class_imbalance_handling_on_credit_card_data.ipynb

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      "name": "class imbalance handling on credit card data.ipynb",
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      "metadata": {
        "id": "nM81eVL4-0HQ",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBI4wcCf-0HX",
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      "source": [
        "df = pd.read_csv('credit_card_data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Merchant_id</th>\n",
              "      <th>Average Amount/transaction/day</th>\n",
              "      <th>Transaction_amount</th>\n",
              "      <th>Is declined</th>\n",
              "      <th>Total Number of declines/day</th>\n",
              "      <th>isForeignTransaction</th>\n",
              "      <th>isHighRiskCountry</th>\n",
              "      <th>Daily_chargeback_avg_amt</th>\n",
              "      <th>6_month_avg_chbk_amt</th>\n",
              "      <th>6-month_chbk_freq</th>\n",
              "      <th>isFradulent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>Y</td>\n",
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            "text/plain": [
              "   Merchant_id  Average Amount/transaction/day  ...  6-month_chbk_freq isFradulent\n",
              "0   3160040998                           100.0  ...                  0           Y\n",
              "1   3160040998                           100.0  ...                  0           Y\n",
              "2   3160041896                           185.5  ...                  0           Y\n",
              "3   3160141996                           185.5  ...                  0           Y\n",
              "4   3160241992                           500.0  ...                  6           Y\n",
              "\n",
              "[5 rows x 11 columns]"
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      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Merchant_id                       0\n",
              "Average Amount/transaction/day    0\n",
              "Transaction_amount                0\n",
              "Is declined                       0\n",
              "Total Number of declines/day      0\n",
              "isForeignTransaction              0\n",
              "isHighRiskCountry                 0\n",
              "Daily_chargeback_avg_amt          0\n",
              "6_month_avg_chbk_amt              0\n",
              "6-month_chbk_freq                 0\n",
              "isFradulent                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
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          },
          "execution_count": 4
        }
      ]
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3075 entries, 0 to 3074\n",
            "Data columns (total 11 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   Merchant_id                     3075 non-null   int64  \n",
            " 1   Average Amount/transaction/day  3075 non-null   float64\n",
            " 2   Transaction_amount              3075 non-null   float64\n",
            " 3   Is declined                     3075 non-null   object \n",
            " 4   Total Number of declines/day    3075 non-null   int64  \n",
            " 5   isForeignTransaction            3075 non-null   object \n",
            " 6   isHighRiskCountry               3075 non-null   object \n",
            " 7   Daily_chargeback_avg_amt        3075 non-null   int64  \n",
            " 8   6_month_avg_chbk_amt            3075 non-null   float64\n",
            " 9   6-month_chbk_freq               3075 non-null   int64  \n",
            " 10  isFradulent                     3075 non-null   object \n",
            "dtypes: float64(3), int64(4), object(4)\n",
            "memory usage: 264.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E41GkBqe-0Hn",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df['Is declined'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N    3018\n",
              "Y      57\n",
              "Name: Is declined, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QFozNBlR-0Hq",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df['isFradulent'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N    2627\n",
              "Y     448\n",
              "Name: isFradulent, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnOujqOa-0Hu",
        "colab_type": "code",
        "outputId": "325c6991-527a-42b5-fba5-b05e3897edf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df['isForeignTransaction'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N    2369\n",
              "Y     706\n",
              "Name: isForeignTransaction, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_ZFkHuG-0Hx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "df['isForeignTransaction'] = le.fit_transform(df['isForeignTransaction'])\n",
        "df['Is declined'] = le.fit_transform(df['Is declined'])\n",
        "df['isHighRiskCountry'] = le.fit_transform(df['isHighRiskCountry'])\n",
        "df['isFradulent'] = le.fit_transform(df['isFradulent'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PfTzKZk8-0H0",
