td>\n",
              "      <td>1.820408</td>\n",
              "      <td>-6.264903</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.336111</td>\n",
              "      <td>0.972755</td>\n",
              "      <td>1.241866</td>\n",
              "      <td>-1.051086</td>\n",
              "      <td>0.038009</td>\n",
              "      <td>0.672317</td>\n",
              "      <td>2.108471</td>\n",
              "      <td>-1.421243</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12261</th>\n",
              "      <td>21419</td>\n",
              "      <td>-17.467710</td>\n",
              "      <td>10.114816</td>\n",
              "      <td>-24.202142</td>\n",
              "      <td>11.805469</td>\n",
              "      <td>-10.198046</td>\n",
              "      <td>-2.579938</td>\n",
              "      <td>-17.656788</td>\n",
              "      <td>2.256902</td>\n",
              "      <td>-6.242149</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.328024</td>\n",
              "      <td>0.940830</td>\n",
              "      <td>1.296817</td>\n",
              "      <td>-1.055104</td>\n",
              "      <td>0.111792</td>\n",
              "      <td>0.679695</td>\n",
              "      <td>2.093541</td>\n",
              "      <td>-1.425491</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12369</th>\n",
              "      <td>21662</td>\n",
              "      <td>-18.018561</td>\n",
              "      <td>10.558600</td>\n",
              "      <td>-24.667741</td>\n",
              "      <td>11.786180</td>\n",
              "      <td>-10.564657</td>\n",
              "      <td>-2.645681</td>\n",
              "      <td>-18.023468</td>\n",
              "      <td>2.693655</td>\n",
              "      <td>-6.219464</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.319479</td>\n",
              "      <td>0.908839</td>\n",
              "      <td>1.352904</td>\n",
              "      <td>-1.059222</td>\n",
              "      <td>0.185751</td>\n",
              "      <td>0.687037</td>\n",
              "      <td>2.078081</td>\n",
              "      <td>-1.429517</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "        dataTable['output_type'] = 'display_data';\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFsRcj0gX-3M",
        "outputId": "69a7a638-e761-4e93-a962-f3c10d9b7ca4"
      },
      "source": [
        "new_dataset['Class'].value_counts()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    492\n",
              "1.0     56\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "jan1xMeWYLrM",
        "outputId": "d9ddd468-75ed-462e-80b1-814efc9bb8bf"
      },
      "source": [
        "new_dataset.groupby('Class').mean()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0.0     9835.859756 -0.184667  0.261596  0.894742  0.407649 -0.125789   \n",
              "1.0    12262.107143 -4.727948  4.660436 -9.328536  6.783464 -2.890388   \n",
              "\n",
              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                          ...                       \n",
              "0.0    0.210587 -0.171763 -0.056370  1.009323  ...  0.009559 -0.074201   \n",
              "1.0   -2.030360 -6.578337  1.028374 -2.981468  ...  0.453283  0.116896   \n",
              "\n",
              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
              "0.0   -0.163802 -0.052527 -0.029867  0.112223  0.078368 -0.008070 -0.005597   \n",
              "1.0    0.048132 -0.225526 -0.452514 -0.124184  0.333325  0.793484 -0.046177   \n",
              "\n",
              "          Amount  \n",
              "Class             \n",
              "0.0    67.515549  \n",
              "1.0    90.815893  \n",
              "\n",
              "[2 rows x 30 columns]"
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              "      <td>0.078368</td>\n",
              "      <td>-0.008070</td>\n",
              "      <td>-0.005597</td>\n",
              "      <td>67.515549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>12262.107143</td>\n",
              "      <td>-4.727948</td>\n",
              "      <td>4.660436</td>\n",
              "      <td>-9.328536</td>\n",
              "      <td>6.783464</td>\n",
              "      <td>-2.890388</td>\n",
              "      <td>-2.030360</td>\n",
              "      <td>-6.578337</td>\n",
              "      <td>1.028374</td>\n",
              "      <td>-2.981468</td>\n",
              "      <td>...</td>\n",
              "      <td>0.453283</td>\n",
              "      <td>0.116896</td>\n",
              "      <td>0.048132</td>\n",
              "      <td>-0.225526</td>\n",
              "      <td>-0.452514</td>\n",
              "      <td>-0.124184</td>\n",
              "      <td>0.333325</td>\n",
              "      <td>0.793484</td>\n",
              "      <td>-0.046177</td>\n",
              "      <td>90.815893</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e53fede5-9e04-4fbd-b25e-20896a83695c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e53fede5-9e04-4fbd-b25e-20896a83695c 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-e53fede5-9e04-4fbd-b25e-20896a83695c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fff457d8-099d-4bfe-ae68-b89b49972e83\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fff457d8-099d-4bfe-ae68-b89b49972e83')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fff457d8-099d-4bfe-ae68-b89b49972e83 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbe0g12oYhyK"
      },
      "source": [
        "Splitting the data into Features & Targets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiC0OOyUYUoD"
      },
      "source": [
        "X = new_dataset.drop(columns='Class', axis=1)\n",
        "Y = new_dataset['Class']"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9u7wbWqzYyrI",
        "outputId": "2a52c1c2-0e8f-4927-829b-42502c443619"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Time         V1         V2         V3         V4         V5        V6  \\\n",
