Credit_Card_Fraud_Detection_Using_Python_ML_Algorithm.ipynb

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        "<a href=\"https://colab.research.google.com/github/pdivya-mca/Credit-Card-Fraud-Detection/blob/main/Credit_Card_Fraud_Detection_Using_Python_ML_Algorithm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "\n",
        "Dataset : https://www.kaggle.com/datasets/zohrehtofighizavareh/credit-card-fraud-detection"
      ],
      "metadata": {
        "id": "xxO5e1Jo-MSC"
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    {
      "cell_type": "markdown",
      "source": [
        "**Importing Dependencies**"
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      "execution_count": null,
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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": "markdown",
      "source": [
        "**Loading Dataset to Pandas Dataframe**"
      ],
      "metadata": {
        "id": "GBJfytUY8Kfx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data = pd.read_csv('/content/CreditCard.csv')"
      ],
      "metadata": {
        "id": "O6_wX-D_8Hux"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Cleaning**"
      ],
      "metadata": {
        "id": "bb0sPCBrR-Q7"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "**Shape of Dataset**"
      ],
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      }
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    {
      "cell_type": "code",
      "source": [
        "# Finding no of rows and cloumns.\n",
        "credit_card_data.shape"
      ],
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        "id": "crMffT6Z8sa3",
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              "(284807, 31)"
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      "source": [
        "**Fetching Top 5 Rows**"
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      "cell_type": "code",
      "source": [
        "credit_card_data.head()"
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
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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",
              "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
              "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
              "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",
              "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \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",
              "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
              "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
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              "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
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              "        Class  \n",
              "284802      0  \n",
              "284803      0  \n",
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              "      <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>284802</th>\n",
              "      <td>172786.0</td>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>...</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>172787.0</td>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>...</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>24.79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>...</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>67.88</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>...</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>172792.0</td>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>...</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>217.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataset Information**"
      ],
      "metadata": {
        "id": "6jZTpVkPLvnu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PSon-VTLzmB",
        "outputId": "5a1775d6-cab7-42af-b918-95eea170de46"
      },
      "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",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 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",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Number of missing values**"
      ],
      "metadata": {
        "id": "z4lit34jMC7u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZW6xJNqrMHt-",
        "outputId": "c6b01c68-2bd2-4389-d174-ab54e91f28f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Distibution of Normal Transaction (Legit) and Fradulent Transactions**"
      ],
      "metadata": {
        "id": "DOU_6X3XMuhZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vy7llNcNMexd",
        "outputId": "b74ffa37-0a84-4bc1-9269-528916d2a9bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above result we can see that the dataset is highly imbalanced.\n",
        "\n",
        "0---> Normal Transaction 1---> Fradulent Transaction"
      ],
      "metadata": {
        "id": "o6yuGCpcM8s9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Separating legit and fraud data for analysis**"
      ],
      "metadata": {
        "id": "mmeeEgLaNS4F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "legit = credit_card_data[credit_card_data.Class == 0]\n",
        "fraud = credit_card_data[credit_card_data.Class == 1]"
      ],
      "metadata": {
        "id": "SW87AVZmNPSd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# printing shape of legit and fraud\n",
        "print(legit.shape)\n",
        "print(fraud.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JupyxIKVNyj-",
        "outputId": "192880e7-a358-4b28-fb26-54f42fca458d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(284315, 31)\n",
            "(492, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Statistical information about amount column**"
      ],
      "metadata": {
        "id": "p-M-mVXVOMBz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "legit.amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXxVnivCORx1",
        "outputId": "9c7bae8c-4e91-45fd-f153-992ad242aaa8"
      },
      "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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraud.amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13IhxNHhOhot",
        "outputId": "1dc2d502-865a-4328-a1c5-9dd0cda4be73"
      },
      "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": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comparing values of both transaction**"
      ],
      "metadata": {
        "id": "kksrwHkOO2Ek"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data.groupby('Class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "NYTFl3vEO6rU",
        "outputId": "af792dab-bc83-4b2b-9061-f3a6676c8203"
      },
      "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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              "      <th>...</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>94838.202258</td>\n",
              "      <td>0.008258</td>\n",
              "      <td>-0.006271</td>\n",
              "      <td>0.012171</td>\n",
              "      <td>-0.007860</td>\n",
              "      <td>0.005453</td>\n",
              "      <td>0.002419</td>\n",
              "      <td>0.009637</td>\n",
              "      <td>-0.000987</td>\n",
              "      <td>0.004467</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000644</td>\n",
              "      <td>-0.001235</td>\n",
              "      <td>-0.000024</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>0.000182</td>\n",
              "      <td>-0.000072</td>\n",
              "      <td>-0.000089</td>\n",
              "      <td>-0.000295</td>\n",
              "      <td>-0.000131</td>\n",
              "      <td>88.291022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3411cdd0-0f27-4beb-af66-d7205400ba59')\"\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Under Sampling**\n",
        "\n",
        "Build a sample dataset containg similar distribution of legit transaction and fraud transactions\n",
        "\n",
        "no of fraud transactions are 492 so we going to take sample of 492 legit transaction to make the data balanced."
