creditcardfraudetection.ipynb

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        "<a href=\"https://colab.research.google.com/github/dhanish03/Credit_Card_Fraud_Detection/blob/main/creditcardfraudetection.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 libraries\n",
        "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": [
        "#load data set\n",
        "credit_card_data = pd.read_csv('/content/creditcard.csv')"
      ],
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        "id": "Lpo11EqZ1VaZ"
      },
      "execution_count": 2,
      "outputs": []
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        "credit_card_data.head()"
      ],
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        "credit_card_data.tail()"
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              "        Time        V1        V2        V3        V4        V5        V6  \\\n",
              "37717  39076  1.117749 -1.544665 -0.145357 -1.307482 -1.392833 -0.630833   \n",
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              "37721  39077  1.106292 -0.525727  0.926440  0.817696 -1.122177 -0.062703   \n",
              "\n",
              "             V7        V8        V9  ...       V21       V22       V23  \\\n",
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        "id": "STo2Ibox20Xk",
        "outputId": "d79bbed3-f8c4-4f84-a0ee-8bc1c1c8b72d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "0.0    37618\n",
              "1.0      103\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "0 --> Normal Transaction                                                                    \n",
        "1 --> Fraudulent Transaction"
      ],
      "metadata": {
        "id": "kBdTjBNZ4MHS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#separating data from analysis\n",
        "legit = credit_card_data[credit_card_data.Class == 0]\n",
        "fraud = credit_card_data[credit_card_data.Class == 1]"
      ],
      "metadata": {
        "id": "4_rKqXpw4TiR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(legit.shape)\n",
        "print(fraud.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NypSK6-O4yHA",
        "outputId": "cdb207a3-8142-4614-8f21-568d9bd4c726"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(37618, 31)\n",
            "(103, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#statistical measures of the data\n",
        "legit.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9aCpdNT44Tm",
        "outputId": "cb4be92a-c90a-47ed-dcbd-7c23321bf8e7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    37618.000000\n",
              "mean        86.017411\n",
              "std        232.957939\n",
              "min          0.000000\n",
              "25%          7.300000\n",
              "50%         22.880000\n",
              "75%         77.675000\n",
              "max       7879.420000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraud.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x865jift5Ilv",
        "outputId": "ba48e63b-f70b-44dd-de3d-a8cad8457f1c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     103.000000\n",
              "mean       90.471165\n",
              "std       247.173335\n",
              "min         0.000000\n",
              "25%         1.000000\n",
              "50%         3.760000\n",
              "75%        99.990000\n",
              "max      1809.680000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#compare values for both transactions\n",
        "credit_card_data.groupby('Class').mean()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "rkd6eL5d5UzH",
        "outputId": "1baee812-ee20-4879-9f68-304aa94df2b1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2         V3        V4        V5  \\\n",
              "Class                                                                    \n",
              "0.0    24778.212797 -0.186787  0.044196   0.749331  0.176750 -0.209569   \n",
              "1.0    20498.291262 -7.762676  5.838647 -10.957218  5.911555 -5.453798   \n",
              "\n",
              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                          ...                       \n",
              "0.0    0.105912 -0.092989  0.025693  0.243321  ...  0.044683 -0.032046   \n",
              "1.0   -2.306085 -7.743100  3.867920 -2.961800  ...  0.652319  0.628539   \n",
              "\n",
              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
              "0.0   -0.111604 -0.040404  0.007159  0.135739  0.021382  0.007030  0.003658   \n",
              "1.0   -0.354537 -0.362138 -0.234731  0.300023  0.180390  0.822304  0.102481   \n",
              "\n",
              "          Amount  \n",
              "Class             \n",
              "0.0    86.017411  \n",
              "1.0    90.471165  \n",
              "\n",
              "[2 rows x 30 columns]"
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              "      <td>-5.453798</td>\n",
              "      <td>-2.306085</td>\n",
              "      <td>-7.743100</td>\n",
              "      <td>3.867920</td>\n",
              "      <td>-2.961800</td>\n",
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              "      <td>0.652319</td>\n",
              "      <td>0.628539</td>\n",
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              "      <td>-0.362138</td>\n",
              "      <td>-0.234731</td>\n",
              "      <td>0.300023</td>\n",
              "      <td>0.180390</td>\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-903dd9be-6a8f-451b-a189-5d458f6fa580 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-903dd9be-6a8f-451b-a189-5d458f6fa580');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
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              "        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",
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              "    (() => {\n",
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              "        document.querySelector('#df-81cdb779-dc8f-4eff-a661-128620b68db9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Under - Sampling\n"
      ],
      "metadata": {
        "id": "rVnOny_X5137"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build sample dataset containing similar distribution of normal transactions and fraudulent transactions."