            "8294   11080   1.325930  -0.039276   0.184189  -0.204363   0.139414  0.499040   \n",
            "1127     874  -0.445099   0.754145   1.361999  -1.184238  -0.036773 -0.941421   \n",
            "12166  21206   1.229514  -0.211045   1.057933  -0.520571  -1.073185 -0.754903   \n",
            "8030   10999   1.231104  -0.010139   0.706038   0.184175  -0.413804 -0.143596   \n",
            "9563   14338  -1.803279   0.101064   1.509077  -0.760388  -1.766393  0.644461   \n",
            "...      ...        ...        ...        ...        ...        ...       ...   \n",
            "11880  20451 -15.819179   8.775997 -22.804686  11.864868  -9.092361 -2.386893   \n",
            "12070  20931 -16.367923   9.223692 -23.270631  11.844777  -9.462037 -2.450444   \n",
            "12108  21046 -16.917468   9.669900 -23.736443  11.824990  -9.830548 -2.514829   \n",
            "12261  21419 -17.467710  10.114816 -24.202142  11.805469 -10.198046 -2.579938   \n",
            "12369  21662 -18.018561  10.558600 -24.667741  11.786180 -10.564657 -2.645681   \n",
            "\n",
            "              V7        V8        V9  ...       V20       V21       V22  \\\n",
            "8294   -0.523791  0.011449  1.477210  ...  0.036311 -0.119524 -0.073381   \n",
            "1127    0.698625 -0.026480 -0.134956  ... -0.001496 -0.015807  0.056658   \n",
            "12166  -0.595141 -0.163353  2.980563  ... -0.094263 -0.146789  0.089747   \n",
            "8030   -0.482836 -0.074320  1.666521  ... -0.064352 -0.056268  0.166509   \n",
            "9563   -2.738748 -3.529287  4.902921  ... -1.522169  2.969896 -0.281443   \n",
            "...          ...       ...       ...  ...       ...       ...       ...   \n",
            "11880 -16.560368  0.948349 -6.310658  ...  0.992279 -2.350634  1.036362   \n",
            "12070 -16.925152  1.384208 -6.287736  ...  0.993585 -2.343674  1.004602   \n",
            "12108 -17.290657  1.820408 -6.264903  ...  0.996745 -2.336111  0.972755   \n",
            "12261 -17.656788  2.256902 -6.242149  ...  1.001519 -2.328024  0.940830   \n",
            "12369 -18.023468  2.693655 -6.219464  ...  1.007703 -2.319479  0.908839   \n",
            "\n",
            "            V23       V24       V25       V26       V27       V28  Amount  \n",
            "8294  -0.250445 -1.348173  0.434017  1.117792 -0.090467 -0.020103   15.95  \n",
            "1127   0.008857  0.450117 -0.311580  0.774707  0.203999  0.088460    7.69  \n",
            "12166 -0.043276  0.371562  0.500848 -0.729114  0.070240  0.032541   11.85  \n",
            "8030  -0.036268 -0.240182  0.202967  1.080781 -0.069350 -0.000014   14.95  \n",
            "9563  -5.110558  0.762979 -1.853282 -0.557814  0.885812  0.145876  570.49  \n",
            "...         ...       ...       ...       ...       ...       ...     ...  \n",
            "11880  1.136051 -1.043414 -0.108923  0.657437  2.136424 -1.411945    1.00  \n",
            "12070  1.188212 -1.047184 -0.035573  0.664900  2.122796 -1.416741    1.00  \n",
            "12108  1.241866 -1.051086  0.038009  0.672317  2.108471 -1.421243    1.00  \n",
            "12261  1.296817 -1.055104  0.111792  0.679695  2.093541 -1.425491    1.00  \n",
            "12369  1.352904 -1.059222  0.185751  0.687037  2.078081 -1.429517    1.00  \n",
            "\n",
            "[548 rows x 30 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHnRPzZvYz-F",
        "outputId": "93c0d0f3-135e-4016-fbb4-1c5651caf8f3"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8294     0.0\n",
            "1127     0.0\n",
            "12166    0.0\n",
            "8030     0.0\n",
            "9563     0.0\n",
            "        ... \n",
            "11880    1.0\n",
            "12070    1.0\n",
            "12108    1.0\n",
            "12261    1.0\n",
            "12369    1.0\n",
            "Name: Class, Length: 548, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78wEbV41Y6j3"
      },
      "source": [
        "Split the data into Training data & Testing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrsqqwT0Y3n5"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUmwXz99ZuvK",
        "outputId": "0528ad93-655c-4da1-be98-fc342586b7b3"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(548, 30) (438, 30) (110, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aowgjqbeZ7d1"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0NkjaxyZ96E"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0HHd1v8Z2Mq"
      },
      "source": [
        "model = LogisticRegression()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "_rshSouvaGrv",
        "outputId": "82b7eb34-f803-4c06-88a7-22cc0b9950f6"
      },
      "source": [
        "# training the Logistic Regression Model with Training Data\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: 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",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "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": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9FYWyKjalvC"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6O6kbwYaoxH"
      },
      "source": [
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pC2kCJhOaage"
      },
      "source": [
        "# accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ijq6gAevbDwm",
        "outputId": "3395d394-2aeb-49c3-910c-0b9afdbadfcb"
      },
      "source": [
        "print('Accuracy on Training data : ', training_data_accuracy)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Training data :  0.9863013698630136\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tryJUs6mbJM7"
      },
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxEuN24ib3hS",
        "outputId": "840dc84f-64cb-449f-9cc4-545119f24969"
      },
      "source": [
        "print('Accuracy score on Test Data : ', test_data_accuracy)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score on Test Data :  0.9636363636363636\n"
          ]
        }
      ]
    }
  ]
}