CreditCard.ipynb

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        "<a href=\"https://colab.research.google.com/github/tejaswiniii-11/Fraud-Transaction-Detection/blob/main/CreditCard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "credit_data = pd.read_csv('/content/drive/MyDrive/creditcard.csv/creditcard.csv')"
      ],
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        "id": "e7TbPWhuX8aW"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "credit_data.head()"
      ],
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          "metadata": {},
          "execution_count": 4
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    },
    {
      "cell_type": "code",
      "source": [
        "credit_data.tail()"
      ],
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        "id": "t_ZRHKzuYJCb",
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              "            Time         V1         V2        V3        V4        V5  \\\n",
              "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
              "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
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              "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
              "\n",
              "              V6        V7        V8        V9  ...       V21       V22  \\\n",
              "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
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              "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
              "\n",
              "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
              "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
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          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dataset information\n",
        "credit_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__Gsxj6aYfzA",
        "outputId": "99de4f78-4e9d-4ce0-ce7b-11af0096d86a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
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            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
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            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
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            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
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    {
      "cell_type": "code",
      "source": [
        "# check number of missing values in each column\n",
        "credit_data.isnull().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RD3qPzVQYqlC",
        "outputId": "aafb19a0-d598-4ebe-e401-e82dc145352e"
      },
      "execution_count": null,
      "outputs": [
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              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#distribution of legit transactions & fradulent transactions\n",
        "credit_data['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2_7Z23uYyjE",
        "outputId": "6639a494-8431-431d-de2e-c446fa0dde8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset is highly imbalanced\n"
      ],
      "metadata": {
        "id": "ot0XvmFMZQeT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "0 --> Normal Transaction\n",
        "\n",
        "1 --> Fradulent Transaction\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "tEOc14CQZgJl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Separate data for analysis\n",
        "legit = credit_data[credit_data.Class == 0]\n",
        "fraud = credit_data[credit_data.Class == 1]"
      ],
      "metadata": {
        "id": "z23BNRQuZHME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(legit.shape)\n",
        "print(fraud.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-MVKmQAaBiZ",
        "outputId": "bc73c31b-4aca-485d-94f3-c217743aae6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(284315, 31)\n",
            "(492, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#statistical measures of data\n",
        "legit.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajdLhIPDaFyU",
        "outputId": "6f4da3b5-8e47-416d-e17a-b9dbf253e34d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    284315.000000\n",
              "mean         88.291022\n",
              "std         250.105092\n",
              "min           0.000000\n",
              "25%           5.650000\n",
              "50%          22.000000\n",
              "75%          77.050000\n",
              "max       25691.160000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraud.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9od7ZqLVaOm_",
        "outputId": "cf5aa581-d74c-48bd-d83d-8fcc6c26e525"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     492.000000\n",
              "mean      122.211321\n",
              "std       256.683288\n",
              "min         0.000000\n",
              "25%         1.000000\n",
              "50%         9.250000\n",
              "75%       105.890000\n",
              "max      2125.870000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#compare values for both transactions\n",
        "credit_data.groupby('Class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "yiOlxaEzas8d",
        "outputId": "dd91ed4e-8f07-46d3-c8f7-fa23d611a757"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0      94838.202258  0.008258 -0.006271  0.012171 -0.007860  0.005453   \n",
              "1      80746.806911 -4.771948  3.623778 -7.033281  4.542029 -3.151225   \n",
              "\n",
              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                          ...                       \n",
              "0      0.002419  0.009637 -0.000987  0.004467  ... -0.000644 -0.001235   \n",