      ],
      "metadata": {
        "id": "hpaw01YQPdK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "legit_sample = legit.sample(n=492)"
      ],
      "metadata": {
        "id": "HUff98GuQKmc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#checking whether both legit and fraud transactions are equal\n",
        "print(legit_sample.shape)\n",
        "print(fraud.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKmrwdsXQVll",
        "outputId": "575ebc50-1642-4a9d-f8f6-8cda4843c0e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(492, 31)\n",
            "(492, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Concatinating legit_sample and fraudin row order**"
      ],
      "metadata": {
        "id": "N7j0cOy7QvlN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset = pd.concat([legit_sample,fraud], axis=0)"
      ],
      "metadata": {
        "id": "svDjmmkyQ6SV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#printing first 5 rows of new dataset\n",
        "new_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mZ4iGqtsQsMo",
        "outputId": "9b9577ab-917b-4892-caa2-9059f975e89a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
              "245611  152829.0  2.018963  0.158078 -1.590802  0.339677  0.438433 -0.648418   \n",
              "88430    62137.0  1.225615  0.379435  0.094645  1.161682  0.137813 -0.324424   \n",
              "270518  164117.0 -1.931769 -0.691516 -0.218049  1.433256 -2.187658  0.934995   \n",
              "273352  165559.0  2.221944 -1.541185 -0.351991 -1.634898 -1.612030 -0.277850   \n",
              "177022  123010.0  2.066407 -0.857911 -0.801287 -0.279220 -0.726389 -0.155982   \n",
              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
              "245611  0.129937 -0.137568  0.125384  ... -0.293179 -0.745846  0.342714   \n",
              "88430   0.265618 -0.120291 -0.325196  ...  0.004272  0.190912 -0.208823   \n",
              "270518  3.115527  0.094724 -0.902347  ...  0.601668  0.560027  1.665401   \n",
              "273352 -1.544542  0.042498 -1.110945  ... -0.324571 -0.502812  0.390124   \n",
              "177022 -0.799386  0.058079 -0.374622  ... -0.305968 -0.387324  0.311120   \n",
              "\n",
              "             V24       V25       V26       V27       V28  amount  Class  \n",
              "245611  0.687179 -0.291834  0.140427 -0.062163 -0.037329    1.98      0  \n",
              "88430   0.057458  0.885829 -0.261198  0.011520  0.001949    1.00      0  \n",
              "270518 -0.085602  0.256549 -0.508149  0.062594  0.226933  789.71      0  \n",
              "273352 -0.404968 -0.597817 -0.442606  0.035902 -0.046252   22.00      0  \n",
              "177022  0.725908 -0.356933  0.208608 -0.024939 -0.048970   22.00      0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <td>-2.187658</td>\n",
              "      <td>0.934995</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-bd261969-2411-4875-a53c-c1f33dc491e9');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fetching last 5 rows of dataset\n",
        "new_dataset.tail()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "F3_D_zxHRb2n",
        "outputId": "33b7d9d9-cc1e-4ae3-f55f-52d23515d5a9"
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      "execution_count": null,
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              "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
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              "281674  0.223050 -0.068384  0.577829  ... -0.164350 -0.295135 -0.072173   \n",
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              "             V24       V25       V26       V27       V28  amount  Class  \n",
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          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#shape of dataset\n",
        "new_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jJ30UUERlQs",
        "outputId": "a60c3b99-bc1f-4096-f67f-0fcc065cdb76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(984, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# counting the class\n",
        "new_dataset['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6z-vPgoRsiF",
        "outputId": "47db2dcb-d1a2-4f21-8924-b2a0cfd09cc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    492\n",
              "1    492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now it is a blanced dataset so checking for the mean value\n",
        "new_dataset.groupby('Class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "AjFNz7-iSN21",
        "outputId": "f2f110ae-6e2b-49cf-8370-981b469db0d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0      89231.046748 -0.063521  0.140265  0.076755  0.057550  0.027246   \n",
              "1      80746.806911 -4.771948  3.623778 -7.033281  4.542029 -3.151225   \n",
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              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
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              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
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              "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
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              "           amount  \n",
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              "      <td>0.046295</td>\n",
              "      <td>0.003593</td>\n",
              "      <td>0.058716</td>\n",
              "      <td>...</td>\n",
              "      <td>0.082541</td>\n",
              "      <td>-0.015689</td>\n",
              "      <td>-0.010192</td>\n",
              "      <td>-0.003871</td>\n",
              "      <td>0.002138</td>\n",
              "      <td>-0.003915</td>\n",
              "      <td>0.048416</td>\n",
              "      <td>0.018263</td>\n",
              "      <td>0.008994</td>\n",
              "      <td>84.838415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27c3ad0a-20b2-4365-9297-43a9446aa190')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-27c3ad0a-20b2-4365-9297-43a9446aa190 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-27c3ad0a-20b2-4365-9297-43a9446aa190');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Encode**\n",
        "\n",
        "-- The data is already cleaned, there is no categorical data in this dataset so no need of encoding."
