PaymentFraudDetector_Generated.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oukoSeyRLsot"
      },
      "source": [
        "**Fraud Detection Project**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "mPR9g2ZOLwBM"
      },
      "outputs": [],
      "source": [
        "#Load libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
        "from sklearn.model_selection import KFold, StratifiedKFold\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sOhOWK2uMCXv",
        "outputId": "575792b7-fcb7-47ef-a2ee-4d5bb5f45a0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fatal: destination path 'PaymentFraudDetector' already exists and is not an empty directory.\n"
          ]
        }
      ],
      "source": [
        "#Connect to GitHub for faster access\n",
        "!git clone https://github.com/ozyuzer/PaymentFraudDetector.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "PiVafxfSMME2"
      },
      "outputs": [],
      "source": [
        "# Upload the gendered datasets\n",
        "female = pd.read_csv(\"datasets/generated_datasets/adults_2550_female_urban.csv\", delimiter='|')\n",
        "male = pd.read_csv(\"datasets/generated_datasets/adults_2550_male_urban.csv\", delimiter='|')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "MesvwJARJo0c"
      },
      "outputs": [],
      "source": [
        "female = female.rename(columns={'merch_long,,': 'merch_long'})\n",
        "\n",
        "# Merge the dataframes\n",
        "df = pd.concat([female, male])\n",
        "\n",
        "df = df.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "myESotLgNQto"
      },
      "outputs": [],
      "source": [
        "df = df.drop(['merch_long'], axis=1)\n",
        "df = df.drop(['lat','long','zip','merch_lat'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxd4_9VrJ0cl",
        "outputId": "964c0921-ab3c-4a23-cf5e-f571d6ef724e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are no null values in the dataframe\n"
          ]
        }
      ],
      "source": [
        "# Check for null values\n",
        "null_df = df.isnull()\n",
        "\n",
        "# Check if there are any null values in the original dataframe\n",
        "if null_df.any().any():\n",
        "    print(\"There are null values in the dataframe\")\n",
        "else:\n",
        "    print(\"There are no null values in the dataframe\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "PWKkGoRjKDj4",
        "outputId": "0a4973ec-195d-4a23-a547-8a4898dfcfc9"
      },
      "outputs": [
        {
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              "      <td>814.04</td>\n",
              "      <td>1</td>\n",
              "      <td>fraud_Lynch Ltd</td>\n",
              "    </tr>\n",
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              "      <td>2022-12-12</td>\n",
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              "      <td>1670808833</td>\n",
              "      <td>gas_transport</td>\n",
              "      <td>969.16</td>\n",
              "      <td>1</td>\n",
              "      <td>fraud_Cummerata-Jones</td>\n",
              "    </tr>\n",
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              "      <td>115-04-4507</td>\n",
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              "      <td>shopping_pos</td>\n",
              "      <td>743.97</td>\n",
              "      <td>1</td>\n",
              "      <td>fraud_Dooley Inc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>791.05</td>\n",
              "      <td>1</td>\n",
              "      <td>fraud_Stoltenberg-Beatty</td>\n",
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              "      <td>kids_pets</td>\n",
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              "      <td>food_dining</td>\n",
              "      <td>9.89</td>\n",
              "      <td>0</td>\n",
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              "      <td>2022-06-08</td>\n",
              "      <td>13:51:13</td>\n",
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              "      <td>misc_pos</td>\n",
              "      <td>1203.06</td>\n",
              "      <td>0</td>\n",
              "      <td>fraud_Hermann-Gaylord</td>\n",
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              "      <td>Theatre manager</td>\n",
              "      <td>...</td>\n",
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              "      <td>adults_2550_male_urban.json</td>\n",
              "      <td>532d383268e8fa9de8720d59dc92d388</td>\n",
              "      <td>2022-12-19</td>\n",
              "      <td>14:14:45</td>\n",
              "      <td>1671455685</td>\n",
              "      <td>personal_care</td>\n",
              "      <td>2.44</td>\n",
              "      <td>0</td>\n",
              "      <td>fraud_Yost-Rogahn</td>\n",
              "    </tr>\n",
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              "      <td>521-67-0496</td>\n",
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              "      <td>Theatre manager</td>\n",
              "      <td>...</td>\n",
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              "      <td>adults_2550_male_urban.json</td>\n",
              "      <td>8e3f0ecf672b22642ba2d95791f0eca2</td>\n",
              "      <td>2022-05-26</td>\n",
              "      <td>20:42:24</td>\n",
              "      <td>1653590544</td>\n",
              "      <td>misc_pos</td>\n",
              "      <td>2.94</td>\n",
              "      <td>0</td>\n",
              "      <td>fraud_Turner, Ruecker and Parisian</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  "
            ],
            "text/plain": [
              "                ssn           cc_num   first     last gender  \\\n",
              "0       115-04-4507    4218196001337   Kathy  Johnson      F   \n",
              "1       115-04-4507    4218196001337   Kathy  Johnson      F   \n",
              "2       115-04-4507    4218196001337   Kathy  Johnson      F   \n",
              "3       115-04-4507    4218196001337   Kathy  Johnson      F   \n",
              "4       115-04-4507    4218196001337   Kathy  Johnson      F   \n",
              "...             ...              ...     ...      ...    ...   \n",
              "111959  521-67-0496  347507819735446  Thomas   Howard      M   \n",
              "111960  521-67-0496  347507819735446  Thomas   Howard      M   \n",
              "111961  521-67-0496  347507819735446  Thomas   Howard      M   \n",
              "111962  521-67-0496  347507819735446  Thomas   Howard      M   \n",
              "111963  521-67-0496  347507819735446  Thomas   Howard      M   \n",
              "\n",
              "                           street       city state  city_pop  \\\n",
              "0            863 Lawrence Valleys  Homosassa    FL     27123   \n",
              "1            863 Lawrence Valleys  Homosassa    FL     27123   \n",
              "2            863 Lawrence Valleys  Homosassa    FL     27123   \n",
              "3            863 Lawrence Valleys  Homosassa    FL     27123   \n",
              "4            863 Lawrence Valleys  Homosassa    FL     27123   \n",
              "...                           ...        ...   ...       ...   \n",
              "111959  777 King Tunnel Suite 964    Turlock    CA     77377   \n",
              "111960  777 King Tunnel Suite 964    Turlock    CA     77377   \n",
              "111961  777 King Tunnel Suite 964    Turlock    CA     77377   \n",
              "111962  777 King Tunnel Suite 964    Turlock    CA     77377   \n",
              "111963  777 King Tunnel Suite 964    Turlock    CA     77377   \n",
              "\n",
              "                          job  ...      acct_num  \\\n",
              "0       Accounting technician  ...  888022315787   \n",
              "1       Accounting technician  ...  888022315787   \n",
              "2       Accounting technician  ...  888022315787   \n",
              "3       Accounting technician  ...  888022315787   \n",
              "4       Accounting technician  ...  888022315787   \n",
              "...                       ...  ...           ...   \n",
              "111959        Theatre manager  ...   35413216676   \n",
              "111960        Theatre manager  ...   35413216676   \n",
              "111961        Theatre manager  ...   35413216676   \n",
              "111962        Theatre manager  ...   35413216676   \n",
              "111963        Theatre manager  ...   35413216676   \n",
              "\n",
              "                              profile                         trans_num  \\\n",
              "0       adults_2550_female_urban.json  59f060ee29e40979a362acdb04315f6e   \n",
              "1       adults_2550_female_urban.json  faec1d6969fc4ed9b5182be0d4a0f15b   \n",
              "2       adults_2550_female_urban.json  53fa6924f0c85c2a9eeee8e5a1f604fd   \n",
              "3       adults_2550_female_urban.json  9869951e93f3dcf1ce8f8a1677741c3e   \n",
              "4       adults_2550_female_urban.json  6e037c9b466baf60e97b7eb63bfe6e49   \n",
              "...                               ...                               ...   \n",
              "111959    adults_2550_male_urban.json  74c50c93ba6df3b74e0290102d87b1c9   \n",
              "111960    adults_2550_male_urban.json  1a2547ce36029c75aca5fcce440c3673   \n",
              "111961    adults_2550_male_urban.json  6fb3ba131d9bea78bed7cf78d3ca8fc7   \n",
              "111962    adults_2550_male_urban.json  532d383268e8fa9de8720d59dc92d388   \n",
              "111963    adults_2550_male_urban.json  8e3f0ecf672b22642ba2d95791f0eca2   \n",
              "\n",
              "        trans_date trans_time   unix_time       category      amt  is_fraud  \\\n",
              "0       2022-12-12   01:47:59  1670806079   shopping_net    10.13         1   \n",
              "1       2022-12-12   03:39:36  1670812776   shopping_pos   814.04         1   \n",
              "2       2022-12-12   02:33:53  1670808833  gas_transport   969.16         1   \n",
              "3       2022-12-13   03:36:20  1670898980   shopping_pos   743.97         1   \n",
              "4       2022-12-13   01:04:54  1670889894   shopping_pos   791.05         1   \n",
              "...            ...        ...         ...            ...      ...       ...   \n",
              "111959  2022-12-13   18:32:07  1670952727      kids_pets     3.30         0   \n",
              "111960  2022-09-18   23:05:58  1663535158    food_dining     9.89         0   \n",
              "111961  2022-06-08   13:51:13  1654689073       misc_pos  1203.06         0   \n",
              "111962  2022-12-19   14:14:45  1671455685  personal_care     2.44         0   \n",
              "111963  2022-05-26   20:42:24  1653590544       misc_pos     2.94         0   \n",
              "\n",
              "                                  merchant  \n",
              "0                  fraud_Fisher-Schowalter  \n",
              "1                          fraud_Lynch Ltd  \n",
              "2                    fraud_Cummerata-Jones  \n",
              "3                         fraud_Dooley Inc  \n",
              "4                 fraud_Stoltenberg-Beatty  \n",
              "...                                    ...  \n",
              "111959                   fraud_Ullrich Ltd  \n",
              "111960                  fraud_Ernser-Lynch  \n",
              "111961               fraud_Hermann-Gaylord  \n",
              "111962                   fraud_Yost-Rogahn  \n",
              "111963  fraud_Turner, Ruecker and Parisian  \n",
              "\n",
              "[111964 rows x 21 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlvXJMtUNjH2"
      },
      "source": [
        "Checking Imbalance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etlGBTqeNXDH",
        "outputId": "c5f9ee35-2541-4d2b-ff29-59fa2065fe6a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Class distribution in the dataset:\n",
            "No Frauds: 111095 (99.22%)\n",
            "Frauds: 869 (0.78%)\n"
          ]
        }
      ],
      "source": [
        "# Check the class balance in the dataset\n",
        "class_counts = df['is_fraud'].value_counts()\n",
        "no_frauds = class_counts[0]\n",