              "1     -1.397737 -5.568731  0.570636 -2.581123  ...  0.372319  0.713588   \n",
              "\n",
              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
              "0     -0.000024  0.000070  0.000182 -0.000072 -0.000089 -0.000295 -0.000131   \n",
              "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
              "\n",
              "           Amount  \n",
              "Class              \n",
              "0       88.291022  \n",
              "1      122.211321  \n",
              "\n",
              "[2 rows x 30 columns]"
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "UnderSampling"
      ],
      "metadata": {
        "id": "yj_4tBGTbH63"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build a sample dataset containing similar distribution of normal transactions & fradulent transactions"
      ],
      "metadata": {
        "id": "VUUQBi5ibK0z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "No. of Fraudulent Transactions = 492"
      ],
      "metadata": {
        "id": "Cd3AvQCFbVCT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "legit_sample = legit.sample(n=492)\n"
      ],
      "metadata": {
        "id": "J5GRpXJnbTdS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concatenate 2 dataframes"
      ],
      "metadata": {
        "id": "RCXLoW1CbkU1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#axis =1 (column) , axis=0 (row)\n",
        "new_dataset = pd.concat([legit_sample, fraud], axis=0)"
      ],
      "metadata": {
        "id": "4Szm3beua-rf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "KlOEjp2rcNSq",
        "outputId": "38b94adc-9daf-4ef4-8690-2c509d5bc350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
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          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "TdLereJYcRMa",
        "outputId": "a2b46600-bca6-449b-ded1-6ad58d138eec"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "            Time        V1        V2        V3        V4        V5        V6  \\\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>279863</th>\n",
              "      <td>169142.0</td>\n",
              "      <td>-1.927883</td>\n",
              "      <td>1.125653</td>\n",
              "      <td>-4.518331</td>\n",
              "      <td>1.749293</td>\n",
              "      <td>-1.566487</td>\n",
              "      <td>-2.010494</td>\n",
              "      <td>-0.882850</td>\n",
              "      <td>0.697211</td>\n",
              "      <td>-2.064945</td>\n",
              "      <td>...</td>\n",
              "      <td>0.778584</td>\n",
              "      <td>-0.319189</td>\n",
              "      <td>0.639419</td>\n",
              "      <td>-0.294885</td>\n",
              "      <td>0.537503</td>\n",
              "      <td>0.788395</td>\n",
              "      <td>0.292680</td>\n",
              "      <td>0.147968</td>\n",
              "      <td>390.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280143</th>\n",
              "      <td>169347.0</td>\n",
              "      <td>1.378559</td>\n",
              "      <td>1.289381</td>\n",
              "      <td>-5.004247</td>\n",
              "      <td>1.411850</td>\n",
              "      <td>0.442581</td>\n",
              "      <td>-1.326536</td>\n",
              "      <td>-1.413170</td>\n",
              "      <td>0.248525</td>\n",
              "      <td>-1.127396</td>\n",
              "      <td>...</td>\n",
              "      <td>0.370612</td>\n",
              "      <td>0.028234</td>\n",
              "      <td>-0.145640</td>\n",
              "      <td>-0.081049</td>\n",
              "      <td>0.521875</td>\n",
              "      <td>0.739467</td>\n",
              "      <td>0.389152</td>\n",
              "      <td>0.186637</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280149</th>\n",
              "      <td>169351.0</td>\n",
              "      <td>-0.676143</td>\n",
              "      <td>1.126366</td>\n",
              "      <td>-2.213700</td>\n",
              "      <td>0.468308</td>\n",
              "      <td>-1.120541</td>\n",
              "      <td>-0.003346</td>\n",
              "      <td>-2.234739</td>\n",
              "      <td>1.210158</td>\n",
              "      <td>-0.652250</td>\n",
              "      <td>...</td>\n",
              "      <td>0.751826</td>\n",
              "      <td>0.834108</td>\n",
              "      <td>0.190944</td>\n",
              "      <td>0.032070</td>\n",
              "      <td>-0.739695</td>\n",
              "      <td>0.471111</td>\n",
              "      <td>0.385107</td>\n",
              "      <td>0.194361</td>\n",
              "      <td>77.89</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281144</th>\n",
              "      <td>169966.0</td>\n",
              "      <td>-3.113832</td>\n",
              "      <td>0.585864</td>\n",
              "      <td>-5.399730</td>\n",
              "      <td>1.817092</td>\n",
              "      <td>-0.840618</td>\n",
              "      <td>-2.943548</td>\n",
              "      <td>-2.208002</td>\n",
              "      <td>1.058733</td>\n",
              "      <td>-1.632333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.583276</td>\n",
              "      <td>-0.269209</td>\n",
              "      <td>-0.456108</td>\n",
              "      <td>-0.183659</td>\n",
              "      <td>-0.328168</td>\n",
              "      <td>0.606116</td>\n",
              "      <td>0.884876</td>\n",
              "      <td>-0.253700</td>\n",
              "      <td>245.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281674</th>\n",
              "      <td>170348.0</td>\n",
              "      <td>1.991976</td>\n",
