Credit_Card_Fraud_Detection.ipynb

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        "id": "Y1ObhcL1ik5Q"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data = pd.read_csv(\"creditcard.csv\")"
      ],
      "metadata": {
        "id": "DhBdsizDldpC"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "# Exploratory Data Analysis (EDA)"
      ],
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        "id": "wBFlH2gdmNFm"
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      "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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        "credit_card_data.tail()"
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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",
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              "             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",
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              "    <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": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqWj1Sb2mXwt",
        "outputId": "64e695ec-fe12-4a31-a943-829c090f57e7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset has 284807 records with 31 columns each (30 features + the binary classification class [fraud/not-fraud])"
      ],
      "metadata": {
        "id": "uRMM3hoomdG2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Time:** Number of seconds elapsed between this transaction and the first transaction in the dataset\n",
        "\n",
        "**V1-V28:** Anonymous features obtained through PCA transformation for confidentiality purposes\n",
        "\n",
        "**Amount:** Transaction amount\n",
        "\n",
        "**Class:** Binary variable indicating whether the transaction is fraudulent (1) or non-fraudulent (0)"
      ],
      "metadata": {
        "id": "CpkITL6mIJp4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJhqukg_m4qA",
        "outputId": "2764a639-7977-4499-8ad6-630391650fc9"
      },
      "execution_count": 7,
      "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": [
        "There are no missing values in the dataset."
      ],
      "metadata": {
        "id": "hMf0trVvrjPk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data cleaning"
      ],
      "metadata": {
        "id": "tTJTqzwzsiNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data.drop_duplicates()"
      ],
      "metadata": {
        "id": "lWH0C8DEskTF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lN_aMwu5srXs",
        "outputId": "4ba22bd3-7136-4aff-c79b-42486a95b955"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The size of the dataset remains the same, hence there are no duplicates."
      ],
      "metadata": {
        "id": "Jv17iYrasxKr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_card_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "D-EDe7iTtVOq",
        "outputId": "eb3f00bf-54f2-4373-db85-0cb17099545a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time            V1            V2            V3            V4  \\\n",
              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
              "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
              "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
              "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
              "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
              "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
              "\n",
              "                 V5            V6            V7            V8            V9  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
              "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
              "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
              "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
              "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
              "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
              "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
              "\n",
              "       ...           V21           V22           V23           V24  \\\n",
              "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
              "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
              "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
              "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
              "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
              "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
              "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
              "\n",
              "                V25           V26           V27           V28         Amount  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
              "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
              "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
              "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
              "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
              "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
              "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
              "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
              "\n",
              "               Class  \n",
              "count  284807.000000  \n",
              "mean        0.001727  \n",
              "std         0.041527  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
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              "      <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>count</th>\n",
              "      <td>284807.000000</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>...</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>1.168375e-15</td>\n",
              "      <td>3.416908e-16</td>\n",
              "      <td>-1.379537e-15</td>\n",
              "      <td>2.074095e-15</td>\n",
              "      <td>9.604066e-16</td>\n",
              "      <td>1.487313e-15</td>\n",
              "      <td>-5.556467e-16</td>\n",
              "      <td>1.213481e-16</td>\n",
              "      <td>-2.406331e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>1.654067e-16</td>\n",
              "      <td>-3.568593e-16</td>\n",
              "      <td>2.578648e-16</td>\n",
              "      <td>4.473266e-15</td>\n",
              "      <td>5.340915e-16</td>\n",
              "      <td>1.683437e-15</td>\n",
              "      <td>-3.660091e-16</td>\n",
              "      <td>-1.227390e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</td>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>1.332271e+00</td>\n",
              "      <td>1.237094e+00</td>\n",
              "      <td>1.194353e+00</td>\n",
              "      <td>1.098632e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>7.345240e-01</td>\n",
              "      <td>7.257016e-01</td>\n",
              "      <td>6.244603e-01</td>\n",
              "      <td>6.056471e-01</td>\n",
              "      <td>5.212781e-01</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.616051e+01</td>\n",
              "      <td>-4.355724e+01</td>\n",
              "      <td>-7.321672e+01</td>\n",
              "      <td>-1.343407e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.483038e+01</td>\n",
              "      <td>-1.093314e+01</td>\n",
              "      <td>-4.480774e+01</td>\n",
              "      <td>-2.836627e+00</td>\n",
              "      <td>-1.029540e+01</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.500000</td>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-7.682956e-01</td>\n",
              "      <td>-5.540759e-01</td>\n",
              "      <td>-2.086297e-01</td>\n",
              "      <td>-6.430976e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.283949e-01</td>\n",
              "      <td>-5.423504e-01</td>\n",
              "      <td>-1.618463e-01</td>\n",
              "      <td>-3.545861e-01</td>\n",
              "      <td>-3.171451e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.000000</td>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-2.741871e-01</td>\n",
              "      <td>4.010308e-02</td>\n",
              "      <td>2.235804e-02</td>\n",
              "      <td>-5.142873e-02</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.945017e-02</td>\n",
              "      <td>6.781943e-03</td>\n",
              "      <td>-1.119293e-02</td>\n",
              "      <td>4.097606e-02</td>\n",
              "      <td>1.659350e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.500000</td>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>3.985649e-01</td>\n",
              "      <td>5.704361e-01</td>\n",
              "      <td>3.273459e-01</td>\n",
              "      <td>5.971390e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>1.863772e-01</td>\n",
              "      <td>5.285536e-01</td>\n",
              "      <td>1.476421e-01</td>\n",
              "      <td>4.395266e-01</td>\n",
              "      <td>3.507156e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>7.330163e+01</td>\n",
              "      <td>1.205895e+02</td>\n",
              "      <td>2.000721e+01</td>\n",
              "      <td>1.559499e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>2.720284e+01</td>\n",
              "      <td>1.050309e+01</td>\n",
              "      <td>2.252841e+01</td>\n",
              "      <td>4.584549e+00</td>\n",
              "      <td>7.519589e+00</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1df31c7b-c043-4ab7-8a8c-e045a4d397e6')\"\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",
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              "  </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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1df31c7b-c043-4ab7-8a8c-e045a4d397e6 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-1df31c7b-c043-4ab7-8a8c-e045a4d397e6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All the features have reasonable values, hence no further cleaning is needed."
      ],
      "metadata": {
        "id": "lBoTq6pztiot"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data visualization"
      ],
      "metadata": {
        "id": "Gfm8iupJtt3H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax = plt.subplots(1,2,figsize=(10,5))\n",
        "\n",
        "credit_card_data['Class'].value_counts().plot.pie(explode=[0,0.1], autopct='%1.1f%%', ax=ax[0])\n",
        "ax[0].set_title('Class')\n",
        "ax[0].set_ylabel('')\n",
        "\n",
        "sns.countplot(x = credit_card_data['Class'], ax = ax[1])\n",
        "ax[1].set_title('Class')\n",
        "\n",
        "N,P = credit_card_data['Class'].value_counts()\n",
        "print('Legit transactions (0): ', N)\n",
        "print('Fraudolent transactions (1): ', P)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "O9P2x0knt2V2",
        "outputId": "209a004f-748f-4e94-d1d1-5c4a3457b53e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Legit transactions (0):  284315\n",
            "Fraudolent transactions (1):  492\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Out of 284315 transactions, only 492 are fraudolent. Hence the dataset is **extremely imbalanced**. \n",
        "To deal with this problem, I'll try to apply an oversampling technique called SMOTE (Synthetic Minority Over-sampling Technique). SMOTE is a popular oversampling technique that generates synthetic examples of the minority class by interpolating between existing minority class examples. The algorithm works by selecting an example from the minority class and finding its k-nearest neighbors. It then creates new synthetic examples by interpolating between the selected example and its k-nearest neighbors."
      ],
      "metadata": {
        "id": "0w8t_zAZ1MSq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Oversampling"
      ],
      "metadata": {
        "id": "91LfDXMn3pym"
      }
    },
    {
      "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": "EhvNUYA53Lu1"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(legit.shape)\n",
        "print(fraud.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_Qt0BDJ3Zt0",
        "outputId": "6ed56948-c507-478d-857a-d08969d3f527"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(284315, 31)\n",
            "(492, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_variable = 'Class'\n",
        "\n",
        "X = credit_card_data.drop(target_variable, axis = 1) #input features\n",
        "y = credit_card_data[target_variable] #target feature"
      ],
      "metadata": {
        "id": "p4soeyOo4Rqq"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "vCtEi_2W4o5P",
        "outputId": "b58f531c-18bd-41cf-8db1-2faa3c989114"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V20       V21       V22       V23       V24  \\\n",
              "0  0.098698  0.363787  ...  0.251412 -0.018307  0.277838 -0.110474  0.066928   \n",
              "1  0.085102 -0.255425  ... -0.069083 -0.225775 -0.638672  0.101288 -0.339846   \n",
              "2  0.247676 -1.514654  ...  0.524980  0.247998  0.771679  0.909412 -0.689281   \n",
              "3  0.377436 -1.387024  ... -0.208038 -0.108300  0.005274 -0.190321 -1.175575   \n",
              "4 -0.270533  0.817739  ...  0.408542 -0.009431  0.798278 -0.137458  0.141267   \n",
              "\n",
              "        V25       V26       V27       V28  Amount  \n",
              "0  0.128539 -0.189115  0.133558 -0.021053  149.62  \n",
              "1  0.167170  0.125895 -0.008983  0.014724    2.69  \n",
              "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66  \n",
              "3  0.647376 -0.221929  0.062723  0.061458  123.50  \n",
              "4 -0.206010  0.502292  0.219422  0.215153   69.99  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db9244be-88d0-44e8-862f-ec1063228beb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>...</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>...</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>...</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db9244be-88d0-44e8-862f-ec1063228beb')\"\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",
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              "  </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-db9244be-88d0-44e8-862f-ec1063228beb 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-db9244be-88d0-44e8-862f-ec1063228beb');\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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DdVzAog4rOM",
        "outputId": "c5327110-4212-4ff6-d67e-5edf6ff24e1e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ],
      "metadata": {
        "id": "mt6gYIbC3yp9"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smote = SMOTE(random_state=42)"
      ],
      "metadata": {
        "id": "eLAK3hA439Ss"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# apply SMOTE to the dataset\n",
        "X_resampled, y_resampled = smote.fit_resample(X, y)"
      ],
      "metadata": {
        "id": "9dXgvRfw4AmL"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Before oversampling, the dataset has \" + str(X.shape[0]) + \" rows\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXDpaS1S5CtU",
        "outputId": "8ee04688-1641-4bbc-deb0-12d6a667ba8d"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before oversampling, the dataset has 284807 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"After oversampling, the dataset has \" + str(X_resampled.shape[0]) + \" rows\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTRddTTX5F2Z",
        "outputId": "8bd46229-691c-48f1-881b-31111a85a570"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After oversampling, the dataset has 568630 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see the new distribution for the target class."