        "frauds = class_counts[1]\n",
        "total = len(df)\n",
        "no_frauds_pct = round(no_frauds / total * 100, 2)\n",
        "frauds_pct = round(frauds / total * 100, 2)\n",
        "\n",
        "# Print the class distribution\n",
        "print('Class distribution in the dataset:')\n",
        "print(f'No Frauds: {no_frauds} ({no_frauds_pct}%)')\n",
        "print(f'Frauds: {frauds} ({frauds_pct}%)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "IzdKgPmwL-MF"
      },
      "outputs": [],
      "source": [
        "y = df['is_fraud']\n",
        "df = df.drop('is_fraud', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3M35hhywKTE1"
      },
      "source": [
        "PCA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "TxT34iVnKSk3",
        "outputId": "5fac2417-d0ec-45f6-fc36-be72f92c2e05"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Explained Variance Ratio:\n",
            "[0.22244938 0.21148036 0.19851167 0.18842897 0.17912961]\n",
            "Cumulative Explained Variance Ratio:\n",
            "[0.22244938 0.43392975 0.63244141 0.82087039 1.        ]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Assuming you have a DataFrame named 'df' containing your data\n",
        "\n",
        "# Step 1: Data Preparation\n",
        "# Extract the numeric features from the DataFrame\n",
        "numeric_features = df.select_dtypes(include=[np.number])\n",
        "\n",
        "# Step 2: Standardize the dataset\n",
        "standardized_data = (numeric_features - numeric_features.mean()) / numeric_features.std()\n",
        "\n",
        "# Step 3: Perform PCA\n",
        "pca = PCA()\n",
        "pca.fit(standardized_data)\n",
        "\n",
        "# Step 4: Determine the optimal number of components\n",
        "explained_variance_ratio = pca.explained_variance_ratio_\n",
        "cumulative_variance_ratio = np.cumsum(explained_variance_ratio)\n",
        "n_components = np.argmax(cumulative_variance_ratio >= 0.99) + 1\n",
        "\n",
        "# Plot the cumulative explained variance ratio\n",
        "plt.plot(range(1, len(cumulative_variance_ratio) + 1), cumulative_variance_ratio, marker='o')\n",
        "plt.xlabel('Number of Components')\n",
        "plt.ylabel('Cumulative Explained Variance Ratio')\n",
        "plt.title('Cumulative Explained Variance Ratio')\n",
        "plt.show()\n",
        "\n",
        "# Step 5: Perform PCA with the selected number of components\n",
        "pca = PCA(n_components=n_components)\n",
        "pca_data = pca.fit_transform(standardized_data)\n",
        "\n",
        "# Step 6: Interpret the results\n",
        "# Print the explained variance ratio of each principal component\n",
        "print(\"Explained Variance Ratio:\")\n",
        "print(explained_variance_ratio)\n",
        "\n",
        "# Print the cumulative explained variance ratio\n",
        "print(\"Cumulative Explained Variance Ratio:\")\n",
        "print(cumulative_variance_ratio)\n",
        "\n",
        "# Optionally, you can access the principal components themselves\n",
        "principal_components = pd.DataFrame(\n",
        "    data=pca.components_,\n",
        "    columns=standardized_data.columns,\n",
        "    index=[f\"PC{i+1}\" for i in range(n_components)]\n",
        ")\n",
        "\n",
        "# The transformed data after PCA can be stored in a DataFrame\n",
        "pca_df = pd.DataFrame(data=pca_data, columns=[f\"PC{i+1}\" for i in range(n_components)])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "JUq8o1uVKhZp",
        "outputId": "25b284a1-2cab-410d-bbb5-8178dd1e4707"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "      <th>PC3</th>\n",
              "      <th>PC4</th>\n",
              "      <th>PC5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.368348</td>\n",
              "      <td>-1.318356</td>\n",
              "      <td>0.020816</td>\n",
              "      <td>0.868969</td>\n",
              "      <td>-1.143343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.272501</td>\n",
              "      <td>2.103935</td>\n",
              "      <td>-0.010257</td>\n",
              "      <td>4.338697</td>\n",
              "      <td>-0.336384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.446994</td>\n",
              "      <td>2.764690</td>\n",
              "      <td>-0.016375</td>\n",
              "      <td>5.007816</td>\n",
              "      <td>-0.180741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.193192</td>\n",
              "      <td>1.799007</td>\n",
              "      <td>-0.005509</td>\n",
              "      <td>4.042715</td>\n",
              "      <td>-0.405638</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.246198</td>\n",
              "      <td>2.000154</td>\n",
              "      <td>-0.007552</td>\n",
              "      <td>4.245211</td>\n",
              "      <td>-0.358497</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>111959</th>\n",
              "      <td>-1.689884</td>\n",
              "      <td>-1.024013</td>\n",
              "      <td>0.431430</td>\n",
              "      <td>0.513764</td>\n",
              "      <td>1.192083</td>\n",
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              "    <tr>\n",
              "      <th>111960</th>\n",
              "      <td>-1.639595</td>\n",
              "      <td>-0.428811</td>\n",
              "      <td>0.256914</td>\n",
              "      <td>-0.008590</td>\n",
              "      <td>1.106236</td>\n",
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              "    <tr>\n",
              "      <th>111961</th>\n",
              "      <td>-0.246450</td>\n",
              "      <td>5.327709</td>\n",
              "      <td>0.002742</td>\n",
              "      <td>4.483603</td>\n",
              "      <td>2.193540</td>\n",
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              "    <tr>\n",
              "      <th>111962</th>\n",
              "      <td>-1.693759</td>\n",
              "      <td>-1.066131</td>\n",
              "      <td>0.443280</td>\n",
              "      <td>0.547400</td>\n",
              "      <td>1.197489</td>\n",
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              "      <th>111963</th>\n",
              "      <td>-1.589926</td>\n",
              "      <td>0.301958</td>\n",
              "      <td>0.023556</td>\n",
              "      <td>-0.777022</td>\n",
              "      <td>0.975299</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>111964 rows × 5 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      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-5e17b996-597a-4d8a-85ac-0bbe7a2cd178 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-5e17b996-597a-4d8a-85ac-0bbe7a2cd178');\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",
              "  "
            ],
            "text/plain": [
              "             PC1       PC2       PC3       PC4       PC5\n",
              "0       0.368348 -1.318356  0.020816  0.868969 -1.143343\n",
              "1       1.272501  2.103935 -0.010257  4.338697 -0.336384\n",
              "2       1.446994  2.764690 -0.016375  5.007816 -0.180741\n",
              "3       1.193192  1.799007 -0.005509  4.042715 -0.405638\n",
              "4       1.246198  2.000154 -0.007552  4.245211 -0.358497\n",
              "...          ...       ...       ...       ...       ...\n",
              "111959 -1.689884 -1.024013  0.431430  0.513764  1.192083\n",
              "111960 -1.639595 -0.428811  0.256914 -0.008590  1.106236\n",
              "111961 -0.246450  5.327709  0.002742  4.483603  2.193540\n",
              "111962 -1.693759 -1.066131  0.443280  0.547400  1.197489\n",
              "111963 -1.589926  0.301958  0.023556 -0.777022  0.975299\n",
              "\n",
              "[111964 rows x 5 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pca_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_R2wHa8-Tcmu"
      },
      "source": [
        "This code splits the data into features and target variable, uses StratifiedKFold to generate training and testing sets, and then checks the distribution of the target variable in each set. Finally, it prints the percentage of non-fraudulent and fraudulent transactions in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIWQDf0qTB9J",
        "outputId": "7e7415df-d0c6-410c-d00e-a11f79479971"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No Frauds 99.22 % of the dataset\n",
            "Frauds 0.78 % of the dataset\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Label Distributions: \n",
            "\n",
            "[0.99222971 0.00777029]\n",
            "[0.99227403 0.00772597]\n"
          ]
        }
      ],
      "source": [
        "# Split data into features and target variable\n",
        "X = pca_df\n",
        "\n",
        "# Use StratifiedKFold to split data into five folds\n",
        "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
        "\n",
        "# Iterate over each fold and generate training and testing sets\n",
        "for train_index, test_index in sss.split(X, y):\n",
        "    original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n",
        "    original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "# Convert training and testing sets to arrays and check distribution of target variable\n",
        "original_Xtrain = original_Xtrain.values\n",
        "original_Xtest = original_Xtest.values\n",
        "original_ytrain = original_ytrain.values\n",
        "original_ytest = original_ytest.values\n",
        "train_counts_label = np.unique(original_ytrain, return_counts=True)[1]\n",
        "test_counts_label = np.unique(original_ytest, return_counts=True)[1]\n",
        "\n",
        "# Print percentage of non-fraudulent and fraudulent transactions in dataset\n",
        "print('No Frauds', round(y.value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
        "print('Frauds', round(y.value_counts()[1]/len(df) * 100,2), '% of the dataset')\n",
        "\n",
        "# Print distribution of target variable in training and testing sets\n",
        "print('-' * 100)\n",
        "print('Label Distributions: \\n')\n",
        "print(train_counts_label/ len(original_ytrain))\n",
        "print(test_counts_label/ len(original_ytest))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vneAny_H1ku0"
      },
      "source": [
        "Function for plotting confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "1ZKQ1z9C1oZL"
      },
      "outputs": [],
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues,\n",
        "                          ax=None):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    if ax is None:\n",
        "        fig, ax = plt.subplots()\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    ax.set_title(title)\n",
        "\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    ax.set_xticks(tick_marks)\n",
        "    ax.set_xticklabels(classes, rotation=45)\n",
        "    ax.set_yticks(tick_marks)\n",
        "    ax.set_yticklabels(classes)\n",
        "    ax.grid(False)\n",
        "\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        ax.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    ax.set_ylabel('True label')\n",
        "    ax.set_xlabel('Predicted label')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OmyC_Xy5U2md"
      },
      "source": [
        "# **Undersampling**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "yPrb-D4dU_ft"
      },
      "outputs": [],
      "source": [
        "# Apply undersampling to balance the dataset\n",
        "undersampler = RandomUnderSampler(random_state=4521)\n",
        "X_train_und, y_train_und = undersampler.fit_resample(original_Xtrain, original_ytrain)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76Mf_v14xGPZ"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pI_gEqMGw3mJ",
        "outputId": "ed9db657-6584-48e9-f8ea-02f3660d74c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.96      0.98     22219\n",
            "           1       0.14      0.75      0.23       173\n",
            "\n",
            "    accuracy                           0.96     22392\n",
            "   macro avg       0.57      0.86      0.61     22392\n",