      ],
      "metadata": {
        "id": "t-2RjO0i57PF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Number of fraudulent transactions --> 103\n"
      ],
      "metadata": {
        "id": "GkCqU_Gc6MAa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "legit_sample = legit.sample(n=103)"
      ],
      "metadata": {
        "id": "krP2hjtI5obv"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "concatenating two dataframes"
      ],
      "metadata": {
        "id": "FODNGc2a6fSt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset = pd.concat([legit_sample,fraud],axis=0)"
      ],
      "metadata": {
        "id": "7_xwx5n96dlt"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "iIWos22L6pHN",
        "outputId": "0bf653ef-842b-42fa-b1a1-f9ecb311a8e7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Time        V1        V2        V3        V4        V5        V6  \\\n",
              "33287  37172  1.307400 -0.557251  0.403531 -0.597468 -1.012611 -0.830117   \n",
              "4266    3755  1.372955 -0.730758 -0.931581 -1.604395  1.443775  3.280036   \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "new_dataset.tail()"
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          "height": 255
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        "id": "ZXtvYf0t6409",
        "outputId": "6e872ae8-978c-447b-c48d-f19df2ca35f7"
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      "execution_count": 18,
      "outputs": [
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          "output_type": "execute_result",
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              "        Time        V1        V2        V3        V4        V5        V6  \\\n",
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              "      <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>30442</th>\n",
              "      <td>35926</td>\n",
              "      <td>-3.896583</td>\n",
              "      <td>4.518355</td>\n",
              "      <td>-4.454027</td>\n",
              "      <td>5.547453</td>\n",
              "      <td>-4.121459</td>\n",
              "      <td>-1.163407</td>\n",
              "      <td>-6.805053</td>\n",
              "      <td>2.928356</td>\n",
              "      <td>-4.917130</td>\n",
              "      <td>...</td>\n",
              "      <td>1.691042</td>\n",
              "      <td>0.920021</td>\n",
              "      <td>-0.151104</td>\n",
              "      <td>0.011007</td>\n",
              "      <td>0.080303</td>\n",
              "      <td>0.412191</td>\n",
              "      <td>0.635789</td>\n",
              "      <td>0.501050</td>\n",
              "      <td>4.56</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30473</th>\n",
              "      <td>35942</td>\n",
              "      <td>-4.194074</td>\n",
              "      <td>4.382897</td>\n",
              "      <td>-5.118363</td>\n",
              "      <td>4.455230</td>\n",
              "      <td>-4.812621</td>\n",
              "      <td>-1.224645</td>\n",
              "      <td>-7.281328</td>\n",
              "      <td>3.332250</td>\n",
              "      <td>-3.679659</td>\n",
              "      <td>...</td>\n",
              "      <td>1.550473</td>\n",
              "      <td>0.614573</td>\n",
              "      <td>0.028521</td>\n",
              "      <td>0.013704</td>\n",
              "      <td>-0.149512</td>\n",
              "      <td>-0.131687</td>\n",
              "      <td>0.473934</td>\n",
              "      <td>0.473757</td>\n",
              "      <td>14.46</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30496</th>\n",
              "      <td>35953</td>\n",
              "      <td>-4.844372</td>\n",
              "      <td>5.649439</td>\n",
              "      <td>-6.730396</td>\n",
              "      <td>5.252842</td>\n",
              "      <td>-4.409566</td>\n",
              "      <td>-1.740767</td>\n",
              "      <td>-6.311699</td>\n",
              "      <td>3.449167</td>\n",
              "      <td>-5.416284</td>\n",
              "      <td>...</td>\n",
              "      <td>1.194888</td>\n",
              "      <td>-0.845753</td>\n",
              "      <td>0.190674</td>\n",
              "      <td>-0.216443</td>\n",
              "      <td>-0.325033</td>\n",
              "      <td>-0.270328</td>\n",
              "      <td>0.210214</td>\n",
              "      <td>0.391855</td>\n",
              "      <td>111.70</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31002</th>\n",
              "      <td>36170</td>\n",
              "      <td>-5.685013</td>\n",
              "      <td>5.776516</td>\n",
              "      <td>-7.064977</td>\n",
              "      <td>5.902715</td>\n",
              "      <td>-4.715564</td>\n",
              "      <td>-1.755633</td>\n",
              "      <td>-6.958679</td>\n",
              "      <td>3.877795</td>\n",
              "      <td>-5.541529</td>\n",
              "      <td>...</td>\n",
              "      <td>1.128641</td>\n",
              "      <td>-0.962960</td>\n",
              "      <td>-0.110045</td>\n",
              "      <td>-0.177733</td>\n",