      ],
      "metadata": {
        "id": "CLPAXk5NEWYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_resampled.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SocfFT45Qq9",
        "outputId": "c8f9097d-d274-4635-887a-779a902f1ca2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    284315\n",
            "1    284315\n",
            "Name: Class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax = plt.subplots(1,2,figsize=(10,5))\n",
        "\n",
        "y_resampled.value_counts().plot.pie(explode=[0,0.1], autopct='%1.1f%%', ax=ax[0])\n",
        "ax[0].set_title('Class')\n",
        "ax[0].set_ylabel('')\n",
        "\n",
        "sns.countplot(x = y_resampled, ax = ax[1])\n",
        "ax[1].set_title('Class')\n",
        "\n",
        "N,P = y_resampled.value_counts()\n",
        "print('Legit transactions (0): ', N)\n",
        "print('Fraudolent transactions (1): ', P)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "Huio3qdY9BeL",
        "outputId": "2df9f557-bd1f-41bb-e2a0-76b2b2278f69"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Legit transactions (0):  284315\n",
            "Fraudolent transactions (1):  284315\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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ARNFANTm9VGz+LVsbPaxUXKyMnAJlbC3Qoq2ZztvqRwTqsgZ/lI+4hhGqG0r5AAAANQtFA1UiI7tAi7dnau3e49r0W7Z2+0CpqKiD2QU6mF1++ejYKFI9WkWpbWy4gQkBAACqHkUDbrP5t2x9ty1Ti7cd0uYD2dVyHIW3OL18TFywQw0ig3RDmygltonWlc3qKMCPE8ABAADfQtHARSsqsSttz1F9tzVTi7dl6kB2gdGRvMZvWSf1Ydov+jDtF4VZ/dT90npKbBul61tFKyLY3+h4AAAAlcbXqLggx/OL9Pm6/Xps5lp1en6R+r+3Wv/+8RdKRiXkFpZo3qaDemr2BnV+YZHufDNN7/x3j/YeOftB6EB5xo8fryuuuEJhYWGKiopSnz59tGPHDpcx1113nUwmk8vPo48+6jJm37596t27t4KDgxUVFaVhw4appKTEZczSpUvVqVMnWa1WtWjRQjNmzCiTZ9q0aWrSpIkCAwMVHx+v1atXuywvKChQSkqK6tSpo9DQUPXt21eZmZlltgMA8E7s0cB5ZZ8s1ufr9uvbTRlau++4bDXlYAsDlNgdWpV+TKvSj+mFedvUvF6IktrF6C9dGqlp3RCj48HDLVu2TCkpKbriiitUUlKiZ555RklJSdq6datCQv54/QwaNEjPPfec8/fg4GDn3202m3r37q2YmBitXLlSBw8e1P333y9/f3/94x//kCSlp6erd+/eevTRRzVz5kwtXrxYDz30kOrXr6/k5GRJ0uzZs5Wamqrp06crPj5ekyZNUnJysnbs2KGoqChJ0lNPPaV58+Zpzpw5ioiI0JAhQ3Tbbbfphx9+qI6nCwBQxUwOBzPpUb7Nv2Xrw7S9+mrDQZ0sthkdp0YzmaSrW9TVvVdeosQ20bKYTUZHghc4fPiwoqKitGzZMnXv3l1S6R6Njh07atKkSeWu8+233+rmm2/WgQMHFB0dLUmaPn26RowYocOHDysgIEAjRozQvHnztHnzZud6/fr1U1ZWlubPny9Jio+P1xVXXKGpU6dKkux2uxo1aqTHH39cTz/9tLKzs1WvXj3NmjVLt99+uyRp+/btatOmjdLS0nTllVee9/Hl5OQoIiJC2dnZCg+/+BMsdB724UWv68sCLNLTCRH6Z1q2ivhfgIu1E+83OoLTvufijI7gkWxmqzbGjVb7Tc/LYi80Oo5HaTxmU6W3UdH3X/ZowEVBsU1fbzyof//4izb8mmV0HPzO4ZD+u/OI/rvziGIjAnVX18bq17Ux1+zAOWVnZ0uSateu7XL7zJkz9dFHHykmJka33HKLRo8e7dyrkZaWpri4OGfJkKTk5GQNHjxYW7Zs0eWXX660tDQlJia6bDM5OVlDhw6VJBUVFWnt2rUaOXKkc7nZbFZiYqLS0tIkSWvXrlVxcbHLdlq3bq3GjRuftWgUFhaqsPCPDww5OTmSpOLiYhUXF1/w83NKgOWiV/Vpp54Xnp+yKvN6czebmf8PlMdmDnD5E39wx+u3otugaECStO/oCX206hfN+elXHT/hOW+gKOtAdoFeXvQ/TVmyS8mXxei+Ky9R16a1z78iahS73a6hQ4eqW7duuuyyy5y333333brkkksUGxurjRs3asSIEdqxY4c+//xzSVJGRoZLyZDk/D0jI+OcY3JycnTy5EkdP35cNput3DHbt293biMgIECRkZFlxpy6nzONHz9e48aNK3P7woULXaZ/XainEyIuet2aILUrz8+ZvvnmG6Mj/CFutNEJPNqWdiOMjuBxNrrh9XvixIkKjaNo1GB2u0NLth/Sv3/8Rct3HuZ0tF6myGbXVxsO6KsNB9Q6Jkz3XHmJbru8gUKs/LOGlJKSos2bN2vFihUutz/88MPOv8fFxal+/fq64YYbtHv3bjVv3ry6Y16QkSNHKjU11fl7Tk6OGjVqpKSkpEpNneo++j/uiOdzAiylJeOV1UydOtPy5+8yOoLT/pcSjI7gkWzmAG1pN0Lttrwki73I6DgepeGItEpv49Qe5fPhE0kNlH2iWDNX/6JZq/Zp//GTRseBG2zPyNXouZv10rfb9efLG+iBbk3UvF6o0bFgkCFDhujrr7/W8uXL1bBhw3OOjY+PlyTt2rVLzZs3V0xMTJmzQ506E1RMTIzzzzPPDpWZmanw8HAFBQXJYrHIYrGUO+b0bRQVFSkrK8tlr8bpY85ktVpltZadJuLv7y9//4s/LTQfos+tyMZzdKbKvN7cjeMPzs1iL+I5OoM7Xr8V3Qant61BThbZNO37XbpmwhJNmL+DkuGD8gpL9O8ff1HSq8s1bM4GHcjiv3FN4nA4NGTIEH3xxRdasmSJmjZtet511q9fL0mqX7++JCkhIUGbNm3SoUOHnGMWLVqk8PBwtW3b1jlm8eLFLttZtGiREhJKv1kNCAhQ586dXcbY7XYtXrzYOaZz587y9/d3GbNjxw7t27fPOQYA4N3Yo1EDlNjs+njNr3pt8U4dyqXV1wQ2u0Nz1u7XlxsO6P4rL1FKjxaqFcIBcb4uJSVFs2bN0pdffqmwsDDnsQ4REREKCgrS7t27NWvWLN10002qU6eONm7cqKeeekrdu3dX+/btJUlJSUlq27at7rvvPk2YMEEZGRkaNWqUUlJSnHsTHn30UU2dOlXDhw/Xgw8+qCVLluiTTz7RvHnznFlSU1PVv39/denSRV27dtWkSZOUn5+vAQMGODMNHDhQqampql27tsLDw/X4448rISGhQmecAgB4PoqGD3M4HPp640G9vHCH9h6t2EE78C1FJXa9syJds9f8qoe7N9PAa5oqOIB/9r7qjTfekFR6CtvTvf/++3rggQcUEBCg7777zvmhv1GjRurbt69GjRrlHGuxWPT1119r8ODBSkhIUEhIiPr37+9y3Y2mTZtq3rx5euqppzR58mQ1bNhQ77zzjvMaGpJ055136vDhwxozZowyMjLUsWNHzZ8/3+UA8VdffVVms1l9+/ZVYWGhkpOT9frrr1fRswMAqG5cR8NHLf/fYU1YsF2bf6vYwTqoGeqFWfXE9S3Ur2tj+VuYOQnvxnU0qhbX0Tg7rqPh+biOxtlxHQ1ctA2/Zuml+du1cvdRo6PAAx3OLdToL7fonRXpSr3xUv2pQ6xMJi7+BwAA3I+i4SN2H87Tvxbs0Lebyz//PHC6X46e0JMfr9eby/ZoeM9Wuq5VlNGRAACAj6FoeLmTRTZNWLBdH6b9IpudWXC4MFsP5uiB99fo6hZ1Nf62ODWqffEXPQMAADgdk7S92Ko9R9Vz8nK9/8NeSgYqZcWuI0qetFwfpu0Vh20BAAB3oGh4oZNFNj37f1vU7+0f9Qtnk4KbnCiyacyXW3T326v06zFeVwAAoHIoGl7m1F6MGSv3ii+eURXS9hxl7wYAAKg0ioaXYC8GqhN7NwAAQGVRNLwAezFgFPZuAACAi0XR8GDsxYAnYO8GAAC4GBQND8VeDHga9m4AAIALQdHwQG8t36272IsBD3Rq70bKrHU6WWQzOg4AAPBgFA0PUlhiU+rs9frHN9