            "weighted avg       0.99      0.96      0.97     22392\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Create a logistic regression model and fit it on the training data\n",
        "lr_model = LogisticRegression()\n",
        "lr_model.fit(X_train_und, y_train_und)\n",
        "\n",
        "# Predict the target variable on the testing data\n",
        "y_pred_lr_und = lr_model.predict(original_Xtest)\n",
        "\n",
        "# Print the classification report to evaluate the model performance\n",
        "print(classification_report(original_ytest, y_pred_lr_und))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "7hFO7RbFy-ZC",
        "outputId": "21a742f9-f148-458c-d439-15aadab0a246"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[21395   824]\n",
            " [   43   130]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create a confusion matrix\n",
        "cm_und = confusion_matrix(original_ytest, y_pred_lr_und)\n",
        "\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plot_confusion_matrix(cm_und,labels,title=\"Confusion Matrix\",cmap=plt.cm.Greens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "otyd_9pqVA95"
      },
      "source": [
        "# **Oversampling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u61oIyfW3rkw"
      },
      "source": [
        "by SMOTE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roLuRhzW3tYe"
      },
      "source": [
        "Synthetic Minority Over-sampling Technique (SMOTE) for oversampling in Python. SMOTE is a popular oversampling technique that generates synthetic samples for the minority class by interpolating between neighboring instances."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "RPsYK1vkVDz1"
      },
      "outputs": [],
      "source": [
        "# Instantiate the SMOTE oversampler\n",
        "oversampler = SMOTE(random_state=42)\n",
        "\n",
        "# Fit and transform the data using the oversampler\n",
        "X_train_over, y_train_over = oversampler.fit_resample(original_Xtrain, original_ytrain)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPIKbTYo4W1d"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "urwwY3mQ4Gm0",
        "outputId": "90ae579c-b1e5-4540-8341-fe4fa3505ba4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.96      0.98     22219\n",
            "           1       0.14      0.75      0.24       173\n",
            "\n",
            "    accuracy                           0.96     22392\n",
            "   macro avg       0.57      0.86      0.61     22392\n",
            "weighted avg       0.99      0.96      0.97     22392\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Create a logistic regression model and fit it on the training data\n",
        "lr_model = LogisticRegression()\n",
        "lr_model.fit(X_train_over, y_train_over)\n",
        "\n",
        "# Predict the target variable on the testing data\n",
        "y_pred_lr_over = lr_model.predict(original_Xtest)\n",
        "\n",
        "# Print the classification report to evaluate the model performance\n",
        "print(classification_report(original_ytest, y_pred_lr_over))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "whaQD_EK4ZdU",
        "outputId": "63bddf2d-8aa6-4aa4-ecc9-7277467b4ec3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[21421   798]\n",
            " [   43   130]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create a confusion matrix\n",
        "cm_over = confusion_matrix(original_ytest, y_pred_lr_over)\n",
        "\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "# Plot the confusion matrix\n",
        "plot_confusion_matrix(cm_over,labels,title=\"Confusion Matrix\",cmap=plt.cm.Greens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5gzJM0J44ir"
      },
      "source": [
        "# **Neural Networks**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZOYc_4-8NYe"
      },
      "source": [
        "Undersampling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "Pi6wfQN08Q0S",
        "outputId": "d9bb8452-a50d-4909-c9b7-49d7713d45bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "313/313 [==============================] - 2s 3ms/step - loss: 0.5614 - accuracy: 0.7436 - val_loss: 0.3647 - val_accuracy: 0.7857\n",
            "Epoch 2/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.5223 - accuracy: 0.7796 - val_loss: 0.4091 - val_accuracy: 0.7857\n",
            "Epoch 3/10\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.5125 - accuracy: 0.7804 - val_loss: 0.4125 - val_accuracy: 0.7786\n",
            "Epoch 4/10\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.5120 - accuracy: 0.7859 - val_loss: 0.3386 - val_accuracy: 0.8071\n",
            "Epoch 5/10\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.5005 - accuracy: 0.7835 - val_loss: 0.3881 - val_accuracy: 0.7786\n",
            "Epoch 6/10\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.5018 - accuracy: 0.7788 - val_loss: 0.3746 - val_accuracy: 0.8071\n",
            "Epoch 7/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4962 - accuracy: 0.7835 - val_loss: 0.3787 - val_accuracy: 0.7786\n",
            "Epoch 8/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.5010 - accuracy: 0.7875 - val_loss: 0.3418 - val_accuracy: 0.8071\n",
            "Epoch 9/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4952 - accuracy: 0.7772 - val_loss: 0.3648 - val_accuracy: 0.8000\n",
            "Epoch 10/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4851 - accuracy: 0.7971 - val_loss: 0.4381 - val_accuracy: 0.7786\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tensorflow.keras.layers import Dropout\n",
        "\n",
        "# Define the neural network model with dropout layers\n",
        "model_und = Sequential()\n",
        "model_und.add(Dense(64, activation='relu', input_dim=X_train_over.shape[1]))\n",
        "model_und.add(Dropout(0.2))\n",
        "model_und.add(Dense(64, activation='relu'))\n",
        "model_und.add(Dropout(0.2))\n",
        "model_und.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Compile the model with binary crossentropy and Adam optimizer\n",
        "model_und.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model with 10 epochs, batch size of 4, and validation split of 0.1\n",
        "history = model_und.fit(X_train_und, y_train_und, epochs=10, batch_size=4, validation_split=0.1)\n",
        "\n",
        "# Plot the training and validation accuracy over time\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "9vTXiC1m8euo",
        "outputId": "3de5ab07-28f2-427f-81d8-8e0523e7418e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "700/700 [==============================] - 1s 1ms/step\n",
            "Confusion matrix, without normalization\n",
            "[[21560   659]\n",
            " [   50   123]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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3mN7q3F1xcXH3eleytRymCwDSq2aFapr431n6Zf8u5XB21rDX+um7D+epbIfaunz1iiQpt6ubvv1lvb79Zb0+7ND/pmMNmDlS076ZZ/988YrjL6BxXQapfpUa6v3pYO0+sk95PbyV18PbvjzQ118rh8zSlJVz1PbDt1Tn0eqa3nOkos+f0XfbN2TsjgP/7++/L6herQZ6uubTWrx8ofLnz6eoQ4fl7e3t0K9eg7qaPG2i/bOLq6v939Eno/VCwyZq9lIzfTx2pC5evKi+vfqrU/su+uKr2fdqV7I9QhgPnGfffdnhc+jIHvpr4a+qUrKCfty9TZI0bslnkq4H9q1cvByn03//dcNlDxcNUufnX1G5jnV04MRhSdLRU8cd+nRq9IqOnDqm3lMHS5L2HTuk6o88rh7NOhLCyDRjRo5VkYce0pTpk+xtgcUCU/VzcXFRId9CNxxj1TerlSNnTo0e/7GcnK4fFB33yWg9WSVYUYcOq0RQ8Ruuh4zF4Wg88FIONZ+/eCHd6/Zr9abOLtqtHZO/Ve+XOsnZydm+7Pkn6+lw9DE1erKuDs/eoiNztmpaz5Hy+cdMuFqZylq7c5PDmKsjNqha2cp3tC9AWnyzYpUqV6mkV1qFqFiRIAU//rRmfDYrVb9NGzepWJEgPfrIY3q7a0+dO3feviwhPl4uLi72AJYkt1y5JElbt2zN/J2AJEL4npg5c2aqw0TIGDabTWM7h2nTbz9rz9H96Vp3/NLP1Wrom6rdu4WmrvxC77buqhEd37MvL164qAIKFdFLNRrp1RFvK3RkT1UpWV4LB0y19/HNWzDVTPr032fl5e4pNxe3u9s54CaOHjmq6VM/V4mgElq6YpHav9Fe7/Toq7mz/3dqpV79upr6+RSt+HaZBg0L06aNm/Xi882VlJQkSapZq4ZOnzqtsaPGKyEhQX//fUEfvBcmSToVfdrEbmVLRkM4NDRUNptNH374oUP70qVLZbPZ7mrsmTNnymazpfqaPn36XY2L+8vEt4aqXGBptRqa+oKq2xmzaJo2/LpVu4/s1dQVX6jX1MF6q0k7ueR0kSQ52Zzk5uKmVz/qrk2//awNv25V+1G99cyjwakuBAPupeTkZFV8tKLChgxUxUcr6rUOoQpt/6o+mzbD3qd5yxf13PP/0SPlH9HzjRtpwdKvFLF9h37ccP3ITZlHymjqZ5M1YewnKuhVWEH+pRRQLEAFCxV0mB0jcxn/Tru5uemjjz7S33//neFje3p6Kjo62uGrbdu2qfolJCRk+LaR+SZ0HaJGVeuqdp8W+vNs9F2Pt23fTuXMkVOBhR6SJEWfP6PEa4k6+OcRe5+9xw5JkooWLCJJOnX+jAr5FHAYp5BPfsVcitXVhKt3XRNwI76FC+nhMqUd2ko/XFonjp+46TrFigcqX/58Ohx12N7WovVLijp+QAeO7tUfpw7r3QH9dPavsyp2g/PLyBzGQ7hu3bry9fXV8OHDb9lv0aJFeuSRR+Tq6qrAwECNGjXqtmPbbDb5+vo6fOXKlUthYWGqVKmSpk+frmLFisnN7fphw2+//VbVq1eXt7e38uXLp0aNGikqKso+3vr162Wz2XThwgV7W2RkpGw2m44ePWpvmzlzpooWLarcuXOradOmOnfuXPq+KbitCV2HqGlwQz3zTstUF0vdqUolHlFSUpLOXLj+32vznl+UM0dOFS8cYO9T6qFikqQ/Tl//Zbd17w7VebS6wzj1KtfQ1t93ZEhNwI08We1JHTxwyKHt0MFD8i/qf9N1/jzxp86fO3/DC7UKFiqoPHnyaNGCxXJzc1PturUyumTchPEQdnZ21rBhwzRhwgSdOHHjv+IiIiLUokULtWrVSrt371ZYWJgGDBigmTNn3vF2Dx06pEWLFmnx4sWKjIyUJF26dEk9e/bU9u3b9f3338vJyUlNmzZVcnJymsfdtm2b2rdvr65duyoyMlK1a9fWkCFD7rhOpDbxraF6uU5TtRneVRcvx6mQTwEV8ingcA62kE8BVSxRVkFFAiVJ5Ys9rIolytovqnqyTGV1b9peFYqXUTHfomrzTFON6fSBvvh+sS7ExUiS1u74UREHftXnvUepUolHVLlkeU19+yN9F7HBPjuesmKOivsW1Ucd3lNp/xLq/PyralGzkcYsnnZPvyfIXt7s3kW/bPtFIz8cpahDh/X1lws0Y/osvd6pgyQpLi5O7/UboJ+3/aI/jv6h9es2qNWLbVS8RHHVrV/HPs7USZ8qcmekDh44pE8nT1Pv7u8obMhArmG5h+6LW5SaNm2qSpUq6YMPPtBnn32Wavno0aNVp04dDRgwQJJUqlQp/f777xo5cqRCQ0NvOm5MTIzy5Mlj/5wnTx6dOnVK0vVD0LNnz1aBAv87lPjii443sn/++ecqUKCAfv/9d5UrVy5N+zJu3Dg1bNhQ77zzjr3WLVu26Ntvv73pOvHx8YqPj7d/jo2NTdO2sqsuL4RIkjaMWujQHjqyh2Z9t0DS9VuHwl7taV/245jFDn3iExPUqnZjhb3aU645XXXk1DGNWTxNoxf9Lzwty9LzA9tpwpuDtXH0Il26elmrfvlBvf7/diTp+i1Lz70fojGdP1D3pq/pxNlodRjdh9uTkKmqPFZZ8xZ8obD3B+mjoSMUEBigD0cNV8s2LSRdn9zs2b1H8+Z8qZgLMSrs56tn6j6jAWHvyfUf9wpH/BKhoYOG61LcJZUqXVLjJo5R65dbmdqtbOm+CGFJ+uijj/TMM8+od+/eqZbt3btXjRs3dmgLDg7W2LFjlZSUJGdn51TrSJKHh4d27PjfYcF/XmwQEBDgEMCSdPDgQQ0cOFDbtm3T2bNn7TPgY8eOpTmE9+7dq6ZNmzq0VatW7ZYhPHz4cIWHh6dpfEi2eg/dtk/4nNEKnzP6pst3HvpN1bq9cNtxos+dVvNBr9+yz4Zft6py54a3HQvISM8+11DPPnfjn7tcuXJp6crFtx3j0xlTb9sHmcv44egUNWrUUIMGDdS//82fbpReTk5OCgoKsn8VL/6/K1rd3d1T9X/++ed1/vx5TZs2Tdu2bdO2bdcf/JBy4VZKiFuWZV8nMTHxruvs37+/YmJi7F/Hj2fMOU4AwP3tvpkJS9KHH36oSpUqqXRpx6v+ypQpo82bNzu0bd68WaVKlbrpLDi9zp07p/3792vatGl6+umnJUmbNjk+hCFl5hwdHS0fHx9Jsp9P/metKeGd4qeffrrltl1dXR0OEQEAsof7KoTLly+vtm3bavz48Q7tvXr10uOPP67BgwerZcuW2rp1qz755BNNmjTpJiOln4+Pj/Lly6dPP/1UhQsX1rFjx9SvXz+HPkFBQfL391dYWJiGDh2qAwcOpLpKu1u3bgoODtbHH3+sxo0ba/Xq1bc8FA0AyL7um8PRKQYNGpTqauTKlSvr66+/1vz581WuXDkNHDhQgwYNuuVFWenl5OSk+fPnKyIiQuXKlVOPHj00cuRIhz45c+bUl19+qX379qlChQr66KOPUl35/OSTT2ratGkaN26cKlasqO+++07vv/9+htUJAMg6bNY/T3DivhAbGysvLy+pVmEpx333dxKQIS5+s8d0CUCmiI2NVZH8RRUTEyNPz9SvUf0nfsMDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCE50tLpv//9b5oHfOGFF+64GAAAspM0hXCTJk3SNJjNZlNSUtLd1AMAQLaRphBOTk7O7DoAAMh27uqc8NWrVzOqDgAAsp10h3BSUpIGDx6sIkWKKE+ePDp8+LAkacCAAfrss88yvEAAALKqdIfw0KFDNXPmTI0YMUIuLi729nLlymn69OkZWhwAAFlZukN49uzZ+vTTT9W2bVs5Ozvb2ytWrKh9+/ZlaHEAAGRl6Q7hP//8U0FBQanak5OTlZiYmCFFAQCQHaQ7hMuWLasff/wxVfvChQv16KOPZkhRAABkB2m6RemfBg4cqJCQEP35559KTk7W4sWLtX//fs2ePVsrVqzIjBoBAMiS0j0Tbty4sZYvX661a9fK3d1dAwcO1N69e7V8+XLVq1cvM2oEACBLSvdMWJKefvpprVmzJqNrAQAgW7mjEJak7du3a+/evZKunyeuUqVKhhUFAEB2kO4QPnHihFq3bq3NmzfL29tbknThwgU99dRTmj9/vh566KGMrhEAgCwp3eeEO3TooMTERO3du1fnz5/X+fPntXfvXiUnJ6tDhw6ZUSMAAFlSumfCGzZs0JYtW1S6dGl7W+nSpTVhwgQ9/fTTGVocAABZWbpnwv7+/jd8KEdSUpL8/PwypCgAALKDdIfwyJEj9dZbb2n79u32tu3bt6t79+76+OOPM7Q4AACyMptlWdbtOvn4+Mhms9k/X7p0SdeuXVOOHNePZqf8293dXefPn8+8arOJ2NhYeXl5SbUKSznu6m2TwH3r4jd7TJcAZIrY2FgVyV9UMTEx8vT0vGXfNJ0THjt2bEbUBQAA/iFNIRwSEpLZdQAAkO3c8cM6JOnq1atKSEhwaLvd1BsAAFyX7hOOly5dUteuXVWwYEG5u7vLx8fH4QsAAKRNukP4nXfe0bp16zR58mS5urpq+vTpCg8Pl5+fn2bPnp0ZNQIAkCWl+3D08uXLNXv2bNWqVUvt2rXT008/raCgIAUEBGju3Llq27ZtZtQJAECWk+6Z8Pnz51W8eHFJ18//ptySVL16dW3cuDFjqwMAIAtLdwgXL15cR44ckSQ9/PDD+vrrryVdnyGnvNABAADcXrpDuF27dtq1a5ckqV+/fpo4caLc3NzUo0cP9enTJ8MLBAAgq0rTE7Nu5Y8//lBERISCgoJUoUKFjKorW+OJWcgOeGIWsqoMf2LWrQQEBCggIOBuhwEAINtJUwiPHz8+zQN269btjosBACA7SVMIjxkzJk2D2Ww2QhgAgDRKUwinXA2Ne+v04h08BhQAHjA5nHKmuS9X/QAAYAghDACAIYQwAACGEMIAABhCCAMAYMgdhfCPP/6ol19+WdWqVdOff/4pSZozZ442bdqUocUBAJCVpTuEFy1apAYNGihXrlzauXOn4uPjJUkxMTEaNmxYhhcIAEBWle4QHjJkiKZMmaJp06YpZ87/3QsVHBysHTt2ZGhxAABkZekO4f3796tGjRqp2r28vHThwoWMqAkAgGwh3SHs6+urQ4cOpWrftGmTihcvniFFAQCQHaQ7hDt27Kju3btr27ZtstlsOnnypObOnavevXurc+fOmVEjAABZUrpfZdivXz8lJyerTp06unz5smrUqCFXV1f17t1bb731VmbUCABAlmSzLMu6kxUTEhJ06NAhxcXFqWzZssqTJ09G15ZtxcbGysvLS6fPR/MCBwB4wMTGxqpQ3sKKiYm57e/wdM+EU7i4uKhs2bJ3ujoAANleukO4du3astlsN12+bt26uyoIAIDsIt0hXKlSJYfPiYmJioyM1G+//aaQkJCMqgsAgCwv3SE8ZsyYG7aHhYUpLi7urgsCACC7yLAXOLz88sv6/PPPM2o4AACyvAwL4a1bt8rNzS2jhgMAIMtL9+HoZs2aOXy2LEvR0dHavn27BgwYkGGFAQCQ1aU7hL28vBw+Ozk5qXTp0ho0aJDq16+fYYUBAJDVpSuEk5KS1K5dO5UvX14+Pj6ZVRMAANlCus4JOzs7q379+rwtCQCADJDuC7PKlSunw4cPZ0YtAABkK+kO4SFDhqh3795asWKFoqOjFRsb6/AFAADSJs0vcBg0aJB69eolDw+P/638j8dXWpYlm82mpKSkjK8ym+EFDgDw4ErPCxzSHMLOzs6Kjo7W3r17b9mvZs2aaa8UN0QIA8CDK1PeopSS1YQsAAAZI13nhG/19iQAAJA+6bpPuFSpUrcN4vPnz99VQQAAZBfpCuHw8PBUT8wCAAB3Jl0h3KpVKxUsWDCzagEAIFtJ8zlhzgcDAJCx0hzCabyTCQAApFGaD0cnJydnZh0AAGQ76X5sJQAAyBiEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggjWxgSPlS5crg7fFV85FH78qtXr+rtt3qoSEF/5fcqqFYvtdHp06cNVgzc2qaNm/Ri4+Yq5l9CuXK467/LltuXJSYm6r1+7+uxSo8rn2cBFfMvofahHXTyZLTDGM2bvKSSxUrL2z2vij1UXK+FtE/VB5mLEEa2UfaRMjpyIsr+9f2GNfZl7/Tqq5UrvtHc+XP03brVij4ZrVbN2xisFri1S5cuqXyF8ho7YUyqZZcvX1bkzkj1e6+ftv6yWfMXfKkD+w/qpaYvOfSrUauGvvhyjnb9Hql5X8/T4agjatOi7b3aBUiyWZZlmS4CjmJjY+Xl5aXT56Pl6elpupwsYUj4UC3/73Jti/gp1bKYmBj5+wZo5hcz1OzFppKk/fv2q1K5ylq/6QdVffKJe10ukC65crjrq0Xz9ULj52/aZ/svEXq6Wg3tP7xPRYv637DPiuUr1aJZS8Vc/ls5c+bMrHKzvNjYWBXKW1gxMTG3/R3OTBjZxqGDUSrmX0JlSj6i0Ffa6dix45KknRE7lZiYqGfq1Lb3Lf1wafkX9de2n7aZKhfIULExMbLZbPL29rrh8vPnz2v+vK/0ZLUnCeB7iBDOZKGhoWrSpInpMrK9x594TJ9+PlX/XblU4z8Zq6NH/lDdWvV08eJFnTp9Wi4uLvL29nZYp2DBgjp9ivPCePBdvXpV7787QC1avZRqZvZev/eVz7OAihT01/Fjx7VgyVeGqsyesmwIh4aGymazpfo6dOiQ6dJgQINnG+jF5s1UvkJ51WtQT0tXLFbMhRgtWrDYdGlApkpMTNTLrV6RZVkaP3FcquU9er+tn7Zv0YpV/5Wzs7M6hHYUZynvnRymC8hMDRs21IwZMxzaChQo4PA5ISFBLi4u97Is3Ae8vb0VVCpIUYeiVKfuM0pISNCFCxccZsNnzpxRId9C5ooE7lJiYqLatnpFx44d06o139zw/GT+/PmVP39+lSxVUqXLPKySgaW07aef9WS1qgYqzn6y7ExYklxdXeXr6+vwVadOHXXt2lVvv/228ufPrwYNGkiSRo8erfLly8vd3V3+/v7q0qWL4uLi7GOFhYWpUqVKDuOPHTtWgYGB9s9JSUnq2bOnvL29lS9fPr3zzjv8RXmfiouL05GoI/It7KtHqzyqnDlz6od16+3LD+w/oOPHjqvqk/wiwoMpJYCjDh3SytUrlC9fvtuuk5ycLElKiI/P7PLw/7L0TPhmZs2apc6dO2vz5s32NicnJ40fP17FihXT4cOH1aVLF73zzjuaNGlSmscdNWqUZs6cqc8//1xlypTRqFGjtGTJEj3zzDO3XC8+Pl7x//ihj42NTf9O4Zb69emv5xr9R0UDiurkyWgNCR8iZ2dntWj1kry8vBT6Woj69u6nvD4+8vD0VM/uvVT1yapcGY37VlxcnKIORdk/Hz1yVLsid8knb14VLuyrNi3aaufOSC1etlBJSUk6deqUJClv3rxycXHRz9t+UcT2CD0VXE3ePj46EnVY4R8MVvESxVWVWfA9k6VDeMWKFcqTJ4/987PPPitJKlmypEaMGOHQ9+2337b/OzAwUEOGDFGnTp3SFcJjx45V//791axZM0nSlClTtHr16tuuN3z4cIWHh6d5O0i/P/88qVdfDtX5c+eVv0B+PRX8lDZs/sF+emLEqI/k5OSk1i3aKj4+XnXr19W4T1LffwncL3Zs36EGdZ+1f+7bu58k6eVX2+r9ge9pxfKVkqSqVao5rLd67SrVqFVDuXPn0rIlyzQkfKguXbok38K+qt+gnvq+21eurq73bkeyuSwdwrVr19bkyZPtn93d3dW6dWtVqVIlVd+1a9dq+PDh2rdvn2JjY3Xt2jVdvXpVly9fVu7cuW+7rZiYGEVHR6tq1f/9BZkjRw499thjtz0k3b9/f/Xs2dP+OTY2Vv7+N76PD3dmzrxZt1zu5uamsRPG3PDBB8D9qEatGrpy7dJNl99qmSSVK19O365dldFlIZ2ydAi7u7srKCjohu3/dPToUTVq1EidO3fW0KFDlTdvXm3atEnt27dXQkKCcufOLScnp1RhmpiYmCF1urq68pcnAGRDWfrCrLSKiIhQcnKyRo0apSeffFKlSpXSyZMnHfoUKFBAp06dcgjiyMhI+7+9vLxUuHBhbdv2v4c7XLt2TREREZlePwDgwUQISwoKClJiYqImTJigw4cPa86cOZoyZYpDn1q1aumvv/7SiBEjFBUVpYkTJ2rVKsdDOd27d9eHH36opUuXat++ferSpYsuXLhwD/cEAPAgIYQlVaxYUaNHj9ZHH32kcuXKae7cuRo+fLhDnzJlymjSpEmaOHGiKlasqJ9//lm9e/d26NOrVy+98sorCgkJUbVq1eTh4aGmTZvey10BADxAeIHDfYgXOADAg4sXOAAA8AAghAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAzJYboApGZZliTpYuxFw5UAANIr5Xd3yu/yWyGE70MXL17/DxgUWMpwJQCAO3Xx4kV5eXndso/NSktU455KTk7WyZMn5eHhIZvNZrqcLC82Nlb+/v46fvy4PD09TZcDZDh+xu8ty7J08eJF+fn5ycnp1md9mQnfh5ycnPTQQw+ZLiPb8fT05BcUsjR+xu+d282AU3BhFgAAhhDCAAAYQggj23N1ddUHH3wgV1dX06UAmYKf8fsXF2YBAGAIM2EAAAwhhAEAMIQQBgDAEEIYuEtfffWV6RIAPKAIYeAuHDx4UG+++abq169vuhQADyBCGLgL/v7+mj59uo4dO6aGDRuaLgdIt3nz5mn58uWmy8i2eGwlcIeSk5Pl5uamJk2ayNnZWX379tVLL72kBQsWmC4NSJNjx47p008/lWVZcnV15YiOAcyEgTuU8nKNdevWaeXKlXJ1ddWiRYvUtGlTw5UBaVO0aFENHDhQ3t7eGjFihL755hvTJWU7hDBwh2w2m1avXq369eurdOnSGjBggPr27avt27fr2WefNV0ecEtJSUmSpGeeeUYdO3aUj4+PRowYoQ0bNhiuLHvhiVnAHUpKSlK3bt106dIlzZw5U5IUHx+v5cuXq3v37nriiSe0ZMkSs0UCN2FZlmw2m1asWKHFixdr9+7dioiI0JNPPqnw8HDVq1fPdInZAjNh4A45OzvrzJkzOnz4sL3N1dVVL7zwgl566SUtW7ZMdevWNVghcHM2m00//vijGjdurMcff1yTJ0/WF198IUkaPny41q5da7jC7IEQBtLp3Llz9n8///zzio+P1+rVq+1tLi4uqly5sp544gldvXpVf/zxh4kygdtas2aNatasqc6dO+uxxx5TmzZtFBYWpri4OA0cOFAbN240XWKWRwgD6fD777/Lz89PK1eulCQFBwcrR44cmjp1qlatWmXvt2fPHj355JNavXq1AgICTJUL3JKXl5f+/vtvnT9/3t5Wv359derUSREREerevbvWrFljsMKsjxAG0qFYsWJq2bKl2rRpo1WrVqlEiRKaNGmS/vrrL73//vuqXLmyGjVqpIkTJ+r111+Xu7u76ZIBB7///rv934GBgTp69Kh++OEH/fPyoKJFi6pSpUp67LHHVLp0aRNlZhtcmAXcQsrFK//895UrV/T2229r9uzZWrRokf7zn//o6NGj2rlzp1atWqUCBQqobdu2Klu2rOHqAUcnTpxQjRo1FBwcrDlz5kiSXn/9dX355Zf67LPPVL16dfn5+endd99VTEyMhgwZIh8fH8NVZ22EMHAbP/zwgwoVKqSyZcumCuI5c+ZoyZIlatCggb3/P4MbuJ/ExMRo1qxZmjx5soKDgzV9+nRJUqdOnbRo0SL5+PjIy8tLe/bs0U8//aQKFSoYrjjrI4SBW0hISFDTpk21du1aRUZGqkyZMvaQjYmJ0Ysvvqg9e/ZoxowZPLYS950b/UEYGxurefPm6eOPP1bt2rU1bdo0SdI333yjkydPKjY2Vi+88IKCgoJMlJztEMLAbRw7dkzdu3fXpk2btGHDBofDzF27dtWMGTPk5eWlgwcPcg4Y951NmzZp7dq1CgsLs7fFxMRo/vz5GjZsmJ577jlNmjTJXIHZHBdmAf/wz79Jk5OTJV2/SGXixImqWrWqatWq5XBhi5ubm77++mvt2rWLAMZ9JyEhQWvWrNFnn32mwYMH29u9vLzUpk0bNWzYUFOmTFG7du0MVpm98QIH4P+lHLpbt26dFixYoNOnT+uZZ55Rp06d5Ofnp2nTpqlTp06qVq2aXn75ZZ07d07ff/+9OnfurAIFCpguH7BL+Vl2cXFRhw4dlCNHDs2bN09JSUn2GbGHh4cqVqyoihUr6uDBgzp58qT8/PzMFp4NMRMG/p/NZtPSpUv14osvKiYmRmXKlFGPHj3Up08fHTt2TIULF9bChQv15ptvav/+/YqLi9P333+vEiVKmC4dkPS/IzmXLl2SZVmKj4+Xv7+/QkJC1LJlSy1YsMDhsHR0dLSaNWumVatWEcCGcE4Y+H87d+5Us2bN1L9/f73++uu6evWq/Pz8dOHCBbVq1UoffvihihYtKkm6cuWKnJ2d5eLiYrhq4LqU2e/q1as1ceJEXbp0SXnz5tWECRPk6+ur48ePa9asWZoyZYry5cungIAA/fDDD4qIiFCpUqVMl59tMRNGtvbPv0HPnj2rV155Ra+//rpOnDihhx9+WO3atdPatWu1cOFCjRw5UgcOHJAk5cqViwDGfcVms2nZsmVq3ry5ypUrp2bNmunMmTOqXr26Dh48KH9/f3Xq1Elz585VhQoVFBgYqJ9++okANoyZMLK9WbNmKSEhQa+++qr279+vsmXLqnnz5sqbN68mT54sZ2dnValSRbt371aHDh00adIk5cjB5RS4v+zbt0+tW7dWx44d1aVLFx0/flzVq1fXxYsXlTNnTm3cuNHh6VdJSUlydnY2WDEkZsLIRv7592bKv6OiovTmm2/q3LlzcnV1VYUKFXTp0iVFR0frmWeekaurq5KTk1W7dm0tWbJEPXv2JIBx30j5OU5ISJCrq6tq1aqlN954QydOnFCdOnVUv359bd68Wd7e3mrcuLH27dtnX5cAvj8wE0a2kJycLCcnJ509e1bJyckqWLCgfv75Z61fv15//fWXRo4cae8bHR2tcuXKqUOHDmrdurUWLFigL7/8UpGRkfL09DS4F0BqCxcu1IIFCzR//nwdPnxYJUqUULt27RQXF6e5c+fKxcVFTZs21bJly1SiRAnt2bOHUyn3EWbCyPJSAnjHjh2qUqWKDh48qL///lvDhw9XeHi4Tpw4Ye+XlJSkwoULa9KkSRo5cqRefPFFzZgxQ4sWLSKAYVTKfetxcXH2toMHDyo8PFx16tSRJJUoUUJXrlzRgQMHVKNGDXvY+vr6avny5dq4cSMBfJ9hJowsLSWAd+3apeDgYHXq1Ekff/yxpOuP6ZswYYI2b96sH374QVWqVFFSUpJsNpucnJx04MABxcbGqkiRIipcuLDhPUF2lvJzHBERoRYtWmjXrl2KiorSV199pT///FPTp0+XzWaznypp1KiRDh8+rIkTJ2rJkiVaunSpNm3aZL+6H/cRC8iikpOTLcuyrF27dlnu7u5W//79Uy3fvXu3Vb9+faty5crWjh07LMuyrGvXrllJSUn3vF7gRlJ+FiMjIy0PDw+rW7dulmVZVuPGjS13d3erSpUq9r6JiYmWZVnW9u3brerVq1v+/v5W2bJl7T/buP8wE0aWdvbsWZUvX14VK1bUt99+a28fMGCAIiIi9M0332jt2rUaP368oqOjNW3aNFWqVMk+8wBMSvk5/PXXX1WtWjW9/fbbGjp0qCQpPj5er7zyinbu3KmuXbvqrbfecviZvXbtmg4fPqz8+fMrb968pnYBt8FvGWRpFy9e1HPPPaeIiAgtXbpUkjR8+HBNnTpVnTt3liTVrVtX3bt3l7+/v5o3b65ff/2VAMZ9wcnJScePH1edOnXUqFEjewBL0vz58+Xm5qZy5cpp4cKFmjlzpn1ZUlKScuTIoVKlShHA9znutUCWVqxYMQ0YMEBubm4KDQ1Vw4YNtX79es2ZM0cNGjSwzzTq1KmjhIQEzZkzRx4eHqbLBuySkpJUrFgxXb16VZs3b1ZwcLCGDx+uIUOGaOvWrSpSpIjeeOMNzZw5U05OTgoNDeX2owcIh6ORLRw9elRjx47VpEmT1LdvXw0ePNjhvuGUd65evnxZuXPnNlUmcEMHDx5Ut27d5OLiokKFCmnZsmWaM2eO6tevL0k6deqUunfvrr1796pv375q27at4YqRVhxzQ7YQGBiot956S506ddKECRO0ZMkSh5edpwQyAYz7UcmSJTVu3DhduXJFX3zxhd555x17AF+7dk2+vr4aM2aMHn30UVWvXt1wtUgPZsLIVo4ePapRo0Zp9uzZmjVrlpo0aWK6JCDNoqKi1KVLFzk7O+vdd9+1B25iYqJy5szJoygfQMyEkaXc7m/KwMBA9erVS+3atVOzZs20YsWKe1QZcPdKlCihTz75RJZlaciQIdq8ebMkKWfOnJJ4FOWDiBBGlpASvilPFUqRlJSUqm9gYKC6deum3r17q2TJkvekPiCjlCxZUuPHj1fOnDnVu3dv/fTTT6ZLwl3gcDQeeNb/v0f1+++/15IlS3ThwgWVK1dOHTt2VL58+W56iO7atWu8jAEPrH379mnAgAEaNWoUT8J6gBHCyBKWLl2q1q1b6+WXX9Yff/yhv//+W2fPntWmTZtUpEgRzpUhS0pISOBZ0A84QhgPnJSZb4qzZ8+qXr16atOmjfr06SNJ+u2339SrVy9FRUXp559/5oEFAO5LnBPGAyPl78XLly9LcnyrTHR0tCpVqmTvW6ZMGY0YMUJeXl6aP3++w/oAcL8ghPHAsNlsOnPmjAIDA/X111/bHy3p6+srf39/bdiwwd7X2dlZFSpUUI4cObR37177+gBwPyGE8UBxcnLSCy+8oFdeeUXLli2zt1WtWlXr1q3T4sWL7X1tNpuKFCkib29vWZbFTBjAfYdzwriv/fv8rySdOXNGQ4cO1YQJE7Ro0SI1bdpU586dU9u2bRUTE6OqVasqODhYGzdu1OzZs7Vt2zY9/PDDhvYAAG6OEMZ9K+XlCpcuXVJSUpI8PT3ty6KjozVs2DBNnDhRCxYs0Isvvqhz587pww8/1ObNm3X27Fn5+vpq/PjxDueKAeB+Qgjjvnbw4EG1aNFCefLkUceOHeXr62t/Zm58fLx69eqlSZMm6auvvtJLL72ka9euyWaz6fz588qdO7fc3d0N7wEA3BxPKsB9Kzk5WTNnztSuXbvk5uamCxcu6PLly8qbN6+eeOIJvfbaa2rXrp3y5cunli1bytPTUw0aNJAkFShQwHD1AHB7zIRxXzt16pQ++ugjRUVFKSgoSG+++abmzp2rH3/8Ub/++qvy5s2r4sWLKyIiQmfOnNH69etVo0YN02UDQJowE8Z9zdfXV3369NGwYcO0adMmlSxZUgMHDpQkbdu2TSdPntSnn36qggUL6syZM8qfP7/higEg7ZgJ44GQciHWtm3b1KRJE7377rv2ZYmJiUpOTlZMTIwKFixosEoASB9CGA+MU6dOaejQofrll1/UpEkT9evXTxIvYgDw4CKE8UBJCeKdO3eqTp06Cg8PN10SANwxnpiFB4qvr6/ee+89lSxZUlu2bNG5c+dMlwQAd4yZMB5Ip0+fliQVKlTIcCUAcOcIYQAADOFwNAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQzghkJDQ9WkSRP751q1auntt9++53WsX79eNptNFy5cuGkfm82mpUuXpnnMsLAwVapU6a7qOnr0qGw2myIjI+9qHGRvhDDwAAkNDZXNZpPNZpOLi4uCgoI0aNAgXbt2LdO3vXjxYg0ePDhNfdMSnAB4nzDwwGnYsKFmzJih+Ph4ffPNN3rzzTeVM2dO9e/fP1XfhIQEubi4ZMh28+bNmyHjAPgfZsLAA8bV1VW+vr4KCAhQ586dVbduXf33v/+V9L9DyEOHDpWfn59Kly4tSTp+/LhatGghb29v5c2bV40bN9bRo0ftYyYlJalnz57y9vZWvnz59M477+jfT7T99+Ho+Ph49e3bV/7+/nJ1dVVQUJA+++wzHT16VLVr15Yk+fj4yGazKTQ0VJKUnJys4cOHq1ixYsqVK5cqVqyohQsXOmznm2++UalSpZQrVy7Vrl3boc606tu3r0qVKqXcuXOrePHiGjBggBITE1P1mzp1qvz9/ZU7d261aNFCMTExDsunT5+uMmXKyM3NTQ8//LAmTZqU7lqAWyGEgQdcrly5lJCQYP/8/fffa//+/VqzZo1WrFihxMRENWjQQB4eHvrxxx+1efNm5cmTRw0bNrSvN2rUKM2cOVOff/65Nm3apPPnz2vJkiW33O6rr76qL7/8UuPHj9fevXs1depU5cmTR/7+/lq0aJEkaf/+/YqOjta4ceMkScOHD9fs2bM1ZcoU7dmzRz169NDLL7+sDRs2SLr+x0KzZs30/PPPKzIyUh06dLC/Nzo9PDw8NHPmTP3+++8aN26cpk2bpjFjxjj0OXTokL7++mstX75c3377rXbu3KkuXbrYl8+dO1cDBw7U0KFDtXfvXg0bNkwDBgzQrFmz0l0PcFMWgAdGSEiI1bhxY8uyLCs5Odlas2aN5erqavXu3du+vFChQlZ8fLx9nTlz5lilS5e2kpOT7W3x8fFWrly5rNWrV1uWZVmFCxe2RowYYV+emJhoPfTQQ/ZtWZZl1axZ0+revbtlWZa1f/9+S5K1Zs2aG9b5ww8/WJKsv//+29529epVK3fu3NaWLVsc+rZv395q3bq1ZVmW1b9/f6ts2bIOy/v27ZtqrH+TZC1ZsuSmy0eOHGlVqVLF/vmDDz6wnJ2drRMnTtjbVq1aZTk5OVnR0dGWZVlWiRIlrHnz5jmMM3jwYKtatWqWZVnWkSNHLEnWzp07b7pd4HY4Jww8YFasWKE8efIoMTFRycnJatOmjcLCwuzLy5cv73AeeNeuXTp06JA8PDwcxrl69aqioqIUExOj6OhoVa1a1b4sR44ceuyxx1Idkk4RGRkpZ2dn1axZM811Hzp0SJcvX1a9evUc2hMSEvToo49Kkvbu3etQhyRVq1YtzdtI8dVXX2n8+PGKiopSXFycrl27Jk9PT4c+RYsWVZEiRRy2k5ycrP3798vDw0NRUVFq3769OnbsaO9z7do1eXl5pbse4GYIYeABU7t2bU2ePFkuLi7y8/NTjhyO/xu7u7s7fI6Li1OVKlU0d+7cVGMVKFDgjmrIlStXuteJi4uTJK1cudIh/KTr57kzytatW9W2bVuFh4erQYMG8vLy0vz58zVq1Kh01zpt2rRUfxQ4OztnWK0AIQw8YNzd3RUUFJTm/pUrV9ZXX32lggULppoNpihcuLC2bdumGjVqSLo+44uIiFDlypVv2L98+fJKTk7Whg0bVLdu3VTLU2biSUlJ9rayZcvK1dVVx44du+kMukyZMvaLzFL89NNPt9/Jf9iyZYsCAgL03nvv2dv++OOPVP2OHTumkydPys/Pz74dJycnlS5dWoUKFZKfn58OHz6stm3bpmv7QHpwYRaQxbVt21b58+dX48aN9eOPP+rIkSNav369unXrphMnTkiSunfvrg8//FBLly7Vvn371KVLl1ve4xsYGKiQkBC99tprWrp0qX3Mr7/+WpIUEBAgm82mFStW6K+//lJcXJw8PDzUu3dv9ejRQ7NmzVJUVJR27NihCRMm2C926tSpkw4ePKg+ffpo//79mjdvnmbOnJmu/S1ZsqSOHTum+fPnKyoqSuPHj7/hRWZubm4KCQnRrl279OOPP6pbt25q0aKFfH19JUnh4eEaPny4xo8frwMHDmj37t2aMWOGRo8ena56gFshhIEsLnfu3Nq4caOKFi2qZs2aqUyZMmrfvr2uXr1qnxn36tVLr7zyikJCQlStWjV5eHioadOmtxx38uTJat68ubp06aKHH35YHTt21KVLlyRJRYoUUXh4uPr166dChQqpa9eukqTBgwdrwIABGj58uMqUKaOGDRtq5cqVKlasmKTr52kXLVqkpUuXqmLFipoyZYqGDRuWrv194YUX1KNHD3Xt2lWVKlXSli1bNGDAgFT9goKC1KxZM/3nP/9R/fr1VaFCBYdbkDp06KDp06drxowZKl++vGrWrKmZM2faawUygs262ZUXAAAgUzETBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMOT/ANOJmt0PbzRsAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Predict the labels of the test data using the trained model\n",
        "y_pred = model_und.predict(original_Xtest)\n",
        "y_pred_nn_und = (y_pred > 0.5).astype('int')\n",
        "\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "cm_und_nn = confusion_matrix(original_ytest, y_pred_nn_und)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plot_confusion_matrix(cm_und_nn,labels,title=\"Confusion Matrix\",cmap=plt.cm.Greens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egYrUXXN8Lnm"
      },
      "source": [
        "Oversampling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "SsGhdQRd49Py",
        "outputId": "28e027d1-20da-4826-cf2f-1677f609e1c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1111/1111 [==============================] - 4s 3ms/step - loss: 0.4273 - accuracy: 0.8296 - val_loss: 0.5251 - val_accuracy: 0.6764\n",
            "Epoch 2/10\n",
            "1111/1111 [==============================] - 4s 3ms/step - loss: 0.3503 - accuracy: 0.8495 - val_loss: 0.5281 - val_accuracy: 0.6859\n",
            "Epoch 3/10\n",
            "1111/1111 [==============================] - 3s 3ms/step - loss: 0.3195 - accuracy: 0.8605 - val_loss: 0.4392 - val_accuracy: 0.7606\n",
            "Epoch 4/10\n",
            "1111/1111 [==============================] - 3s 3ms/step - loss: 0.2986 - accuracy: 0.8703 - val_loss: 0.4493 - val_accuracy: 0.7564\n",
            "Epoch 5/10\n",
            "1111/1111 [==============================] - 3s 3ms/step - loss: 0.2827 - accuracy: 0.8769 - val_loss: 0.4687 - val_accuracy: 0.7619\n",