              "      <td>-0.089175</td>\n",
              "      <td>-0.049447</td>\n",
              "      <td>0.303445</td>\n",
              "      <td>0.219380</td>\n",
              "      <td>111.70</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33276</th>\n",
              "      <td>37167</td>\n",
              "      <td>-7.923891</td>\n",
              "      <td>-5.198360</td>\n",
              "      <td>-3.000024</td>\n",
              "      <td>4.420666</td>\n",
              "      <td>2.272194</td>\n",
              "      <td>-3.394483</td>\n",
              "      <td>-5.283435</td>\n",
              "      <td>0.131619</td>\n",
              "      <td>0.658176</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.734308</td>\n",
              "      <td>-0.599926</td>\n",
              "      <td>-4.908301</td>\n",
              "      <td>0.410170</td>\n",
              "      <td>-1.167660</td>\n",
              "      <td>0.520508</td>\n",
              "      <td>1.937421</td>\n",
              "      <td>-1.552593</td>\n",
              "      <td>12.31</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvEeiMgO6-xD",
        "outputId": "8a18a03e-66de-4a10-b123-7dac3ccb5f60"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "0.0    103\n",
              "1.0    103\n",
              "Name: count, 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": "o0GI7LNm7H03",
        "outputId": "8b2f2435-3cf5-46a3-9e3f-85c439cff093"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2         V3        V4        V5  \\\n",
              "Class                                                                    \n",
              "0.0    22723.601942 -0.187790 -0.021728   0.569876  0.034937 -0.243983   \n",
              "1.0    20498.291262 -7.762676  5.838647 -10.957218  5.911555 -5.453798   \n",
              "\n",
              "             V6        V7       V8        V9  ...       V20       V21  \\\n",
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              "0.0   -0.067600 -0.199343  0.05622  0.288712  ...  0.025744 -0.048553   \n",
              "1.0   -2.306085 -7.743100  3.86792 -2.961800  ...  0.652319  0.628539   \n",
              "\n",
              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
              "0.0   -0.108536 -0.037392  0.051054  0.175615  0.014245  0.049793  0.034324   \n",
              "1.0   -0.354537 -0.362138 -0.234731  0.300023  0.180390  0.822304  0.102481   \n",
              "\n",
              "          Amount  \n",
              "Class             \n",
              "0.0    83.074175  \n",
              "1.0    90.471165  \n",
              "\n",
              "[2 rows x 30 columns]"
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              "      <td>-0.048553</td>\n",
              "      <td>-0.108536</td>\n",
              "      <td>-0.037392</td>\n",
              "      <td>0.051054</td>\n",
              "      <td>0.175615</td>\n",
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              "      <td>-7.762676</td>\n",
              "      <td>5.838647</td>\n",
              "      <td>-10.957218</td>\n",
              "      <td>5.911555</td>\n",
              "      <td>-5.453798</td>\n",
              "      <td>-2.306085</td>\n",
              "      <td>-7.743100</td>\n",
              "      <td>3.86792</td>\n",
              "      <td>-2.961800</td>\n",
              "      <td>...</td>\n",
              "      <td>0.652319</td>\n",
              "      <td>0.628539</td>\n",
              "      <td>-0.354537</td>\n",
              "      <td>-0.362138</td>\n",
              "      <td>-0.234731</td>\n",
              "      <td>0.300023</td>\n",
              "      <td>0.180390</td>\n",
              "      <td>0.822304</td>\n",
              "      <td>0.102481</td>\n",
              "      <td>90.471165</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-8f4319c5-545c-4beb-97f6-a1717bc1ae14')\"\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-8f4319c5-545c-4beb-97f6-a1717bc1ae14 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-8f4319c5-545c-4beb-97f6-a1717bc1ae14');\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-3dccc8ff-996a-4102-9200-36bf8e4e6c22\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3dccc8ff-996a-4102-9200-36bf8e4e6c22')\"\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-3dccc8ff-996a-4102-9200-36bf8e4e6c22 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": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "splitting data into features and target\n"
      ],
      "metadata": {
        "id": "pDYL4O6v7Xfu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = new_dataset.drop(columns='Class',axis=1)\n",
        "Y = new_dataset['Class']"
      ],
      "metadata": {
        "id": "d15MXtD37NsM"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-M4JStTf7ll4",
        "outputId": "8879e15f-c93c-422e-f8c9-e8fc71814fdd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Time        V1        V2        V3        V4        V5        V6  \\\n",
            "33287  37172  1.307400 -0.557251  0.403531 -0.597468 -1.012611 -0.830117   \n",
            "4266    3755  1.372955 -0.730758 -0.931581 -1.604395  1.443775  3.280036   \n",