              "      <td>0.158476</td>\n",
              "      <td>-2.583441</td>\n",
              "      <td>0.408670</td>\n",
              "      <td>1.151147</td>\n",
              "      <td>-0.096695</td>\n",
              "      <td>0.223050</td>\n",
              "      <td>-0.068384</td>\n",
              "      <td>0.577829</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.164350</td>\n",
              "      <td>-0.295135</td>\n",
              "      <td>-0.072173</td>\n",
              "      <td>-0.450261</td>\n",
              "      <td>0.313267</td>\n",
              "      <td>-0.289617</td>\n",
              "      <td>0.002988</td>\n",
              "      <td>-0.015309</td>\n",
              "      <td>42.53</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuXE4Dm_cXYy",
        "outputId": "51b3f65c-44c8-4762-d7ed-2079891d0fce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    492\n",
              "1    492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset.groupby('Class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "CmT0Khm9ciDo",
        "outputId": "daa1178c-f6e3-400b-ad7f-8e900b4acb57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0      95272.560976 -0.035613 -0.002749  0.040768  0.038277  0.065102   \n",
              "1      80746.806911 -4.771948  3.623778 -7.033281  4.542029 -3.151225   \n",
              "\n",
              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                          ...                       \n",
              "0      0.066337 -0.032326  0.039704 -0.034636  ...  0.033728  0.019973   \n",
              "1     -1.397737 -5.568731  0.570636 -2.581123  ...  0.372319  0.713588   \n",
              "\n",
              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
              "0     -0.026519 -0.011371  0.002506  0.019416 -0.002648 -0.010976 -0.003448   \n",
              "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
              "\n",
              "           Amount  \n",
              "Class              \n",
              "0       92.579736  \n",
              "1      122.211321  \n",
              "\n",
              "[2 rows x 30 columns]"
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              "      <td>0.038277</td>\n",
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              "      <td>0.039704</td>\n",
              "      <td>-0.034636</td>\n",
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              "      <td>0.033728</td>\n",
              "      <td>0.019973</td>\n",
              "      <td>-0.026519</td>\n",
              "      <td>-0.011371</td>\n",
              "      <td>0.002506</td>\n",
              "      <td>0.019416</td>\n",
              "      <td>-0.002648</td>\n",
              "      <td>-0.010976</td>\n",
              "      <td>-0.003448</td>\n",
              "      <td>92.579736</td>\n",
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              "      <th>1</th>\n",
              "      <td>80746.806911</td>\n",
              "      <td>-4.771948</td>\n",
              "      <td>3.623778</td>\n",
              "      <td>-7.033281</td>\n",
              "      <td>4.542029</td>\n",
              "      <td>-3.151225</td>\n",
              "      <td>-1.397737</td>\n",
              "      <td>-5.568731</td>\n",
              "      <td>0.570636</td>\n",
              "      <td>-2.581123</td>\n",
              "      <td>...</td>\n",
              "      <td>0.372319</td>\n",
              "      <td>0.713588</td>\n",
              "      <td>0.014049</td>\n",
              "      <td>-0.040308</td>\n",
              "      <td>-0.105130</td>\n",
              "      <td>0.041449</td>\n",
              "      <td>0.051648</td>\n",
              "      <td>0.170575</td>\n",
              "      <td>0.075667</td>\n",
              "      <td>122.211321</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-6ce57eb5-1a5f-46c6-b292-f7615c124c53')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6ce57eb5-1a5f-46c6-b292-f7615c124c53 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-6ce57eb5-1a5f-46c6-b292-f7615c124c53');\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-6e10ac06-0c40-4387-8934-9c9f35574c24\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6e10ac06-0c40-4387-8934-9c9f35574c24')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-6e10ac06-0c40-4387-8934-9c9f35574c24 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since mean values are simmilar so we have got good sample and the nature of dataset is not changed."
      ],
      "metadata": {
        "id": "vAAC8BDGcx8l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitiing data into Features & Targets"
      ],
      "metadata": {
        "id": "ywIT8itPdE8F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = new_dataset.drop(columns = 'Class', axis=1)\n",
        "Y = new_dataset['Class']"
      ],
      "metadata": {
        "id": "pRTuhKk9cpqK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X contains features\n",
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-2OHkFzdalN",
        "outputId": "45d21d65-9b69-4f00-bd05-57129fe18c2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Time        V1        V2        V3        V4        V5        V6  \\\n",
            "268689  163342.0 -2.097841 -0.716559  1.249165 -0.097467 -0.643958 -0.243363   \n",
            "210993  138216.0 -0.392156  1.618595  0.785635  2.863419  0.796569  0.511742   \n",
            "234399  147960.0 -0.345544  1.417532 -1.237071 -1.114887  1.109206 -1.354227   \n",
            "218002  141129.0  0.707585 -0.916563 -1.147199 -3.353519  1.079277  4.047110   \n",
            "217078  140754.0 -0.503752  0.921753  0.875294 -0.504641  0.447939 -0.075455   \n",