            "Epoch 6/10\n",
            "1111/1111 [==============================] - 3s 3ms/step - loss: 0.2696 - accuracy: 0.8834 - val_loss: 0.3641 - val_accuracy: 0.8325\n",
            "Epoch 7/10\n",
            "1111/1111 [==============================] - 3s 3ms/step - loss: 0.2578 - accuracy: 0.8907 - val_loss: 0.3646 - val_accuracy: 0.8271\n",
            "Epoch 8/10\n",
            "1111/1111 [==============================] - 4s 4ms/step - loss: 0.2468 - accuracy: 0.8963 - val_loss: 0.3680 - val_accuracy: 0.8168\n",
            "Epoch 9/10\n",
            "1111/1111 [==============================] - 3s 2ms/step - loss: 0.2373 - accuracy: 0.9015 - val_loss: 0.3264 - val_accuracy: 0.8650\n",
            "Epoch 10/10\n",
            "1111/1111 [==============================] - 3s 3ms/step - loss: 0.2284 - accuracy: 0.9059 - val_loss: 0.2951 - val_accuracy: 0.8800\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Define the neural network model\n",
        "model_over = Sequential()\n",
        "model_over.add(Dense(64, activation='relu', input_dim=X_train_over.shape[1]))\n",
        "model_over.add(Dense(32, activation='relu'))\n",
        "model_over.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Compile the model\n",
        "model_over.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model_over.fit(X_train_over, y_train_over, epochs=10, batch_size=128, validation_split=0.2)\n",
        "\n",
        "# Plot the training and validation accuracy over time\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "x4hDBiki6NEe",
        "outputId": "01104143-39bf-4c13-cc96-573a49e45992"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "700/700 [==============================] - 1s 1ms/step\n",
            "Confusion matrix, without normalization\n",
            "[[20869  1350]\n",
            " [   54   119]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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igrn8VNg7QM0avqbdv4bZ9ClfvKJyOXvZvMaNGm/T5+D+g/pPrUbK715QZYqW14TREzN827IyQhhPnerlqmjSD7P1Yo/GqvtBa2VzyKafPpuvHM7ZrX3GdvlEr75YV68Pe1fV+7SQT+58WjJkunW6i3MO/Rjyjc5c+lOVu7+qqr2a6/rNWK0NmScHewdrv/Fdh6pjw9bqO22YSr5dQ40Hd9Cvh8Ot0zs3aqeQ/36gIXPG6JmOtfTJnNGa1H2EGr1Y57H8LZB1xN68qTJln9HIcZ/fc3pAsaL6fOxn2rZ7s1ZvWClfP1+91uh1Xbl8xabfwMEf6NCpA9ZXp64drdNiYq7rtUavy7eQrzbsXK/gkCH6fPgohc6Yk6HblpVZDMMwzC4CtmJiYuTu7i7VyC858DvpYfK459LlRb+rWu/XtHX/LrnlcNXlRfvUJqS7Fm9dJUkq4VtUh7/erBd7NNauQ3tUqXg57Z60Wr5tnte5y5GSpDL+JbV/+noFBFVVxPlTKlkoQL9/tU5lOtXW0XMn7rnu7eOWafvB3eo/fbi17Yt3B6lyyWf1cq/mGb/xT7GoleFml/DUyuXspbnfz9Yrjf9z3z4xMdfln7eIlq5erOq1qkm6OxLu3P0ddene+Z7zfD1tloZ/8qkOnz4oR0dHSVLwx0O1+oc12vX7zvTfkEwq+W8fHR0tNze3B/blGx5PPXeXux/yqOvXJEmVipeVYzZHrd+z1drnyNkInb54TlVKVbS+vxIdpbcb3B1JOzs66+2GrfTH6aM6deGsJOnVF+vqROQZNXqxjk7M2aGTc3dqeu9R8nT1sC7XKZujbsfftqnnVtxtvVCigs2IGnic4uPjNXvmHLm5u6lMuWdspo3/YoKK+hRX9co1NWHMl7pz54512m+/7NZLVatYA1iSatWppWNHj+vaX9ceV/lZCiH8GISGhsrDw8PsMjIli8WicV2GaNuBX3Xw1BFJkrdnXsXFxyk6Nsam78W/rsg7V15J0o1bsarR93W9Wbu5bq08rhs/HFGD52qo4YftlJiUKEkqkr+Q/PIV0OvVGumtke+r/ajeqlSsrBYN+sq6zLVhm9WxYWtVLFZWklSpeDl1bNhajtkclcc91+P4EwBWa1f/JN/cfsrvXlBTJ07VklWLlDtPbuv0d97rpBlzpuuHtUvVvmOQxo4cp08+DLZOv3jxkrzyetks0yufl3Ua0p+pIdy+fXtZLBZ99tlnNu3Lli2TxWL5V8sODQ2VxWJJ8ZoxY8a/Wi6eLJO6j1AZ/xJqNeK9NM3n7Oismb2/0PaDv+nFHo0V2KuZDpw6olXDZ8vZ0VmSZGexk7Ojs976vKe2HfhVm3/fqbdH91WtZwOtJ4IN+2a81vy2Ub9M+EEJP57S8uCvNXvdIklSUlJS+m4s8BBVqwdq868b9eOm1apVt5b+27ajLl+6bJ3+Xs8uqlo9UM+UfUYdOrXXsM+CNX3yDMXFxZlYddZm+kjY2dlZn3/+uf766690X7abm5siIyNtXm3btk3RLz4+Pt3XjYw3sdtwNapcRzX7tdSfVyKt7Rf+uiQnRyfrbupk+Tzz6ELU3V/zbWo1lb93QXX4ord2H92nXYf2qE1INxX2LqQmL9WTJEVGXVLCnQQd+/OkdRmHzhyXJBXKW0CSdDv+tt4e3Vc5GhWT/5tVVKjtCzp14axiYq/rcvTVDN1+4J9cXFxUpGgRPV/5OU38arwcHOz1Tei8+/av9EIl3blzR2dO3z0Eky9fXpvQlqTLFy9bpyH9mR7CderUkbe3t0JCQh7Yb/HixXrmmWfk5OQkf39/jR49+qHLtlgs8vb2tnllz55dQ4YMUYUKFTRjxgwVLlxYzs53Rz4//vijqlatKg8PD+XOnVuNGjVSRESEdXmbNm2SxWLRtWvXrG3h4eGyWCw6deqUtS00NFSFChVSjhw51KxZM129ypdxepvYbbiaBTZQrf5vWI/hJgs7ul/xCfGq/WxVa1vxgkXkl6+gdh7aI0nK4ZRdSUlJ+vt5iUlJSTJkyM5y93+L7Qd/UzaHbCqS3+9vyyksSTp98ZzNOu8k3tGfVyKVlJSkVjWbaOWun8U5jzBbUpKhuLj7DzIO7DsgOzs7eXndvXzv+Ref045tO5WQkGDts+nnTSpWPEAenh4ZXW6WZHoI29vb69NPP9XEiRN17ty5e/YJCwtTy5Yt1apVK+3fv19DhgzRoEGDFBoa+sjrPX78uBYvXqwlS5YoPDxckhQbG6vevXtr9+7d+vnnn2VnZ6dmzZqlabfirl279Pbbb6tbt24KDw9XzZo1NXz48IfPiFSb1H2E3qzdTG1Cuun6zRvK5+mlfJ5e1t3IMTeva+aPCzSm82DVKP+SKhYrq1l9x2jHwd3a9f8hvG7PFnm6umtS9xEqWShApf2Ka1a/MbqTeEcb9+2QJK3fs1VhR3/X131Hq0LRZ1SxWFl99f7n+ilss3V0XKxAYbWt3VwBBQrr+RIV9O2Hk1TGv4Q+/PqzexcPPKIbN25o/7792r9vvyTp9Kkz2r9vv86dOafY2FgNGzRcv+3arbOnzyp8zz51e6eHIs9HqslrjSVJv/7ym6ZMnKoDvx/QqROntPDbRfqo/yC1bN3CGrAt3nhNjo7Z1OPd93Xoj8NasnCpvpo0XV16dDFrszO9J+L0zWbNmqlChQr65JNPNHPmzBTTx4wZo9q1a2vQoEGSpOLFi+uPP/7QqFGj1L59+/suNzo6Wjlz5rS+z5kzpy5cuCDp7i7oOXPmyMvrfychvPaa7U0Wvv76a3l5eemPP/5QmTJlUrUt48ePV4MGDdS/f39rrTt27NCPP/5433ni4uJsjsnExMTcty/u3hxDkjaPXmTT3n5UL83+aaEkqdeUYCUZSVo8eJqcsjlqbdhmdZ3wobXvkbMRenVQB33Srpd2jl+upCRDeyMOqMGH7ay7rA3D0KuDO2jie8O0Zcxixd6+qTW/bVSfr4ZZl2Nvb68+Ld5RiYJFlZCYoI3hO/RSzyYpRsrAvxUetk+N6ze1vv+4/93vw9ZvvqHRX36hY0ePa0HrDrp6JUq5cnvq2UrPatXPK1SqdElJkpOTo5YsXKbPh49SfFy8CvkXUpfu76prz/8FrJu7mxavXKh+PQeoVpU6ypUnl/p92EftO771WLc1K3kiQliSPv/8c9WqVUt9+/ZNMe3QoUNq0qSJTVtgYKDGjRunxMRE2dvb33OZrq6u2rNnj/W9nd3/Bv5+fn42ASxJx44d0+DBg7Vr1y5duXLFOgI+c+ZMqkP40KFDatasmU1blSpVHhjCISEhCg4Ovu902LLULfjQPnEJceo28WN1m/jxffus37PV5jKme4m8elEthr5z3+mHzxxXxS4NHloP8G9VrR6oqNuX7zt9znehD5y//LPltW7L/b+Hkj1T9hmt3rAyreXhEZm+OzpZtWrVVL9+fQ0cODDdlmlnZ6eAgADrq0iR/93a0MXFJUX/V199VVFRUZo+fbp27dqlXbt2SfrfiVvJIf73Y31/P3byqAYOHKjo6Gjr6+zZsw+fCQDw1HtiRsKS9Nlnn6lChQoqUaKETXupUqW0fft2m7bt27erePHi9x0Fp9XVq1d15MgRTZ8+XS+//LIkadu2bTZ9kkfOkZGR8vT0lCTr8eS/15oc3sl++eWXB67byclJTk5O/6Z8AMBT6IkK4bJly6pt27aaMGGCTXufPn30/PPPa9iwYXrjjTe0c+dOffnll5o8eXK6rdvT01O5c+fWtGnTlD9/fp05c0YffPCBTZ+AgAD5+vpqyJAhGjFihI4ePZriLO0ePXooMDBQX3zxhZo0aaK1a9c+cFc0ACDremJ2RycbOnRoirORK1asqO+//14LFixQmTJlNHjwYA0dOvSBJ2WllZ2dnRYsWKCwsDCVKVNGvXr10qhRo2z6ZMuWTd9++60OHz6scuXK6fPPP09x5vOLL76o6dOna/z48Spfvrx++uknffzx/Y9LAgCyLh7g8ATiAQ7ICniAAzIrHuAAAMBTgBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABMQggDAGASQhgAAJMQwgAAmIQQBgDAJIQwAAAmIYQBADAJIQwAgEkIYQAATEIIAwBgEkIYAACTEMIAAJiEEAYAwCSEMAAAJiGEAQAwCSEMAIBJCGEAAExCCAMAYBJCGAAAkxDCAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABMQggDAGASQhgAAJMQwgAAmIQQBgDAJIQwAAAmIYQBADAJIQwAgEkIYQAATEIIAwBgEkIYAACTEMIAAJiEEAYAwCSEMAAAJiGEAQAwCSEMAIBJCGEAAExCCAMAYBJCGAAAkxDCAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABM4pCaTj/88EOqF9i4ceNHLgYAgKwkVSHctGnTVC3MYrEoMTHx39QDAECWkaoQTkpKyug6AADIcv7VMeHbt2+nVx0AAGQ5aQ7hxMREDRs2TAUKFFDOnDl14sQJSdKgQYM0c+bMdC8QAIDMKs0hPGLECIWGhmrkyJFydHS0tpcpU0YzZsxI1+IAAMjM0hzCc+bM0bRp09S2bVvZ29tb28uXL6/Dhw+na3EAAGRmaQ7hP//8UwEBASnak5KSlJCQkC5FAQCQFaQ5hEuXLq2tW7emaF+0aJGeffbZdCkKAICsIFWXKP3d4MGDFRQUpD///FNJSUlasmSJjhw5ojlz5mjlypUZUSMAAJlSmkfCTZo00YoVK7R+/Xq5uLho8ODBOnTokFasWKG6detmRI0AAGRKaR4JS9LLL7+sdevWpXctAABkKY8UwpK0e/duHTp0SNLd48SVKlVKt6IAAMgK0hzC586dU+vWrbV9+3Z5eHhIkq5du6aXXnpJCxYsUMGCBdO7RgAAMqU0HxPu2LGjEhISdOjQIUVFRSkqKkqHDh1SUlKSOnbsmBE1AgCQKaV5JLx582bt2LFDJUqUsLaVKFFCEydO1Msvv5yuxQEAkJmleSTs6+t7z5tyJCYmysfHJ12KAgAgK0hzCI8aNUrdu3fX7t27rW27d+9Wz5499cUXX6RrcQAAZGYWwzCMh3Xy9PSUxWKxvo+NjdWdO3fk4HB3b3byv11cXBQVFZVx1WYRMTExcnd3l2rklxz+1dMmgSdW1Mpws0sAMkRMzHX55y2i6Ohoubm5PbBvqo4Jjxs3Lj3qAgAAf5OqEA4KCsroOgAAyHIe+WYdknT79m3Fx8fbtD1s6A0AAO5K8wHH2NhYdevWTXnz5pWLi4s8PT1tXgAAIHXSHML9+/fXhg0bNGXKFDk5OWnGjBkKDg6Wj4+P5syZkxE1AgCQKaV5d/SKFSs0Z84c1ahRQx06dNDLL7+sgIAA+fn5ad68eWrbtm1G1AkAQKaT5pFwVFSUihQpIunu8d/kS5KqVq2qLVu2pG91AABkYmkO4SJFiujkyZOSpJIlS+r777+XdHeEnPxABwAA8HBpDuEOHTpo3759kqQPPvhAkyZNkrOzs3r16qV+/fqle4EAAGRWqbpj1oOcPn1aYWFhCggIULly5dKrriyNO2YhK+COWcis0v2OWQ/i5+cnPz+/f7sYAACynFSF8IQJE1K9wB49ejxyMQAAZCWpCuGxY8emamEWi4UQBgAglVIVwslnQ+PxurhkD7cBBYCnTILDnVT35awfAABMQggDAGASQhgAAJMQwgAAmIQQBgDAJI8Uwlu3btWbb76pKlWq6M8//5QkzZ07V9u2bUvX4gAAyMzSHMKLFy9W/fr1lT17du3du1dxcXGSpOjoaH366afpXiAAAJlVmkN4+PDhmjp1qqZPn65s2bJZ2wMDA7Vnz550LQ4AgMwszSF85MgRVatWLUW7u7u7rl27lh41AQCQJaQ5hL29vXX8+PEU7du2bVORIkXSpSgAALKCNIdwp06d1LNnT+3atUsWi0Xnz5/XvHnz1LdvX3Xp0iUjagQAIFNK86MMP/jgAyUlJal27dq6efOmqlWrJicnJ/Xt21fdu3fPiBoBAMiULIZhGI8yY3x8vI4fP64bN26odOnSypkzZ3rXlmXFxMTI3d1dF6MieYADADxlYmJilC9XfkVHRz/0OzzNI+Fkjo6OKl269KPODgBAlpfmEK5Zs6YsFst9p2/YsOFfFQQAQFaR5hCuUKGCzfuEhASFh4frwIEDCgoKSq+6AADI9NIcwmPHjr1n+5AhQ3Tjxo1/XRAAAFlFuj3A4c0339TXX3+dXosDACDTS7cQ3rlzp5ydndNrcQAAZHpp3h3dvHlzm/eGYSgyMlK7d+/WoEGD0q0wAAAyuzSHsLu7u817Ozs7lShRQkOHDlW9evXSrTAAADK7NIVwYmKiOnTooLJly8rT0zOjagIAIEtI0zFhe3t71atXj6clAQCQDtJ8YlaZMmV04sSJjKgFAIAsJc0hPHz4cPXt21crV65UZGSkYmJibF4AACB1Uv0Ah6FDh6pPnz5ydXX938x/u32lYRiyWCxKTExM/yqzGB7gAABPr7Q8wCHVIWxvb6/IyEgdOnTogf2qV6+e+kpxT4QwADy9MuQpSslZTcgCAJA+0nRM+EFPTwIAAGmTpuuEixcv/tAgjoqK+lcFAQCQVaQphIODg1PcMQsAADyaNIVwq1atlDdv3oyqBQCALCXVx4Q5HgwAQPpKdQin8komAACQSqneHZ2UlJSRdQAAkOWk+baVAAAgfRDCAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABMQggDAGASQhgAAJMQwgAAmIQQBgDAJIQwAAAmIYQBADAJIQwAgEkIYQAATEIIAwBgEkIYAACTEMIAAJiEEAYAwCSEMAAAJiGEAQAwCSEMAIBJCGEAAExCCAMAYBJCGAAAkxDCAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABMQggDAGASQhgAAJMQwgAAmIQQBgDAJIQwAAAmIYQBADAJIQwAgEkIYQAATEIIAwBgEkIYAACTEMIAAJiEEAYAwCSEMAAAJiGEAQAwCSEMAIBJCGEAAExCCCNLGB48QtkdXGxe5Z95NkU/wzDU5JWmyu7goh+WrzChUiB1tm3ZpteatFBh36L3/LwuW7pcjRq8qgJ5fZXdwUX7wvelWMaJiBNq+Vor+Xr7Ka+nt9q2aqeLFy8+rk2ACGFkIaWfKaWT5yKsr583r0vRZ+L4L2WxWEyoDkib2NhYlS1XVuMmjr3n9JuxsXop8CUNDxl23/kbNWwsi8WiNetWacOW9YqPj9drTV5XUlJSRpaOv3EwuwDgcXFwcJC3t/d9p+8L36fxYydo+66tKlyw6GOsDEi7+g3rq37D+ved3ubNNpKk06dO33P6zu07dfrUaf2ye4fc3NwkSTNmTVP+PAW0acMm1apTK/2LRgqMhJFlHD8WocK+RVWq2DNq366Dzpw5a5128+ZNtW/3X42bOPaBQQ1kFnFx8bJYLHJycrK2OTs7y87OTju27zSxsqyFEM5g7du3V9OmTc0uI8t7/oXnNO3rr/TDqmWa8OU4nTp5WnVq1NX169clSf37DNCLVSrr1caNTK4UeDxeePF5ubi46KOBH+vmzZuKjY3VB/0/VGJioi5cuGB2eVlGpg3h9u3by2KxpHgdP37c7NJggvoN6+u1Fs1VtlxZ1a1fV8tWLlH0tWgtXrhEK1es0qaNmzVqzEizywQeGy8vL81bMFerV65RHve8ypcrv6KvXdOzFSvIzi7TRsMTJ1MfE27QoIFmzZpl0+bl5WXzPj4+Xo6Ojo+zLDwBPDw8FFA8QBHHI3TgwAGdiDgh79w+Nn1av95GgVUD9dOGH02qEshYderV0R9HD+jKlStycHCQh4eH/AsUln9Lf7NLyzIy9c8dJycneXt727xq166tbt266f3331eePHlUv/7dExvGjBmjsmXLysXFRb6+vuratatu3LhhXdaQIUNUoUIFm+WPGzdO/v7+1veJiYnq3bu3PDw8lDt3bvXv31+GYTyOTUUa3bhxQycjTso7v7f69u+j3/bu0q6wndaXJI0c/bmmzZxqcqVAxsuTJ488PDy0acMmXbp0WY1efcXskrKMTD0Svp/Zs2erS5cu2r59u7XNzs5OEyZMUOHChXXixAl17dpV/fv31+TJk1O93NGjRys0NFRff/21SpUqpdGjR2vp0qWqVevBZxnGxcUpLi7O+j4mJibtG4UH+qDfQL3S6D8q5FdI589HanjwcNnb26tlq9fl5eV1z5OxfAv5yr+w/+MvFkiFGzduKOJ4hPX9qZOntC98nzxz5VKhQr6KiorS2TNnFXk+UpJ09OgxSVI+73zWz/uc0DkqUbKkvLzyaNcvu9S3V39179lNxUsUf/wblEVl6hBeuXKlcubMaX3fsGFDSVKxYsU0cqTt8b/333/f+m9/f38NHz5cnTt3TlMIjxs3TgMHDlTz5s0lSVOnTtXatWsfOl9ISIiCg4NTvR6k3Z9/ntdbb7ZX1NUo5fHKo5cCX9Lm7RtTHJ4AnhZ7du9R/ToNre8H9P1AkvTmW201/etpWrVild55u7N1+lttgiRJHw36UB9/8pEk6eiRYxr80SeKivpLfv5+6j+wn3q83/0xbgUydQjXrFlTU6ZMsb53cXFR69atValSpRR9169fr5CQEB0+fFgxMTG6c+eObt++rZs3bypHjhwPXVd0dLQiIyNVuXJla5uDg4Oee+65h+6SHjhwoHr37m19HxMTI19f39RsIlJp7vzZaep/605sBlUCpI9qNao98HPaLqid2gW1e+AyhocMu+/NPPB4ZOoQdnFxUUBAwD3b/+7UqVNq1KiRunTpohEjRihXrlzatm2b3n77bcXHxytHjhyys7NLEaYJCQnpUqeTk5PNtXoAgKwhU5+YlVphYWFKSkrS6NGj9eKLL6p48eI6f/68TR8vLy9duHDBJojDw8Ot/3Z3d1f+/Pm1a9cua9udO3cUFhaW4fUDAJ5OhLCkgIAAJSQkaOLEiTpx4oTmzp2rqVNtz4qtUaOGLl++rJEjRyoiIkKTJk3SmjVrbPr07NlTn332mZYtW6bDhw+ra9euunbt2mPcEgDA04QQllS+fHmNGTNGn3/+ucqUKaN58+YpJCTEpk+pUqU0efJkTZo0SeXLl9evv/6qvn372vTp06eP2rVrp6CgIFWpUkWurq5q1qzZ49wUAMBTxGJwIesTJyYmRu7u7roYFWm9sToA4OkQExNz9w5k0dEP/Q5nJAwAgEkIYQAATEIIAwBgEkIYAACTEMIAAJiEEAYAwCSEMAAAJiGEAQAwCSEMAIBJCGEAAExCCAMAYBJCGAAAkxDCAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABMQggDAGASQhgAAJMQwgAAmIQQBgDAJIQwAAAmIYQBADAJIQwAgEkIYQAATEIIAwBgEkIYAACTEMIAAJiEEAYAwCSEMAAAJiGEAQAwCSEMAIBJCGEAAExCCAMAYBJCGAAAkxDCAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABMQggDAGASQhgAAJMQwgAAmIQQBgDAJIQwAAAmIYQBADAJIQwAgEkIYQAATEIIAwBgEkIYAACTEMIAAJiEEAYAwCSEMAAAJiGEAQAwCSEMAIBJCGEAAExCCAMAYBJCGAAAkxDCAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABMQggDAGASQhgAAJMQwgAAmIQQBgDAJIQwAAAmIYQBADAJIQwAgEkIYQAATEIIAwBgEkIYAACTEMIAAJiEEAYAwCSEMAAAJiGEAQAwCSEMAIBJCGEAAExCCAMAYBJCGAAAkxDCAACYxMHsApCSYRiSpOsx102uBACQVsnf3cnf5Q9CCD+Brl+/+x8wwL+4yZUAAB7V9evX5e7u/sA+FiM1UY3HKikpSefPn5erq6ssFovZ5WR6MTEx8vX11dmzZ+Xm5mZ2OUC64zP+eBmGoevXr8vHx0d2dg8+6stI+AlkZ2enggULml1GluPm5sYXFDI1PuOPz8NGwMk4MQsAAJMQwgAAmIQQRpbn5OSkTz75RE5OTmaXAmQIPuNPLk7MAgDAJIyEAQAwCSEMAIBJCGEAAExCCAP/0nfffWd2CQCeUoQw8C8cO3ZM7733nurVq2d2KQCeQoQw8C/4+vpqxowZOnPmjBo0aGB2OUCazZ8/XytWrDC7jCyL21YCjygpKUnOzs5q2rSp7O3tNWDAAL3++utauHCh2aUBqXLmzBlNmzZNhmHIycmJPTomYCQMPKLkh2ts2LBBq1atkpOTkxYvXqxmzZqZXBmQOoUKFdLgwYPl4eGhkSNHavXq1WaXlOUQwsAjslgsWrt2rerVq6cSJUpo0KBBGjBggHbv3q2GDRuaXR7wQImJiZKkWrVqqVOnTvL09NTIkSO1efNmkyvLWrhjFvCIEhMT1aNHD8XGxio0NFSSFBcXpxUrVqhnz5564YUXtHTpUnOLBO7DMAxZLBatXLlSS5Ys0f79+xUWFqYXX3xRwcHBqlu3rtklZgmMhIFHZG9vr0uXLunEiRPWNicnJzVu3Fivv/66li9frjp16phYIXB/FotFW7duVZMmTfT8889rypQp+uabbyRJISEhWr9+vckVZg2EMJBGV69etf771VdfVVxcnNauXWttc3R0VMWKFfXCCy/o9u3bOn36tBllAg+1bt06Va9eXV26dNFzzz2nNm3aaMiQIbpx44YGDx6sLVu2mF1ipkcIA2nwxx9/yMfHR6tWrZIkBQYGysHBQV999ZXWrFlj7Xfw4EG9+OKLWrt2rfz8/MwqF3ggd3d3/fXXX4qKirK21atXT507d1ZYWJh69uypdevWmVhh5kcIA2lQuHBhvfHGG2rTpo3WrFmjokWLavLkybp8+bI+/vhjVaxYUY0aNdKkSZP0zjvvyMXFxeySARt//PGH9d/+/v46deqUNm7cqL+fHlSoUCFVqFBBzz33nEqUKGFGmVkGJ2YBD5B88srf/33r1i29//77mjNnjhYvXqz//Oc/OnXqlPbu3as1a9bIy8tLbdu2VenSpU2uHrB17tw5VatWTYGBgZo7d64k6Z133tG3336rmTNnqmrVqvLx8dGHH36o6OhoDR8+XJ6eniZXnbkRwsBDbNy4Ufny5VPp0qVTBPHcuXO1dOlS1a9f39r/78ENPEmio6M1e/ZsTZkyRYGBgZoxY4YkqXPnzlq8eLE8PT3l7u6ugwcP6pdfflG5cuVMrjjzI4SBB4iPj1ezZs20fv16hYeHq1SpUtaQjY6O1muvvaaDBw9q1qxZ3LYST5x7/SCMiYnR/Pnz9cUXX6hmzZqaPn26JGn16tU6f/68YmJi1LhxYwUEBJhRcpZDCAMPcebMGfXs2VPbtm3T5s2bbXYzd+vWTbNmzZK7u7uOHTvGMWA8cbZt26b169dryJAh1rbo6GgtWLBAn376qV555RVNnjzZvAKzOE7MAv7m779Jk5KSJN09SWXSpEmqXLmyatSoYXNii7Ozs77//nvt27ePAMYTJz4+XuvWrdPMmTM1bNgwa7u7u7vatGmjBg0aaOrUqerQoYOJVWZtPMAB+H/Ju+42bNighQsX6uLFi6pVq5Y6d+4sHx8fTZ8+XZ07d1aVKlX05ptv6urVq/r555/VpUsXeXl5mV0+YJX8WXZ0dFTHjh3l4OCg+fPnKzEx0ToidnV1Vfny5VW+fHkdO3ZM58+fl4+Pj7mFZ0GMhIH/Z7FYtGzZMr322muKjo5WqVKl1KtXL/Xr109nzpxR/vz5tWjRIr333ns6cuSIbty4oZ9//llFixY1u3RA0v/25MTGxsowDMXFxcnX11dBQUF64403tHDhQpvd0pGRkWrevLnWrFlDAJuEY8LA/9u7d6+aN2+ugQMH6p133tHt27fl4+Oja9euqVWrVvrss89UqFAhSdKtW7dkb28vR0dHk6sG7koe/a5du1aTJk1SbGyscuXKpYkTJ8rb21tnz57V7NmzNXXqVOXOnVt+fn7auHGjwsLCVLx4cbPLz7IYCSNL+/tv0CtXrqhdu3Z65513dO7cOZUsWVIdOnTQ+vXrtWjRIo0aNUpHjx6VJGXPnp0AxhPFYrFo+fLlatGihcqUKaPmzZvr0qVLqlq1qo4dOyZfX1917txZ8+bNU7ly5eTv769ffvmFADYZI2FkebNnz1Z8fLzeeustHTlyRKVLl1aLFi2UK1cuTZkyRfb29qpUqZL279+vjh07avLkyXJw4HQKPFkOHz6s1q1bq1OnTuratavOnj2rqlWr6vr168qWLZu2bNlic/erxMRE2dvbm1gxJEbCyEL+/nsz+d8RERF67733dPXqVTk5OalcuXKKjY1VZGSkatWqJScnJyUlJalmzZpaunSpevfuTQDjiZH8OY6Pj5eTk5Nq1Kihd999V+fOnVPt2rVVr149bd++XR4eHmrSpIkOHz5snZcAfjIwEkaWkJSUJDs7O125ckVJSUnKmzevfv31V23atEmXL1/WqFGjrH0jIyNVpkwZdezYUa1bt9bChQv17bffKjw8XG5ubiZuBZDSokWLtHDhQi1YsEAnTpxQ0aJF1aFDB924cUPz5s2To6OjmjVrpuXLl6to0aI6ePAgh1KeIIyEkeklB/CePXtUqVIlHTt2TH/99ZdCQkIUHBysc+fOWfslJiYqf/78mjx5skaNGqXXXntNs2bN0uLFiwlgmCr5uvUbN25Y244dO6bg4GDVrl1bklS0aFHdunVLR48eVbVq1axh6+3trRUrVmjLli0E8BOGkTAyteQA3rdvnwIDA9W5c2d98cUXku7epm/ixInavn27Nm7cqEqVKikxMVEWi0V2dnY6evSoYmJiVKBAAeXPn9/kLUFWlvw5DgsLU8uWLbVv3z5FRETou+++059//qkZM2bIYrFYD5U0atRIJ06c0KRJk7R06VItW7ZM27Zts57djyeIAWRSSUlJhmEYxr59+wwXFxdj4MCBKabv37/fqFevnlGxYkVjz549hmEYxp07d4zExMTHXi9wL8mfxfDwcMPV1dXo0aOHYRiG0aRJE8PFxcWoVKmStW9CQoJhGIaxe/duo2rVqoavr69RunRp62cbTx5GwsjUrly5orJly6p8+fL68ccfre2DBg1SWFiYVq9erfXr12vChAmKjIzU9OnTVaFCBevIAzBT8ufw999/V5UqVfT+++9rxIgRkqS4uDi1a9dOe/fuVbdu3dS9e3ebz+ydO3d04sQJ5cmTR7ly5TJrE/AQfMsgU7t+/bpeeeUVhYWFadmyZZKkkJAQffXVV+rSpYskqU6dOurZs6d8fX3VokUL/f777wQwngh2dnY6e/asateurUaNGlkDWJIWLFggZ2dnlSlTRosWLVJoaKh1WmJiohwcHFS8eHEC+AnHtRbI1AoXLqxBgwbJ2dlZ7du3V4MGDbRp0ybNnTtX9evXt440ateurfj4eM2dO1eurq5mlw1YJSYmqnDhwrp9+7a2b9+uwMBAhYSEaPjw4dq5c6cKFCigd999V6GhobKzs1P79u25/Ogpwu5oZAmnTp3SuHHjNHnyZA0YMEDDhg2zuW44+ZmrN2/eVI4cOcwqE7inY8eOqUePHnJ0dFS+fPm0fPlyzZ07V/Xq1ZMkXbhwQT179tShQ4c0YMAAtW3b1uSKkVrsc0OW4O/vr+7du6tz586aOHGili5davOw8+RAJoDxJCpWrJjGjx+vW7du6ZtvvlH//v2tAXznzh15e3tr7NixevbZZ1W1alWTq0VaMBJGlnLq1CmNHj1ac+bM0ezZs9W0aVOzSwJSLSIiQl27dpW9vb0+/PBDa+AmJCQoW7Zs3IryKcRIGJnKw35T+vv7q0+fPurQoYOaN2+ulStXPqbKgH+vaNGi+vLLL2UYhoYPH67t27dLkrJlyyaJW1E+jQhhZArJ4Zt8V6FkiYmJKfr6+/urR48e6tu3r4oVK/ZY6gPSS7FixTRhwgRly5ZNffv21S+//GJ2SfgX2B2Np57x/89R/fnnn7V06VJdu3ZNZcqUUadOnZQ7d+777qK7c+cOD2PAU+vw4cMaNGiQRo8ezZ2wnmKEMDKFZcuWqXXr1nrzzTd1+vRp/fXXX7py5Yq2bdumAgUKcKwMmVJ8fDz3gn7KEcJ46iSPfJNduXJFdevWVZs2bdSvXz9J0oEDB9SnTx9FRETo119/5YYFAJ5IHBPGUyP59+LNmzcl2T5VJjIyUhUqVLD2LVWqlEaOHCl3d3ctWLDAZn4AeFIQwnhqWCwWXbp0Sf7+/vr++++tt5b09vaWr6+vNm/ebO1rb2+vcuXKycHBQYcOHbLODwBPEkIYTxU7Ozs1btxY7dq10/Lly61tlStX1oYNG7RkyRJrX4vFogIFCsjDw0OGYTASBvDE4Zgwnmj/PP4rSZcuXdKIESM0ceJELV68WM2aNdPVq1fVtm1bRUdHq3LlygoMDNSWLVs0Z84c7dq1SyVLljRpCwDg/ghhPLGSH64QGxurxMREubm5WadFRkbq008/1aRJk7Rw4UK99tprunr1qj777DNt375dV65ckbe3tyZMmGBzrBgAniSEMJ5ox44dU8uWLZUzZ0516tRJ3t7e1nvmxsXFqU+fPpo8ebK+++47vf7667pz544sFouioqKUI0cOubi4mLwFAHB/3KkAT6ykpCSFhoZq3759cnZ21rVr13Tz5k3lypVLL7zwgv773/+qQ4cOyp07t9544w25ubmpfv36kiQvLy+TqweAh2MkjCfahQsX9PnnnysiIkIBAQF67733NG/ePG3dulW///67cuXKpSJFiigsLEyXLl3Spk2bVK1aNbPLBoBUYSSMJ5q3t7f69eunTz/9VNu2bVOxYsU0ePBgSdKuXbt0/vx5TZs2TXnz5tWlS5eUJ08ekysGgNRjJIynQvKJWLt27VLTpk314YcfWqclJCQoKSlJ0dHRyps3r4lVAkDaEMJ4aly4cEEjRozQb7/9pqZNm+qDDz6QxIMYADy9CGE8VZKDeO/evapdu7aCg4PNLgkAHhl3zMJTxdvbWx999JGKFSumHTt26OrVq2aXBACPjJEwnkoXL16UJOXLl8/kSgDg0RHCAACYhN3RAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABMQggDAGASQhjAPbVv315Nmza1vq9Ro4bef//9x17Hpk2bZLFYdO3atfv2sVgsWrZsWaqXOWTIEFWoUOFf1XXq1ClZLBaFh4f/q+UgayOEgadI+/btZbFYZLFY5OjoqICAAA0dOlR37tzJ8HUvWbJEw4YNS1Xf1AQnAJ4nDDx1GjRooFmzZikuLk6rV6/We++9p2zZsmngwIEp+sbHx8vR0TFd1psrV650WQ6A/2EkDDxlnJyc5O3tLT8/P3Xp0kV16tTRDz/8IOl/u5BHjBghHx8flShRQpJ09uxZtWzZUh4eHsqVK5eaNGmiU6dOWZeZmJio3r17y8PDQ7lz51b//v31zzva/nN3dFxcnAYMGCBfX185OTkpICBAM2fO1KlTp1SzZk1JkqenpywWi9q3by9JSkpKUkhIiAoXLqzs2bOrfPnyWrRokc16Vq9ereLFiyt79uyqWbOmTZ2pNWDAABUvXlw5cuRQkSJFNGjQICUkJKTo99VXX8nX11c5cuRQy5YtFR0dbTN9xowZKlWqlJydnVWyZElNnjw5zbUAD0IIA0+57NmzKz4+3vr+559/1pEjR7Ru3TqtXLlSCQkJql+/vlxdXbV161Zt375dOXPmVIMGDazzjR49WqGhofr666+1bds2RUVFaenSpQ9c71tvvaVvv/1WEyZM0KFDh/TVV18pZ86c8vX11eLFiyVJR44cUWRkpMaPHy9JCgkJ0Zw5czR16lQdPHhQvXr10ptvvqnNmzdLuvtjoXnz5nr11VcVHh6ujh07Wp8bnRaurq4KDQ3VH3/8ofHjx2v69OkaO3asTZ/jx4/r+++/14oVK/Tjjz9q79696tq1q3X6vHnzNHjwYI0YMUKHDh3Sp59+qkGDBmn27Nlprge4LwPAUyMoKMho0qSJYRiGkZSUZKxbt85wcnIy+vbta52eL18+Iy4uzjrP3LlzjRIlShhJSUnWtri4OCN79uzG2rVrDcMwjPz58xsjR460Tk9ISDAKFixoXZdhGEb16tWNnj17GoZhGEeOHDEkGevWrbtnnRs3bjQkGX/99Ze17fbt20aOHDmMHTt22PR9++23jdatWxuGYRgDBw40SpcubTN9wIABKZb1T5KMpUuX3nf6qFGjjEqVKlnff/LJJ4a9vb1x7tw5a9uaNWsMOzs7IzIy0jAMwyhatKgxf/58m+UMGzbMqFKlimEYhnHy5ElDkrF37977rhd4GI4JA0+ZlStXKmfOnEpISFBSUpLatGmjIUOGWKeXLVvW5jjwvn37dPz4cbm6utos5/bt24qIiFB0dLQiIyNVuXJl6zQHBwc999xzKXZJJwsPD5e9vb2qV6+e6rqPHz+umzdvqm7dujbt8fHxevbZZyVJhw4dsqlDkqpUqZLqdST77rvvNGHCBEVEROjGjRu6c+eO3NzcbPoUKlRIBQoUsFlPUlKSjhw5IldXV0VEROjtt99Wp06drH3u3Lkjd3f3NNcD3A8hDDxlatasqSlTpsjR0VE+Pj5ycLD939jFxcXm/Y0bN1SpUiXNmzcvxbK8vLweqYbs2bOneZ4bN25IklatWmUTftLd49zpZefOnWrbtq2Cg4NVv359ubu7a8GCBRo9enSaa50+fXqKHwX29vbpVitACANPGRcXFwUEBKS6f8WKFfXdd98pb968KUaDyfLnz69du3apWrVqku6O+MLCwlSxYsV79i9btqySkpK0efNm1alTJ8X05JF4YmKita106dJycnLSmTNn7juCLlWqlPUks2S//PLLwzfyb3bs2CE/Pz999NFH1rbTp0+n6HfmzBmdP39ePj4+1vXY2dmpRIkSypcvn3x8fHTixAm1bds2TesH0oITs4BMrm3btsqTJ4+aNGmirVu36uTJk9q0aZN69Oihc+fOSZJ69uypzz77TMuWLdPhw4fVtWvXB17j6+/vr6CgIP33v//VsmXLrMv8/vvvJUl+fn6yWCxauXKlLl++rBs3bsjV1VV9+/ZVr169NHv2bEVERGjPnj2aOHGi9WSnzp0769ixY+rXr5+OHDmi+fPnKzQ0NE3bW6xYMZ05c0YLFixQRESEJkyYcM+TzJydnRUUFKR9+/Zp69at6tGjh1q2bClvb29JUnBwsEJCQjRhwgQdPXpU+/fv16xZszRmzJg01QM8CCEMZHI5cuTQli1bVKhQITVv3lylSpXS22+/rdu3b1tHxn369FG7du0UFBSkKlWqyNXVVc2aNXvgcqdMmaIWLVqoa9euKlmypDp16qTY2FhJUoECBRQcHKwPPvhA+fLlU7du3SRJw4YN06BBgxQSEqJSpUqpQYMGWrVqlQoXLizp7nHaxYsXa9myZSpfvrymTp2qTz/9NE3b27hxY/Xq1UvdunVThQoVtGPHDg0aNChFv4CAADVv3lz/+c9/VK9ePZUrV87mEqSOHTtqxowZmjVrlsqWLavq1asrNDTUWiuQHizG/c68AAAAGYqRMAAAJiGEAQAwCSEMAIBJCGEAAExCCAMAYBJCGAAAkxDCAACYhBAGAMAkhDAAACYhhAEAMAkhDACASQhhAABM8n/s1e6wi/Tw6AAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Predict the labels of the test data using the trained model\n",
        "y_pred = model_over.predict(original_Xtest)\n",
        "y_pred_nn_over = (y_pred > 0.5).astype('int')\n",
        "\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "cm_over_nn = confusion_matrix(original_ytest, y_pred_nn_over)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plot_confusion_matrix(cm_over_nn,labels,title=\"Confusion Matrix\",cmap=plt.cm.Greens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jz68tiE9say"
      },
      "source": [
        "Comparison of All"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eDOTRtRG9vtK",
        "outputId": "552b8b7d-8a81-4436-d101-fc05cd34a8d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[21395   824]\n",
            " [   43   130]]\n",
            "Confusion matrix, without normalization\n",
            "[[21560   659]\n",
            " [   50   123]]\n",
            "Confusion matrix, without normalization\n",
            "[[21421   798]\n",
            " [   43   130]]\n",
            "Confusion matrix, without normalization\n",
            "[[20869  1350]\n",
            " [   54   119]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x1200 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Initialize the figure\n",
        "fig = plt.figure(figsize=(12, 12))\n",
        "\n",
        "# Create the subplots\n",
        "ax1 = fig.add_subplot(221)\n",
        "ax2 = fig.add_subplot(222)\n",
        "ax3 = fig.add_subplot(223)\n",
        "ax4 = fig.add_subplot(224)\n",
        "\n",
        "# Plot Undersampling LR\n",
        "plot_confusion_matrix(cm_und, labels, title=\"UnderSampling Logistic Regression\", cmap=plt.cm.Blues, ax=ax1)\n",
        "\n",
        "# Plot Undersampling NN\n",
        "plot_confusion_matrix(cm_und_nn, labels, title=\"UnderSampling Neural Network\", cmap=plt.cm.Reds, ax=ax2)\n",
        "\n",
        "# Plot Oversampling LR\n",
        "plot_confusion_matrix(cm_over, labels, title=\"OverSampling Logistic Regression\", cmap=plt.cm.Oranges, ax=ax3)\n",
        "\n",
        "# Plot Oversampling NN\n",
        "plot_confusion_matrix(cm_over_nn, labels, title=\"OverSampling Neural Network\", cmap=plt.cm.Greens, ax=ax4)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcwMG1xOk5eG"
      },
      "source": [
        "CBA and MCC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCVOaocvkO6W",
        "outputId": "523703d6-8d69-4a4a-d502-c4de97f83e07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Set 1 - CBA: 0.7236, MCC: 0.3097\n",
            "Set 2 - CBA: 0.6899, MCC: 0.3249\n",
            "Set 3 - CBA: 0.7245, MCC: 0.3143\n",
            "Set 4 - CBA: 0.6461, MCC: 0.2218\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import matthews_corrcoef, confusion_matrix\n",
        "\n",
        "# Calculate the confusion matrix and MCC for each set of predicted labels\n",
        "cm_1 = cm_und\n",
        "cm_2 = cm_und_nn\n",
        "cm_3 = cm_over\n",
        "cm_4 = cm_over_nn\n",
        "\n",
        "mcc_1 = matthews_corrcoef(original_ytest, y_pred_lr_und)\n",
        "mcc_2 = matthews_corrcoef(original_ytest, y_pred_nn_und)\n",
        "mcc_3 = matthews_corrcoef(original_ytest, y_pred_lr_over)\n",
        "mcc_4 = matthews_corrcoef(original_ytest, y_pred_nn_over)\n",
        "\n",
        "# Calculate the CBA for each set of predicted labels\n",
        "cba_1 = (cm_1[0][0] / (cm_1[0][0] + cm_1[0][1])) * (cm_1[1][1] / (cm_1[1][0] + cm_1[1][1]))\n",
        "cba_2 = (cm_2[0][0] / (cm_2[0][0] + cm_2[0][1])) * (cm_2[1][1] / (cm_2[1][0] + cm_2[1][1]))\n",
        "cba_3 = (cm_3[0][0] / (cm_3[0][0] + cm_3[0][1])) * (cm_3[1][1] / (cm_3[1][0] + cm_3[1][1]))\n",
        "cba_4 = (cm_4[0][0] / (cm_4[0][0] + cm_4[0][1])) * (cm_4[1][1] / (cm_4[1][0] + cm_4[1][1]))\n",
        "\n",
        "# Print the CBA and MCC values for each set of predicted labels\n",
        "print(\"Set 1 - CBA: {:.4f}, MCC: {:.4f}\".format(cba_1, mcc_1))\n",
        "print(\"Set 2 - CBA: {:.4f}, MCC: {:.4f}\".format(cba_2, mcc_2))\n",
        "print(\"Set 3 - CBA: {:.4f}, MCC: {:.4f}\".format(cba_3, mcc_3))\n",
        "print(\"Set 4 - CBA: {:.4f}, MCC: {:.4f}\".format(cba_4, mcc_4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E--RFHJGKIgc",
        "outputId": "35d6c58a-29c4-4f46-8933-50b216d46381"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Set 1 - CBA: 0.7236, MCC: 0.3097, F1 score: 0.2307, Accuracy: 0.9613\n",
            "Set 2 - CBA: 0.6899, MCC: 0.3249, F1 score: 0.2576, Accuracy: 0.9683\n",
            "Set 3 - CBA: 0.7245, MCC: 0.3143, F1 score: 0.2361, Accuracy: 0.9624\n",
            "Set 4 - CBA: 0.6461, MCC: 0.2218, F1 score: 0.1449, Accuracy: 0.9373\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Calculate the F1 score for each set of predicted labels\n",
        "f1_1 = f1_score(original_ytest, y_pred_lr_und)\n",
        "f1_2 = f1_score(original_ytest, y_pred_nn_und)\n",
        "f1_3 = f1_score(original_ytest, y_pred_lr_over)\n",