            "31472  36381  1.135098 -1.422280  1.059353 -0.501487 -1.576462  0.788810   \n",
            "12988  22815  0.196935  1.977465 -1.796104  1.678595  1.119891 -1.197373   \n",
            "17024  28376 -1.617374 -0.069821  1.477316 -0.830521 -1.718512 -0.680189   \n",
            "...      ...       ...       ...       ...       ...       ...       ...   \n",
            "30442  35926 -3.896583  4.518355 -4.454027  5.547453 -4.121459 -1.163407   \n",
            "30473  35942 -4.194074  4.382897 -5.118363  4.455230 -4.812621 -1.224645   \n",
            "30496  35953 -4.844372  5.649439 -6.730396  5.252842 -4.409566 -1.740767   \n",
            "31002  36170 -5.685013  5.776516 -7.064977  5.902715 -4.715564 -1.755633   \n",
            "33276  37167 -7.923891 -5.198360 -3.000024  4.420666  2.272194 -3.394483   \n",
            "\n",
            "             V7        V8        V9  ...       V20       V21       V22  \\\n",
            "33287 -0.497491 -0.147617 -0.849683  ...  0.074705  0.318928  0.842961   \n",
            "4266  -1.150876  0.706540  0.358377  ...  0.251987 -0.269204 -0.906016   \n",
            "31472 -1.485158  0.382476  0.374725  ...  0.253154  0.222936  0.603012   \n",
            "12988  0.683799 -0.082904  1.021657  ...  0.284793 -0.268007 -0.116640   \n",
            "17024 -0.678319  0.171298  0.195828  ... -0.720619 -0.221856  0.515223   \n",
            "...         ...       ...       ...  ...       ...       ...       ...   \n",
            "30442 -6.805053  2.928356 -4.917130  ...  0.757287  1.691042  0.920021   \n",
            "30473 -7.281328  3.332250 -3.679659  ...  0.579098  1.550473  0.614573   \n",
            "30496 -6.311699  3.449167 -5.416284  ...  0.284555  1.194888 -0.845753   \n",
            "31002 -6.958679  3.877795 -5.541529  ...  0.299489  1.128641 -0.962960   \n",
            "33276 -5.283435  0.131619  0.658176  ... -2.169811 -0.734308 -0.599926   \n",
            "\n",
            "            V23       V24       V25       V26       V27       V28  Amount  \n",
            "33287 -0.077433  0.440288  0.496940 -0.077697  0.012102  0.016065   35.09  \n",
            "4266   0.068290  0.920775  0.351633 -0.509949 -0.022707  0.018000   56.66  \n",
            "31472 -0.279977 -0.476777  0.460320 -0.045050  0.047191  0.026024  112.03  \n",
            "12988  0.201285 -0.187615 -0.493007 -0.429774  0.456220  0.132518    7.74  \n",
            "17024  0.090304  0.749402  0.062343 -0.379224 -0.728425 -0.478121   35.49  \n",
            "...         ...       ...       ...       ...       ...       ...     ...  \n",
            "30442 -0.151104  0.011007  0.080303  0.412191  0.635789  0.501050    4.56  \n",
            "30473  0.028521  0.013704 -0.149512 -0.131687  0.473934  0.473757   14.46  \n",
            "30496  0.190674 -0.216443 -0.325033 -0.270328  0.210214  0.391855  111.70  \n",
            "31002 -0.110045 -0.177733 -0.089175 -0.049447  0.303445  0.219380  111.70  \n",
            "33276 -4.908301  0.410170 -1.167660  0.520508  1.937421 -1.552593   12.31  \n",
            "\n",
            "[206 rows x 30 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7NvNUcE7ngm",
        "outputId": "96910bd7-2b96-49e3-eeec-63758424ad31"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33287    0.0\n",
            "4266     0.0\n",
            "31472    0.0\n",
            "12988    0.0\n",
            "17024    0.0\n",
            "        ... \n",
            "30442    1.0\n",
            "30473    1.0\n",
            "30496    1.0\n",
            "31002    1.0\n",
            "33276    1.0\n",
            "Name: Class, Length: 206, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "split data into training data and testing data"
      ],
      "metadata": {
        "id": "MdqdGuiw7vW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test =train_test_split(X, Y, test_size=0.2,stratify=Y, random_state=2)"
      ],
      "metadata": {
        "id": "NiIKa0Fw7tRF"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dw_VkO4c8dmn",
        "outputId": "838c9c08-e371-4e01-9dbc-7e01490a9733"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(206, 30) (164, 30) (42, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "logistic regression"
      ],
      "metadata": {
        "id": "Ime8iP8w8rU6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "d3p5R9Jp8m_S"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy Score"
      ],
      "metadata": {
        "id": "E2MZd0pL9T78"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "metadata": {
        "id": "Qu703C3B9CP3"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on training data',training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GhjUsDL_9o9R",
        "outputId": "07021eff-b8f7-4754-fc76-bf1c75b55454"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data 0.9817073170731707\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": "10bILong95MG"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score on data', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UbkWY-w-ZWp",
        "outputId": "554fb388-d41f-4d0c-9053-bdbbe2591069"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score on data 0.9523809523809523\n"
          ]
        }
      ]
    }
  ]
}