Imbalanced_Dataset.ipynb

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      "name": "Imbalanced Dataset.ipynb",
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        "<a href=\"https://colab.research.google.com/github/SuperDataWorld/Python/blob/main/Imbalanced_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Imbalanced Dataset - Sampling Techniques"
      ],
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        "id": "pvRz-eAQEG-c"
      }
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd #Data manipulation lib\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "from imblearn.over_sampling import RandomOverSampler, SMOTE "
      ],
      "metadata": {
        "id": "8J__YYIAEaP1"
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      "execution_count": 417,
      "outputs": []
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      "source": [
        "#Import Data  \n",
        "df = pd.read_csv('https://raw.githubusercontent.com/SuperDataWorld/Python/main/Data/loanpred.csv')\n",
        "df.dropna(axis=1, inplace = True)\n",
        "df.drop(['ID','Lead_Creation_Date'], axis=1, inplace= True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Data Prep"
      ],
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      "source": [
        "df.info()"
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      "execution_count": 419,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 69713 entries, 0 to 69712\n",
            "Data columns (total 7 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Gender           69713 non-null  object \n",
            " 1   Monthly_Income   69713 non-null  float64\n",
            " 2   Contacted        69713 non-null  object \n",
            " 3   Source           69713 non-null  object \n",
            " 4   Source_Category  69713 non-null  object \n",
            " 5   Var1             69713 non-null  int64  \n",
            " 6   Approved         69713 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(4)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
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        "df.describe()"
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          "data": {
            "text/plain": [
              "       Monthly_Income          Var1      Approved\n",
              "count    6.971300e+04  69713.000000  69713.000000\n",
              "mean     5.622283e+03      3.948446      0.014631\n",
              "std      1.747671e+05      3.819214      0.120073\n",
              "min      0.000000e+00      0.000000      0.000000\n",
              "25%      1.650000e+03      0.000000      0.000000\n",
              "50%      2.500000e+03      2.000000      0.000000\n",
              "75%      4.000000e+03      7.000000      0.000000\n",
              "max      3.838384e+07     10.000000      1.000000"
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              "      <th>count</th>\n",
              "      <td>6.971300e+04</td>\n",
              "      <td>69713.000000</td>\n",
              "      <td>69713.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.622283e+03</td>\n",
              "      <td>3.948446</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
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              "      <th>25%</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000e+03</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.838384e+07</td>\n",
              "      <td>10.000000</td>\n",
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      "execution_count": 421,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Monthly_Income Contacted Source Source_Category  Var1  Approved\n",
              "0  Female          2000.0         N   S122               G     0         0\n",
              "1    Male          3500.0         Y   S122               G    10         0\n",
              "2    Male          2250.0         Y   S143               B     0         0\n",
              "3    Male          3500.0         Y   S143               B     7         0\n",
              "4    Male         10000.0         Y   S134               B    10         0"
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              "      <th>Source_Category</th>\n",
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              "      <th>Approved</th>\n",
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              "      <th>0</th>\n",
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              "      <td>N</td>\n",
              "      <td>S122</td>\n",
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              "      <th>1</th>\n",
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              "      <td>3500.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>2250.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>S143</td>\n",
              "      <td>B</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>S143</td>\n",
              "      <td>B</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>S134</td>\n",
              "      <td>B</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "# Encode Categorical Fields\n",
        "\n",
        "# Categorical Encoding \n",
        "for i in ['Gender','Contacted','Source','Source_Category','Var1']:\n",
        "  df[i] = df[i].astype('category').cat.codes"
      ],
      "metadata": {
        "id": "PrJaLdhOnNRb"
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      "execution_count": 422,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "X = df.drop(['Approved'], axis=1)\n",
        "y = df[['Approved']]"
      ],
      "metadata": {
        "id": "gbvvvNPlFg1e"
      },
      "execution_count": 423,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "rhu0nTB0F9DF",
        "outputId": "a1f256eb-abc2-4780-8f4c-ba775645364e"
      },
      "execution_count": 424,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Approved\n",
              "0           68693\n",
              "1            1020\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 424
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(1020/68693,4)"
      ],
      "metadata": {
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        },
        "id": "qcFAtOMcG_k7",
        "outputId": "b2ab8835-4e58-4a7a-ad60-89f105a61f0f"
      },
      "execution_count": 425,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0148"
            ]
          },
          "metadata": {},
          "execution_count": 425
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Stratified Split"
      ],
      "metadata": {
