RF_LGBM_HyperparameterTun_credit_loan_approval.ipynb

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        "<a href=\"https://colab.research.google.com/github/gulabpatel/ML_Pipeline/blob/main/RF_LGBM_HyperparameterTun_credit_loan_approval.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Code walkthrough video : https://www.youtube.com/watch?v=wXQRLpDF-ms\n",
        "### Part 1\n",
        "Some data wrangling\n",
        "\n",
        "Some outlier removal based on domain knowledge\n",
        "\n",
        "Use Column Transformer and Pipeline to streamline process\n",
        "\n",
        "Use Randomized Search to find optimal set of parameters\n",
        "\n",
        "Automate the procedure for multiple classifiers\n",
        "\n",
        "Plot Precision-Recall Curve\n",
        "\n",
        "Plot Learning Curve (for bias-variance tradeoff / check for overfitting-underfitting) \n",
        "\n",
        "\n",
        "### Part 2\n",
        "Rectify existing model based on inferences from the learning curve and make a better one"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "KyrnUo5PwEG1"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split, learning_curve, RandomizedSearchCV\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "from sklearn.metrics import plot_precision_recall_curve\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
        "from sklearn.metrics import plot_confusion_matrix, confusion_matrix, classification_report\n",
        "from lightgbm import LGBMClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/gulabpatel/ML_Pipeline/master/credit_risk_dataset.csv')\n",
        "df.head()"
      ],
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              "   person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "0          22          59000                  RENT              123.0   \n",
              "1          21           9600                   OWN                5.0   \n",
              "2          25           9600              MORTGAGE                1.0   \n",
              "3          23          65500                  RENT                4.0   \n",
              "4          24          54400                  RENT                8.0   \n",
              "\n",
              "  loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "0    PERSONAL          D      35000          16.02            1   \n",
              "1   EDUCATION          B       1000          11.14            0   \n",
              "2     MEDICAL          C       5500          12.87            1   \n",
              "3     MEDICAL          C      35000          15.23            1   \n",
              "4     MEDICAL          C      35000          14.27            1   \n",
              "\n",
              "   loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \n",
              "0                 0.59                         Y                           3  \n",
              "1                 0.10                         N                           2  \n",
              "2                 0.57                         N                           3  \n",
              "3                 0.53                         N                           2  \n",
              "4                 0.55                         Y                           4  "
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        "dups = df.duplicated()"
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            "text/plain": [
              "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "15975          23          42000                  RENT                5.0   \n",
              "15989          23          90000              MORTGAGE                7.0   \n",