vFZTHgyb7ZlKG+b6zUb1knjY4CAAA8FEXDQxzKKdCdb/6oz3/+zegoQIVsPZijP01ZoTV7jxkdBQAAeCCKhgfY8GuWbpm6Qut/zTI6CnBBjuYX6e63f9THq/cZHQUAAHgYiobB5v78m/7yZpoycwqNjgJclGKbQ09/vkljv9ysEpvd6DgAAMBDUDQMYrc7NP6bbRo6e70KS/hwBu/3Qdovuv+91co6UWR0FAAA4AEoGgbILSjWwA/W6M3le4yOArjVyt1H9aepP+h/mblGRwEAAAajaFSz9CP56jPtB32/47DRUYAqse/YCd32+kot2pppdBQAAGAgikY1WrHziG6dukK7D+cbHQWoUnmFJXr43z9p2ve7jI4CAAAMQtGoJou2ZurBGWuUU1BidBSgWjgc0sQFO/TivK1GRwEAAAagaFSDeRsP6rGZa1XEGXlQA73933SN+XKzHA6uQgkAQE1C0ahiX/y8X098/LOKbXzIQs31YdovGvn5Jtm55D0AADUGRaMKfbx6n/76yQbZ+HAF6OM1v+pvc/j3AABATUHRqCL//vEXjfxik/hMBfzh859/0xMf/0zZAACgBqBoVIGPV+/7fU660UkAzzNv40GlfrKeaVQAAPg4ioabfb5uv575YhMlAziHL9cf0IjPNnKAOAAAPoyi4UZfbzygYZ9uZLoUUAFz1u7XqLmbjY4BAACqCEXDTRZuydDQj9cz9xy4ADNX7dNzX3GdDQAAfBFFww3+u/Owhsz6WSWUDOCCvfdDuv61YIfRMQAAgJtRNCpp9+E8PTZzHRfjAyph6ve79Pm6/UbHAAAAbkTRqITsk8Ua9MFPyi0oMToK4PWe/nyTft533OgYAADATSgaF8lmd2jIrHXacyTf6CiATygqseuRf69VZk6B0VEAAIAbUDQu0vhvtum/O48YHQPwKYdyC/Xwhz+poNhmdBQAAFBJfkYH8Eafrt2vd1akGx3DZ2WtmKnsH/7jcptf7YZqMGi6JMlRUqRjS97ViW3L5bAVK6hpJ9VOGixLSK2zbtPhcCh7xUzlbVgge2G+rA3aqHbSY/Kv3eD3bRbr6PzXdGLnj7KE1FLtpMcU1KSjc/3sVZ/JlnNYtW981P0PGC427M/WyM836dU7OxodBQAAVAJ7NC7Qun3H9cwXm4yO4fP86zZWw5R/O39i7nnJuezY4rd1ctdq1e3ztKLv/qdK8o7q8Bf/OOf2clZ9ppy1X6l2copi7ntZJv9AHfpkjBwlRZKk3A3zVZSxSzH3/kuhHXrqyFcTnReTK87KUN6GBYrsfn/VPWC4+OLn3zR92W6jYwAAgEqgaFyAjOwCPfLvtSoq4QxTVc5skSW01h8/wRGSJHthvvI2LlKt6wcq6JIOssa0UN2bhqrwt20q/G17uZtyOBzK/elLRSTcqeCWVyogqqnq3pyqkrxjOvG/NElS8dFfFdQiXgH1LlFYp96yn8iW/WSOJOnYwtdV67oHZLYGV89jhyRpwvzt+n77IaNjAACAi0TRqKCCYpse/vdPOpxbaHSUGqHk+AHtn3a/fps+UIe/mqiSnNIPnIUZuyR7icu0Jv86jWQJr6fCA+UXjZLsTNnyj7usY7aGyBrbyrlOQFRTFe7fKntxoQrS18kSWlvmoHDlbfleJr8ABV96VZU9VpTP7pCe+M/P2nUoz+goAADgIlA0KmjEZxu1cX+20TFqBGv9Vqpz01OKumOcaic9JltWpjJmjpC98ITs+ccli5/MgaEu61hCImXLL//UqLa80tvNIZGu6wRHypafJUkKjbtR/lFNdeDdx5Sd9onq3jpC9oI8Za+YqdqJj+j48n/rtzcHKXP2aJXkchKA6pJbWKJBH/6k7JPFRkcBAAAXiIPBK+D1pbv05foDRseoMYKad/njl6imssa20v43HlT+9hUy+wdUyX2aLH6qkzTY5bYj8yYprPMtKsrco5M701R/wBTlrPpMx797S/X+/EyV5EBZ6UfyNWTWOs0Y0FUWs8noOAAAoILYo3EeP+w6on8t2GF0jBrNHBgq/9oNVJJ1QOaQWpKtRPYC1+k0tvyss551yhJaerv9970XznVOZMlyxl6OUwp+2ajio78orNPNKti3UUHNusgcEKjg1lerYB8nA6hu/915RC8v5N8hAADehKJxDnmFJRr+6UbZHUYnqdnsRSdVknVQlpDassa0kMx+OvnLBufy4qP7Zcs5LGts63LX94uIliWklgp+Wf/HNgtPqPDAjnLXcZQU6diiN1QneYhMZovksMth//26DnabHA5OBmCEN5fv4crhAAB4EYrGObw4b5t+yzppdIwa5/iSd1Wwb5NKsjNVsH+bDn/+omQyK6TttTJbQxTa/kYdX/KOCn7ZqMKMXTr6zSRZY1vL2uCP0vDb24/qxP9WSpJMJpPCutyq7JWzdWLnKhUd3qsj816RX2htBV+aUOb+s1Z+rKBmXRQQ3VySZG3QVif+t1JFh9KVu+5rBTZoUz1PBFzY7A4N+3SjCku4mB8AAN6AYzTO4r87D+s/q/cZHaNGKsk9oiNfTZTtZI4sQRGyNmyrmPtedp7itvYNg3TMZNbhuf+Qw1aswKadVOfGx1y3cWy/7IUnnL+Hx/eVo7hARxdMkb0gX4EN2yrqL8/J5Od6zEfR4b06sf2/qv/AFOdtwa27qeDXTcqYOUL+dRqo7i3DqvDR41x2HcrTK4v+p5G9KHsAAHg6ikY58gpL9PRnzMM3Sr1bR5xzuckvQHWSBpc5ePt0l4z42nUdk0mR19yryGvuPee2A+o1UYOH3z5jXbPqJD2mOkmPnWUtVKd3/puunu1idHnjs18JHgAAGI+pU+VgyhTguZhCBQCAd6BonIEpU4DnOzWFCgAAeC6KxmmYMgV4j3f+m85ZqAAA8GAUjdMwZQrwHkyhAgDAs1E0fseUKcD7MIUKAADPRdEQU6YAb8YUKgAAPBNFQ9I/vmHKFOCtmEIFAIBnqvFFY+uBHKZMAV5u16E8/TvtF6NjAACA09T4ojFhwXY5HEanAFBZ077fpZyCYqNjAACA39XoovHjnqNauuOw0TEAuMHxE8V6a9keo2MAAIDf1eii8dL87UZHAOBG7/2QrkO5BUbHMMz48eN1xRVXKCwsTFFRUerTp4927NjhMqagoEApKSmqU6eOQkND1bdvX2VmZrqM2bdvn3r37q3g4GBFRUVp2LBhKikpcRmzdOlSderUSVarVS1atNCMGTPK5Jk2bZqaNGmiwMBAxcfHa/Xq1RecBQDgvWps0ViwJUM/78syOgYANzpRZNOUxbuMjmGYZcuWKSUlRT/++KMWLVqk4uJiJSUlKT8/3znmqaee0ldffaU5c+Zo2bJlOnDggG677TbncpvNpt69e6uoqEgrV67UBx98oBkzZmjMmDHOMenp6erdu7d69Oih9evXa+jQoXrooYe0YMEC55jZs2crNTVVY8eO1bp169ShQwclJyfr0KFDFc4CAPBuJoej5h2hYLM71HPScu08lGd0FABu5m8x6bvUa3VJnRCjoxju8OHDioqK0rJly9S9e3dlZ2erXr16mjVrlm6//XZJ0vbt29WmTRulpaXpyiuv1Lfffqubb75ZBw4cUHR0tCRp+vTpGjFihA4fPqyAgACNGDFC8+bN0+bNm5331a9fP2VlZWn+/PmSpPj4eF1xxRWaOnWqJMlut6tRo0Z6/PHH9fTTT1coy/nk5OQoIiJC2dnZCg8Pv+jnqfOwDy96XV8WYJGeTojQP9OyVcRJ3VysnXi/0RGc9j0XZ3QEj2QzW7UxbrTab3peFnuh0XE8SuMxlb+kQ0Xff/0qfU9e6LN1+ykZgI8qtjn08sL/6bW7Ljc6iuGys7MlSbVr15YkrV27VsXFxUpMTHSOad26tRo3buz8cJ+Wlqa4uDhnyZCk5ORkDR48WFu2bNHll1+utLQ0l22cGjN06FBJUlFRkdauXauRI0c6l5vNZiUmJiotLa3CWc5UWFiowsI/PjDk5ORIkoqLi1VcfPEnAgiwXPSqPu3U88LzU1ZlXm/uZjNbjY7gkWzmAJc/8Qd3vH4ruo0aVzQKS2yaxJWEAZ/21cYDeuTaZmoXG2F0FMPY7XYNHTpU3bp102WXXSZJysjIUEBAgCIjI13GRkdHKyMjwznm9JJxavmpZecak5OTo5MnT+r48eOy2Wzljtm+fXuFs5xp/PjxGjduXJnbFy5cqODg4LM9Fef1dELNfZ1URGpXnp8zffPNN0ZH+EPcaKMTeLQt7UYYHcHjbHTD6/fEiRMVGlfjisa/037Rgeyae7AoUBM4HNKE+Tv0wYNdjY5imJSUFG3evFkrVqwwOorbjBw5Uqmpqc7fc3Jy1KhRIyUlJVVq6lT30f9xRzyfE2ApLRmvrGbq1JmWP3+X0RGc9r+UYHQEj2QzB2hLuxFqt+UlWexFRsfxKA1HpFV6G6f2KJ9PjSoauQXFmvZ9zT1QFKhJlv3vsNJ2H1VC8zpGR6l2Q4YM0ddff63ly5erYcOGzttjYmJUVFSkrKwslz0JmZmZiomJcY458+xQp84EdfqYM88OlZmZqfDwcAUFBclischisZQ75vRtnC/LmaxWq6zWstNE/P395e/vf66n5Jz4EH1uRTaeozNV5vXmbhx/cG4WexHP0Rnc8fqt6DZq1Fmn3lq+R8dPeM68SgBVq6adwtrhcGjIkCH64osvtGTJEjVt2tRleefOneXv76/Fixc7b9uxY4f27dunhITSb0UTEhK0adMml7NDLVq0SOHh4Wrbtq1zzOnbODXm1DYCAgLUuXNnlzF2u12LFy92jqlIFgCAd6sxezQO5xbq3RXpRscAUI3W/5ql+Zsz1POy8r8h9zUpKSmaNWuWvvzyS4WFhTmPdYiIiFBQUJAiIiI0cOBApaamqnbt2goPD9fjjz+uhIQE58HXSUlJatu2re677z5NmDBBGRkZGjVqlFJSUpx7Ex599FFNnTpVw4cP14MPPqglS5bok08+0bx585xZUlNT1b9/f3Xp0kVdu3bVpEmTlJ+frwEDBjgznS8LAMC71ZiiMWNluk6w7xeocd5YuqvGFI033nhDknTddde53P7+++/rgQcekCS9+uqrMpvN6tu3rwoLC5WcnKzXX3/dOdZisejrr7/W4MGDlZCQoJCQEPXv31/PPfecc0zTpk01b948PfXUU5o8ebIaNmyod955R8nJyc4xd955pw4fPqwxY8YoIyNDHTt21Pz5810OED9fFgCAd6sR19EoKrHrqn8u0ZE85ugBNdH/Demm9g0jjY4BN+M6GlWL62icHdfR8HxcR+PsqvM6GjXiGI1vNx+kZAA12L/TfjE6AgAANU6NKBof/ciHDKAm+2rjAWVzIggAAKqVzxeN7Rk5WrP3uNExABiooNiuOWt/NToGAAA1is8XjQ+ZMgFApXs2a8AhaQAAeAyfLhq5BcX68uffjI4BwAPsPXpCy3ceMToGAAA1hk8Xjc/X/aZ8TpUB4HccFA4AQPXx6aLBQeAATvf9jkP6Leuk0TEAAKgRfLZopO0+qp2H8oyOAcCD2OwOzVrFFxAAAFQHny0a7M0AUJ7Za/arqMRudAwAAHyeTxaNQzkFWrg1w+gYADzQkbxCfbv5oNExAADweT5ZNGav+VXFNk5jCaB8M1ftMzoCAAA+zyeLxrxNfFsJ4OzW7D2mw7mFRscAAMCn+VzR2H/8hLZn5BodA4AHczikxdsyjY4BAIBP87mi8d1WPjwAOL/vKBoAAFQp3ysa2w4ZHQGAF1ix64gKirmgJwAAVcWnikZuQbFWpR81OgYAL1BQbNeKnUeMjgEAgM/yqaKx7H+HOdsUgApj+hQAAFXHp4oGx2cAuBCLtx+Sw8GXEwAAVAWfKRolNru+33HY6BgAvMjh3EKt/zXL6BgAAPgknykaa/YeV/bJYqNjAPAyTJ8CAKBq+EzR4MMCgIvx3VbOVAcAQFXwmaLBxbcAXIwdmbn69dgJo2MAAOBzfKJo7DqUq71H+aAA4OKwRxQAAPfziaLBRfoAVAZFAwAA9/OJorE6/ZjREQB4sXW/ZMlm5zS3AAC4k08UjU2/ZRsdAYAXO1ls065DeUbHAADAp3h90cjMKdDh3EKjYwDwcnxhAQCAe3l90di0nw8HACpvM0UDAAC38vqisZEPBwDcgD0aAAC4l9cXDb6FBOAOWw/kcEA4AABu5PVFg28hAbgDB4QDAOBeXl00OBAcgDvxxQUAAO7j1UVjIweCA3AjpmICAOA+Xl00+PYRgDvxngIAgPt4ddHg20cA7sQB4QAAuI9XFw2+fQTgThwQDgCA+3ht0cjI5kBwAO7HFxgAALiH1xaNbRk5RkcA4IO2HeS9BQAAd/DaopGRXWB0BAA+KCOH9xYAANzBa4vGoRymTQFwv0MUDQAA3MJri0ZmLh8GALjfIY79AgDALby2aLBHA0BVMOK95frrr1dWVlaZ23NycnT99ddXex4AANzBe4sGezQAVIGTxTblFBRX630uXbpURUVFZW4vKCjQf//732rNAgCAu/gZHeBisUcDQFU5lFOo8ED/Kr+fjRs3Ov++detWZWRkOH+32WyaP3++GjRoUOU5AACoCl5ZNOx2h47kUTQAVI1DOQVqERVa5ffTsWNHmUwmmUymcqdIBQUFacqUKVWeAwCAquCVReNofpFK7A6jYwDwUdV1QHh6erocDoeaNWum1atXq169es5lAQEBioqKksViqZYsAAC4m1cWDY7PAFCVMqvpFLeXXHKJJMlut1fL/QEAUJ28tGgwbQpA1THiPWbnzp36/vvvdejQoTLFY8yYMdWeBwCAyvLOosEFtQBUoerao3HK22+/rcGDB6tu3bqKiYmRyWRyLjOZTBQNAIBX8tKiwR4NAFWnuvdovPDCC3rxxRc1YsSIar1fAACqkldeR4OrggOoStW91/T48eO64447qvU+AQCoal5ZNLJOVO/FtADULMer+T3mjjvu0MKFC6v1PgEAqGpeOXWq2MYZWgBUnZJqfo9p0aKFRo8erR9//FFxcXHy93e9WOATTzxRrXkAAHAHr9yjYeMaGgCqUHVfp+ett95SaGioli1bpqlTp+rVV191/kyaNKnC21m+fLluueUWxcbGymQyae7cuS7LH3jgAecFAk/99OzZ02XMsWPHdM899yg8PFyRkZEaOHCg8vLyXMZs3LhR11xzjQIDA9WoUSNNmDChTJY5c+aodevWCgwMVFxcnL755huX5Q6HQ2PGjFH9+vUVFBSkxMRE7dy5s8KPFQDg+byyaHCxPgBVqbq/zEhPTz/rz549eyq8nfz8fHXo0EHTpk0765iePXvq4MGDzp///Oc/LsvvuecebdmyRYsWLdLXX3+t5cuX6+GHH3Yuz8nJUVJSki655BKtXbtWEydO1LPPPqu33nrLOWblypW66667NHDgQP3888/q06eP+vTpo82bNzvHTJgwQa+99pqmT5+uVatWKSQkRMnJySoo4Bg8APAVXjl1qsRG0QBQdbz1y4xevXqpV69e5xxjtVoVExNT7rJt27Zp/vz5WrNmjbp06SJJmjJlim666Sb961//UmxsrGbOnKmioiK99957CggIULt27bR+/Xq98sorzkIyefJk9ezZU8OGDZMkPf/881q0aJGmTp2q6dOny+FwaNKkSRo1apRuvfVWSdKHH36o6OhozZ07V/369XPXUwIAMJB3Fg2uogugipXY7PKzVM9O3wcffPCcy9977z233dfSpUsVFRWlWrVq6frrr9cLL7ygOnXqSJLS0tIUGRnpLBmSlJiYKLPZrFWrVunPf/6z0tLS1L17dwUEBDjHJCcn66WXXtLx48dVq1YtpaWlKTU11eV+k5OTnVO50tPTlZGRocTEROfyiIgIxcfHKy0t7axFo7CwUIWFf5x6OCcnR5JUXFys4uKLP4A/wHLRq/q0U88Lz09ZlXm9uZvNbDU6gkeymQNc/sQf3PH6reg2vLJocIwGgKpWYnfIr5o+YB0/ftzl9+LiYm3evFlZWVm6/vrr3XY/PXv21G233aamTZtq9+7deuaZZ9SrVy+lpaXJYrEoIyNDUVFRLuv4+fmpdu3aysjIkCRlZGSoadOmLmOio6Ody2rVqqWMjAznbaePOX0bp69X3pjyjB8/XuPGjStz+8KFCxUcHFyRp6BcTydEXPS6NUFqV56fM515zJGh4kYbncCjbWnH9YnOtNENr98TJ05UaJxXFo0X+sQpr7DE6BgAfFhANe3NkKQvvviizG12u12DBw9W8+bN3XY/p+8piIuLU/v27dW8eXMtXbpUN9xwg9vup6qMHDnSZU9JTk6OGjVqpKSkJIWHh1/0druP/s/5B9VAAZbSkvHK6mwV2YxO41mWP3+X0RGc9r+UYHQEj2QzB2hLuxFqt+UlWexFRsfxKA1HpFV6G6f2KJ+PVxaNVjFhRkcAgCplNpuVmpqq6667TsOHD6+S+2jWrJnq1q2rXbt26YYbblBMTIwOHTrkMqakpETHjh1zHtcRExOjzMxMlzGnfj/fmNOXn7qtfv36LmM6dux41rxWq1VWa9lpIv7+/mVOCXwh+BB9bkU2nqMzVeb15m4We+H5B9VgFnsRz9EZ3PH6reg2vPKsUwBQE+zevVslJVW393b//v06evSo88N+QkKCsrKytHbtWueYJUuWyG63Kz4+3jlm+fLlLvNzFy1apFatWqlWrVrOMYsXL3a5r0WLFikhofSb16ZNmyomJsZlTE5OjlatWuUcAwDwfl65RwMAfMmZB047HA4dPHhQ8+bNU//+/Su8nby8PO3atcv5e3p6utavX6/atWurdu3aGjdunPr27auYmBjt3r1bw4cPV4sWLZScnCxJatOmjXr27KlBgwZp+vTpKi4u1pAhQ9SvXz/FxsZKku6++26NGzdOAwcO1IgRI7R582ZNnjxZr776qvN+n3zySV177bV6+eWX1bt3b3388cf66aefnKfANZlMGjp0qF544QW1bNlSTZs21ejRoxUbG6s+ffpc7NMIAPAwFA0AMNjPP//s8rvZbFa9evX08ssvn/eMVKf76aef1KNHD+fvpwpM//799cYbb2jjxo364IMPlJWVpdjYWCUlJen55593mY40c+ZMDRkyRDfccIPMZrP69u2r1157zbk8IiJCCxcuVEpKijp37qy6detqzJgxLtfauOqqqzRr1iyNGjVKzzzzjFq2bKm5c+fqsssuc44ZPny48vPz9fDDDysrK0tXX3215s+fr8DAwIo/cQAAj2ZyOBycwgkA4HVycnIUERGh7OzsSh0M3nnYh25M5TsCLKVn5PpnGgeDn2ntxPuNjuC077k4oyN4JJvZqo1xo9V+0/Mco3GGxmM2VXobFX3/ZY8GAHiIw4cPa8eOHZKkVq1aqV69egYnAgDg4nEwOAAYLD8/Xw8++KDq16+v7t27q3v37oqNjdXAgQMrfK5yAAA8DUUDAAyWmpqqZcuW6auvvlJWVpaysrL05ZdfatmyZfrrX/9qdDwAAC4KU6cAwGCfffaZPv30U1133XXO22666SYFBQXpL3/5i9544w3jwgEAcJHYowEABjtx4oSio6PL3B4VFcXUKQCA16JoAIDBEhISNHbsWBUUFDhvO3nypMaNG8cF7AAAXoupUwBgsEmTJqlnz55q2LChOnToIEnasGGDrFarFi5caHA6AAAuDkUDAAwWFxennTt3aubMmdq+fbsk6a677tI999yjoKAgg9MBAHBxvLNofPesdPh/RqcA4MvueF/ys55/nBuMHz9e0dHRGjRokMvt7733ng4fPqwRI0ZUSw4AANzJO4vGr6ulX34wOgUAn2aqtnt68803NWvWrDK3t2vXTv369aNoAAC8knceDG62GJ0AgK8zV9/3MBkZGapfv36Z2+vVq6eDBw9WWw4AANzJS4uGd+6IAeAtTJK5+t4eGzVqpB9+KLuX9ocfflBsbGy15QAAwJ288xO72d/oBAB8maV632MGDRqkoUOHqri4WNdff70kafHixRo+fDhXBgcAeC0vLRreGRuAl6jm95hhw4bp6NGjeuyxx1RUVCRJCgwM1IgRIzRy5MhqzQIAgLt45yd2jtEAUJWquWiYTCa99NJLGj16tLZt26agoCC1bNlSVmv1nPUKAICq4J1Fwz/Y6AQAfJm/MdeuCA0N1RVXXGHIfQMA4G7eeTB4aJTRCQD4Mt5jAACoNO8sGmFlTwMJAG7DewwAAJXmpUUj2ugEAHxZKO8xAABUlncWjdAYoxMA8GXs0QAAoNK8s2iEUTQAVCH2mgIAUGkUDQA4E3s0AACoNO8sGgEhUkCY0SkA+CqmZwIAUGneWTQk9moAqDpMnQIAoNIoGgDgwsRZpwAAcAOKBgCcLriOZPE3OgUAAF7Pe4sG3zgCqAocCA4AgFt4b9FgjwaAqsDxGQAAuIUXFw2+dQRQBfgSAwAAt/DeolGrqdEJAPgi3lsAAHAL7y0a0e0ks5/RKQD4mtiORicAAMAneG/R8A+U6rUxOgUAX1P/cqMTAADgE7y3aEh88wjAvSIaSSF1jE4BAIBPoGgAwCm8pwAA4DbeXTSY4gDAnep3NDoBAAA+w7uLRnQ7ycwVfAG4CXs0AABwG+8uGv6BUlRro1MA8BXsJQUAwG28u2hITHUA4B4cCA4AgFt5f9FgqgMAd6jfwegEAAD4FO8vGkx1AOAOsbyXAADgTt5fNGIu44BwAJXH3lEAANzK+4uGn5UDwgFUHntHAQBwK+8vGpIU28noBAC8WURjDgQHAMDNfKNotLjB6AQAvBnvIQAAuJ1vFI3mN0gWq9EpAHirVjcZnQAAAJ/jG0XDGio1vcboFAC8kX+I1Oxao1MAAOBzfKNoSFKrXkYnAOCNmvcoPakEAABwK98pGpdSNABcBL6kAACgSvhO0YhoIMW0NzoFAG9iMkuX9jQ6BQAAPsl3iobEAZ0ALkzDK6SQukanAADAJ/lY0WAKBIALwHsGAABVxreKRmxHKbyB0SkAeAv2ggIAUGV8q2hIzLcGUDG1m0n1WhmdAgAAn+V7RYOpEAAqgjPVAQBQpXyvaDTtLgWEGp0CgKfzwS8lli9frltuuUWxsbEymUyaO3euy3KHw6ExY8aofv36CgoKUmJionbu3Oky5tixY7rnnnsUHh6uyMhIDRw4UHl5eS5jNm7cqGuuuUaBgYFq1KiRJkyYUCbLnDlz1Lp1awUGBiouLk7ffPPNBWcBAHg33ysaflap+fVGpwDgyYJqSY0TjE7hdvn5+erQoYOmTZtW7vIJEybotdde0/Tp07Vq1SqFhIQoOTlZBQUFzjH33HOPtmzZokWLFunrr7/W8uXL9fDDDzuX5+TkKCkpSZdcconWrl2riRMn6tlnn9Vbb73lHLNy5UrdddddGjhwoH7++Wf16dNHffr00ebNmy8oCwDAu/le0ZCkjvcYnQCAJ2t/p2TxMzqF2/Xq1UsvvPCC/vznP5dZ5nA4NGnSJI0aNUq33nqr2rdvrw8//FAHDhxw7vnYtm2b5s+fr3feeUfx8fG6+uqrNWXKFH388cc6cOCAJGnmzJkqKirSe++9p3bt2qlfv3564okn9Morrzjva/LkyerZs6eGDRumNm3a6Pnnn1enTp00derUCmcBAHg/3/s/rSS1TJIiG0tZ+4xOAsATXfGQ0QmqXXp6ujIyMpSYmOi8LSIiQvHx8UpLS1O/fv2UlpamyMhIdenSxTkmMTFRZrNZq1at0p///GelpaWpe/fuCggIcI5JTk7WSy+9pOPHj6tWrVpKS0tTamqqy/0nJyc7S0RFspSnsLBQhYWFzt9zcnIkScXFxSouLr7o5ybActGr+rRTzwvPT1mVeb25m81sNTqCR7KZA1z+xB/c8fqt6DZ8s2iYzVLnAdLicUYnAeBpmnaX6rY0OkW1y8jIkCRFR0e73B4dHe1clpGRoaioKJflfn5+ql27tsuYpk2bltnGqWW1atVSRkbGee/nfFnKM378eI0bV/Z9feHChQoODj7reufzdELERa9bE6R25fk505nHHBkqbrTRCTzalnYjjI7gcTa64fV74sSJCo3zzaIhSZ3ul5b+U7IVnn8sgJqjBu7N8BUjR4502VOSk5OjRo0aKSkpSeHh4Re93e6j/+OOeD4nwFJaMl5Zna0im9FpPMvy5+8yOoLT/pd873gzd7CZA7Sl3Qi12/KSLPYio+N4lIYj0iq9jVN7lM/Hd4tGSF2p7a3Spk+MTgLAU4TFSq16G53CEDExMZKkzMxM1a9f33l7ZmamOnbs6Bxz6NAhl/VKSkp07Ngx5/oxMTHKzMx0GXPq9/ONOX35+bKUx2q1ymotO03E399f/v7+Z13vfPgQfW5FNp6jM1Xm9eZuFjtfqJ6LxV7Ec3QGd7x+K7oN3zwY/BS+uQRwus79ffIg8Ipo2rSpYmJitHjxYudtOTk5WrVqlRISSr8RTUhIUFZWltauXescs2TJEtntdsXHxzvHLF++3GV+7qJFi9SqVSvVqlXLOeb0+zk15tT9VCQLAMD7+XbRaBwvxcQZnQKAJzD7SZ0fMDpFlcrLy9P69eu1fv16SaUHXa9fv1779u2TyWTS0KFD9cILL+j//u//tGnTJt1///2KjY1Vnz59JElt2rRRz549NWjQIK1evVo//PCDhgwZon79+ik2NlaSdPfddysgIEADBw7Uli1bNHv2bE2ePNllStOTTz6p+fPn6+WXX9b27dv17LPP6qefftKQIUMkqUJZAADez/e/2usyUPp6qNEpABitdW8pLMboFFXqp59+Uo8ePZy/n/rw379/f82YMUPDhw9Xfn6+Hn74YWVlZenqq6/W/PnzFRgY6Fxn5syZGjJkiG644QaZzWb17dtXr732mnN5RESEFi5cqJSUFHXu3Fl169bVmDFjXK61cdVVV2nWrFkaNWqUnnnmGbVs2VJz587VZZdd5hxTkSwAAO9mcjgcDqNDVKmifOnlNlJhttFJABip/1elZ5yCz8jJyVFERISys7MrdTB452EfujGV7wiwlJ6R659pHAx+prUT7zc6gtO+55i5UR6b2aqNcaPVftPzHKNxhsZjNlV6GxV9//XtqVOSFBAidSj/nOwAaoi6rSgZAABUM98vGhIHhQM13RUDjU4AAECNUzOKRr1LpSbXGJ0CgBH8Q6QOnnPOewAAaoqaUTQkKf5RoxMAMEKHflLgxc/fBwAAF6fmFI3WvaXYy41OAaA6+QVJ3YcZnQIAgBqp5hQNk0m6YazRKQBUp/iHpfD65x8HAADcruYUDUlq3kNqdp3RKQBUh8AI6eqnjE4BAECNVbOKhvT7Xg2T0SkAVLVuQ6WgWkanAACgxqp5RaNBJ6ntrUanAFCVwupLVw42OgUAADVazSsaknTDGMnsZ3QKAFXl2hGSf5DRKQAAqNFqZtGo01y6/F6jUwCoCnVaSJffZ3QKAABqvJpZNCTp2qdLT30JwLdcP0qysMcSAACj1dyiEV5fin/E6BQA3Cn2cqltH6NTAAAA1eSiIZWe+jIw0ugUANzlhrGl18wBAACGq9lFIyiS8+wDvqLZdaXXygEAAB6hZhcNqXT6VHhDo1MAqAyTWUp81ugUAADgNBQN/yDp5leNTgGgMuIfLT0+AwAAeAyKhiRdmiR1vMfoFAAuRu3mpdfGAQAAHoWicUrP8VJYrNEpAFwIk1nq8zoX5wMAwANRNE4JjJBumWx0CgAXIv5RqfGVRqcAAADloGicjilUgPdgyhQAAB6NonEmplABno8pUwAAeDw/owN4nFNTqGbdYXSSGuvZpQUat6zI5bZWdczaPiRUklRQ4tBfFxTo4y0lKixxKLmFn16/KVDRoWfvzQ6HQ2OXFurtdcXKKnCoWyOL3ugdqJZ1LJKkwhKHHvqqQF9uL1ZMqFmv9w5UYrM//nlM/KFQ+7LtmnITH2w9AlOmAADweOzRKA9TqAzXrp5ZB/8a6vxZ8WCwc9lT8wv01f9KNOeOIC17IEQHch267ZOT59zehB+K9NqqIk3vHahVD4UoJMCk5I9OqKDEIUl6a22x1h6wKW1giB7u7K+7Pzsph6N0Wfpxu95eV6wXbwisugeMimPKFAAAXoGicTZMoTKUn1mKCTU7f+oGl75UswscevfnYr2SHKjrm/qpc6xF798aqJW/2vTj/pJyt+VwODRpVZFGdbfq1tb+ah9t0Yd9gnQg16G520vX2XbEpj+18lO7KItSrgjQ4RMOHTlRWjQGzzuplxKtCreaqufB4+yYMgUAgNegaJwNZ6Ey1M5jdsW+nKtmk3N1z+cntC/bLklae9CmYrtcpjW1rmtR4wiT0n61lbut9CyHMvIcLutEBJoU39DiXKdDtEUr9tl0stihBbtLVD/UpLrBJs3cWKxAP5P+3Ma/Ch8tKowpUwAAeA2KxrkwhcoQ8Q0smnFrkObfG6w3egcp/bhD17