            "...          ...       ...       ...       ...       ...       ...       ...   \n",
            "279863  169142.0 -1.927883  1.125653 -4.518331  1.749293 -1.566487 -2.010494   \n",
            "280143  169347.0  1.378559  1.289381 -5.004247  1.411850  0.442581 -1.326536   \n",
            "280149  169351.0 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
            "281144  169966.0 -3.113832  0.585864 -5.399730  1.817092 -0.840618 -2.943548   \n",
            "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
            "\n",
            "              V7        V8        V9  ...       V20       V21       V22  \\\n",
            "268689  0.622753  0.596449 -0.204497  ...  0.680060  0.123575 -0.620479   \n",
            "210993  0.780376 -1.848562 -1.105875  ... -0.524741  1.610166  0.060797   \n",
            "234399  1.396381 -0.041075 -0.725102  ... -0.088027  0.292471  0.927703   \n",
            "218002 -1.984700 -0.647111 -1.518908  ... -0.672994  1.348885 -0.671418   \n",
            "217078  0.621285  0.058884  0.105744  ... -0.074726 -0.185733 -0.528624   \n",
            "...          ...       ...       ...  ...       ...       ...       ...   \n",
            "279863 -0.882850  0.697211 -2.064945  ...  1.252967  0.778584 -0.319189   \n",
            "280143 -1.413170  0.248525 -1.127396  ...  0.226138  0.370612  0.028234   \n",
            "280149 -2.234739  1.210158 -0.652250  ...  0.247968  0.751826  0.834108   \n",
            "281144 -2.208002  1.058733 -1.632333  ...  0.306271  0.583276 -0.269209   \n",
            "281674  0.223050 -0.068384  0.577829  ... -0.017652 -0.164350 -0.295135   \n",
            "\n",
            "             V23       V24       V25       V26       V27       V28  Amount  \n",
            "268689  0.464623 -0.031187  0.422240 -0.812981 -0.121951 -0.112994  289.93  \n",
            "210993 -0.019436 -0.135964 -0.076035  0.013654 -0.021080 -0.175540   36.24  \n",
            "234399 -0.167967  0.810744 -0.226117  0.054080  0.270246  0.214492    7.63  \n",
            "218002  0.163340  0.660329  0.580621 -0.103472  0.186302  0.123084   12.40  \n",
            "217078  0.085172  0.481572 -0.645770 -0.098334  0.033272  0.181761   35.00  \n",
            "...          ...       ...       ...       ...       ...       ...     ...  \n",
            "279863  0.639419 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00  \n",
            "280143 -0.145640 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76  \n",
            "280149  0.190944  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89  \n",
            "281144 -0.456108 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00  \n",
            "281674 -0.072173 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53  \n",
            "\n",
            "[984 rows x 30 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Y contains the labels\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jcqvTDoded6",
        "outputId": "2e25bf7f-8792-4024-a2d8-23ca13ac2bc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "268689    0\n",
            "210993    0\n",
            "234399    0\n",
            "218002    0\n",
            "217078    0\n",
            "         ..\n",
            "279863    1\n",
            "280143    1\n",
            "280149    1\n",
            "281144    1\n",
            "281674    1\n",
            "Name: Class, Length: 984, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split data into Train and Test data"
      ],
      "metadata": {
        "id": "vsPAkpS9d3GS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Train data = 80% of data  & Test data = 20% of data\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2 )"
      ],
      "metadata": {
        "id": "Yo4Eq2ecdiFP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5UNq949eWOP",
        "outputId": "3fa0c08a-c3a1-45c4-c20d-1da439e4c26a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(984, 30) (787, 30) (197, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the model"
      ],
      "metadata": {
        "id": "xOI7OtAefFH_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Logistic Regression\n",
        "\n",
        "which is used generally for binary classification problems"
      ],
      "metadata": {
        "id": "nwZVz-awfHje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "F5qt6ZJufAkO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the Logistic Regression model with Train data\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "gMU1K09LfaVg",
        "outputId": "76be71c8-e161-461d-a44c-f8167b10a0a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation based on Accuracy score"
      ],
      "metadata": {
        "id": "lksGuN75f8Dp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Accuracy Score on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "metadata": {
        "id": "4fOqVKNYfnVQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on Train data :', training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IX6AfhnogaD6",
        "outputId": "16aec608-c5e1-4521-d09a-ba8aace1d26c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Train data : 0.9224904701397713\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": "ZfKCzMY_gl2f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score on Test data: ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jauHmylhEYd",
        "outputId": "0f0d0f6f-710b-49ef-eff7-ab3af24e1d36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score on Test data:  0.9137055837563451\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the accuracy of Train data and Test data are similar so the model is not overfitted(over-tested) or underfitted (over-trained)"
      ],
      "metadata": {
        "id": "LpRcIEymhOMR"
      }
    }
  ]
}