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              "16025          24          10000                  RENT                8.0   \n",
              "16028          23         100000              MORTGAGE                7.0   \n",
              "...           ...            ...                   ...                ...   \n",
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              "32172          49         120000              MORTGAGE               12.0   \n",
              "32259          39          40000                   OWN                4.0   \n",
              "32279          43          11340                  RENT                4.0   \n",
              "\n",
              "             loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "15975            VENTURE          B       6000           9.99            0   \n",
              "15989          EDUCATION          B       8000          10.36            0   \n",
              "15995            MEDICAL          A       4000           5.42            0   \n",
              "16025           PERSONAL          A       3000           7.90            1   \n",
              "16028          EDUCATION          A      15000           7.88            0   \n",
              "...                  ...        ...        ...            ...          ...   \n",
              "32010    HOMEIMPROVEMENT          A       2500           5.42            0   \n",
              "32047  DEBTCONSOLIDATION          A      20000           7.88            0   \n",
              "32172            MEDICAL          B      12000          10.99            0   \n",
              "32259            VENTURE          B       1000          10.37            0   \n",
              "32279          EDUCATION          C       1950            NaN            1   \n",
              "\n",
              "       loan_percent_income cb_person_default_on_file  \\\n",
              "15975                 0.14                         N   \n",
              "15989                 0.09                         N   \n",
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              "16025                 0.30                         N   \n",
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              "...                    ...                       ...   \n",
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              "       cb_person_cred_hist_length  \n",
              "15975                           4  \n",
              "15989                           3  \n",
              "15995                           4  \n",
              "16025                           3  \n",
              "16028                           4  \n",
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              "      <th>32172</th>\n",
              "      <td>49</td>\n",
              "      <td>120000</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>12.0</td>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>B</td>\n",
              "      <td>12000</td>\n",
              "      <td>10.99</td>\n",
              "      <td>0</td>\n",
              "      <td>0.10</td>\n",
              "      <td>N</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32259</th>\n",
              "      <td>39</td>\n",
              "      <td>40000</td>\n",
              "      <td>OWN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>VENTURE</td>\n",
              "      <td>B</td>\n",
              "      <td>1000</td>\n",
              "      <td>10.37</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>N</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32279</th>\n",
              "      <td>43</td>\n",
              "      <td>11340</td>\n",