        "colab_type": "code",
        "outputId": "48c69d95-75dd-4140-e72b-7bb10c772646",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df['isForeignTransaction'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2369\n",
              "1     706\n",
              "Name: isForeignTransaction, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df['isFradulent'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2627\n",
              "1     448\n",
              "Name: isFradulent, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngycvWdh-0H6",
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          "height": 257
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      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Merchant_id</th>\n",
              "      <th>Average Amount/transaction/day</th>\n",
              "      <th>Transaction_amount</th>\n",
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              "      <th>Total Number of declines/day</th>\n",
              "      <th>isForeignTransaction</th>\n",
              "      <th>isHighRiskCountry</th>\n",
              "      <th>Daily_chargeback_avg_amt</th>\n",
              "      <th>6_month_avg_chbk_amt</th>\n",
              "      <th>6-month_chbk_freq</th>\n",
              "      <th>isFradulent</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3160141996</td>\n",
              "      <td>185.5</td>\n",
              "      <td>5008.5</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3160241992</td>\n",
              "      <td>500.0</td>\n",
              "      <td>26000.0</td>\n",
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              "      <td>800</td>\n",
              "      <td>677.2</td>\n",
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            "text/plain": [
              "   Merchant_id  Average Amount/transaction/day  ...  6-month_chbk_freq  isFradulent\n",
              "0   3160040998                           100.0  ...                  0            1\n",
              "1   3160040998                           100.0  ...                  0            1\n",
              "2   3160041896                           185.5  ...                  0            1\n",
              "3   3160141996                           185.5  ...                  0            1\n",
              "4   3160241992                           500.0  ...                  6            1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 12
        }
      ]
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      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Merchant_id', 'Average Amount/transaction/day', 'Transaction_amount',\n",
              "       'Is declined', 'Total Number of declines/day', 'isForeignTransaction',\n",
              "       'isHighRiskCountry', 'Daily_chargeback_avg_amt', '6_month_avg_chbk_amt',\n",
              "       '6-month_chbk_freq', 'isFradulent'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
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          "height": 507
        }
      },
      "source": [
        "df.hist()"
      ],
      "execution_count": 0,
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          "output_type": "display_data",
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FC+nWrZor39uPziD7+PHjZ5nZeu11v86s484qZ0fruLPkG3QeWTtaxx1OuU+s1Pq2zTbbWD4PPvjgSuc6C51BdvI+etzWW2fWcWeVs6N13FnyzazzyNrROu7ordOP7ArxwrR5jDz+zqLhk87cvR2lSbQFSceJxhL6h1QGEk1Jc3aJRCKRqHvqcmSXSCQS1aDc6BHgyhG1P1+XSMYukUjUKZUYqsQnh+TGTCQSiUTdk4xdIpFIJOqe5MZMJBI1R7nVtolEc0kju0QiURZJkyS9IOlZSU/HuV6S7pX0Wvz2jPOSdL6kiZKelzSkY6VPJJKxSyQSlfNVM9vazLaN4+OB+81sM+D+OAb/v7PNYjsC/zfuRKJDScYukUi0lD3xP9AkfvfKnL86PtTxOP4v5n07QsBEIkdV5uwkDQSuBvrg/1B8iZmdJ6kX/s+7jcAkYF8zmyNJwHn4X9kvAkaa2TOR1qHASZH0aWZ2FYkOZ8qUKRxyyCHMnDkTYCtJRycdf6Iw4B5JBlxsZpcAfcxseoTPwOs/QH+a/jP21Dg3PXMOSUfgIz/69OnDuHHjlof1WQOOGZz9s/faZcGCBU1kr1U6i5xtRbUWqCzF/7zwGUndgfGS7gVG4m6OMyUdj7s5jqOpm2Mo7uYYGg3nycC2eOUaL+k2M5tTJTkTLaShoYGzzz6bIUOGIOll4Mik408Uw8xsmqT1gXslvZINNDMLQ1gxYTAvAdh2221t+PDhy8P+eM2tnP1C51g/d+WIbmRlr1XGjRvXKeRsK6rixjSz6bleu5nNB17Ge3LNdXPsAtxrZrOj8bsXGFENGROto2/fvgwZsnydwTKSjj9RmNm0+H0H/yfy7YCZOfdk/L4T0acBAzOXD4hziUSHUfWuk6RG4PPAEzTfzVHsfP49iro/oLwLpJaH8p3E1bAaSccV0Un0WVLO+GfoVcxsfuzvDJwC3AYciv/r+qHArXHJbcBRkq7HR/XzMmUkkegQqmrsJK0F3AT8xMze92kbpyVujmKUcn9AeRfIpAOHFw3raGrd1bBgwQKATYCDk47LU+v6zFFGzj7AzaHrBuBaM/unpKeAGyWNAiYD+0b8u/C52on4fO1hbSh6IlERVTN2klbFDd01Zvb3OD1TUl8zm16hm2MaMDzv/LhqyZhoHUuWLGHvvfcGmJ10/MnBzN4APlfg/HvAjgXOG3BkO4iWSFRMVebsYuXdZcDLZvaHTFDOzQEruzkOiZdPt2eFm+NuYGdJPeMF1Z3jXKKDMTNGjRrFlltuCTAzE5R0nEgkap5qjey+BBwMvCDp2Th3Au7Lr9jNYWazJZ0KPBXxTjGz2VWSMdEKHn30UcaMGcPgwYMBBoWek44TiUSnoCrGzsweAVQkuFluDjO7HLi8GnIlqsewYcNwtYGklzJf0YCk40QiUeOkL6gkEolEou5Jxi6RSCQSdU8ydolEIpGoe5KxSyQSiUTdk4xdIpFIJOqeZOwSiUQiUfckY5dIJBKJuicZu0QikUjUPcnYJRKJRKLuScYukUgkEnVPMnaJRCKRqHuSsUskEolE3ZOMXSKRSCTqnmTsEolEIlH3JGOXSCQSibonGbtEIpFI1D3J2CUSiUSi7knGLpFIJBJ1TzJ2iUQikah7krFLJBKJRN2TjF0ikUgk6p6aNHaSRkiaIGmipOM7Wp5E9Uk6rn+SjhO1RIcYO0kbSlogqUscj5N0eOx3AS4EdgUGAftLGtTSe8249njmP3d3NcRuNY2Njdx3331VTXPcuHEMGDCgqmm2NdXWcXtRDf2NHDmSk046CYCHH36YLbbYohqilbxPR9BZdZyoXxpaeqGkSUAfYCnwMfAScDVwiZktK3Wtmb0FrFUkeDtgopm9Efe5Htgz0q8KjcffWa2kmsXUOYs58NInWOO+D4vGOWbwUkY2Q74P3nqeWfM+aNNnmnTm7tVOslU6Hj58OH03/Qz03rWimzU2NnLppZey0047rTjXgvyqRH/55Otz1vipdHn1A8bmzn3rD22iu/z7tIEOy9Hm9bi9mHzWN+h3xCWs2rNfh8lwxhln8MYbb3DppZcWDG9sbGTx9t9njcati6Zx5YhubSVep0Bm1rIL3dgdbmb3SVoH+ApwHjDOzA5rZlrjgLFmdqmkfYARZpYb6R0MDDWzozLxjwCOiMMtgAl5SfYGZmXC38scdySDgUnA/BJxsrJXQndgY+D5lovVbDYys/VaenEVdLwF8CGel5VQSb63VTr5+mwEPgLebqUs5WjuffLl7Ggdl6oHg4FV8TK/NHN+ELAG8AL+7NViG+C/eJkrRHPrbI5q1t2WtC2t0nGnw8xatOEZu1Peue2AZcBngN2B/wDvA1OA0Zl4jYABDXE8DjgcWA1X1k2ZuEcCS4D1ysizJ/Bs3O8DvKLl0j4VeDTSvgfonbnur8AMYB7wELBVJuxK4M/AXcBCYCdgSDzX/Lj2BuC0zDXfCDnmAo8Bn83Ls1/gvds5wBVA1wjrCdwRzzon9gdkru0V8d+O8Fvi/HBgaibe/0b6A0rkVe5e7+bfC/gu8HRe/J8Ct8X+usDtkc9PAacBj1RQXs6LcvA+8Dpweybs78BEYGzk6wvA5pFX78R1O0fc03FPwjJgAXBBc8oqsCnwr9D3LOCGEtcdDEzGO0sn5qWzCnB8PMt7wI1Ar8y1w0L/S0P+kZkydVoR3U0CjsUbv3lRtrpWWLY+DzwT+XcDcD2ZcllBHj1dadwK09sHuDQvL8vqqhJ5Ip8mAD/OnBsc5wxobKasDWXCDdi02nmXr/9W5vfystleOu5sW1Xn7MzsSWAqsANuHA4BeuCG738k7VXm+o9wY/T5zOkRuDvk3WLXSdoOd6H+LO43gaa9/gOAw4D1cYN6bCbsH8BmEfYMcE1e8gfgDWx34EngZrzB6gVcB3wrI8fngcuBH+BG4WLgNkmrZ9I7ENgF2ARv0HMTK6vgxuwFYENgMXBB5roxwJrAViHrOQXy4VfASOArZjY1PzxD7l4bFbjX7cAWkjbLy4NrY/9CXLcbAIfGVglPAVvj+XYnsLOkrhG2dsgyBjfE/wHuDjn7A6fgeYmZnQg8DLxlZmtZZqRQIafiZawnMAD4Y6FIMb/0Z7yR7ofrMzs5+mNgL9yj0Q/vNFwY126El6s/As/Fcz9boXz74mV+Y+CzuD5Lli1JqwG34PnXC++E7V3h/dqKacDAzPGAOFctxuDtS45D8TYAgMiX30t6S9JMSRdJWiPChkuaKuk4STOAKyR1kXSCpNclzZc0XlJW/p0kvSZprqQLJSnS2gTYXNJ7kmZJukZSj4wckyQdK+l5SfMk3SCpq6RueBnpF+sXFkgq6ieVNFrS2MzxwZImx31PbF1WfkKodk8CeBw4scD5c4FzYr+RAiO72P9/eG94Y9wwLQL+r4wsF+fSzu/BRNonZY5/BPyzSDo9Qq514vhK4OpM+JfxCqvMuUdY0VP/M3BqXpoTcOOTy7MfZsJ2A17Pi/90/G4NzIn9vvhIpmcBmYeHTH8IWdZpgS6X3yuOxwK/iv3N8NHCmkAXfOS5RSZuRSO7vPs14KOzXUPHM4DHMuF74KO2LnHcPfTSI6PTSS0pq8S8MiVGvhHvV8D1meNuuGssl87LwI6Z8L6RNw34iPTm/LKYKVOlRnYHZY5/C1xUrmxFuXw7r1w+RseO7BqAN1hRj58j4zVpjTw5fcbzbxnlcireYTK8fTkHuA03/t3xTtxvMvm+FDgLWB13ff4M72huAQj4HLBuxDfc+9ED7xy+ywrP0abAq5HOerh36Nw8WZ/EO0S9otz8sJD+y+THaHyqB9xduyD0vjpe95eSRnYlt7ZYjdkfmC1pqKQHJb0raR7wQ9xnXBIzewyYjheaiXjB+1OZywbi7qQcl+SFz8jsLyIWx0Rv7szozb3PitFgVs4pmf1+wDSLklMgfCPgmOj9zZU0N2TrVyT+5FyYpDUlXQxsHLI8BPSIVW0DgdlmNqfw49MDn/v4jZnNKxJnObl7Rc8w/17go7j9Y/8A3GW6CK/MDXnPkN0vdc9jJb0cZWEWrtdL8Mr/It4w5lgMzDKzjzPH0HRR00OV3LcAP497PynpRUnfKxKvH5lnM7OFuLsyx0bAzRk9v4wb8D40LY/5ZbEcBcsqpctWoXI5uZn3ba6cJTGzpcBR+Aj9ZeBGM3uxyvLkRndfj3vkRo7C68NPzWy2mc0HzgD2y1y7DDjZzD40s8X4NMpJZjbBnOfMLKvvM81srvniugfxDiJmNhH4faTzLm54vpIn5/lm9raZzcaNbvFVJJWxD3CHmT1kZh8Cv4znKUdVddzZqKqxk/QF3Ng9gjeYtwEDzWwd4CK8EFbCn3BX0xjgGjP7oEz8KbhbEAAzq1SpB+BzfTsB6+A9QvLkzDYg04H+ORdGkHV1TAFON7MemW1NM7uuSPwNWbGA4Bi8V7mVma2N99pyskwBemXdI3nMwedzrpD0paJPu4LcvYYWuBfAvcB6krbGjV7Ohfku3oPMuvOyz1MQSTvgRmZffHTaA5+TOszMNsHdks3BaKGxM7MZZvZ9M+uHuwT/JGnTAlGnk3k2SWvi7sMcU4Bd83Td1cymkSmPzSiL5ShVtgqVyw2bk3gV5cymeZeZbW5mm5jZ6W0gzxi8Do8k48LEO2VrAuMzHYN/xvkc7+a1K/kd5nyKdZj7AF+TNC06jmNZuVNfrAPTUsp1xArSFjruTFTF2ElaW9I38EnxsWb2Au46mG1mH8Sc2gHNSHIsPhd2EE0LcTEuAw6TtKOkVST1l/TpCq7rjq+weg+vHGeUif9vvPd+lKQGSXvii3Jy/AX4YYxqJambpN0ldc/EOVLSAEm98EUPN2RkWQzMjbCTcxeY2XTcv/8nST0lrSrpy5k0MbNx+Hzg3yO/yz13wXtFWkvweZ/f4a6Xe+P8x/hiktExOvw0TedNSt1vKW4sG2Juce0KrivGTOBTLblQ0nck5Yz1HNxwFuoV/w34hqRhMSd2Ck3ry0XA6fL5OSStF+UBfN53J0n7RjlZNzoOraFU2fo3nr//G2Xj2zQtl3WJmU0G3sSnA/6eCZqFl++tMh2Ddcwsa2SynVjI6zA3gzMircHRcTyIyjv1+TJUSrmOWKIArTV2t0uajxeUE/EhfO61gx8Bp0T4r/DVahVhZlPwxSJGBb1+84Uxh+F++nn4aruNKrjV1bi7Zxq+gvHxMvf5CPg2MApfEXcQ7sv/MMKfBr6PL/aYg7thR+Ylcy0+an0D70meFufPxecOZoUc/8y77mB8TugVfIXiTwrIdy/wPVwvQ0o8Srl75eTcCfhruKRyHIWPgmfgPevrKL4kO8fdcY9X8fz+gArdn0U4D9hH0hxJ5zfz2i8AT0hagHsejrZ4FyxLuNyOxPNhOq7P7KKf8+L6e6KMPw4MjWvfwhvgY4DZ+OKUzzVTznx5ipatTLkcGff7Lk0b/3pmFPC1GN3kWIZ3Ds6RtD5AdIB3KZHOpcCpkjaLzsRnJVViQLrj82fzJPXH5/4qZSawrvzVreZQriOWKERHTxoW2/CVZxVPsMc1I/BJ64nA8e0k5xO4O6616UzCJ8ifpRNNJOOT/Fe14/3aXcfN1RsrRsOvxW/POC/g/JD9eWBIG8t2Od4x+m/mXLNlw1c6vhbboR2tY4ovjmtgxQKVrvio6w38dZeXgf+NeMPJWxiCL3I5CR8pLontlQgz/F27XL69jc/Tga+Ofgc3sIvxDv/UTL4twTtJh8a50cRCk4yO3sM7z/1K5En+dYfinfQlcf+PgAs7i447YutwAYootjGUv3EzrumCj5Q+xYrVX4PaQLav4MvuG6KALAb6ViHdSWTe/6vVDfg0viReuKtsFrBXO927XXTcWr3hqyiPj/3jgbNifzfcHS1ge+CJNpbty/h7of9tqWzRcL4Rvz1jf6VVwfWk486Sb/gK4CGx3x33nAyqRVlrYau5oa+kU/GvFfzOzN7MnD9BK95HyW7/iCjLP09k7tbJfZ6o2myBV8C5uJtqH/M5tZqigvxqKd1xF9lCfL7xbOBWSTsUud+C1j5LhvbScWvZE7gq9q/C38fLnb/anMfxFbB920oIM3sId2u2RrZdgHvNVzXOwUcKI9pKZmpAx8NVSLcAACAASURBVJ0l38xsupk9E/vz8dFr/1qUtRZo8bcx2woz+yW+lDb//BmUXkDSn6bzQFOJOZRqYr6iqS1WNRk+/2PAxdbKlVMV5FdL030Kf7con4dp/SqzcrSLjptJIb31yXSAZuCvI0Bh+fvjc4LtRXNlK3a+rahFHUM75Ft0RHcoEHRG1OdS1zbiH+N4oj1k7YzUnLH7BDPMzKbFhPq9kl6JHmaitllJb9lAM7MwhDVHLctWy7RVvplZZV82z0PSWsBNwE/M7P3sGyhJxyto8Yega4XevXtbY2Njk3MLFy6kW7fa/8J3Z5Vz/Pjxs6wdPyCbdNz2FNDxx/jy9uHAcDP7AYD8wwfjrOm7o62mkI6rTWfRRTGqLX9767jD6ehJw9Zu22yzjeXz4IMPrnSuFumsctLOq0WTjtueAjpe6D/0wlco9oztTTIfvK7WVkjHbf2MnY1qy9/eOu7orS7dmC9Mm1fyP+E64L+9ElUm6bjNmQxgZrNj0dhTcf4U889eJWqMcv+LWKBOfKJ0XJfGLpFItJpFuR0zuxx/HyxRX3yidFxzrx4kEolEIlFtkrFLJBKJRN2TjF0ikUgk6p40Z5dIJOqS/AUbxwxeutKiprSQ6ZNDGtklEolEou5JI7tEIpFoQ8q9EgBphNkeJGOXSCQSRWgvQ1XJfQq5YROVk4xdIpH4xFKJkWmPNBJtT1Xm7CQNlPSgpJckvSjp6DjfS9K9kl6L355xXpLOlzRR0vPZf9WWdGjEf03SodWQL5FIJBKfbKo1slsKHGNmz0jqDoyXdC8wErjfzM6UdDz+R4LHAbsCm8U2FPgzMFRSL+BkYFv8r1PGS7rN/D+WEh3IlClTOOSQQ5g5cybAVpKONrPzQmc34H+4OwnY18zmyD+9fh7+h5GLgJEW/70VnZiTIunTzOwqEolmkkZUieZQlZGdpT8RrHsaGho4++yzeemll8D1e6SkQXgH5n4z2wy4P46haYfmCLxDQ6ZDMxT/o86TcyP+RG1SwnMzWtI0Sc/Gtlvmml+E52aCpF06TvpEwqn6nF17/ImgpCPwBpQ+ffowbty4JuF91vDJ3GLkx+8oFixYUDOylGLBggVMmDABWJ53y2jaoRkeUa8CxuGj9+UdGuBxSbkOzXCiQwMQHoARQH39nUh9UcxzA3COmf0+Gzk6QfsBWwH9gPskbW5mH7er1IlEhqoau/b6E0HL/Fv4tttua8OHD28S/sdrbuXsF4o/2qQDhxcNa0/GjRtHvuy1SAE5VyN1aCqiM3VoiskZ+p0e+/Ml5To6xdgTuN7MPgTelDQRH8X/u6pCJxLNoGrGTtKquKG7xsz+HqdnSuprZtOjV/9OnJ+G/2lgjgFxbhorRgm58+OqJWOi9SxYsABgE+Dg1KEpTyfu0BQkz3PzJeAoSYcAT+Ojvzm4IXw8c1mLOjTlKNXZKUS5DlKt01r5O0Onqy2pirGLxQiXAS+b2R8yQbcBhwJnxu+tmfNHSboen7uZFwbxbuCMzBzOzsAvqiFjovUsWbKEvffeG2B26tB88ijgufkzcCq+mOxU4Gzge5WmV65DU47mvnN2zOClJTtItU5r5a+VDmBHUa3PhX0JOBj4Wt5k9ZnA1yW9BuwUxwB3AW8AE4G/AD8C/xNBvNI8FVtd/olgZ8TMGDVqFFtuuSXAzExQrkMDK3doDonXTLYnOjTA3cDOknpGp2bnOJeoYQp5bsxsppl9bGbL8Hq8XUQv1tFJJDqMqnRzzOwRQEWCdywQ34Aji6RV938i2Bl59NFHGTNmDIMHDwYYJOlZ4AS8A3OjpFH4Px/vG5fchb92MBF/9eAw+OT8K3I9UcxzkxvRx+G3gP/G/m3AtZL+gC9Q2Qx4sh1FTiRWovOO6RPtyrBhw/A+Ckh6ycy2zQSnDk19k/PcvBCdHPCOzv6StsbdmJOAHwCY2YuSbgRewldyHplWYiY6mmTsEolESUp4bu4qcc3pwOltJlQi0UzSX/wkEolEou5JI7tEIlFzpE+BJapNGtklEolEou5Jxi6RSCQSdU8ydolEIpGoe5KxSyQSiUTdk4xdIpFIJOqeZOwSiUQiUfckY5dIJBKJuicZu0QikUjUPcnYJRKJRKLuScYukUgkEnVPMnaJRCKRqHuSsUskEolE3ZOMXSKRSCTqnmTsEolEIlH3JGOXSCQSibonGbtEIpFI1D01aewkjZA0QdJEScd3tDyJ6pN0XP8kHSdqiZozdpK6ABcCuwKDgP0lDepYqRLVJOm4/kk6TtQaDR0tAIAkAzYzs4nAdsBEM3sjwq4H9gReag9ZRo8ezcSJExk7dmx73G4lTjrpJC666CIaGhqYMWNG2fiS6HfEJazas1/B8Eln7l42ja4bDqbbVl+l++d2YcGLD7Lwvw/Q57unLg+/ckS3yh+gMkrqWNJI4HAzG1btGyfaFkkvAkcCH9KB9bhQOU7AVlttxYUXXsjw4cM7WpR2R2ZWPFBakDlcEy/AH8fxD8zsmgLXDAfGmtmAioXIGDtJ+wAjzOzwCHsZ2AzY2MymxLk/4BXqBWALYEJekr2BWZXeP49+wOrAmy28vjnky7ka8BngeWBphWlsA/wX101L2QJ4j+J5li/nRma2XktvltMxsAOwBzAUGGpmR0X4SOBEYF5GvmrquFp8HngR+KhEnFqQsxLaRMeZenwwruff43VrId7ZXpUV7Uq+jqtNS3SxFV4vwT1hFhvAdKB8j7R6NFf+Rrxsvl0kvFU67myUHNmZ2Vq5fUmT8N72fW0tVAGWAL8Ejojju4Bvm9m2hSJLerpYWDkkjQY2NbODWnJ9XloNZlbUaOXLKWkYcL2Zbd2MexjwrRgVt1TOcXgH5dJK5GzFfXKdpy5AV7zReAYvhw/lRZ9ZamRXLZkqpVwelbiuXeVsKa2sM1nPTKX0KFU3KrhnybpV5JpW6aJUGWiJPC24f7Pkl3QlMNXMTmo7qToPLZqzk7S6pHMlvR3buXGuG/APoJ+kBbH1k7SdpH9LmitpuqQLJK1WJPlpwMDM8erAI7jPf5Mi8pikTTOnGiWdFmHDJU2V9HNJ78T995K0m6RXJc2WdEJekl0l3SBpvqRnJH0uc69+km6S9K6kNyX9byZstKS/SRor6X1gZAFZ15F0taR3gcGSTpK0iqSdgHszeXdlkWf9WTzD25K+lxe2uqTfS3pL0kxJF0laIxO+p6RnJb0v6XVJIwqkP1LSI9m8BdaT9Fro70JJyoR/T9LLkuZIulvSRnFeks6JPH9f0gvA9tGB+hregbk1jn8HDIh4TwKb5sl0nqQpET5e0g5xfgNJiyStm4k7JHSzaqH8S7QtkiZFWV4b+E7obCbeUZ1W5tp+km6LOjlR0vczYSvVrahLl0V9mCbpNPlcYaFyvDPwGUnzJP1J0r8kHZ6NG3VnTtTrXcvI2hjtzihJbwEPxPm/SpoR93lI0laZa66M+nNntC1PKNq0QvVF0mcibHdJ/wE+H/VgdJ4swyQ9FvVzSjzPEcCBwM+jPbk9Tz9F2/EIy7Wbx2hFu3lYqTypecysog2YBOwU+6cAjwPrA+sBjwGnRthwvDeRvXYbYHu8B98IvAz8JBNu+GiKiPMGsDHuPlgA/Ar4A96rAtgJmFTo+jieBZyWkWdppLEq8H3gXeBaoDvupliMu0kBRuMN8T4R/1jc7bIq3jkYH2mtBnwqZN0l79q9Iu4aBfLxauDWuPfzwKvAqGJ5l3ftCGAm7ursFs+QzbtzgNuAXpH+7cBvImw73C349ZCtP/DpCBuHj9rBDfQjeXk7F+gBbBh5NyLC9gQmAluG3k4CHouwXSKvegCKOH0zOl4GnBX5OB/4IK6fBSwC/g3cEfdbCNwNbAQcg7uO5gOnArPj2ntwN885wJ+Asbhrdi7wFNAn7n0YXv7mh+5+kJfHewLPAu8Dr0een4672T7Ay+MFBcrtOqHbd4HJ8SyrAE/n8hR34c3By9OuFdS5orLmygrwc+Ad3KW2F7AbXqZmAydk4q8OnIu7tN6O/dUzOp+fd+/ss12JLza5M2R5Atgkwh6KuAsjb97B6+e/Y39joCfwGl7XGiN+Q4HnfSh01xXYOvLya8XqFnAzcDFeF9YHnszlEZlyHOXifbysNgBHR1rZMr8Ebxu6AP8TeaQ8+cZlrsk9x9Vx/zXi/PfwupfL72cz11+Jl8ntQo5rcE8OlK4vw4HBeFn6LN4G7BVhG4VO9sfbqHWBrTP3O60V7fjSiLMqXq4WAT0rtRm1trXU2L0O7JYJ24UwPpRpsCPOT4CbC1WsOM5V2NfxSn54KGMeXmHKGbvHaGrsFgNd4rh7xB+aiT8+U3hGA49nwlbBG5Id8DmHt/Ke5RfAFZlrHyrx3F1wH/qgOD4C+AEwrpK8Ay4Hzswcb5579qggC4lGKMK/CLwZ+xcD5xRJdxyljd1vM8c3AsfH/j8IQ53Jq0V4Bfxa6HB7YJW8+62JG7upoeOP8Yp1Ft5I/BavhHtH3O7AX4Fb4vo5eHl8PcrSv+MZzsIN4W9wQ79m5Pk2wNpx7e7AJpFfXwl5h0RYRR2CQuWOpp2Yxnj2UaHjkVTQmBbQSylZh9O8Tlyphm0k8FqJZ7uSIo10gbiT8Pr5UMSbGHo6McIbWdGBym3H4t6cj4HumXR/A1xZqG4BffC5vjUy5/YHHswvx8AheBk5Io4FTKFpmZ+YVz4N2KBEPck9x6dK6K9HxFknk4+X5rVzr8R+0fqSiZ+T/1yiLuPtz81F4l9JaWNXrh1fTKZTgndeti9VZmt5a+mrB/3w3muOyXGuIJI2l3RHDO/fB87Ae1sFMbO7zGxzM9sEeCvOvQtcgFfacryad/yemeUW1iyO35mZ8MXAWpnjKRlZco1yP7wR7xfugrmS5gIn4BVvpWsL0BtvmCZH2pfEfv8KnomQIZt+Vgfr4ZV0fEa2f8Z58Mbk9Qrvk88lmf1FrMirjYDzMvebjTck/c3sAVxfFwLvSLpE0tpx3Y74KGkk8CXcsCwDTjazD0POpWZ2k5ktwjsE2wB7xn3WwRvJK4CLgE8D90e68/BRxLp4A/yxmY03s/cBzOxOM3vdnH/hI8IdQq5RwOVmdq+ZLTOzaWb2SrnMCdfZfsAvzGy+mU0CzgYODh0DTDazv0Q5vAroS9NysxJlZAU3oKeb2RLgerx8nRcyvIivfMy54A8ETjGzd6Iu/Ro4OJNWtj4U4mYze9J8XuoafORVilG4Xnvg5eK5vPDeZtYjtt/jZXu2mc3PxMmvG9myvxFel6Znyt/FuDHPpx8wJacL85Z7al6c5QtNosxB0zahGMtlktRF0pnyKYL3ccMCTdu67IKW5XWpVH2RNFTSg8DpkuYBP8yk2Zp6Xa4df8+azkNm636no6XG7m28sOXYkBUrfqxA/D8Dr+CT2GvjBkIF4pXjd8BX8YYvyyK8oc+xQQvSzrJ8zlDSKsAA/Pmm4COlHpmtu5ntlrm20PPnmIU3UPl5V3IuI8N0ms5nbpiX9mJgq4xs69iKRUZT8FFCNZmCu42y+bGGmT0GYGbnm9k2+HtWmwM/i+t2Y0Wn4128Rz/HzD7IPNcqki6WNAMf6eUasXVxgyZgRlxzI74yckNgTGx3A9fHXMRvc3N4knaV9HjMC80NWVrbcDTpxAT5DXWzG9MyskLzOnHN6qAWoGAjXQwze83M9sf1dhbwN/mcfjHeBnpJ6p45l183snVrCj6yyxrNtc1sK1ZmOl6HAZ8fyx63kqxMB+Bu8J3wDllj7pYVJVS8vlyLT08MNLN18A5eLs1S9bpUWwSl2/G6o6XG7jrgJEnrSeqNu1JyL6bNBNaVtE4mfnfcZ75A0qdxN06zMbO5eI/553lBzwIHRM9qBO7yaQ3bSPq2pAbcTfYh7gJ6Epgv6ThJa8T9PiPpCxXK/zHeMJ8uqbt8Mcf/sSLvynEjPjE/SNKawMmZtJcBfwHOkbQ+gKT+knaJKJcBh0naUb4gpn/oojVcBPwiNwkvXzDwndj/QvRIV8Xdqx/gozfwF40Xhdwf4y6v7pLWlL94fChuKLZgxRzdnnHtL/HFD1muBobhc5VjzGyJmf3azAYB/w/4BnBITL7fhM+d9TGzHvjK3tY2HK3txKxEBbI2l1IN20IynUVJre0sIukgSetFuZwbp5cVi2/+WtFjwG8kdZX0WXx0WLBumNl0fKR7tqS1o0xvIqlQ3b8TXwy2V9TpI2l9h7gQ3fG24j08P8+o9MIy9aU7Pur9QNJ2uFHNcQ2wk6R9JTVIWldSbtQ9E19XUIxS7Xjd0VJjdxo+Wfo8/q7bM3GOcPtcB7wR7oV+uE/+AHwi9S/ADa2Q+TxWvOuX42j8fa35kfZSvJFrKbcC38Xnhg7GX3NYEg3zN3AXzpt4I3cp3otbiVj59IJ8BeTTcfqXuF9+Dv5e0d+By6O3+WNgA0nPSxqSn56Z/QP31z+Az4U8kBfluDj/eLhR7sMNBmb2JL7g4Rx8ZPRyxPtvRt5euK62kXSvpJ6ZsPMlTQS+STQUZnYz8CjwjKSP8fnV3Cq2tXFdz8FHEe8Bv5OvMFtAUx2eR4zU8HmGK/C5rW64QV8Lb9jAG4Gc66hB0g24W7AnsMjMJkv6qqTB4V58HzdEy/DFMKvjo8ml8hV3O2fkKNUhKNpwRLl4GHgiXFhnsnInZn35KtFnJT1bKJ08ysnaXK4DrggZXqZpw/YcsJWkrSVdyIr33Sr94kmhvBkBvCh/3eQ8YD8zW7zSlU3ZHx8NvY0vPjnZSr/qdAieTy/h5exvuHu4CWY2C6/P1+Oj3UF4+9Wad1MLcTVe1qeFTI8349qC9SXCfgScKmkJXucbiBG+mb2Fj/iPwd3Fz7LCdX0ZMCja4VsK3LNoO16XtHbSr1Y2vHF8Ha90q+EVeFAHyzQJd7Nkz/2WFQs8jgfOiv3d8AUfwo3hE20s25eBIcB/WyobPpJ6I357xn7J1Vr4qPy3NJ0oH87KK3gH4A3TQnwOdgqZVXz4YoGxwEVxPJVYUYg3mhPi2pnA+Znrjoxzc3F35/VkJvGBb+GVfz7eccittP1iyDEHOD/O5RYHdcE7P7fgxukj4I/EQgN8bvJVYhVn9toyeVVU1vw8wxtAAxoz5x4BDor9rrgxeBc3/ucDXTNxcy/xLwYOirSejbAr8/Io/94/xF2Fc4F9O7LOlcjL/8PdgXfgnfy3ga92tFzNkP8qViyOWQ1/T7HD5epMW4cLUMXC8EXg7szxL/AFAx0p0yRWNnYTWLGkuC8wIfYvBvYvFK8N5WukqbFrlmy4Ubk4c75JvCL33BfYshr6xOflvgh8IRra9yizwrGjyh5u8C5oT7kq0XleWLuXwXZ65gHAf3Dvz134ayHTKfBqUC1uuOfozY4o2/W01dyHoFtBf5qu1ppK5asc2woD7pG/CJ37+ksf8/kGcLddbkVeLcjfXNmaLbOZ3WhmL1cgSyVp98d77Pfhc6tz8QUs7U2l+bB3uKj/JmlggfCOphbKYFtwLj7fPhZ3Be+Bv2pUzq1aK2yMj8ivkPQfSZeWWeyTKEA9GbtaZJiZDcHnsY6U9OVsoHm3rdyKqQ6hgGzrS3pQ0kv4+4b7xfk1Yn7vNXxhyRqw/IsQ58u/hNFkDlLSofIvsrwm6dBWivpT81WnV7YynbbmdtzF+Fn8SzlXgX9Crci2Q8nUEhUh6RvAO2b2A3yR0z/NbKiZPdHBojWHBnzK4c9m9nncPZ/+MqmZ1JOxy//M2ABasRquGpjZtPh9B59w3w6YKakvQPy+E9FrQf5SsvUBjjFf4fg2PrcF/uLz/Wa2Ge5GzK1M3RX/gPdm+IvVf450e+GLTobi+XFydiFMhkryY3mcWGW3TsjQ3pSV1czeM3+HEHxR0zZxfq0i28PtInlTaqEMVpsvAd+Uf9v3euBrkjrbisOp+BxpzkD/DTd+iWZQ8l8POgO9e/e2xsbGJucWLlxIt261P8rvrHKOHz9+KT73cRXegOQ+YfQc8KGZbSbpYvzLMNcBSJqAL2wYDgyPnjb58XKE8XoV2HHdddd9ozPpuB5kGz9+/Fz8qyrbtb1UK9fjWs7DfDqLrAXq8Sz7BP3rQYdPGrZ222abbSyfBx98cKVztUhnlRP/Qslb+HLpRfiqxYn4qw1zPQp34G5c4vh+YFv81YaTMud/CRxrBXRLfDaus+m4HmQLvW5rHVSPazkP8+ksshaox09bDbTh7bXVxJ+3VpsXps1j5PF3Fg2v5A9NE4VZsGAB+Jc3Djaz9yV9ZGbL/6VA/l+DrSYW9BwBvD916lTGjRvXJPyd2fP44zW3Fr1+cP+Crz62CwsWLFhJ3lqhGbK9ZGZPl4/WNpSrw5DqcaJ51KWxS7QNS5YsYe+99wb/msPf4/RMSX3NbHqFc5DTcFdm9vy4/HuZf8fwEoBtt93W8v9Z+Y/X3MrZLxQvvpMOHF40rK0ZN25czf4TdC3Llki0JfW0QCXRhpgZo0aNYsstt4Sm31+8DV+FSfzemjl/SKzK3B6YZ/5aw93AzpJ6xsKUneNcIpFItBnJ2CUq4tFHH2XMmDE88MAD4J8gelbSbsCZwNfj1YOd4hh8Acsb+FzeX/BPHmFms/H/oXsqtlPiXKKDmTJlCl/96lcZNGgQ+OfDjgZfQZt7vST7Gbl2fL0kkWg1yY2ZqIhhw4blFo0g6SUz2zYTvGN+/JgAP7JQWmZ2Of7ffIkaoqGhgbPPPpshQ4YQ3888UtK9+Ndf7jezMyUdj7/jdRxNXy8Zir9eMjTzesm2+Lua4yXdZmZz2v+pEgmnKiM7SQNzLxxLejH1CBOJzkffvn0ZMmR5VVyGfyy8P/4y9lVx/ir838KJ81fH4r7HgR4xb7sLcK+ZzQ4Ddy/+YehEosOo1shuKf7C8TPy/6Man3qEiUSnZjX8PwKfoI0+I5dZcUufPn2arBLtswYcM3hp/iVNqJUVr7W8+jZLZ5GzraiKsYuKMD3254cLJNcjHB7RrsJX3R1HpkeI/81Mrkc4nOgRAoTBHIH/PUkikWgH4vWSTVjxesnyMDMzSVX5EkWpFbflVttCx664zdJZVrh2FjnbiqovUJHUSBv3CBOJRNtQ6vUSqPgTd/X42bFEJ6eqC1QkrYX/u/JP2rJHWMr9AeVdILUylO8sboXOImeidWRfL7nnnnsKvV5yJiu/XnKUpOvx6Yh58b7l3cAZmW+e7oz/7VEi0WFUzdjJ/07+JuCa9MJxZXQWt0JnkTPROnKvlwwePBji9RLgBNzI3ShpFP4v2vvGJXfhn3SbiH9e7DDw10sk5V4vgfR6SaIGqIqxkw/hLgNeNrPs56JSjzCR6CSk10sS9Uy1RnZfAg4GXojeIKQeYSKRSCRqhGqtxnwEUJHg1CNMJBKJRIeSPheWSCQSibonGbtEIpFI1D3J2CUSiUSi7knGLpFIJBJ1TzJ2iUQikah7krFLJBKJRN2TjF0ikUgk6p5k7BKJRCJR9yRjl0gkEom6Jxm7RCKRSNQ9ydglEolEou5Jxi6RSCQSdU9V/7w1kagVGo+/s2T4pDN3bydJEolELZBGdolEIpGoe5KxSyQSiUTdk4xdIpFIJOqeNGeXSCQSdUC5eeorR3RrJ0lqkzSySyQSiUTdk4xdIpFIJOqeZOwSiUQiUfckY5dIJBKJuicZu0QikUjUPcnYJRKJRKLuSa8eJD6RlFumDemTYolEPZFGdolEIpGoe2pyZCdpBHAe0AW41MzOrGb6lfTqy5F6/a2jrXWc6HiSjhO1RM0ZO0ldgAuBrwNTgack3WZmL1X7Xm9f+iN67fxDum742WZf29qv6o8ePZpHH32U4cOHl73X6NGjmThxImPHjm2OiDVLER0fDhxuZuNamu7S99/h7Ut/xMCf3IBW6VI67ryZTLtoFBv+7NaycQvx8MMPc/jhhzNhwoSS8epNd5XSnvU4kaiEmjN2wHbARDN7A0DS9cCeQNUrSb/D/1QyPNcgatWuy8819NiAft+7oGza5Yzh3Ede5QtrVCZntZg0aRIbb7wxS5YsoaGhQ1VfSMeUMnTFZJ915zl06d6bnl8+mIa112fD//tbVQSc+8g1qMueqMuqsEoXVlt3ID2/NorV+2+5ItK3/lBQz8cMXsrIOD+y60rBTXj11Vc58cQTefDBB1myZAkbbbQRI0eO5Oijj6ZLl+Yb4UoZOXIkAwYM4LTTTmurW7RbPU4kKkFm1tEyNEHSPsAIMzs8jg8GhprZUZk4RwBHxOEWQH73ujcwqwrirAYMBsZXIa18+gFrA69UGHd14M1W3rOlz5OfnxuZ2XotFaKFOn4zT/acTI3AR8DbzRSjXF7k53l/YF3g+QrSzuZXKd2tDmwZcWcCS+JcP+At4OMK7tVcKs23jtBxth5Xqw63B51F1qrW406HmdXUBuyD+/dzxwcDFzQzjacrjDcJ2AnvhT4NvI83On+I8EbAgIa864bjrpnjgBnAGKAncAfwLjAn9gdkrtkY+BcwH7gXuAB4L5teIdlifzQwNhO2PfAYMBd4DhieCRsHnAo8Gve6B+gdYW/F8yyI7YvVzM9W6vj95ugiJxNwJXBakTgbAw9FPtyHu9XG5sU9NPJlFnBiRqb8PB8U8dcrpLMoC9PiXh8AO+anA6wKXAfchBvbscCdZfLqm8CLoetxwJaZMAM2zRxn82I4XkaPAd4BpgOHRd4egRvWj6Ic3J4pc8fhBv1D4GfATXnynA+c19b1uNplri23ziJrZ5GzrbZaXI05DRiYOR4Q59qS8/AKvDawCXBjBddsAPQCNsIbj1WAK+J4Q2AxbtByXIuPInrjxujQlggqqT9wJ3Ba3P9Y4CZJ2R7aAXjDtj7eqB4b578cvz3MbC0z+3dLZKgChXScG8W0RBfFuBZ4Eh+RjcYb3HyG4aOKHYFfSdoyP4Kk1YBDgPfwjkx++BbAUcAXzKw78CpuOLJx1gBuwY3Ivmb2EW7ci/pd8Ser+wAADxBJREFUJW2OG8efAOsBdwG3hzyVsAGwDj4qHYUb+y5mdglwDfDbKAd7ZK7ZH9gd6IEb4xGSeoQ8DcB+wNUV3Lsj6nEiUZRaNHZPAZtJ2jgq9X7AbW18zyXAppJ6m9kCM3s8L3yWpLmx5QzHMuBkM/vQzBab2XtmdpOZLTKz+cDpwFcAJG0IfAH4ZcR/CLi9hbIeBNxlZneZ2TIzuxfvre+WiXOFmb1qZotxY7F1C+/VVhTS8aIIq0gXwNbxe0ChG2Ty/Fdm9pGZPULhcvTr0N9z+Cj5c5mwfeMei4HvA/uY2dICaXyMux8HSVoV+MjMXs+Erw38E3gdOMzMcoZ9XXzEVYzv4iO/e81sCfB7YA3g/5W4JssS4BQzW2Jmd+GjuDKziJxvZlMiT6bjI