        "f1_4 = f1_score(original_ytest, y_pred_nn_over)\n",
        "\n",
        "# Calculate the accuracy score for each set of predicted labels\n",
        "acc_1 = accuracy_score(original_ytest, y_pred_lr_und)\n",
        "acc_2 = accuracy_score(original_ytest, y_pred_nn_und)\n",
        "acc_3 = accuracy_score(original_ytest, y_pred_lr_over)\n",
        "acc_4 = accuracy_score(original_ytest, y_pred_nn_over)\n",
        "\n",
        "# Print the CBA, MCC, F1 score, and accuracy values for each set of predicted labels\n",
        "print(\"Set 1 - CBA: {:.4f}, MCC: {:.4f}, F1 score: {:.4f}, Accuracy: {:.4f}\".format(cba_1, mcc_1, f1_1, acc_1))\n",
        "print(\"Set 2 - CBA: {:.4f}, MCC: {:.4f}, F1 score: {:.4f}, Accuracy: {:.4f}\".format(cba_2, mcc_2, f1_2, acc_2))\n",
        "print(\"Set 3 - CBA: {:.4f}, MCC: {:.4f}, F1 score: {:.4f}, Accuracy: {:.4f}\".format(cba_3, mcc_3, f1_3, acc_3))\n",
        "print(\"Set 4 - CBA: {:.4f}, MCC: {:.4f}, F1 score: {:.4f}, Accuracy: {:.4f}\".format(cba_4, mcc_4, f1_4, acc_4))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "uV-75z5OmHPz",
        "outputId": "1ddf5ae9-16fd-4102-94e4-1bc84dba503f"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the CBA and MCC values for each set of predicted\n",
        "# Define the CBA and MCC values\n",
        "cba_values = [cba_1, cba_2, cba_3, cba_4]\n",
        "mcc_values = [mcc_1, mcc_2, mcc_3, mcc_4]\n",
        "\n",
        "# Define the x-axis labels\n",
        "x_labels = [\"Set 1\", \"Set 2\", \"Set 3\", \"Set 4\"]\n",
        "\n",
        "# Create a figure with two subplots\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot the CBA values\n",
        "ax1.bar(x_labels, cba_values)\n",
        "ax1.set_title(\"CBA Values\")\n",
        "ax1.set_ylim(0, 1)\n",
        "\n",
        "# Plot the MCC values\n",
        "ax2.bar(x_labels, mcc_values)\n",
        "ax2.set_title(\"MCC Values\")\n",
        "ax2.set_ylim(0, 1)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rROb2FlYx0Ne"
      },
      "source": [
        "The CBA metric is defined as the product of the precision and recall, both of which are ratios between the correctly classified instances and the total instances. The precision measures the accuracy of the positive predictions made by the classifier, while the recall measures the completeness of the positive predictions.\n",
        "\n",
        "The CBA metric is a good way to evaluate the overall performance of a binary classifier because it takes into account both the accuracy and completeness of the predictions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79g1YvKByVoG"
      },
      "source": [
        "MCC stands for Matthews Correlation Coefficient and is a metric used to evaluate the performance of binary classifiers. It takes into account all four values in the confusion matrix, i.e., true positives, true negatives, false positives, and false negatives.\n",
        "\n",
        "The MCC metric is defined as the covariance between the predicted binary labels and the true binary labels, divided by the product of their standard deviations. It ranges from -1 to 1, where a score of 1 indicates perfect classification, 0 indicates a random classification, and -1 indicates perfect inverse classification.\n",
        "\n",
        "MCC is a useful metric because it is not affected by imbalanced class distributions, unlike some other metrics such as accuracy or precision. It is also useful when the classes are not equally important, as it considers all four values in the confusion matrix."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FI9OpeNyeqg"
      },
      "source": [
        "So MCC will be the main criteria for comparing algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0zfU2V5MHH4"
      },
      "source": [
        "# **XGBoost**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "ozPGPDwSMLDp",
        "outputId": "814fe429-7e45-4796-9750-ca200ae39fef"
      },
      "outputs": [
        {
          "data": {
            "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>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=42, ...)</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\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=42, ...)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Compute the ratio of negative to positive examples\n",
        "neg_pos_ratio = len(original_ytrain[original_ytrain==0]) / len(original_ytrain[original_ytrain==1])\n",
        "\n",
        "# Create the XGBClassifier with scale_pos_weight\n",
        "xgb_clf = xgb.XGBClassifier(n_estimators=500, learning_rate=0.01, max_depth=5, scale_pos_weight=neg_pos_ratio, random_state=42)\n",
        "\n",
        "# Fit the model to the training data\n",
        "xgb_clf.fit(original_Xtrain, original_ytrain)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "Gzf0yWT2NrNi",
        "outputId": "e000900e-6c81-4f06-cead-8eecd26a56d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[21661   558]\n",
            " [   55   118]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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O1qxZ9eGHH+rf//63fQSSVjw9PRUWFuYw727HWuPi4uTq6pqm68ajN2XKFDVr1ky1atXSn3/+mar3RkZGSpJ+//13+7y4uDidOHFChQsXvmefixcv6uLFi/Y+QHqRM2dOlSpVSsePH79re7Vq1SRJx48fV4kSJfT111/rjz/+0K5du+w/Sr/66ivlyZNH3377rdq2bfvYas/MjO+ObtCggXx8fDR27Nj79lu+fLnKly8vNzc3FS1aVBMnTnzgsm02m3x8fBymbNmyacSIEapUqZJmz56tYsWKKWvWrJKk9evXq2bNmsqZM6dy586tZs2a2X81StLWrVtls9l05coV+7zQ0FDZbDZFRETY5wUHB6tw4cLKnj27WrZsqUuXLqXuj4IHmjJlilq2bKl69eo5/O1TKiQkRDdv3lTp0qXt81xcXFS0aFH98ccfkqRdu3ZJkkOfXLlyKU+ePPY+QHoRGxur8PBwFShQ4K7toaGhkmRvv379upycnBz2Oia9TkxMfOT14g7jIezs7KwxY8ZoypQpOnPmzF37hISEqE2bNnrttdd08OBBjRgxQkOHDlVwcPA/Xu/x48e1fPlyrVixwv7hvHbtmt555x3t3btXP/zwg5ycnNSyZctUfSD37NmjN954Q3369FFoaKjq1q2rUaNG/eM6kdy0adPUsWNHtW/fXlevXlX+/PmVP39++48pSfaTUZLO8KxQoYICAwPte1uuXr2qmTNnKigoSA0bNlSpUqU0Y8YMSbLvzjt27JhWrVqlTz/9VNWrV1f58uU1d+5cHTlyRFu2bLGvq2zZsgoMDJS3t7e8vLwUGBiowMDAx/XnQCY1aNAgbdu2TREREdq9e7dat24tZ2dnvfbaawoPD9eoUaMUEhKiiIgIrV69Wl27dtXzzz+vihUrSrozALp8+bL69u2rw4cP69ChQ3rjjTfk4uKiOnXqmN24zMQyqHPnzlaLFi0sy7KsZ5991vrXv/5lWZZlrVy50vp7ae3bt7caNmzo8N5BgwZZ5cqVu+ey58yZY0my3N3d7VP+/Pkty7Ks4cOHW1myZLHOnz9/3/ouXLhgSbIOHjxoWZZlbdmyxZJkXb582d5n//79liTr5MmTlmVZVrt27awXXnjBYTlt27a1vLy87rmemzdvWtHR0fbp9OnTliSme0z30rlzZ3uf4cOHP7CPi4uLNWHCBOvcuXNWdHS09f3331vlypVzWJeHh4c1e/ZsKyoqyrp48aK1fPlyq1ChQg59kv7b/y/Tf6f0PsXHxzM9xNSmTRurQIEClqurq1WwYEGrTZs2VlhYmBUfH2+dPHnSev755y1vb2/Lzc3N8vf3twYMGGBFRUU5LGPdunXWc889Z3l5eVm5cuWy6tata+3cudP4tj3pU1RUlCXJio6Ovuf3VRLjx4STfPjhh6pXr54GDhyYrO3w4cNq0aKFw7waNWpo0qRJSkhIkLOz812X6eHhoX379tlfJx33kKQiRYoob968Dv2PHTumYcOGac+ePbp48aJ9BHzq1CkFBASkaDsOHz6c7DrR6tWra/369fd8z9ixYxUUFJSi5UMpOmkvKCjogX/T+Ph4DRo0SIMGDbpnn6tXr6pbt27q1q3bPfsUK1bsgfUAae1/byzzd35+fg57a+6lYcOGatiwYVqWhVQyvjs6Sa1atdS4cWMNGTIkzZbp5OQkf39/+5R0WYokubu7J+v/0ksvKSoqSrNmzdKePXu0Z88eSXdO2ElaniRZf7u/SVrcWWbIkCGKjo62T6dPn37oZQIA0r90MxKWpHHjxqlSpUoOJ8JId465JZ0kk2TXrl0qVarUPUfBqXXp0iWFhYVp1qxZev755yVJO3fudOiTNHKOjIy0H1tMOp7891qTwjvJTz/9dN91u7m5yc3N7WHKBwA8gdJVCFeoUEEdOnTQ5MmTHeYPGDBATz/9tEaOHKm2bdvqxx9/1NSpUzV9+vQ0W3euXLmUO3duff755ypQoIBOnTql9957z6GPv7+//Pz8NGLECI0ePVpHjx5NdpZ2v379VKNGDX300Udq0aKFNmzYcN9d0QCAzCvd7I5O8sEHHyQ7G7lKlSpasmSJFi1apICAAA0bNkwffPCBunTpkmbrdXJy0qJFixQSEqKAgAC9/fbbmjBhgkOfLFmy6Ouvv9aRI0dUsWJFffjhh8nOfH722Wc1a9YsffrppwoMDNT333+v//73v2lWJwAg4+ABDukQD3BAZhDPAxyQQfEABwAAngCEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhrikpNO3336b4gU2b978HxcDAEBmkqIQfvnll1O0MJvNpoSEhIepBwCATCNFIZyYmPio6wAAINN5qGPCN2/eTKs6AADIdFIdwgkJCRo5cqQKFiyoHDly6MSJE5KkoUOH6osvvkjzAgEAyKhSHcKjR49WcHCwxo8fL1dXV/v8gIAAzZ49O02LAwAgI0t1CM+bN0+ff/65OnToIGdnZ/v8wMBAHTlyJE2LAwAgI0t1CP/555/y9/dPNj8xMVG3b99Ok6IAAMgMUh3C5cqV044dO5LNX7ZsmSpXrpwmRQEAkBmk6BKlvxs2bJg6d+6sP//8U4mJiVqxYoXCwsI0b948rVmz5lHUCABAhpTqkXCLFi20evVqbdq0Se7u7ho2bJgOHz6s1atXq2HDho+iRgAAMiSbZVmW6SLgKCYmRl5eXqbLAB6p+Ph40yUAj0RMTIy8vb0VHR0tT0/P+/ZN9e7oJHv37tXhw4cl3TlOXLVq1X+6KAAAMqVUh/CZM2fUrl077dq1Szlz5pQkXblyRc8995wWLVqkQoUKpXWNAABkSKk+JtytWzfdvn1bhw8fVlRUlKKionT48GElJiaqW7duj6JGAAAypFQfE86WLZt2796d7HKkkJAQPf/887p+/XqaFpgZcUwYmQHHhJFRpeaYcKpHwn5+fne9KUdCQoJ8fX1TuzgAADKtVIfwhAkT1LdvX+3du9c+b+/everfv78++uijNC0OAICMLEW7o3PlyiWbzWZ/fe3aNcXHx8vF5c55XUn/dnd3V1RU1KOrNpNgdzQyA3ZHI6NK80uUJk2alBZ1AQCAv0lRCHfu3PlR1wEAQKbzj2/WIUk3b95UXFycw7wHDb0BAMAdqT4x69q1a+rTp4/y5csnd3d35cqVy2ECAAApk+oQfvfdd7V582bNmDFDbm5umj17toKCguTr66t58+Y9ihoBAMiQUn2zjsKFC2vevHmqU6eOPD09tW/fPvn7+2v+/Pn6+uuvtXbt2kdVa6bB2dHIDDg7GhnVI71ZR1RUlIoXLy7pzvHfpEuSatasqe3bt/+DcgEAyJxSHcLFixfXyZMnJUllypTRkiVLJEmrV6+2P9ABAAA8WKpDuGvXrjpw4IAk6b333tO0adOUNWtWvf322xo0aFCaFwgAQEaV6mPC/+uPP/5QSEiI/P39VbFixbSqK1PjmDAyA44JI6NK8ztm3U+RIkVUpEiRh10MAACZTopCePLkySleYL9+/f5xMQAAZCYp2h1drFixlC3MZtOJEyceuqjMjt3RyAzYHY2MKs13RyedDY3HKyX/AQEA6Yuzs3OK+6b67GgAAJA2CGEAAAwhhAEAMIQQBgDAEEIYAABD/lEI79ixQx07dlT16tX1559/SpLmz5+vnTt3pmlxAABkZKkO4eXLl6tx48bKli2b9u/fr1u3bkm6cznNmDFj0rxAAAAyqlSH8KhRozRz5kzNmjVLWbJksc+vUaOG9u3bl6bFAQCQkaU6hMPCwlSrVq1k8728vHTlypW0qAkAgEwh1SHs4+Oj48ePJ5u/c+dOFS9ePE2KAgAgM0h1CHfv3l39+/fXnj17ZLPZdPbsWS1YsEADBw5Uz549H0WNAABkSKl+lOF7772nxMRE1a9fX9evX1etWrXk5uamgQMHqm/fvo+iRgAAMqQUPUXpbuLi4nT8+HHFxsaqXLlyypEjR1rXlmklPUWJBzgAwJMnNd/hqR4JJ3F1dVW5cuX+6dsBAMj0Uh3CdevWlc1mu2f75s2bH6ogAAAyi1SHcKVKlRxe3759W6Ghofrtt9/UuXPntKoLAIAML9Uh/Mknn9x1/ogRIxQbG/vQBQEAkFmk2QMcOnbsqC+//DKtFgcAQIaXZiH8448/KmvWrGm1OAAAMrxU745u1aqVw2vLshQZGam9e/dq6NChaVYYAAAZXapD2MvLy+G1k5OTSpcurQ8++ECNGjVKs8IAAMjoUhXCCQkJ6tq1qypUqKBcuXI9qpoAAMgUUnVM2NnZWY0aNeJpSQAApIFUn5gVEBCgEydOPIpaAADIVFIdwqNGjdLAgQO1Zs0aRUZGKiYmxmECAAApk+IHOHzwwQcaMGCAPDw8/u/Nf7t9pWVZstlsSkhISPsqMxke4AAAT67UfIenOISdnZ0VGRmpw4cP37df7dq1U14p7ooQBoAn1yN5ilJSVhOyAACkjVQdE77f05MAAEDqpOo64VKlSj0wiKOioh6qIAAAMotUhXBQUFCyO2YBAIB/JlUh/NprrylfvnyPqhYAADKVFB8T5ngwAABpK8UhnMIrmQAAQAqleHd0YmLio6wDAIBMJ9W3rQQAAGmDEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhAAAMIYQBADCEEAYAwBBCGAAAQwhhZAojRoyQzWZzmMqUKWNvr1OnTrL2Hj16GKwYuL/t27frpZdekq+vr2w2m1atWuXQvmLFCjVq1Ei5c+eWzWZTaGhosmWcO3dOnTp1ko+Pj9zd3VWlShUtX7788WwAJBHCyETKly+vyMhI+7Rz506H9u7duzu0jx8/3lClwINdu3ZNgYGBmjZt2j3ba9asqQ8//PCey3j99dcVFhamb7/9VgcPHlSrVq3Upk0b7d+//1GVjf/hYroA4HFxcXGRj4/PPduzZ89+33YgPWnatKmaNm16z/ZOnTpJkiIiIu7ZZ/fu3ZoxY4aeeeYZSdJ///tfffLJJwoJCVHlypXTtF7cHSNhZBrHjh2Tr6+vihcvrg4dOujUqVMO7QsWLFCePHkUEBCgIUOG6Pr164YqBR6P5557TosXL1ZUVJQSExO1aNEi3bx5U3Xq1DFdWqbBSPgR69Kli65cuZLseA0er2rVqik4OFilS5dWZGSkgoKC9Pzzz+u3336Th4eH2rdvryJFisjX11e//vqrBg8erLCwMK1YscJ06cAjs2TJErVt21a5c+eWi4uLsmfPrpUrV8rf3990aZlGhg3hLl26aO7cucnmHzt2jA9YJvT33XYVK1ZUtWrVVKRIES1ZskRvvPGG3nzzTXt7hQoVVKBAAdWvX1/h4eEqUaKEiZKBR27o0KG6cuWKNm3apDx58mjVqlVq06aNduzYoQoVKpguL1PIsCEsSU2aNNGcOXMc5uXNm9fhdVxcnFxdXR9nWUgHcubMqVKlSun48eN3ba9WrZok6fjx44QwMqTw8HBNnTpVv/32m8qXLy9JCgwM1I4dOzRt2jTNnDnTcIWZQ4Y+Juzm5iYfHx+HqX79+urTp4/eeust5cmTR40bN5Ykffzxx6pQoYLc3d3l5+enXr16KTY21r6sESNGqFKlSg7LnzRpkooWLWp/nZCQoHfeeUc5c+ZU7ty59e6778qyrMexqUil2NhYhYeHq0CBAndtT7qc417twJMu6ZwHJyfHGHB2dlZiYqKJkjKlDB3C9zJ37ly5urpq165d9l97Tk5Omjx5sg4dOqS5c+dq8+bNevfdd1O13IkTJyo4OFhffvmldu7cqaioKK1cufKB77t165ZiYmIcJqStgQMHatu2bYqIiNDu3bvVsmVLOTs7q127dgoPD9fIkSMVEhKiiIgIffvtt3r99ddVq1YtVaxY0XTpwF3FxsYqNDTU/oPx5MmTCg0NtZ9wGBUVpdDQUP3++++SpLCwMIWGhurcuXOSpDJlysjf31///ve/9fPPPys8PFwTJ07Uxo0b9fLLL5vYpMzJyqA6d+5sOTs7W+7u7vbplVdesWrXrm1Vrlz5ge9funSplTt3bvvr4cOHW4GBgQ59PvnkE6tIkSL21wUKFLDGjx9vf3379m2rUKFCVosWLe67ruHDh1uSkk3R0dEp2lY8WNu2ba0CBQpYrq6uVsGCBa22bdtax48ftyzLsk6dOmXVqlXL8vb2ttzc3Cx/f39r0KBB/P2Rrm3ZsuWu3xudO3e2LMuy5syZc9f24cOH25dx9OhRq1WrVla+fPms7NmzWxUrVrTmzZtnZoMykOjo6BR/h2foY8J169bVjBkz7K/d3d3Vrl07Va1aNVnfTZs2aezYsTpy5IhiYmIUHx+vmzdv6vr168qePfsD1xUdHa3IyEj7sUTpznWpTz311AN3SQ8ZMkTvvPOO/XVMTIz8/PxSsolIoUWLFt2zzc/PT9u2bXuM1QAPr06dOvf9bunSpYu6dOly32WULFmSO2QZlqFD2N3d/a5nQru7uzu8joiIULNmzdSzZ0+NHj1a3t7e2rlzp9544w3FxcUpe/bscnJySvaBv337dprU6ebmJjc3tzRZFgDgyZEpjwn/r5CQECUmJmrixIl69tlnVapUKZ09e9ahT968eXXu3DmHIP77vVi9vLxUoEAB7dmzxz4vPj5eISEhj7x+AMCTiRCW5O/vr9u3b2vKlCk6ceKE5s+fn+z0/Dp16ujChQsaP368wsPDNW3aNK1bt86hT//+/TVu3DitWrVKR44cUa9evXTlypXHuCUAgCcJIaw718Z9/PHH+vDDDxUQEKAFCxZo7NixDn3Kli2r6dOna9q0aQoMDNTPP/+sgQMHOvQZMGCAOnXqpM6dO6t69ery8PBQy5YtH+emAACeIDbrQWcN4bGLiYmRl5eXoqOj5enpabocAEAqpOY7nJEwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGuJguAMlZliVJiomJMVwJACC1kr67k77L74cQToeuXr0qSfLz8zNcCQDgn7p69aq8vLzu28dmpSSq8VglJibq7Nmz8vDwkM1mM11OhhcTEyM/Pz+dPn1anp6epssB0hyf8cfLsixdvXpVvr6+cnK6/1FfRsLpkJOTkwoVKmS6jEzH09OTLyhkaHzGH58HjYCTcGIWAACGEMIAABhCCCPTc3Nz0/Dhw+Xm5ma6FOCR4DOefnFiFgAAhjASBgDAEEIYAABDCGEAAAwhhIGHtHjxYtMlAHhCEcLAQzh27Jh69+6tRo0amS4FwBOIEAYegp+fn2bPnq1Tp06pSZMmpssBUm3hwoVavXq16TIyLW5bCfxDiYmJypo1q15++WU5Oztr8ODBevXVV7V06VLTpQEpcurUKX3++eeyLEtubm7s0TGAkTDwDyU9XGPz5s367rvv5ObmpuXLl6tly5aGKwNSpnDhwho2bJhy5syp8ePHa+3ataZLynQIYeAfstls2rBhgxo1aqTSpUtr6NChGjx4sPbu3aumTZuaLg+4r4SEBElSvXr11L17d+XKlUvjx4/Xtm3bDFeWuXDHLOAfSkhIUL9+/XTt2jUFBwdLkm7duqXVq1erf//+euaZZ7Ry5UqzRQL3YFmWbDab1qxZoxUrVujgwYMKCQnRs88+q6CgIDVs2NB0iZkCI2HgH3J2dtb58+d14sQJ+zw3Nzc1b95cr776qr755hs1aNDAYIXAvdlsNu3YsUMtWrTQ008/rRkzZuirr76SJI0dO1abNm0yXGHmQAgDqXTp0iX7v1966SXdunVLGzZssM9zdXVVlSpV9Mwzz+jmzZv6448/TJQJPNDGjRtVu3Zt9ezZU0899ZTat2+vESNGKDY2VsOGDdP27dtNl5jhEcJAKvz+++/y9fXVd999J0mqUaOGXFxc9Nlnn2ndunX2focOHdKzzz6rDRs2qEiRIqbKBe7Ly8tLly9fVlRUlH1eo0aN1KNHD4WEhKh///7auHGjwQozPkIYSIVixYqpbdu2at++vdatW6cSJUpo+vTpunDhgv773/+qSpUqatasmaZNm6Y333xT7u7upksGHPz+++/2fxctWlQRERHasmWL/n56UOHChVWpUiU99dRTKl26tIkyMw1OzALuI+nklb//+8aNG3rrrbc0b948LV++XC+88IIiIiK0f/9+rVu3Tnnz5lWHDh1Urlw5w9UDjs6cOaNatWqpRo0amj9/viTpzTff1Ndff60vvvhCNWvWlK+vr95//31FR0dr1KhRypUrl+GqMzZCGHiALVu2KH/+/CpXrlyyIJ4/f75Wrlypxo0b2/v/PbiB9CQ6Olpz587VjBkzVKNGDc2ePVuS1KNHDy1fvly5cuWSl5eXDh06pJ9++kkVK1Y0XHHGRwgD9xEXF6eWLVtq06ZNCg0NVdmyZe0hGx0drdatW+vQoUOaM2cOt61EunO3H4QxMTFauHChPvroI9WtW1ezZs2SJK1du1Znz55VTEyMmjdvLn9/fxMlZzqEMPAAp06dUv/+/bVz505t27bNYTdznz59NGfOHHl5eenYsWMcA0a6s3PnTm3atEkjRoywz4uOjtaiRYs0ZswYvfjii5o+fbq5AjM5TswC/ubvv0kTExMl3TlJZdq0aapWrZrq1KnjcGJL1qxZtWTJEh04cIAARroTFxenjRs36osvvtDIkSPt8728vNS+fXs1adJEM2fOVNeuXQ1WmbnxAAfg/0vadbd582YtXbpUf/31l+rVq6cePXrI19dXs2bNUo8ePVS9enV17NhRly5d0g8//KCePXsqb968pssH7JI+y66ururWrZtcXFy0cOFCJSQk2EfEHh4eCgwMVGBgoI4dO6azZ8/K19fXbOGZECNh4P+z2WxatWqVWrdurejoaJUtW1Zvv/22Bg0apFOnTqlAgQJatmyZevfurbCwMMXGxuqHH35QiRIlTJcOSPq/PTnXrl2TZVm6deuW/Pz81LlzZ7Vt21ZLly512C0dGRmpVq1aad26dQSwIRwTBv6//fv3q1WrVhoyZIjefPNN3bx5U76+vrpy5Ypee+01jRs3ToULF5Yk3bhxQ87OznJ1dTVcNXBH0uh3w4YNmjZtmq5duyZvb29NmTJFPj4+On36tObOnauZM2cqd+7cKlKkiLZs2aKQkBCVKlXKdPmZFiNhZGp//w168eJFderUSW+++abOnDmjMmXKqGvXrtq0aZOWLVumCRMm6OjRo5KkbNmyEcBIV2w2m7755hu98sorCggIUKtWrXT+/HnVrFlTx44dk5+fn3r06KEFCxaoYsWKKlq0qH766ScC2DBGwsj05s6dq7i4OL3++usKCwtTuXLl9Morr8jb21szZsyQs7OzqlatqoMHD6pbt26aPn26XFw4nQLpy5EjR9SuXTt1795dvXr10unTp1WzZk1dvXpVWbJk0fbt2x3ufpWQkCBnZ2eDFUNiJIxM5O+/N5P+HR4ert69e+vSpUtyc3NTxYoVde3aNUVGRqpevXpyc3NTYmKi6tatq5UrV+qdd94hgJFuJH2O4+Li5Obmpjp16ujf//63zpw5o/r166tRo0batWuXcubMqRYtWujIkSP29xLA6QMjYWQKiYmJcnJy0sWLF5WYmKh8+fLp559/1tatW3XhwgVNmDDB3jcyMlIBAQHq1q2b2rVrp6VLl+rrr79WaGioPD09DW4FkNyyZcu0dOlSLVq0SCdOnFCJEiXUtWtXxcbGasGCBXJ1dVXLli31zTffqESJEjp06BCHUtIRRsLI8JICeN++fapataqOHTumy5cva+zYsQoKCtKZM2fs/RISElSgQAFNnz5dEyZMUOvWrTVnzhwtX76cAIZRSdetx8bG2ucdO3ZMQUFBql+/viSpRIkSunHjho4ePapatWrZw9bHx0erV6/W9u3bCeB0hpEwMrSkAD5w4IBq1KihHj166KOPPpJ05zZ9U6ZM0a5du7RlyxZVrVpVCQkJstlscnJy0tGjRxUTE6OCBQuqQIEChrcEmVnS5zgkJERt2rTRgQMHFB4ersWLF+vPP//U7NmzZbPZ7IdKmjVrphMnTmjatGlauXKlVq1apZ07d9rP7kc6YgEZVGJiomVZlnXgwAHL3d3dGjJkSLL2gwcPWo0aNbKqVKli7du3z7Isy4qPj7cSEhIee73A3SR9FkNDQy0PDw+rX79+lmVZVosWLSx3d3eratWq9r63b9+2LMuy9u7da9WsWdPy8/OzypUrZ/9sI/1hJIwM7eLFi6pQoYICAwO1fv16+/yhQ4cqJCREa9eu1aZNmzR58mRFRkZq1qxZqlSpkn3kAZiU9Dn89ddfVb16db311lsaPXq0JOnWrVvq1KmT9u/frz59+qhv374On9n4+HidOHFCefLkkbe3t6lNwAPwLYMM7erVq3rxxRcVEhKiVatWSZLGjh2rzz77TD179pQkNWjQQP3795efn59eeeUV/frrrwQw0gUnJyedPn1a9evXV7NmzewBLEmLFi1S1qxZFRAQoGXLlik4ONjelpCQIBcXF5UqVYoATue41gIZWrFixTR06FBlzZpVXbp0UZMmTbR161bNnz9fjRs3to806tevr7i4OM2fP18eHh6mywbsEhISVKxYMd28eVO7du1SjRo1NHbsWI0aNUo//vijChYsqH//+98KDg6Wk5OTunTpwuVHTxB2RyNTiIiI0KRJkzR9+nQNHjxYI0eOdLhuOOmZq9evX1f27NlNlQnc1bFjx9SvXz+5uroqf/78+uabbzR//nw1atRIknTu3Dn1799fhw8f1uDBg9WhQwfDFSOl2OeGTKFo0aLq27evevTooSlTpmjlypUODztPCmQCGOlRyZIl9emnn+rGjRv66quv9O6779oDOD4+Xj4+Pvrkk09UuXJl1axZ03C1SA1GwshUIiIiNHHiRM2bN09z587Vyy+/bLokIMXCw8PVq1cvOTs76/3337cH7u3bt5UlSxZuRfkEYiSMDOVBvymLFi2qAQMGqGvXrmrVqpXWrFnzmCoDHl6JEiU0depUWZalUaNGadeuXZKkLFmySOJWlE8iQhgZQlL4Jt1VKElCQkKyvkWLFlW/fv00cOBAlSxZ8rHUB6SVkiVLavLkycqSJYsGDhyon376yXRJeAjsjsYTz/r/z1H94YcftHLlSl25ckUBAQHq3r27cufOfc9ddPHx8TyMAU+sI0eOaOjQoZo4cSJ3wnqCEcLIEFatWqV27dqpY8eO+uOPP3T58mVdvHhRO3fuVMGCBTlWhgwpLi6Oe0E/4QhhPHGSRr5JLl68qIYNG6p9+/YaNGiQJOm3337TgAEDFB4erp9//pkbFgBIlzgmjCdG0u/F69evS3J8qkxkZKQqVapk71u2bFmNHz9eXl5eWrRokcP7ASC9IITxxLDZbDp//ryKFi2qJUuW2G8t6ePjIz8/P23bts3e19nZWRUrVpSLi4sOHz5sfz8ApCeEMJ4oTk5Oat68uTp16qRvvvnGPq9atWravHmzVqxYYe9rs9lUsGBB5cyZU5ZlMRIGkO5wTBjp2v8e/5Wk8+fPa/To0ZoyZYqWL1+uli1b6tKlS+rQoYOio6NVrVo11ahRQ9u3b9e8efO0Z88elSlTxtAWAMC9EcJIt5IernDt2jUlJCTI09PT3hYZGakxY8Zo2rRpWrp0qVq3bq1Lly5p3Lhx2rVrly5evCgfHx9NnjzZ4VgxAKQnhDDStWPHjqlNmzbKkSOHunfvLh8fH/s9c2/duqUBAwZo+vTpWrx4sV599VXFx8fLZrMpKipK2bNnl7u7u+EtAIB7404FSLcSExMVHBysAwcOKGvWrLpy5YquX78ub29vPfPMM/rXv/6lrl27Knfu3Grbtq08PT3VuHFjSVLevHkNVw8AD8ZIGOnauXPn9OGHHyo8PFz+/v7q3bu3FixYoB07dujXX3+Vt7e3ihcvrpCQEJ0/f15bt25VrVq1TJcNACnCSBjpmo+PjwYNGqQxY8Zo586dKlmypIYNGyZJ2rNnj86ePavPP/9c+fLl0/nz55UnTx7DFQNAyjESxhMh6USsPXv26OWXX9b7779vb7t9+7YSExMVHR2tfPnyGawSAFKHEMYT49y5cxo9erR++eUXvfzyy3rvvfck8SAGAE8uQhhPlKQg3r9/v+rXr6+goCDTJQHAP8Yds/BE8fHx0X/+8x+VLFlSu3fv1qVLl0yXBAD/GCNhPJH++usvSVL+/PkNVwIA/xwhDACAIeyOBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIYAABDCGEAAAwhhAHcVZcuXfTyyy/bX9epU0dvvfXWY69j69atstlsunLlyj372Gw2rVq1KsXLHDFihCpVqvRQdUVERMhmsyk0NPShloPMjRAGniBdunSRzWaTzWaTq6ur/P399cEHHyg+Pv6Rr3vFihUaOXJkivqmJDgB8Dxh4InTpEkTzZkzR7du3dLatWvVu3dvZcmSRUOGDEnWNy4uTq6urmmyXm9v7zRZDoD/w0gYeMK4ubnJx8dHRYoUUc+ePdWgQQN9++23kv5vF/Lo0aPl6+ur0qVLS5JOnz6tNm3aKGfOnPL29laLFi0UERFhX2ZCQoLeeecd5cyZU7lz59a7776r/72j7f/ujr5165YGDx4sPz8/ubm5yd/fX1988YUiIiJUt25dSVKuXLlks9nUpUsXSVJiYqLGjh2rYsWKKVu2bAoMDNSyZcsc1rN27VqVKlVK2bJlU926dR3qTKnBgwerVKlSyp49u4oXL66hQ4fq9u3byfp99tln8vPzU/bs2dWmTRtFR0c7tM+ePVtly5ZV1qxZVaZMGU2fPj3VtQD3QwgDT7hs2bIpLi7O/vqHH35QWFiYNm7cqDVr1uj27dtq3LixPDw8tGPHDu3atUs5cuRQkyZN7O+bOHGigoOD9eWXX2rnzp2KiorSypUr77ve119/XV9//bUmT56sw4cP67PPPlOOHDnk5+en5cuXS5LCwsIUGRmpTz/9VJI0duxYzZs3TzNnztShQ4f09ttvq2PHjtq2bZukOz8WWrVqpZdeekmhoaHq1q2b/bnRqeHh4aHg4GD9/vvv+vTTTzVr1ix98sknDn2OHz+uJUuWaPXq1Vq/fr3279+vXr162dsXLFigYcOGafTo0Tp8+LDGjBmjoUOHau7cuamuB7gnC8ATo3PnzlaLFi0sy7KsxMREa+PGjZabm5s1cOBAe3v+/PmtW7du2d8zf/58q3Tp0lZiYqJ93q1bt6xs2bJZGzZssCzLsgoUKGCNHz/e3n779m2rUKFC9nVZlmXVrl3b6t+/v2VZlhUWFmZJsjZu3HjXOrds2WJJsi5fvmyfd/PmTSt79uzW7t27Hfq+8cYbVrt27SzLsqwhQ4ZY5cqVc2gfPHhwsmX9L0nWypUr79k+YcIEq2rVqvbXw4cPt5ydna0zZ87Y561bt85ycnKyIiMjLcuyrBIlSlgLFy50WM7IkSOt6tWrW5ZlWSdPnrQkWfv377/neoEH4Zgw8IRZs2aNcuTIodu3bysxMVHt27fXiBEj7O0VKlRwOA584MABHT9+XB4eHg7LuXnzpsLDwxUdHa3IyEhVq1bN3ubi4qKnnnoq2S7pJKGhoXJ2dlbt2rVTXPfx48d1/fp1NWzY0GF+XFycKleuLEk6fPiwQx2SVL169RSvI8nixYs1efJkhYeHKzY2VvHx8fL09HToU7hwYRUsWNBhPYmJiQoLC5OHh4fCw8P1xhtvqHv37vY+8fHx8vLySnU9wL0QwsATpm7dupoxY4ZcXV3l6+srFxfH/43d3d0dXsfGxqpq1apasGBBsmXlzZv3H9WQLVu2VL8nNjZWkvTdd985hJ905zh3Wvnxxx/VoUMHBQUFqXHjxvLy8tKiRYs0ceLEVNc6a9asZD8KnJ2d06xWgBAGnjDu7u7y9/dPcf8qVapo8eLFypcvX7LRYJICBQpoz549qlWrlqQ7I76QkBBVqVLlrv0rVKigxMREbdu2TQ0aNEjWnjQST0hIsM8rV66c3NzcdOrUqXuOoMuWLWs/ySzJTz/99OCN/Jvdu3erSJEi+s9//mOf98cffyTrd+rUKZ09e1a+vr729Tg5Oal06dLKnz+/fH19deLECXXo0CFV6wdSgxOzgAyuQ4cOypMnj1q0aKEdO3bo5MmT2rp1q/r166czZ85Ikvr3769x48Zp1apVOnLkiHr16nXfa3yLFi2qzp0761//+pdWrVplX+aSJUskSUWKFJHNZtOaNWt04cIFxcbGysPDQwMHDtTbb7+tuXPnKjw8XPv27dOUKVPsJzv16NFDx44d06BBgxQWFqaFCxcqODg4VdtbsmRJnTp1SosWLVJ4eLgmT55815PMsmbNqs6dO+vAgQPasWOH+vXrpzZt2sjHx0eSFBQUpLFjx2ry5Mk6evSoDh48qDlz5ujjjz9OVT3A/RDCQAaXPXt2bd++XYULF1arVq1UtmxZvfHGG7p586Z9ZDxgwAB16tRJnTt3VvXq1eXh4aGWLVved7kzZszQK6+8ol69eqlMmTLq3r27rl27JkkqWLCggoKC9N577yl//vzq06ePJGnkyJEaOnSoxo4dq7Jly6pJkyb67rvvVKxYMUl3jtMuX75cq1atUmBgoGbOnKkxY8akanubN2+ut99+W3369FGlSpW0e/duDR06NFk/f39/tWrVSi+88IIaNWqkihUrOlyC1K1bN82ePVtz5sxRhQoVVLt2bQUHB9trBdKCzbrXmRcAAOCRYiQMAIAhhDAAAIYQwgAAGEIIAwBgCCEMAIAhhDAAAIYQwgAAGEIIAwBgCCEMAIAhhDAAAIYQwgAAGEIIAwBgyP8Dt+QCdkat73kAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Predict the labels of the test data using the trained model\n",
        "y_pred = xgb_clf.predict(original_Xtest)\n",
        "y_pred_xgb = (y_pred > 0.5).astype('int')\n",
        "\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "cm_xgb = confusion_matrix(original_ytest, y_pred_xgb)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plot_confusion_matrix(cm_xgb,labels,title=\"XGBoost\",cmap=plt.cm.Greys)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "finw4Ly_N9yf",
        "outputId": "8e940a39-10ee-49f0-b08b-fc0a6b5f669e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.33617528679087355"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mcc = matthews_corrcoef(original_ytest, y_pred_xgb)\n",
        "mcc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0j7tGCXRS41",
        "outputId": "b6dcbd07-62bc-4a4a-e224-148e80902171"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.656785696353717"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cba = (cm_1[0][0] / (cm_xgb[0][0] + cm_xgb[0][1])) * (cm_xgb[1][1] / (cm_xgb[1][0] + cm_xgb[1][1]))\n",
        "cba"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbdceeovL1An",
        "outputId": "060f83f2-dd7a-4cf1-bc20-ac5f6a04915f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.2779740871613663\n",
            "0.9726241514826723\n"
          ]
        }
      ],
      "source": [
        "f1 = f1_score(original_ytest, y_pred_xgb)\n",
        "acc = accuracy_score(original_ytest, y_pred_xgb)\n",
        "print(f1)\n",
        "print(acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHRStGvaHChV"
      },
      "source": [
        "# **Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UK24xU-VHFfW",
        "outputId": "2180931c-548a-4380-a2ad-fc92d000e276"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.98      0.99     22219\n",
            "           1       0.13      0.36      0.19       173\n",
            "\n",
            "    accuracy                           0.98     22392\n",
            "   macro avg       0.56      0.67      0.59     22392\n",
            "weighted avg       0.99      0.98      0.98     22392\n",
            "\n",
            "Confusion matrix:\n",
            " [[21783   436]\n",
            " [  110    63]]\n",
            "Share of Non-Fraud in Test Data: 0.9923\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(random_state=36)\n",
        "rf.fit(X_train_over,y_train_over)\n",
        "predicted=rf.predict(original_Xtest)\n",
        "print('Classification report:\\n', classification_report(original_ytest, predicted))\n",
        "conf_mat = confusion_matrix(y_true=original_ytest, y_pred=predicted)\n",
        "print('Confusion matrix:\\n', conf_mat)\n",
        "print('Share of Non-Fraud in Test Data:', 1-round(original_ytest.sum()/len(original_ytest),4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "7q0kTbrpIrfm",
        "outputId": "be057179-1c87-4566-9b40-a8ab545d009f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[21783   436]\n",
            " [  110    63]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Predict the labels of the test data using the trained model\n",
        "predicted\n",
        "y_pred_rf = (predicted > 0.5).astype('int')\n",
        "\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "cm_rf = confusion_matrix(original_ytest, y_pred_rf)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plot_confusion_matrix(cm_rf,labels,title=\"Random Forest\",cmap=plt.cm.Greys)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYdLCXH4Il9m",
        "outputId": "6e18c05f-25bd-44a7-9347-ab2354dc3413"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.20437171488581934"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import matthews_corrcoef, confusion_matrix\n",
        "mcc = matthews_corrcoef(original_ytest, y_pred_rf)\n",
        "mcc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKSV6IWaM8yz",
        "outputId": "e5e441cd-501c-43ca-ee01-31ea90e9267f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.35701595806536457"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cba = (cm_rf[0][0] / (cm_rf[0][0] + cm_rf[0][1])) * (cm_rf[1][1] / (cm_rf[1][0] + cm_rf[1][1]))\n",
        "cba"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFZ5YFUpM_An",
        "outputId": "717d22b6-d2a8-4dba-c9ac-85aa15d2f781"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.18749999999999997\n",
            "0.9756162915326902\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "f1 = f1_score(original_ytest, y_pred_rf)\n",
        "acc = accuracy_score(original_ytest, y_pred_rf)\n",
        "print(f1)\n",
        "print(acc)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyNvXT+mAK3aFrjBk36c9vFa",
      "include_colab_link": true,
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}