              "      <td>RENT</td>\n",
              "      <td>4.0</td>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>C</td>\n",
              "      <td>1950</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0.17</td>\n",
              "      <td>N</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>165 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf0eebfe-9e2e-4396-b216-dc2f6c6d70f0')\"\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "metadata": {},
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      ],
      "source": [
        "df[dups]"
      ]
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    {
      "cell_type": "code",
      "execution_count": 5,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "6464           23          42000                  RENT                5.0   \n",
              "15975          23          42000                  RENT                5.0   \n",
              "\n",
              "      loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "6464      VENTURE          B       6000           9.99            0   \n",
              "15975     VENTURE          B       6000           9.99            0   \n",
              "\n",
              "       loan_percent_income cb_person_default_on_file  \\\n",
              "6464                  0.14                         N   \n",
              "15975                 0.14                         N   \n",
              "\n",
              "       cb_person_cred_hist_length  \n",
              "6464                            4  \n",
              "15975                           4  "
            ],
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              "      <th>person_age</th>\n",
              "      <th>person_income</th>\n",
              "      <th>person_home_ownership</th>\n",
              "      <th>person_emp_length</th>\n",
              "      <th>loan_intent</th>\n",
              "      <th>loan_grade</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>loan_int_rate</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>loan_percent_income</th>\n",
              "      <th>cb_person_default_on_file</th>\n",
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              "      <th>6464</th>\n",
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              "      <td>42000</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>VENTURE</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 5
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      ],
      "source": [
        "df.query(\"person_age==23 & person_income==42000 &\\\n",
        "person_home_ownership=='RENT' & loan_int_rate==9.99\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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        "outputId": "42ffc582-e1fa-4235-9e16-fb3ca8ba2381",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32581, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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        "df.shape"
      ]
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      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
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      },
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      "source": [
        "df.drop_duplicates(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "vgg90xLUwEG9",
        "outputId": "b911a2e0-0ae4-4496-d5ec-c0ffa25d7ae3",
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          "base_uri": "https://localhost:8080/"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32416, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.shape"
      ]