+PvRNgIYJaZVeIG74h6nEgUpeaMXTQmRwF3Ay8DN5rZi81M5pJmxh8FbA68IukpSd/IC+9tZj1i+32ce9fMPshFkLSmpIslTZb0Pt5I9IhVaf2AOWa2MJPmZOC1ZsoJPnL8Tsb4zsVHJ30zcWZk9hcBa7XgPlmam58lKaRjvGGGCnUB/Ch+ry1ym37AbDNblDk3pUC8Unl1Y9yjD/BfYJsizzMRH32Nxl2GJqlfJsr2wGeBMy38ScF7NNVboWeYnLnPsniG/iWuyfJennFeBPyjzDX5eXQV3sEifsdUcuMq1OOqlrk2prPI2lnkbBNqztgBxKhlczPbxMxOb8H1zVKqmb1mZvvjbr+zgL9JKvdPh/kre47B3WFDwwWXcxkK7733zEtzQ2Bi7C8E1swFhIEsNnE8BRiTMb49zKybVfYOU4tWIzU3PytMs6COK9VFBTJNB3pJWjNzbmCxyGVknYW7qkdLKmiczOxaMxuGd0Ymhuw57gF+A9wvqU/m/H3A3iVu/XakB4AkxTPk3IGLyJQb3G1ZjrtyIhcJzz9/C/BZSZ8BvoG7PyuiNfW4LcpcW9FZZO0scrYVNWns2htJB0laL3rOc+P0smYm0x13d82V1As4ORdgZpNxV+OvJa0maRiQnSd5Fegqafdwg52Eu8UKMRbYQ9IukrpI6ippuKQBFcj4bjzXp5r5bO1GlXSRzfPRkedfpGmeNze9Cfgo5ecFZN5C0tckrY4vTlmcL7OZ/RYfhd4vqXecPhn4f5J+J2mDSGtTSWNjnuxGYHdJO0a5OAaf83ssrn8WOCDKwQjCbV4hM6mgHIT34m8h+5Nm9lYz7pFI1AzJ2DkjgBclLcAXSOwX813N4Vx8PmUW8Dg+R5PlAGAoMBtv5JZP8pvZPOBHwKV4r30hvpJuJcxsCv6+0gm48ZqCr5orq8tw6Z0OPBou0O0rf7x2oxq6yHEg8EXcXXgacANuLFrK74AjJK2fd3514Exc9zPwUekv8i82s1PxkdJ9knrFvN4X8ZWhL0qah6/UfBqYHwb2IOCPkfYewB6xuAXg6Dg3N571lmY8y2X4HONcSeWuuwp/TaMiF2YiUZNUa1lnLWx4QzkBdyMd39Hy5Ml2OT6f89/MuV74awivxW/PGpBzIPAg/vLvi8DRtSRra3SMG7tfNyP+JOAFfASVe9WhYD7g7urzQ67ngSGZdA6N+K8Bh2bObxPpT4xrVY3yU01Z4h4P4yPVB6qt93L6xDsSN0T4E0BjLZY7YCTe+Xw2tsM7SM6VykleeNGyUe9bhwtQRSV3wVe7fQpfbv8cMKij5crI92VgSF5j9dtcxQGOB86qATn75ioA7pp9FX/HrMNlba6O8dWYm+Cj3hG4i/HzzbjfJOIdxXI6w1fD/iMak+2BJ+J8L+CN+O0Z+zmj9GTEVVy7azXKTzVliXs8Eo1oVfVeiT5xj8dFsb8fcEMtljvc2DXrfeA2knWlcpIXXrBsfBK2enJjLv88kbmbJ/d5oprA/HWD2Xmn98RdRMTvXu0qVAHMbLqZPRP78/GVdP2pDVmbq+MN8BexF+C92f8xs/+0UoZi+bAncLU5j+Mrcfvy/9u7/1C76zqO488XS8MMzRqMoaNbsYnXJrSNJjTkXoQ5TTZiERuhWyz2RxMLDNT+aLGgZlGQFdnQlQ6Zygi8S2MN2yWS5nRgzim2JcE2TMvFlatYXnr3x+dzt3N/nHvPvX7P93zP974ecLjnfL/fne/7nPflvvf9fD/f9xduAA5GxNmI+DfpCGxNXndJRByO9FfoIab4Tmf4+1NILHli0DdJk6W2U3zeW8ln42fcB1yfJ+qUqdJ/Wxo1+T1p1Ox3o/bqVOwuZ+y06dO0PkW7UxZEupYJ0rmeBVNtXDZJPcBnSMNHVYh1RjmOiP0RsSgiPhRpVuCvZri/AH4v6WhubQXNv4dmsU21/PQky2eirbFEulTmrYi4MtK54qLz3ko+z20T6XKGIdL1iWVq9fduvaQXJO2TNKuZvyXoxr+ThahTsetq+X/Us7o0oB0kfZg0WeIbEfFW47qqxdpGqyJiGXAjsE3SdY0rq/Q9lBFLlT5vBe0nnU+8hnTE/OA021vJKtcIeqbmz58fPT09Y5a9/fbbXHzxdJfJdV63xnn06NGzpE4aV5axf+e4/SbJ8bsRcZGkV0i9V1/Lw12DReU9Xw7ynYi4Ib++GyAivt+wzYG8zZ9zu7J/kPqTlvaHq5U4x20/j9TQ4NKyYhy3/x7gtxHx6UnW/ZKUw7359bn8lhpkJ3T6pOH7fSxfvjzGO3To0IRlVdStcZKGPn4QzvG0ujVO4ET6wQ8ZOwmmsLyTbjH2Kqlh9+jEj6vHbbONsRNUHitq/wXHubDh+ReAw2XH2bD/HppPUPk8YyeoHOlUnGU/qng/O6u+S0jXlVl9DeWfO4HHJG0htS77UlE7iIgRSaMtxeYBuyPiuKQdpEs9BkjXA+6RdJI08WJDUfsvOM7bJa0FRnKcm8uOE0DSXtIdL+ZLOk2aWHQBQETcR+qgcxPp0oN3SA3j54RaFrtjZ4bO3TxzMtPdRdym9deImGrGV9s5x+WIiDdJd4Ro1/s/yfkWZqPLvt3w/F3ON6LumBbivJtJGgmULVKrvanWB+loec7xBBUzM6s9FzszM6s9FzszM6s9FzszM6s9FzszM6u9QoqdpEWSDkl6SdJxSV/Pyz8q6aCkE/nnZXm5JN0r6WRur7Os4b025e1PSNpURHxmZja3FXVkNwLcERG9pAsVt0nqJV2E+lRELAaeyq8htV9anB9bgV9AKo6k60JWkpqvbh8tkGZmZrNVSLGLmXfKn1FX9iJiNDOzuavwc3YtdsqfaVd267BTp07R399Pb28vwNUeqjazblJoB5XxnfIbbzsVESGpkOat+XYrWwEWLFjA4ODgmPULLoI7lo40/ffjt++U4eHhysQyleHhYY4cOcLGjRtZsmQJ/f39L5OGqg+S2iI9FRE7Jd1FGqq+k7FD1StJQ9UrG4aqV5A66B+VNJCP5M3M2qKwYifpAlKhezgifpMXvy5pYZzvmP5GXn4GaLzf0xV52RlSX7fG5YPj9xURu4BdACtWrIi+vr4x63/68OP86Fjzj/b3L/c1XVemwcFBxsdeRYODg9x8882Ni/7H2KHqvrz8QVK+7qRhqBo4LGl0qLqPPFQNkAvmGmBv2z+Imc1ZhRS7fOfgB4CXI+LHDasGgE2kZrKbgMcblt8m6RHS//qHckE8AHyvYVLKairQb84muJA2D1X76L1c3RKn2WwVdWT3OeAW4Jik5/Oyb9G8Y/qknbcj4qyk7wLP5u12dLrhsI01PDwM8CnglnYOVfvovVzdEqfZbBVS7CLiT6T7I01mQsf0qTpvR8RuYHcRcVmx3nvvPdavXw/pxpRtHao2MyuSO6hYSyKCLVu2cNVVVwG83rBqdKgaJg5V35pnZV5LHqom3RNstaTL8nD16rzMzKxtank/Oyve008/zZ49e1i6dClAbx6u9lC1mXUFFztryapVq0ijzyDppYhY0bDaQ9VmVmkexjQzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9pzsTMzs9rzncqNnruemHL9r9dcXFIkZmbt4SM7MzOrPRc7MzOrPRc7MzOrvUqes5O0BvgJMA+4PyJ2djgkK5hzXC6fl7W5rnJHdpLmAT8HbgR6gY2SejsblRXJOTazslWu2AGfBU5GxKsR8V/gEWBdh2OyYjnHZlaqKg5jXg6canh9GljZuIGkrcDW/HJY0ivj3mM+8K9mO9A9BURZjCnjrIr+eybE+fH3+ZbOccW0IcdmlVLFYjetiNgF7Gq2XtJzEbGixJBmxXE25xyXq1viNJutKg5jngEWNby+Ii+z+nCOzaxUVSx2zwKLJX1C0oXABmCgwzFZsZxjMytV5YYxI2JE0m3AAdK09N0RcXyGb9N0+Kti5mScznEldUucZrOiiOh0DGZmZm1VxWFMMzOzQrnYmZlZ7XVtsZO0RtIrkk5KumuS9R+U9Ghe/4yknvKjPBfLdLFulvRPSc/nx1c7EONuSW9IerHJekm6N3+GFyQtKyEm57jYGCuXY7PSRETXPUiTGv4GfBK4EPgL0Dtum68B9+XnG4BHKxzrZuBnHf5OrwOWAS82WX8T8DtAwLXAMxX43pzjLs6xH36U+ejWI7tW2k2tAx7Mz/cB10tSiTGO6orWWBHxR+DsFJusAx6K5DDwEUkL2xiSc1ywCubYrDTdWuwmazd1ebNtImIEGAI+Vkp0TeLIJosVYH0eOtonadEk6zut1c9R5v6c42KVnWOz0nRrsaub/UBPRFwDHOT80YrVh3Ns1kHdWuxaaTd1bhtJHwAuBd4sJbomcWQTYo2INyPiP/nl/cDykmKbibJbfDnH5XMbN6utbi12rbSbGgA25edfBP4QEZ24gn7aWMedF1kLvFxifK0aAG7NM/auBYYi4rU27s85Ll/ZOTYrTeXahbUimrSbkrQDeC4iBoAHgD2STpJOym+ocKy3S1oLjORYN5cdp6S9QB8wX9JpYDtwQf4M9wFPkmbrnQTeAb7Sznic4+JVLcdmZXK7MDMzq71uHcY0MzNrmYudmZnVnoudmZnVnoudmZnVnoudmZnVnoudmZnVnoudmZnV3v8Byz0W/VUbz2IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cf7WJP9MA5gO",
        "colab_type": "code",
        "outputId": "404868f9-fdca-4d2b-bdb6-a078c293fa2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x=df['isFradulent'].value_counts().values\n",
        "sns.barplot(['Not fraud','fraud'],x)\n",
        "plt.title('Target variable count')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Target variable count')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxBnCIzJ-0IA",
        "colab_type": "code",