        "id": "aV8kc1PxJulW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=21)\n",
        "print(y_train.value_counts())  \n",
        "print(y_test.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLIU_IMOKKTU",
        "outputId": "68c754b6-3c11-4315-9d58-b3ddf04985cd"
      },
      "execution_count": 426,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Approved\n",
            "0           54929\n",
            "1             841\n",
            "dtype: int64\n",
            "Approved\n",
            "0           13764\n",
            "1             179\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(841/54929,4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZsa4ftDKPW5",
        "outputId": "5102ed2e-2928-4064-98e5-0fc40274e7c8"
      },
      "execution_count": 427,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0153"
            ]
          },
          "metadata": {},
          "execution_count": 427
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(179/13764,4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcrUETJxKPOT",
        "outputId": "982fb249-afde-4c63-a145-c0ead6945b6b"
      },
      "execution_count": 428,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.013"
            ]
          },
          "metadata": {},
          "execution_count": 428
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "splitter=StratifiedShuffleSplit(n_splits=1,random_state=12, test_size=0.20) \n",
        "for train,test in splitter.split(X,y):     \n",
        "    X_train = X.iloc[train]\n",
        "    y_train = y.iloc[train]\n",
        "    X_test = X.iloc[test]\n",
        "    y_test = y.iloc[test]\n",
        "print(y_train.value_counts())  \n",
        "print(y_test.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4efjvJvG6_j",
        "outputId": "26e24222-bd01-44bc-aa87-2d3ba5eb632d"
      },
      "execution_count": 429,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Approved\n",
            "0           54954\n",
            "1             816\n",
            "dtype: int64\n",
            "Approved\n",
            "0           13739\n",
            "1             204\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "816/54954"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tOxDEtygJ29d",
        "outputId": "a07b8819-36b6-4eb0-8a2b-ed5d0614e42c"
      },
      "execution_count": 430,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01484878261818976"
            ]
          },
          "metadata": {},
          "execution_count": 430
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "204/13739"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-01lN7WJ6Cr",
        "outputId": "4c827fea-672c-4f64-abd4-116484bf4ae0"
      },
      "execution_count": 431,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.014848242230147754"
            ]
          },
          "metadata": {},
          "execution_count": 431
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. SMOTE Sample"
      ],
      "metadata": {
        "id": "2EJWyif5hKpT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# SMOTE Resample\n",
        "sm = SMOTE(random_state=42)\n",
        "X_sm, y_sm = sm.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "pxhhdZTcg2US"
      },
      "execution_count": 432,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_sm.value_counts()) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PAxvid5cg2QZ",
        "outputId": "e85adcb5-85e2-4578-d291-d49c021b6e4a"
      },
      "execution_count": 433,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Approved\n",
            "0           54954\n",
            "1           54954\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_sm.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "OjloBM14g2Mk",
        "outputId": "6b1e4e57-af47-4fd0-85d8-59b15f4c8246"
      },
      "execution_count": 434,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Gender  Monthly_Income  Contacted  Source  Source_Category  Var1\n",
              "109898       0     4900.000000          0       0                6     2\n",
              "109899       0    10610.588107          0      11                1     4\n",
              "109900       0     2800.000000          0       0                6     2\n",
              "109901       1     4000.000000          1       0                6     3\n",
              "109902       0     2500.000000          0       6                1     3\n",
              "109903       0     8000.000000          0       8                1     4\n",
              "109904       1     4000.000000          1      24                2     3\n",
              "109905       0     3028.711050          1      19                1     2\n",
              "109906       0    15000.000000          1       4                2     3\n",
              "109907       1     5800.000000          1       2                1     3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44bcd907-66ed-48c6-a8d7-4c743738cc51\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Monthly_Income</th>\n",
              "      <th>Contacted</th>\n",
              "      <th>Source</th>\n",
              "      <th>Source_Category</th>\n",
              "      <th>Var1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>109898</th>\n",
              "      <td>0</td>\n",
              "      <td>4900.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109899</th>\n",
              "      <td>0</td>\n",
              "      <td>10610.588107</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109900</th>\n",
              "      <td>0</td>\n",
              "      <td>2800.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109901</th>\n",
              "      <td>1</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109902</th>\n",
              "      <td>0</td>\n",
              "      <td>2500.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109903</th>\n",
              "      <td>0</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109904</th>\n",
              "      <td>1</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109905</th>\n",
              "      <td>0</td>\n",
              "      <td>3028.711050</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109906</th>\n",
              "      <td>0</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109907</th>\n",
              "      <td>1</td>\n",
              "      <td>5800.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44bcd907-66ed-48c6-a8d7-4c743738cc51')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-44bcd907-66ed-48c6-a8d7-4c743738cc51 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-44bcd907-66ed-48c6-a8d7-4c743738cc51');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 434