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    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "yNQGK5FLwEG-"
      },
      "outputs": [],
      "source": [
        "# X and y will be thought of as the entire training data\n",
        "# X_test and y_test will be thought of as the out of sample data for model evaluation\n",
        "\n",
        "X, X_test, y, y_test = train_test_split(df.drop('loan_status', axis=1), df['loan_status'],\n",
        "                                        random_state=0,  test_size=0.2, stratify=df['loan_status'],\n",
        "                                        shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "07zWXpHwwEG-",
        "outputId": "c86b5fb9-0632-462c-87ea-331e3ae6e42b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.781312\n",
              "1    0.218688\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df['loan_status'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "OvysXEohwEG_",
        "outputId": "f84964d6-ac68-480e-c123-1098e27e9e0b",
        "colab": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.781313\n",
              "1    0.218687\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "y.value_counts(normalize=True)"
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    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "uXduo5SSwEG_",
        "outputId": "fcf9a92f-cea3-469b-e2d3-4ace6899ac86",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.781308\n",
              "1    0.218692\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "y_test.value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "BBiY5f42wEG_",
        "outputId": "e7b2a699-c46f-4ffc-ad2a-85b03ae4c8f2",
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        {
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          "data": {
            "text/plain": [
              "person_age                    0.000\n",
              "person_income                 0.000\n",
              "person_home_ownership         0.000\n",
              "person_emp_length             2.800\n",
              "loan_intent                   0.000\n",
              "loan_grade                    0.000\n",
              "loan_amnt                     0.000\n",
              "loan_int_rate                 9.614\n",
              "loan_percent_income           0.000\n",
              "cb_person_default_on_file     0.000\n",
              "cb_person_cred_hist_length    0.000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "np.round(X.isna().sum()* 100 / X.shape[0], 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "pNAk3auQwEHA",
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        {
          "output_type": "execute_result",
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              "(25932, 11)"
            ]
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          "metadata": {},
          "execution_count": 14
        }
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22763, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "X.dropna().shape"
      ]
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    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "n-mHF5lVwEHB",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.12220422643837729"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
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      "source": [
        "(25932-22763)/25932"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "PxwuzgM0wEHB",
        "outputId": "8e10a431-a923-4d98-d932-1f0a54edd66d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       person_income  loan_amnt  loan_percent_income\n",
              "21415          48000      10000                 0.21\n",
              "12916          85000       7500                 0.09\n",