yfr9zC0sIQYJEiA133LkSHmJSR5yh3exl5dueYMuvkly578HJ/dYg2q+3reXrxv4X65I4gHS+Qxiwt0JRegRq1pEAtXstV8kf5+i3HXgWPGufFlCkAALwKB4OfT89/Sr+ulo7uNDpJjdGr5R97D9pHS/ENLbpkUq4+2VKsIP+qmb7kbzFpWm/X6TgDvjypJ7oG6OcMm+ZuL9GGR0M14YdCPTG/QJ/9JfgsW0KV8AuU+r7NlCkAALwIezTOJzBcuuvj0qlUMERkoEmX1jFr1zG7YkJNKrJJWQWuey8y8x2KCS2/hMT8fjaqzPxy1gkp/5/A9+kl2nLIpiFdA7R0r003tfRTSIBJf2nnr6V7y5+ihSr0pylSg85GpwAAABeAolERdVtIfd+TTBajk9RIeUUO7T5mV/0wkzrXt8jfLC3e88eB3zuO2LQv26GERuX/92kaaVJMqMllnZxCh1btt5W7TkGJQynfFOjNm4NkMZtks0vFv3eLYrtks5c/RQtV5KonpPZ/MToFAAC4QBSNimqZKN04zugUNcLfFhZo2d4S7c2ya+WvJfrz7BOymE266zJ/RQSaNPByf6UuLND36SVae8CmAV8WKKGhRVc2PO0A8al5+mJbsSTJZDJpaHyAXvhvof5vR7E2Zdp0/xcnFRtmUp/WZWcPPr+sUDe19NPl9UtLSLfGFn2+vVgbM22aurpI3Roz47DatEySEvl3BwCAN+IT04W46nEpc6u0YZbRSXza/hy77vrspI6edKhesElXN7box4Ehqvf7NKdXewbKvKBAfT85oUKblNzcT6/3dr3GxY6jdmUX/rHnYXi3AOUXO/TwVwXKKnDo6sYWzb83WIF+rtOtNh+y6ZOtJVr/SIjzttvb+mnpXj9d836+WtUxa1Zfjs+oFnUvlfq+K5n5PgQAAG9E0bhQt0wqPTB8/xqjk/isj28/9wf5QL/SA7fPPHj7dI6x4S6/m0wmPdcjUM/1OPdF9y6Lsmjn46Eut5lNJr3eO0ivn+P+4GaBEb8fGxV+/rEAAMAj8VXhhfKzSnd+xMX8gKpiski3vy/VaW50EgAAUAkUjYsRFiP1m1l6yk0A7pX0vNTiBqNTAACASqJoXKwGnaQ/TTU6BeBbOt4rJaQYnQIAALgBRaMy2t8hdRtqdArANzTsKt38qtEpAACAm1A0KuuGsdKlPY1OAXi38Aalxz75BRidBAAAuAlFo7LMZun296TGCUYnAbxTcF3pvi+ksGijkwAAADeiaLhDQIh0zxypQRejkwDeJaiWdP+XUr1WRicBAABuRtFwF2uYdO9nUkx7o5MA3sEaUbonI+Yyo5MAAIAqQNFwp6BI6b65UlRbo5MAni0gVLr3Uyn2cqOTAACAKkLRcLeQOqVTQeq0NDoJ4Jn8g6W7Z0uNuhqdBAAAVCGKRlUIjZIemCfVa210EsCzBISWHs/U5GqjkwAAgCpG0agqYdGlZSM6zugkgGc4dUwGJQMAgBqBolGVQupK/f+PeehAUC3p/rlMlwIAoAahaFS14Nqlx2w0ijc6CWCM4LpS/6+kBp2MTgIAAKoRRaM6BEZI934uNbnG6CRA9QqNKZ1CGMMUQgAAahqKRnWxhpaWjcvvMzoJUD1iL5ce/l6K4qQIAADURBSN6uQXIN06Ver5kmSyGJ0GqDqX3S4N+FYKjzU6CQAAMAhFwwhXPlp6FfHASKOTAO5lMks3jJVuf1fyDzI6DQAAMBBFwyjNe0iDlnCtDfiOgDCp33+ka1KNTgIAADwARcNIdZpLAxdJl/Y0OglQObWbSQ99J7XitQwAAEpRNIwWGF76LfDVTxmdBLg4za4r3TvHQd8e79lnn5XJZHL5ad36j/9uBQUFSklJUZ06dRQaGqq+ffsqMzPTZRv79u1T7969FRwcrKioKA0bNkwlJSUuY5YuXapOnTrJarWqRYsWmjFjRpks06ZNU5MmTRQYGKj4+HitXr26Sh4zAMA4FA1PYDZLic9Kfd+V/JjXDi/S9RHpns9KL8gHr9CuXTsdPHjQ+bNixQrnsqeeekpfffWV5syZo2XLlunAgQO67bbbnMttNpt69+6toqIirVy5Uh988IFmzJihMWPGOMekp6erd+/e6tGjh9avX6+hQ4fqoYce0oIFC5xjZs+erdTUVI0dO1br1q1Thw4dlJycrEOHDlXPkwAAqBYUDU8Sd7s04BspjDP1wMNZAqQ/TZFumiBZ/IxOgwvg5+enmJgY50/dunUlSdnZ2Xr33Xf1yiuv6Prrr1fnzp31/vvva+XKlfrxxx8lSQsXLtTWrVv10UcfqWPHjurVq5eef/55TZs2TUVFRZKk6dOnq2nTpnr55ZfVpk0bDRkyRLfffrteffVVZ4ZXXnlFgwYN0oABA9S2bVtNnz5dwcHBeu+996r/CQEAVBk+IXiaBp2kh5dKnw6QfvnB6DRAWeENpNvfkxpfaXQSXISdO3cqNjZWgYGBSkhI0Pjx49W4cWOtXbtWxcXFSkxMdI5t3bq1GjdurLS0NF155ZVKS0tTXFycoqOjnWOSk5M1ePBgbdmyRZdffrnS0tJctnFqzNChQyVJRUVFWrt2rUaOHOlcbjablZiYqLS0tHNmLywsVGFhofP3nJwcSVJxcbGKi4sv+jkJ4Gzj5Tr1vPD8lFWZ15u72cxWoyN4JJs5wOVP/MEdr9+KboOi4YnCoqX+X0ur35QWPycVnzA6EVDq8nul5H+UXu0eXic+Pl4zZsxQq1atdPDgQY0bN07XXHONNm/erIyMDAUEBCgyMtJlnejoaGVkZEiSMjIyXErGqeWnlp1rTE5Ojk6ePKnjx4/LZrOVO2b79u3nzD9+/HiNGzeuzO0LFy5UcHDw+Z+As3g6gdfzuaR25fk50zfffGN0hD/EjTY6gUfb0m6E0RE8zkY3vH5PnKjYZ1OKhqcym6UrB0stk6QvU6R95/6mD6hS4Q2kWyZLLW80OgkqoVevXs6/t2/fXvHx8brkkkv0ySefKCjI848PGzlypFJT/zh9ck5Ojho1aqSkpCSFh4df9Ha7j/6PO+L5nABLacl4ZXW2imxGp/Esy5+/y+gITvtfSjA6gkeymQO0pd0Itdvykiz2IqPjeJSGIyr/mfLUHuXzoWh4ujrNpQe+Ye8GjMNeDJ8VGRmpSy+9VLt27dKNN96ooqIiZWVluezVyMzMVExMjCQpJiamzNmhTp2V6vQxZ56pKjMzU+Hh4QoKCpLFYpHFYil3zKltnI3VapXVWnaaiL+/v/z9/Sv2oMvBh+hzK7LxHJ2pMq83d7PYC88/qAaz2It4js7gjtdvRbfBweDe4NTejUdXSI355gLVJLyBdM+n0q3TKBk+Ki8vT7t371b9+vXVuXNn+fv7a/Hixc7lO3bs0L59+5SQUPq+k5CQoE2bNrmcHWrRokUKDw9X27ZtnWNO38apMae2ERAQoM6dO7uMsdvtWrx4sXMMAMA3UDS8yam9Gz3/Kflf/Hxk4Lwuv1d6LI2pUj7mb3/7m5YtW6a9e/dq5cqV+vOf/yyLxaK77rpLERERGjhwoFJTU/X9999r7dq1GjBggBISEnTllaUH/iclJalt27a67777tGHDBi1YsECjRo1SSkqKc0/Do48+qj179mj48OHavn27Xn/9dX3yySd66qk/rhWUmpqqt99+Wx988IG2bdumwYMHKz8/XwMGDDDkeQEAVA2mTnkbjt1AVeJYDJ+2f/9+3XXXXTp69Kjq1aunq6++Wj/++KPq1asnSXr11VdlNpvVt29fFRYWKjk5Wa+//rpzfYvFoq+//lqDBw9WQkKCQkJC1L9/fz333HPOMU2bNtW8efP01FNPafLkyWrYsKHeeecdJScnO8fceeedOnz4sMaMGaOMjAx17NhR8+fPL3OAOADAu5kcDofD6BC4SHY7x27AfTgWA14mJydHERERys7OrtTB4J2HfejGVL4jwFJ6Rq5/pnEw+JnWTrzf6AhO+56LMzqCR7KZrdoYN1rtNz3PMRpnaDxmU6W3UdH3X6ZOebNTezcG/yC1vdXoNPBWMXHSfXM5FgMAALgVU6d8Qe1m0l8+lH5bK333rJS+3OhE8Aa1mkrXj5Iu6yuZTEanAQAAPoai4UsadJb6fyXtXlJaOA5uMDoRPFFotNR9mNT5AcniOadoBAAAvoWi4YuaXy816yFt+UJa8oJ0bLfRieAJrOFStyekKx+TAkKMTgMAAHwcRcNXmUzSZbdJbf4krftAWjZBysswOhWMYLFKXQdJ1/xVCq5tdBoAAFBDUDR8ncVPumKg1OEuadUb0g+TpYJso1OhOpgspf/de4yUIhoanQYAANQwnHWqpggILv1G+4n10lVPSAGhRidCVTGZpTa3SINXSn2mUTIAAIAh2KNR0wTXlpKel64dLm34WFrzrnR4m9Gp4A7BdaVO90mdB0i1LjE6DQAAqOEoGjWVNax03n7XQdLeFdKad6RtX0v2YqOT4UI1ipeueEhq20fyCzA6DQAAgCSKBiSpydWlP7mZpQeOr50h5fxmdCqci3+IFHd7acGo397oNAAAAGVQNPCHsOjSKVXX/FXa8U3pXo49yyQ5jE6GU+peKnUZKHW8i6t4AwAAj0bRQFlmS+nBxG1ukY7skn56V1o/k7NVGcXsJ7W6qXTvRbNrjU4DAABQIRQNnFvdFlLP8VLis6V7N3Z8I/1vgZR7wOhkvi0gVGreo7RgtEyWQuoYnQgAAOCCUDRQMX5W6dKk0h+HQzq4XtrxbWnxyNhkdDrfEN5AurRnabloek3pcw4AAOClKBq4cCaTFHt56U+PZ6Ts/X+Ujr0rJFuR0Qm9R0z70mLRqpcU29HoNAAAAG5D0UDlRTT841S5hbnSrsWlxWPnQunkMaPTeRaLtXRvRate0qW9pIgGRicCAACoEhQNuJc1TGrXp/THbpeO/K90mtWBn6UD60unWRXnG5uxupgsUr3WpXsqYi+X6neUYi6T/IOMTgYAAFDlKBqoOmazFNW69KdDv9Lb7HbpyI7S0nFwve+Uj9NLRf2OpcWCUgEAAGowigaql9ksRbUp/el4V+ltdlvpno9T5SNzi5R7UMrNkIryjExbliVACo0pveZI3Ut/LxUdpZg4SgUAAMBpKBowntlStnycUpgn5WX+UTxyM6S8jNKrmOce/H1ZhlSYU7kMfoFSaLQUFlP6Expz2t+jpbD6pX8Prl25+wEAAKghKBrwbNbQ0p86zc89ruiEVJAl2Yole4nrj61EkqO00Jj9Sy+AZ/aTLL//GRAqBUVWw4MBAACoOSga8A0BwaU/AAAA8AhmowMAAAAA8D0UDQAAAABuR9EAAAAA4HYUDQAAAABuR9EAAAAA4HYUDQAAAABuR9EAAAAA4HYUDfiM5cuX65ZbblFsbKxMJpPmzp1rdCQAAIAai6IBn5Gfn68OHTpo2rRpRkcBAACo8bgyOHxGr1691KtXL6NjAAAAQOzRAAAAAFAFKBoAAAAA3I6iAQAAAMDtKBoAAAAA3I6iAQAAAMDtOOsUfEZeXp527drl/D09PV3r169X7dq11bhxYwOTAQAA1DwUDfiMn376ST169HD+npqaKknq37+/ZsyYYVAqAACAmomiAZ9x3XXXyeFwGB0DAAAA4hgNAAAAAFWAogEAAADA7SgaAAAAANyOogEAAADA7SgaAAAAANyOogEAAADA7SgaAAAAANyOogEAAADA7SgaAAAAANyOogEAAADA7SgaAAAAANyOogEAAADA7SgaAAAAANyOogEAAADA7SgaAADDTJs2TU2aNFFgYKDi4+O1evVqoyMBANyEogEAMMTs2bOVmpqqsWPHat26derQoYOSk5N16NAho6MBANyAogEAMMQrr7yiQYMGacCAAWrbtq2mT5+u4OBgvffee0ZHAwC4gZ/RAQAANU9RUZHWrl2rkSNHOm8zm81KTExUWlpauesUFhaqsLDQ+Xt2drYk6dixYyouLr7oLOaSkxe9ri8zO6QTJ/xlLjkps83oNJ7l6NGjRkdwyirio1x5bGazTpw4oawisyx2nqPThbjh9ZubmytJcjgc5xzHMw8AqHZHjhyRzWZTdHS0y+3R0dHavn17ueuMHz9e48aNK3N706ZNqyQjpJVGB/BQdSc9anQEVMjdRgfwTC/WddumcnNzFRERcdblFA0AgFcYOXKkUlNTnb/b7XYdO3ZMderUkclkMjCZb8rJyVGjRo3066+/Kjw83Og4wAXh9Vu1HA6HcnNzFRsbe85xFA0AQLWrW7euLBaLMjMzXW7PzMxUTExMuetYrVZZrVaX2yIjI6sqIn4XHh7OBzV4LV6/VedcezJO4WBwAEC1CwgIUOfOnbV48WLnbXa7XYsXL1ZCQoKByQAA7sIeDQCAIVJTU9W/f3916dJFXbt21aRJk5Sfn68BAwYYHQ0A4AYUDQCAIe68804dPnxYY8aMUUZGhjp27Kj58+eXOUAcxrBarRo7dmyZ6WqAN+D16xlMjvOdlwoAAAAALhDHaAAAAABwO4oGAAAAALejaAAAAABwO4oGAAAAALejaAAAABfTpk1TkyZNFBgYqPj4eK1evdroSECFLF++XLfccotiY2NlMpk0d+5coyPVaBQNAADgNHv2bKWmpmrs2LFat26dOnTooOTkZB06dMjoaMB55efnq0OHDpo2bZrRUSBObwsAAE4THx+vK664QlOnTpVUesX2Ro0a6fHHH9fTTz9tcDqg4kwmk7744gv16dPH6Cg1Fns0AACAJKmoqEhr165VYmKi8zaz2azExESlpaUZmAyAN6JoAAAASdKRI0dks9nKXJ09OjpaGRkZBqUC4K0oGgAAAADcjqIBAAAkSXXr1pXFYlFmZqbL7ZmZmYqJiTEoFQBvRdEAAACSpICAAHXu3FmLFy923ma327V48WIlJCQYmAyAN/IzOgAAAPAcqamp6t+/v7p06aKuXbtq0qRJys/P14ABA4yOBpxXXl6edu3a5fw9PT1d69evV+3atdW4cWMDk9VMnN4WAAC4mDp1qiZOnKiMjAx17NhRr732muLj442OBZzX0qVL1aNHjzK39+/fXzNmzKj+QDUcRQMAAACA23GMBgAAAAC3o2gAAAAAcDuKBgAAAAC3o2gAAAAAcDuKBgAAAAC3o2gAAAAAcDuKBgAAAAC3o2gAAAAAcDuKBgAAQBUwmUyaO3eu0TEAw1A0AAAALkJGRoYef/xxNWvWTFarVY0aNdItt9yixYsXGx0N8Ah+RgcAAADwNnv37lW3bt0UGRmpiRMnKi4uTsXFxVqwYIFSUlK0fft2oyMChmOPBgAAwAV67LHHZDKZtHr1avXt21eXXnqp2rVrp9TUVP3444/lrjNixAhdeumlCg4OVrNmzTR69GgVFxc7l2/YsEE9evRQWFiYwsPD1blzZ/3000+SpF9++UW33HKLatWqpZCQELVr107ffPNNtTxW4GKxRwMAAOACHDt2TPPnz9eLL76okJCQMssjIyPLXS8sLEwzZsxQbGysNm3apEGDBiksLEzDhw+XJN1zzz26/PLL9cYbb8hisWj9+vXy9/eXJKWkpKioqEjLly9XSEiItm7dqtDQ0Cp7jIA7UDQAAAAuwK5du+RwONS6desLWm/UqFHOvzdp0kR/+9vf9PHHHzuLxr59+zRs2DDndlu2bOkcv2/fPvXt21dxcXGSpGbNmlX2YQBVjqlTAAAAF8DhcFzUerNnz1a3bt0UExOj0NBQjRo1Svv27XMuT01N1UMPPaTExET985//1O7du53LnnjiCb3wwgvq1q2bxo4dq40bN1b6cQBVjaIBAABwAVq2bCmTyXRBB3ynpaXpnnvu0U033aSvv/5aP//8s/7+97+rqKjIOebZZ5/Vli1b1Lt3by1ZskRt27bVF198IUl66KGHtGfPHt13333atGmTunTpoilTprj9sQHuZHJcbC0HAACooXr16qVNmzZpx44dZY7TyMrKUmRkpEwmk7744gv16dNHL7/8sl5//XWXvRQPPfSQPv30U2VlZZV7H3fddZfy8/P1f//3f2WWjRw5UvPmzWPPBjwaezQAAAAu0LRp02Sz2dS1a1d99tln2rlzp7Zt26bXXntNCQkJZca3bNlS+/bt08cff6zdu3frtddec+6tkKSTJ09qyJAhWrp0qX755Rf98MMPWrNmjdq0aSNJGjp0qBYsWKD09HStW7dO33//vXMZ4Kk4GBwAAOACNWvWTOvWrdOLL76ov/71rzp48KDq1aunzp0764033igz/k9/+pOeeuopDRkyRIWFherdu7dGjx6tZ599VpJksVh09OhR3X///crMzFTdunV12223ady4cZIkm82mlJQU7d+/X+Hh4erZs6deffXV6nzIwAVj6hQAAAAAt2PqFAAAAAC3o2gAAAAAcDuKBgAAAAC3o2gAAAAAcDuKBgAAAAC3o2gAAAAAcDuKBgAAAAC3o2gAAAAAcDuKBgAAAAC3o2gAAAAAcDuKBgAAAAC3+3/yXLF0Gk5WlgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After applying SMOTE, the dataset is perfectly balanced."
      ],
      "metadata": {
        "id": "szkPCNNe820u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset preprocessing"
      ],
      "metadata": {
        "id": "JUxV5hNj2-YS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#split the dataset intro train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size = 0.2, random_state = 7)"
      ],
      "metadata": {
        "id": "xMktGCOV3Bnx"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71dzguku-Ujg",
        "outputId": "432ff297-f418-4845-dd78-88355488ed87"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(454904, 30)\n",
            "(454904,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8eME96rb-d0q",
        "outputId": "85ab410d-dd71-4268-e2c9-6c83e4ee03ae"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(113726, 30)\n",
            "(113726,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification algorithm"
      ],
      "metadata": {