      ],
      "metadata": {
        "id": "x6dNFxJ_SlDt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Split the data into Features and Targets**"
      ],
      "metadata": {
        "id": "eltKsISfS0RE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = new_dataset.drop(columns='Class', axis=1)\n",
        "Y = new_dataset['Class']"
      ],
      "metadata": {
        "id": "vfJI7O9dS6tk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXjZ9kWaTPvm",
        "outputId": "5381d8ba-be2d-473e-ff55-ce9b71a4f1ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Time        V1        V2        V3        V4        V5        V6  \\\n",
            "245611  152829.0  2.018963  0.158078 -1.590802  0.339677  0.438433 -0.648418   \n",
            "88430    62137.0  1.225615  0.379435  0.094645  1.161682  0.137813 -0.324424   \n",
            "270518  164117.0 -1.931769 -0.691516 -0.218049  1.433256 -2.187658  0.934995   \n",
            "273352  165559.0  2.221944 -1.541185 -0.351991 -1.634898 -1.612030 -0.277850   \n",
            "177022  123010.0  2.066407 -0.857911 -0.801287 -0.279220 -0.726389 -0.155982   \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",
            "245611  0.129937 -0.137568  0.125384  ... -0.109856 -0.293179 -0.745846   \n",
            "88430   0.265618 -0.120291 -0.325196  ... -0.090231  0.004272  0.190912   \n",
            "270518  3.115527  0.094724 -0.902347  ...  1.789028  0.601668  0.560027   \n",
            "273352 -1.544542  0.042498 -1.110945  ... -0.408834 -0.324571 -0.502812   \n",
            "177022 -0.799386  0.058079 -0.374622  ... -0.600756 -0.305968 -0.387324   \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",
            "245611  0.342714  0.687179 -0.291834  0.140427 -0.062163 -0.037329    1.98  \n",
            "88430  -0.208823  0.057458  0.885829 -0.261198  0.011520  0.001949    1.00  \n",
            "270518  1.665401 -0.085602  0.256549 -0.508149  0.062594  0.226933  789.71  \n",
            "273352  0.390124 -0.404968 -0.597817 -0.442606  0.035902 -0.046252   22.00  \n",
            "177022  0.311120  0.725908 -0.356933  0.208608 -0.024939 -0.048970   22.00  \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": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_mSald2Taoc",
        "outputId": "468c7900-6bd9-480e-9de6-64ec6ec9d14e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "245611    0\n",
            "88430     0\n",
            "270518    0\n",
            "273352    0\n",
            "177022    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 the data into train and test**"
      ],
      "metadata": {
        "id": "UefI9mjxTjJP"
      }
    },
    {
      "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": "3pFVvYuoTqR_"
      },
      "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": "obi-nZVzUMFM",
        "outputId": "a31ff8bd-c384-4bb8-a7d7-ad07ca3e2df7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(984, 30) (787, 30) (197, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Scaling**\n",
        "\n",
        "Here i am going to use logistic regression algorithm so scaling is not mandatory."
      ],
      "metadata": {
        "id": "zzRsRRCCUiRb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Training**\n",
        "\n",
        "**Logistic Regression**"
      ],
      "metadata": {
        "id": "Q4aSjmzEUw5b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "ZGVqwb3nU8-9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# traing the logistic regression model with training data\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-WitDGjVPPU",
        "outputId": "ecafd68d-0805-49e5-f402-605ccb170798"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Evaluation**\n",
        "**Accuracy Score**"
      ],
      "metadata": {
        "id": "ZsRaK0fYWDij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
        "print(\" Accuracy on Training Data: \" , training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nuDeJj6BWLJr",
        "outputId": "4803bb8b-ab25-4c92-d6de-31fccc3838f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Accuracy on Training Data:  0.9237611181702668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy on testing data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
        "print(\" Accuracy on Testing Data: \", test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R2yAe5wbYQVW",
        "outputId": "eba8588d-930b-4ee0-cad9-3ae8985cfdd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Accuracy on Testing Data:  0.8934010152284264\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**F1 Score**"
      ],
      "metadata": {
        "id": "whbjt1ygZcRb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import plot_roc_curve, f1_score\n",
        "\n",
        "f1_score(Y_test,X_test_prediction) # f1 score\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhnb6wCRY2k7",
        "outputId": "0e33e32b-7d91-46eb-82d4-a765bef96722"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8877005347593584"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**AUROC**"
      ],
      "metadata": {
        "id": "evbiLMKPZ8HU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc_curve(model, X_test, Y_test) #AUROC"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "Ahgasjo7ZmZz",
        "outputId": "6cc8806f-444c-472e-db52-b822690d2bf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fb86f24de80>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Conclusion:**\n",
        "\n",
        "We are getting very good AUROC score , so this logistic regression algorithm is a good model for Credit Card Fraud Detection Analysis. "
      ],
      "metadata": {
        "id": "Ti-wOZPdad3D"
      }
    }
  ]
}