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              "19114          62000       2300                 0.04\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "X[['person_income', 'loan_amnt', 'loan_percent_income']].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "yuJAPLm4wEHB"
      },
      "outputs": [],
      "source": [
        "X.drop('loan_percent_income', axis=1, inplace=True)\n",
        "X_test.drop('loan_percent_income', axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "scrolled": true,
        "id": "cZy08wakwEHC",
        "outputId": "697b2365-89a4-4a26-b6dd-32e1a54de0ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "person_age ---> 58\n",
            "\n",
            "person_income ---> 3680\n",
            "\n",
            "person_home_ownership ---> 4\n",
            "RENT        50.320068\n",
            "MORTGAGE    41.439149\n",
            "OWN          7.916859\n",
            "OTHER        0.323924\n",
            "Name: person_home_ownership, dtype: float64\n",
            "\n",
            "person_emp_length ---> 36\n",
            "\n",
            "loan_intent ---> 6\n",
            "EDUCATION            19.809502\n",
            "MEDICAL              18.787598\n",
            "VENTURE              17.542033\n",
            "PERSONAL             16.878760\n",
            "DEBTCONSOLIDATION    15.968687\n",
            "HOMEIMPROVEMENT      11.013420\n",
            "Name: loan_intent, dtype: float64\n",
            "\n",
            "loan_grade ---> 7\n",
            "A    32.932284\n",
            "B    32.126330\n",
            "C    19.902052\n",
            "D    11.121394\n",
            "E     3.004010\n",
            "F     0.732685\n",
            "G     0.181243\n",
            "Name: loan_grade, dtype: float64\n",
            "\n",
            "loan_amnt ---> 710\n",
            "\n",
            "loan_int_rate ---> 346\n",
            "\n",
            "cb_person_default_on_file ---> 2\n",
            "N    82.392411\n",
            "Y    17.607589\n",
            "Name: cb_person_default_on_file, dtype: float64\n",
            "\n",
            "cb_person_cred_hist_length ---> 29\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for col in X:\n",
        "    print(col, '--->', X[col].nunique())\n",
        "    if X[col].nunique()<20:\n",
        "        print(X[col].value_counts(normalize=True)*100)\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "uI_8z91QwEHD",
        "outputId": "260422dc-d0ce-499d-9606-0ebefe8e4255",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         person_age  person_income  person_emp_length     loan_amnt  \\\n",
              "count  25932.000000   2.593200e+04       25206.000000  25932.000000   \n",
              "mean      27.721155   6.589884e+04           4.811315   9611.395187   \n",
              "std        6.382311   6.333831e+04           4.172822   6339.054572   \n",
              "min       20.000000   4.000000e+03           0.000000    500.000000   \n",
              "25%       23.000000   3.849500e+04           2.000000   5000.000000   \n",
              "50%       26.000000   5.500000e+04           4.000000   8000.000000   \n",
              "75%       30.000000   7.900000e+04           7.000000  12250.000000   \n",
              "max      144.000000   6.000000e+06         123.000000  35000.000000   \n",
              "\n",
              "       loan_int_rate  cb_person_cred_hist_length  \n",
              "count   23439.000000                25932.000000  \n",
              "mean       11.013784                    5.793460  \n",
              "std         3.240104                    4.052297  \n",