        "outputId": "6d06dea1-b7d9-4d37-9c45-3d870c5ed8a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X = df.drop(['isFradulent'],axis=1)\n",
        "y = df['isFradulent']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
        "X_train.shape, y_train.shape,X_test.shape,y_test.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2460, 10), (2460,), (615, 10), (615,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tn3ukaNsDBgl",
        "colab_type": "code",
        "outputId": "5eed92b3-e9db-4e0b-e069-b6f735b77130",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "X_train.columns"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Merchant_id', 'Average Amount/transaction/day', 'Transaction_amount',\n",
              "       'Is declined', 'Total Number of declines/day', 'isForeignTransaction',\n",
              "       'isHighRiskCountry', 'Daily_chargeback_avg_amt', '6_month_avg_chbk_amt',\n",
              "       '6-month_chbk_freq'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EasQyeQIDapS",
        "colab_type": "code",
        "outputId": "274bd60c-81ae-42c1-f294-d4891d1840ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "X.columns"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Merchant_id', 'Average Amount/transaction/day', 'Transaction_amount',\n",
              "       'Is declined', 'Total Number of declines/day', 'isForeignTransaction',\n",
              "       'isHighRiskCountry', 'Daily_chargeback_avg_amt', '6_month_avg_chbk_amt',\n",
              "       '6-month_chbk_freq'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2i1l-wJu-0ID",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df_main = X_train\n",
        "#df_main['isFradulent'] = y_train\n",
        "df_new=pd.concat([X_train,y_train],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "leoOhjjs-0IG",
        "colab_type": "code",
        "outputId": "5ed8dece-ed5f-4ba7-d4a5-36215cd624c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df_new.head()\n",
        "df_new.shape\n",
        "df_new['isFradulent'].value_counts()\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2115\n",
              "1     345\n",
              "Name: isFradulent, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xx6JWWAy-0II",
        "colab_type": "code",
        "outputId": "046a714a-eaf0-44fe-8487-469dc8130f0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "from sklearn.utils import resample\n",
        "# Separate majority and minority classes\n",
        "df_majority = df_new[df_new['isFradulent']==0]\n",
        "df_minority = df_new[df_new['isFradulent']==1]\n",
        "\n",
        "# Upsample minority class\n",
        "df_minority_upsampled = resample(df_minority, replace=True,n_samples=2115,random_state=111) \n",
        " \n",
        "# Combine majority class with upsampled minority class\n",
        "df_upsampled = pd.concat([df_majority, df_minority_upsampled])\n",
        " \n",
        "# Display new class counts\n",
        "df_upsampled['isFradulent'].value_counts()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2115\n",
              "0    2115\n",
              "Name: isFradulent, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVwwJHJuEvGs",
        "colab_type": "code",
        "outputId": "326cbc3d-6068-49ae-b0a6-686398aa756e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "x=df_upsampled['isFradulent'].value_counts().values\n",
        "sns.barplot(['Not fraud','fraud'],x)\n",
        "#sns.scatterplot(df_upsampled['Merchant_id'],df_upsampled['isFradulent'],hue =\"isFradulent\")\n",
        "#sns.pairplot(df_upsampled, hue =\"isFradulent\", palette ='coolwarm') \n",
        "plt.title('Target variable count')"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Target variable count')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chfVP2zi-0IP",
        "colab_type": "code",
        "outputId": "a593c4a2-5021-425b-e03e-858003d8120f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df_upsampled.shape"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4230, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51WXHjxWdORh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z = df_upsampled['isFradulent']\n",
        "f = z.to_numpy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfnEzv_U-0IS",
        "colab_type": "text"
      },
      "source": [
        "### undersampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVYgNFCc-0IS",
        "colab_type": "code",
        "outputId": "1f1c837b-5996-4a71-afcf-bcf048952dd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "from sklearn.utils import resample\n",
        "# Separate majority and minority classes\n",
        "df_majority = df_new[df_new['isFradulent']==0]\n",
        "df_minority = df_new[df_new['isFradulent']==1]\n",
        "\n",
        "df_majority_downsampled = resample(df_majority, replace=False, n_samples=345,random_state=121) \n",
        " \n",
        "# Combine minority class with downsampled majority class\n",
        "df_downsampled = pd.concat([df_majority_downsampled, df_minority])\n",
        " \n",
        "# Display new class counts\n",
        "df_downsampled['isFradulent'].value_counts()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    345\n",
              "0    345\n",
              "Name: isFradulent, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tno0qXAW-0IY",
        "colab_type": "code",
        "outputId": "f4103406-6cb5-41ed-db09-64a2b268ff75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df_downsampled.shape"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(690, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNNlKfrfFjZ1",
        "colab_type": "code",
        "outputId": "897035c3-1a4a-482a-c11c-759b9eebc3b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "x=df_downsampled['isFradulent'].value_counts().values\n",
        "sns.barplot(['Not fraud','fraud'],x)\n",
        "plt.title('Target variable count')"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Target variable count')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yk1N5Wsj-0Ib",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df_new.drop(['isFradulent'],axis=1)\n",
        "y = df_new['isFradulent']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOD4hYtE_0uP",
        "colab_type": "code",
        "outputId": "f54d4db4-4a1e-4822-8911-b594c66546a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X.shape,y.shape"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2460, 10), (2460,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itbDgqWqXo7s",
        "colab_type": "text"
      },
      "source": [
        "## SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJEbM3Oh-0Ii",
        "colab_type": "code",
        "outputId": "ee82ba9d-cb30-4c13-a5f1-7f0f1e741afe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "from imblearn.over_sampling import SMOTE \n",
        "sm = SMOTE(sampling_strategy='auto', random_state=None,k_neighbors=3)\n",
        "X_train_res, y_train_res = sm.fit_sample(X, y) "
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vv_OI-N9-0Im",
        "colab_type": "code",
        "outputId": "a08d5959-11d6-49f0-e23b-fd5370b16758",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(\"After applying SMOTE, counts '1': {}\".format(sum(y_train_res == 1))) \n",
        "print(\"After applying SMOTE, counts '0': {}\".format(sum(y_train_res == 0))) \n"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After applying SMOTE, counts '1': 2115\n",
            "After applying SMOTE, counts '0': 2115\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2Zov8h-MGqo",
        "colab_type": "code",
        "outputId": "9edb2e49-668a-437c-9f95-8d135aea7629",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(\"After applying SMOTE, counts of label '1': {}\".format(sum(y_tl == 1))) \n",
        "print(\"After applying SMOTE, counts of label '0': {}\".format(sum(y_tl == 0))) "
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After applying SMOTE, counts of label '1': 345\n",
            "After applying SMOTE, counts of label '0': 1938\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L160bcJBamOo",
        "colab_type": "text"
      },
      "source": [
        "## Clustering based method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sk9wfX-N-k9Y",
        "colab_type": "code",
        "outputId": "ecf9c893-c896-47b2-fd71-5100fb69ec35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "from imblearn.under_sampling import ClusterCentroids\n",
        "\n",
        "cluster_centroid = ClusterCentroids(ratio={0: 300})\n",
        "X_cluster, y_cluster = cluster_centroid.fit_sample(X, y)\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTXQOn92UxYw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d6abe55f-29f9-431e-a10f-d5405a369ffa"
      },
      "source": [
        "print(\"After applying cluster centroid algorithm, counts '1': {}\".format(sum(y_cluster == 1))) \n",
        "print(\"After applying cluster centroid algorithm, counts '0': {}\".format(sum(y_cluster == 0))) "
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After applying cluster centroid algorithm, counts '1': 345\n",
            "After applying cluster centroid algorithm, counts '0': 300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCAV366paydc",
        "colab_type": "text"
      },
      "source": [
        "## Tomek Links"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsHcwbZZ6MNO",
        "colab_type": "code",
        "outputId": "7e82fdba-47a0-4b68-fbea-d1903f2c22aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "from imblearn.under_sampling import TomekLinks\n",
        " \n",
        "tomeklinks = TomekLinks()\n",
        "X_tomeklinks, y_tomeklinks = tomeklinks.fit_sample(X, y)\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_zWwPtdMZaK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1cf15598-0bf0-4e7b-fbfa-8a48018fafa1"
      },
      "source": [
        "print(\"Using tomek links, counts '1': {}\".format(sum(y_tomeklinks == 1))) \n",
        "print(\"Using tomek links, counts '0': {}\".format(sum(y_tomeklinks == 0))) "
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using tomek links, counts '1': 345\n",
            "Using tomek links, counts '0': 1938\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}