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_sm.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "-3h7l2FbpY-k",
        "outputId": "5f6d80cc-f247-41d6-c9ff-a7a34f468405"
      },
      "execution_count": 435,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Approved\n",
              "109898         1\n",
              "109899         1\n",
              "109900         1\n",
              "109901         1\n",
              "109902         1\n",
              "109903         1\n",
              "109904         1\n",
              "109905         1\n",
              "109906         1\n",
              "109907         1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9324e06d-089a-4949-9f23-718e3d6b3cbd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Approved</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>109898</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109899</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109900</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109901</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109902</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109903</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109904</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109905</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109906</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109907</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9324e06d-089a-4949-9f23-718e3d6b3cbd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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-9324e06d-089a-4949-9f23-718e3d6b3cbd 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-9324e06d-089a-4949-9f23-718e3d6b3cbd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 435
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Random Oversampling Sample"
      ],
      "metadata": {
        "id": "ryoNuPTmiUl2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define oversampling strategy\n",
        "oversample = RandomOverSampler(sampling_strategy='minority')\n",
        "# fit and apply the transform\n",
        "X_over, y_over = oversample.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "NX1h1DD8iUQF"
      },
      "execution_count": 436,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_over.value_counts()) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHm2a0EQih7E",
        "outputId": "2155460e-4fe1-4dee-d8fe-9b5cc173d4e9"
      },
      "execution_count": 437,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Approved\n",
            "0           54954\n",
            "1           54954\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_over.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "9tqDq6KEl1-u",
        "outputId": "db6e6d8a-2f19-4b5f-a261-a7843a3735be"
      },
      "execution_count": 438,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Gender  Monthly_Income  Contacted  Source  Source_Category  Var1\n",
              "109898       1          2150.0          1       0                2     2\n",
              "109899       1         13500.0          1       0                6     4\n",
              "109900       1          5000.0          1       0                1     4\n",
              "109901       1          2656.8          1       0                6     3\n",
              "109902       1          4800.0          1       7                1     4\n",
              "109903       1          5500.0          1       0                6     4\n",
              "109904       1          4400.0          1       0                6     4\n",
              "109905       1          3500.0          1       6                4     4\n",
              "109906       1          3748.9          1       7                2     3\n",
              "109907       1          6700.0          1       0                6     4"
            ],
            "text/html": [
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              "      <div>\n",
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              "      <th>109904</th>\n",
              "      <td>1</td>\n",
              "      <td>4400.0</td>\n",
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              "      <th>109907</th>\n",
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              "      <td>6700.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ff23c451-0286-4deb-8d03-308ad37a8243');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 438
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_over.query('Monthly_Income == 6500 & Contacted == 1 & Source == 6 & Var1 == 4 & Gender == 1 & Source_Category == 1').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDT-64NXoH8q",
        "outputId": "a7fe66bc-bdf6-4979-db38-0cb0588b8119"
      },
      "execution_count": 439,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender             244\n",
              "Monthly_Income     244\n",
              "Contacted          244\n",
              "Source             244\n",
              "Source_Category    244\n",
              "Var1               244\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 439
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_sm.query('Monthly_Income == 6500 & Contacted == 1 & Source == 6 & Var1 == 4 & Gender == 1 & Source_Category == 1').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6t6vEtXmrtdm",
        "outputId": "b33d29cf-ab6d-4524-b578-8ee1751c0429"
      },
      "execution_count": 440,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender             151\n",
              "Monthly_Income     151\n",
              "Contacted          151\n",
              "Source             151\n",
              "Source_Category    151\n",
              "Var1               151\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 440
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.query('Monthly_Income == 6500 & Contacted == 1 & Source == 6 & Var1 == 4 & Gender == 1 & Source_Category == 1').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lU9HlIJGrBt5",
        "outputId": "45d28ec4-f0dd-41a8-bba3-ea4ac5822754"
      },
      "execution_count": 441,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender             48\n",
              "Monthly_Income     48\n",
              "Contacted          48\n",
              "Source             48\n",
              "Source_Category    48\n",
              "Var1               48\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 441
        }
      ]
    }
  ]
}