        "id": "Wvy4VeYW-qiF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression(solver = 'liblinear', multi_class = 'ovr')\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "LnyYyj9g-v3T",
        "outputId": "6f1d2444-1e09-413b-fe91-3bd79f0bee85"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(multi_class='ovr', solver='liblinear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(multi_class=&#x27;ovr&#x27;, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(multi_class=&#x27;ovr&#x27;, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_pred_test = lr.predict(X_test)"
      ],
      "metadata": {
        "id": "wgQDqPah_GWL"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model evaluation"
      ],
      "metadata": {
        "id": "_R6Iosmw_KK9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import roc_auc_score, roc_curve"
      ],
      "metadata": {
        "id": "kDHUybPX_OX1"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_pred_train = lr.predict(X_train)"
      ],
      "metadata": {
        "id": "nYgbbjw0BbE2"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Train score and Test score of Logistic Regression\n",
        "print(\"Train accuracy: \", accuracy_score(y_train,lr_pred_train)*100)\n",
        "print(\"Test accuracy: \", accuracy_score(y_test, lr_pred_test)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFM_3MfC_PSh",
        "outputId": "9a64da39-5ef5-4b17-e81a-0faa4b04aac9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train accuracy:  97.1596204913564\n",
            "Test accuracy:  97.17215060760073\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_lr = confusion_matrix(y_test, lr_pred_test)\n",
        "TN, FP, FN, TP = cm_lr[0,0], cm_lr[0,1], cm_lr[1,0], cm_lr[1,1]"
      ],
      "metadata": {
        "id": "Vn2uVmp0B5NC"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(cm_lr, annot = True, fmt = 'd')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "bxuAacdzCIWK",
        "outputId": "87e2321d-bc4b-497e-dcd0-8a5eea1dabd2"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Precision score: \", precision_score(y_test, lr_pred_test)*100)\n",
        "print(\"Recall: \", recall_score(y_test, lr_pred_test)*100)\n",
        "print(\"F1-score: \", f1_score(y_test, lr_pred_test)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCxfUNJOCRJ_",
        "outputId": "4feb4878-359e-4511-aff7-35e51d6a50a8"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision score:  98.33450621627962\n",
            "Recall:  95.96224620965327\n",
            "F1-score:  97.13389419649224\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report :\\n\", classification_report(y_test, lr_pred_test, digits=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBJAUJSWCYjL",
        "outputId": "bab6efe7-6420-4e0a-84c4-7958481eba19"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9607    0.9838    0.9721     56937\n",
            "           1     0.9833    0.9596    0.9713     56789\n",
            "\n",
            "    accuracy                         0.9717    113726\n",
            "   macro avg     0.9720    0.9717    0.9717    113726\n",
            "weighted avg     0.9720    0.9717    0.9717    113726\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auc = roc_auc_score(y_test, lr_pred_test)\n",
        "\n",
        "print(\"ROC AUC score: \", auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iLEu01tRCdgh",
        "outputId": "64a90da6-aa07-408a-dde4-e38bb9f07e07"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC score:  0.9717057811650622\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, thresholds = roc_curve(y_test, lr_pred_test)\n",
        "plt.plot(fpr, tpr, color='orange', label='ROC')\n",
        "plt.plot([0,1], [0,1], color='darkblue', linestyle=\"--\", label=\"ROC curve (area = %0.2f)\" % auc)\n",
        "plt.xlabel(\"False positive rate\")\n",
        "plt.ylabel(\"True positive rate\")\n",
        "plt.title(\"ROC Curve of Logistic Regression Classifier\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "vfL1EBGTCqPe",
        "outputId": "f49cc080-098e-4042-92f3-8233f434127e"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusions"
      ],
      "metadata": {
        "id": "Zrli6tyTC3Bp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The logistic regression model achieved high precision and recall scores of 98.33% and 95.96%, respectively. The F1-score, which is a measure of the balance between precision and recall, was also high at 97.13%. The ROC AUC score, which measures the model's ability to distinguish between positive and negative classes, was 0.97. Overall, these results suggest that the model is performing well in identifying fraud cases while minimizing false positives and false negatives. "
      ],
      "metadata": {
        "id": "Sza90HonFgzm"
      }
    }
  ]
}