              "min         5.420000                    2.000000  \n",
              "25%         7.900000                    3.000000  \n",
              "50%        10.990000                    4.000000  \n",
              "75%        13.480000                    8.000000  \n",
              "max        23.220000                   30.000000  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
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              "      <th>person_emp_length</th>\n",
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              "      <td>25206.000000</td>\n",
              "      <td>25932.000000</td>\n",
              "      <td>23439.000000</td>\n",
              "      <td>25932.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>27.721155</td>\n",
              "      <td>6.589884e+04</td>\n",
              "      <td>4.811315</td>\n",
              "      <td>9611.395187</td>\n",
              "      <td>11.013784</td>\n",
              "      <td>5.793460</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
              "      <td>6.382311</td>\n",
              "      <td>6.333831e+04</td>\n",
              "      <td>4.172822</td>\n",
              "      <td>6339.054572</td>\n",
              "      <td>3.240104</td>\n",
              "      <td>4.052297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>4.000000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>5.420000</td>\n",
              "      <td>2.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>3.849500e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>5.500000e+04</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>10.990000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>7.900000e+04</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12250.000000</td>\n",
              "      <td>13.480000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>144.000000</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>35000.000000</td>\n",
              "      <td>23.220000</td>\n",
              "      <td>30.000000</td>\n",
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              "\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-8033408c-05df-4f78-bb2d-59c7ad8e04c6');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "X.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "RzylEV8NwEHE",
        "outputId": "06856718-d7c0-43d6-d152-1c8530c80390",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['person_age',\n",
              " 'person_income',\n",
              " 'person_emp_length',\n",
              " 'loan_amnt',\n",
              " 'loan_int_rate',\n",
              " 'cb_person_cred_hist_length']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "num_cols = [col for col in X if X[col].dtypes != 'O']\n",
        "num_cols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "scrolled": true,
        "id": "hh1LeUSawEHE",
        "outputId": "343ae81b-ef80-4aa3-e86a-253621819318",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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NwIZFLE2SJoJPwklSJwawJHViAI+BQ9skzccAHgOHtkmajwE8Jg5tk7QzA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA3iMnJRH0jADeIyclEfSMAN4zJyUR9IcA1iSOjGAJakTA1iSOjGAJakTA7gDh6NJAgO4C4ejSQIDuBuHo0kygCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwO4I+eEkJY3A7gj54SQljcDuDPnhJCWLwNYkjrpEsBJvpzktiQ3J9nc2g5JsinJ3e3nwa09SS5IsjXJrUme1aNmSVpsPe+AX1BVx1bVmrZ+DnBtVa0Grm3rACcDq9trPXDh2CuVpBGYpC6IU4FL2/KlwEuH2t9fA58DDkpyWI8CR8XRENLy1CuAC/jLJDclWd/aDq2q+9ry14BD2/LhwL1Dx25rbf9GkvVJNifZvGPHjlHVPRKOhpCWpxWdzvvTVbU9yQ8Dm5J8cXhjVVWSvbodrKqLgIsA1qxZM3W3ko6GkJafLnfAVbW9/bwf+ARwPPD1ua6F9vP+tvt24Mihw49obZI01cYewEmekOTAuWXgROB2YCNwZtvtTOCTbXkj8Mo2GuIE4MGhrgpJmlo9uiAOBT6RZO78f15VVye5Ebg8yVnAV4DT2v5XAacAW4HvAK8ef8njMfdh3IEHHki7PpKWsLEHcFV9CfiJedq/CbxonvYCzh5Dad3Nzs6y/uK/4bI3vpiVK1f2LkfSiE3SMDThh3HScmIAS1InBrAkdWIAS1InBvCE8vFkaekzgCeUjydLS58BPMEcESEtbQawJHViAEtSJwawJHViAEtSJwawJHViAE84xwNLS5cBPOEcDywtXQbwFHA8sLQ0GcCS1IkBLEmdGMBTwg/jpKXHAJ4SfhgnLT0G8BTxwzhpaTGAp4xdEdLSYQBPGbsipKXDAJ5CK/Y/wDthaQkwgKeUd8LS9DOAp5gfyknTzQCecnZFSNPLAJ5ydkVI08sAXgLsipCmkwG8RNgVIU0fA3iJsCtCmj4G8BJiV4Q0XQzgJWa4K8JuCWmyGcBLzHBXhN0S0mQzgJeg4a6I4ceWv/e973lHLE0QA3gZmLsT/upXv8q6869kZmbGIJYmgAG8TMzdFa/Y/wC7JqQJYQAvU3ZNSP0ZwMvYrromDGRpPKYmgJOsTXJXkq1Jzuldz1IxX9fEfH3FC7lbdtibtHemIoCT7Ae8GzgZOAY4Pckxi3kOw2NgV33Fe7pbnnsN9y3PF9o7X+ed99lVwDu+WUvRit4FLNDxwNaq+hJAksuAU4E7FusEs7Oz/NzvfYQLznguD//Td5idnd2nnzMzMwBL4j3mgnTuveauE8BXv/pVXveBv+OCM57Lmy6/GYC3n3bsv9l/ZmaGV7/nGi4447m87gN/x/tecxIAr37PNbzvNSexcuXKR+wzvO/KlSu/f965/eZ7D2lcFvv3LdNwF5Hk5cDaqvrltn4G8Oyqeu3QPuuB9W31acBd+3CqJwHfeJTljsu01DotdYK1joq1wjeqau3OjdNyB7xHVXURcNGjeY8km6tqzSKVNFLTUuu01AnWOirWumtT0QcMbAeOHFo/orVJ0tSalgC+EVid5OgkjwPWARs71yRJj8pUdEFU1cNJXgtcA+wHbKiqLSM41aPqwhizaal1WuoEax0Va92FqfgQTpKWomnpgpCkJccAlqROlmUAJzkyyXVJ7kiyJcnrW/shSTYlubv9PLh3rXOS7JfkC0muaOtHJ7m+PZr94fbhZHdJDkry0SRfTHJnkudM6nVN8sb23//2JB9Ksv+kXNckG5Lcn+T2obZ5r2MGLmg135rkWRNQ6x+234Fbk3wiyUFD285ttd6V5KSedQ5te1OSSvKktj6Wa7osAxh4GHhTVR0DnACc3R5tPge4tqpWA9e29UnxeuDOofXfB86vqh8FvgWc1aWqR/oj4OqqejrwEwxqnrjrmuRw4HXAmqp6JoMPd9cxOdf1EmDngfu7uo4nA6vbaz1w4ZhqnHMJj6x1E/DMqvpx4P8A5wK0v2frgGe0Y97TphroVSdJjgROBP7vUPN4runcs/XL+QV8EvgZBk/PHdbaDgPu6l1bq+UIBn/hXghcAYTB0zor2vbnANdMQJ0/CNxD+3B3qH3iritwOHAvcAiD0UBXACdN0nUFjgJu39N1BN4LnD7ffr1q3Wnby4APtuVzgXOHtl0DPKdnncBHGdwsfBl40jiv6XK9A/6+JEcBxwHXA4dW1X1t09eAQzuVtbN3Av8N+F5b/yHgH6rq4ba+jUGg9HY0sAN4X+su+dMkT2ACr2tVbQfexuCu5z7gQeAmJvO6ztnVdZz7x2TOpNX9S8D/assTVWuSU4HtVXXLTpvGUueyDuAkTwQ+BryhqmaGt9Xgn73uY/SSvAS4v6pu6l3LAqwAngVcWFXHAd9mp+6GCbquBzOY0Olo4MnAE5jnf08n1aRcxz1J8mYGXX4f7F3LzpIcAPwm8Fu9ali2AZzksQzC94NV9fHW/PUkh7XthwH396pvyPOAn03yZeAyBt0QfwQclGTuQZpJeTR7G7Ctqq5v6x9lEMiTeF3/M3BPVe2oqn8BPs7gWk/idZ2zq+s4kY/qJ3kV8BLgFe0fDJisWp/K4B/gW9rfryOAzyf5d4ypzmUZwEkCXAzcWVXvGNq0ETizLZ/JoG+4q6o6t6qOqKqjGHx48emqegVwHfDyttuk1Po14N4kT2tNL2IwZejEXVcGXQ8nJDmg/T7M1Tpx13XIrq7jRuCV7ZP7E4AHh7oqukiylkG32c9W1XeGNm0E1iV5fJKjGXzIdUOPGqvqtqr64ao6qv392gY8q/0ej+eajrOjflJewE8z+N+3W4Gb2+sUBn2r1wJ3A58CDuld6051Px+4oi0/hcEv7lbgI8Dje9fX6joW2Nyu7f8EDp7U6wr8DvBF4HbgA8DjJ+W6Ah9i0Df9LwyC4axdXUcGH8q+G/h74DYGIzt617qVQR/q3N+vPx7a/82t1ruAk3vWudP2L/OvH8KN5Zr6KLIkdbIsuyAkaRIYwJLUiQEsSZ0YwJLUiQEsSZ0YwFo2hh6wkCaCAaypkuSoNs3hB9t0lx9tD1P8ZJLPJLkpyTVDT4z9VZJ3JtkMvD7Jz7fpJ29J8tdtn/2TvC/JbW0Oixe09lcl+XiSq9sUkH+wh9ouTLI5gykuf2eo/ZRW801tisO5KUWf0KZIvKGd99SRXThNJO8INI2exmAQ/WeTbADOZjDj1qlVtSPJLwDnMZgEBuBx1b5qPMltwElVtX1ojtqzGUyv8B+TPB34yyQ/1rYdy2CypoeAu5K8q6qGJ2kZ9uaqeqBNr3htkrmpGN8L/KequifJh4b3Z/Bk4y+1Wm5I8qmq+vajvkKaCt4BaxrdW1Wfbct/xmAayWcCm5LcDPx3Bs/uz/nw0PJngUuS/AqDOYBh8GTknwFU1ReBrwBzAXxtVT1YVf/E4FHlf7+buk5L8nngCwzmuz0GeDrwpaq6p+0zHMAnAue0mv8K2B/4kT3/8bVUeAesabTz45uzwJaqes4u9v/+HWVV/ZckzwZeDNyU5Cf3cK6Hhpa/yy7+zrR5Df4r8FNV9a0klzAI1N0J8HNVddce9tMS5R2wptGPJJkL218EPgesmmtL8tgkz5jvwCRPrarrq+q3GMxdfCTwN8Ar2vYfY3AXurehuJJB0D+Y5FAG36hAe5+ntHmnAX5h6JhrgF9rkwGR5Li9PKemnHfAmkZ3MfgaqQ0MugXexSDMLkjygwx+r98JbJnn2D9MsprB3ee1wC0MJuS5sPUPPwy8qqoearm4IFV1S5IvtPe6l0FXB1X1/5K8Brg6ybeBG4cOe2ur89Ykj2HwbSIvWfBJNfWcjEdTpd1JXlGD73GbCkmeWFX/2O503w3cXVXn965L/dkFIY3er7QP2rYw+N6893auRxPCO2BpLyW5nsHcwcPOqKrbetSj6WUAS1IndkFIUicGsCR1YgBLUicGsCR1YgBLUif/H1zuYwak+dYQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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TPBU4DLgf8KfAhiTrWy93E7C97b8d2AxsS7IeuD/wzemXLUnjNfUecFW9uqo2VdUxwHOBD1bV84EPAc9su50OvK8tX9bWads/WBN4RO0Hjx+P+8SStIjVdB/wfwNelmQrwzHe81r7ecADW/vLgFdN4uJVxRl/8U9gBEuakh5DED9QVR8GPtyWbwYes8A+3wOeNY16vLlC0jStph6wJB1QDGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODOAlDJyUXdIEGcCS1IkBvAi/kkjSpBnAC6jBwCkhJE2cASxJnRjAktSJASxJnRjAktSJASxJnRjAC6jBgPI2CEkTZgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicG8CKcjlLSpBnAC3A6SknTYABLUicGsCR1YgBLUidTD+Akm5N8KMnnknw2yUtb+wOSXJnki+39iNaeJG9KsjXJ9UlOmERdAyfgkTRlPXrA88DLq+p44ETgrCTHA68Crqqq44Cr2jrAU4Dj2msLcPY0ivQuCEmTNvUArqpbq+qTbfnbwE3ARuA04IK22wXAM9ryacCFNXQ1sCHJ0VMuW5LGrusYcJJjgEcC1wBHVdWtbdPXgKPa8kbglpHDtrW23c+1JclckrkdO3ZMrGZJGpduAZzkR4B3A79VVf8yuq2qij28E7eqzqmq2aqanZmZGWOlkjQZXQI4ycEMw/eiqnpPa/76rqGF9n5ba98ObB45fFNrk6Q1rcddEAHOA26qqj8Z2XQZcHpbPh1430j7C9vdECcCd44MVUjSmrW+wzVPAl4A3JDk063tNcDrgUuSnAl8BXh223Y58FRgK/Bd4IzplitJkzH1AK6qfwCyyOaTF9i/gLMmWtQiBu1WtHXrfF5F0viZLEuYn59nfn6+dxmS9lMG8CJ8EEPSpBnAktSJASxJnRjAzfz8PMPP+yRpOgxgSerEAF7GwA/jJE2IASxJnRjAktSJASxJnRjAktSJAbyIqhp+T5y3pkmaEAN4MVX82oWfMoAlTYwBvJQsNmmbJO07A1iSOjGAF1GDAUX5IIakiTGAlzD6QZxBLGncDOClVHHGeZ/gnnvu6V2JpP2QAbyEGgwoP4eTNCEGsCR1YgAvoQYD6p4B8/Pzjv9KGjsDWJI6MYAlqRMDeBnOCSFpUgzg5VRx+rnXsHPnTkNY0lgZwEvY9TRcDQa84NyrDWBJY2UAL2NXCDsxj6RxM4CXUYMBtI6v48GSxskAbgYjQTuqBgPumd/JPTt3snPnTp7z1n/4QQA7P4SkfbG+dwFrwWgvOImhK2ks1kwPOMmpSb6QZGuSV03z2jUYMCh7u5LGa00EcJKDgDcDTwGOB56X5PhxXmOw68O2BdRgwODunTz/7I8xf/fd3H333T94PNlxYUl7a60MQTwG2FpVNwMkuRg4DfjcWK9Sde9rNIyrGOzcSQ3u4Z7v383tt9/OIYccwvr16znssMN44XlX885fezzr1g1/nu16B37Qa97Vtvv67hbavtwxkqZj3P8G10oAbwRuGVnfBjx2dIckW4AtbfWuJF/Yi+scCXxjuZ02//cfbjv0JXtxtX23onpXEeudvLVW84FS7xVVderujWslgJdVVecA5+zLOZLMVdXsmEqaOOudrLVWL6y9mg/0etfK77Tbgc0j65tamyStWWslgK8FjktybJJDgOcCl3WuSZL2yZoYgqiq+SS/CbwfOAg4v6o+O4FL7dMQRgfWO1lrrV5YezUf0PXG26ckqY+1MgQhSfsdA1iSOjGA6fuY895Icn6S25Lc2LuWlUiyOcmHknwuyWeTvLR3TUtJcliSTyT5TKv393rXtBJJDkryqSR/27uWlUjy5SQ3JPl0krne9SwnyYYklyb5fJKbkjxun895oI8Bt8ec/xn4Dwwf8LgWeF5VjfcpuzFK8gTgLuDCqnpE73qWk+Ro4Oiq+mSSHwWuA56xWv8fJwlw36q6K8nBwD8AL62qqzuXtqQkLwNmgftV1dN617OcJF8GZqtqTTyIkeQC4GNVdW67G+vwqvrWvpzTHvDIY85VdTew6zHnVauqPgrc3ruOlaqqW6vqk23528BNDJ9uXJVq6K62enB7reqeSpJNwM8D5/auZX+U5P7AE4DzAKrq7n0NXzCAYeHHnFdtOKx1SY4BHglc07eSpbVf5z8N3AZcWVWrul7gjcArgbU0ZV8BH0hyXZtKYDU7FtgBvL0N85yb5L77elIDWFOT5EeAdwO/VVX/0ruepVTVPVX10wyfunxMklU71JPkacBtVXVd71r20OOr6gSGsxye1YbWVqv1wAnA2VX1SOA7wD5/XmQA+5jzVLSx1HcDF1XVe3rXs1Lt18wPAT80kcoqchLw9DamejHwpCR/2bek5VXV9vZ+G/BehsOBq9U2YNvIb0KXMgzkfWIA+5jzxLUPtc4DbqqqP+ldz3KSzCTZ0Jbvw/AD2s/3rWpxVfXqqtpUVccw/Pv7war6pc5lLSnJfdsHsrRf5U8BVu1dPVX1NeCWJA9rTSczhulw18SjyJM0xcecxybJu4AnAkcm2Qa8tqrO61vVkk4CXgDc0MZVAV5TVZd3rGkpRwMXtDtk1gGXVNWauLVrDTkKeO/wZzPrgXdW1RV9S1rWi4GLWkftZuCMfT3hAX8bmiT14hCEJHViAEtSJwawJHViAEtSJwawJC1iTye+SvLskUmn3rns/t4FIUkL25OJr5IcB1wCPKmq7kjyY+0hk0XZA5ZWIMnlux7O0IFjoYmvkjw4yRVtDouPJXl42/SrwJur6o527JLhCwaw9hNJJvpQUVU9dRyzX2m/cA7w4qp6FPBfgbe09ocCD03yj0muTrLs4+sGsFaNJMe0ya4vahNeX5rk8CSPSvKR1uN4f5tfmCQfTvLGNpn3S5M8K8mNbSL1j7Z9Dkvy9jbx96eS/Gxrf1GS97SezBeT/M9lavtykiNbjTcleVsb5/tAe1yZJA9J8vft+p9sPaUk+aNW1w1JntP2fWL7b3pfkpuTvD7J8zOcCP6GJA9u+80keXeSa9vrpAn+EWgZbUKpnwH+uj3V+RcMn5yE4RN9xzF8SvV5wNuW/a2pqnz5WhUv4BiGUxSe1NbPB14B/BMw09qew/BxcYAPA28ZOf4GYGNb3tDeXz6y/8OBrwKHAS9i+Djp/dv6V4DNS9T2ZeDIVuM88NOt/RLgl9ryNcAvtOXDgMOB/wxcyfAx96Pa9Y9u/0i/1ZYPZTgB1O+1Y18KvLEtv5PhrGEAD2I4n0b3P6sD6dX+zG9sy/cDbl1kv7cCZ4ysXwU8eqlz2wPWanNLVf1jW/5L4MnAI4ArW4/jdxjOWLfLX40s/yPwjiS/yjDwAB7fzkNVfZ5h0D60bbuqqu6squ8xnFjl36ywxi9V1a45La4DjmkTy2ysqve2a32vqr7brv+uGk5v+XXgI8Cj27HX1nCy+u8D/xf4QGu/geE/eoCfA/68/bdfBtyv9cLUQQ2nUf1SkmfBcKKpJD/VNv9vhj9YSXIkw79nNy91vgN+Mh6tOrvflvNt4LNVtdj3b33nBwdW/XqSxzL8ZojrkjxqmWt9f2T5Hlb+72H34+6zwuOWOs9gZH0wUss64MT2Q0JTttDEV8DzgbOT/A7Db0u5GPgMwwm9TknyOYZ/L15RVd9c6vz2gLXaPCj3ftnhLwJXAzO72pIcnOQnFjowyYOr6pqq+l2G316wGfgYw38wJHkow1/jvzDuomv4VUvbkjyjXevQJIe36z8nw2/YmGH4tTaf2INTf4DhLFy08/70GMvWMqrqeVV1dFUdXMMpP8+rqi9V1alV9VNVdXxVva7tW1X1stb2k1V18XLnN4C12nyB4bcj3AQcAfwZ8EzgD5N8Bvg0ww9BFvJH7QOsGxmOG3+G4SfU65LcwHC44kXtV/5JeAHwkiTXt+v/OMOJxq9vtXwQeGUN55ZdqZcAs0mubz2rXx9zzerIBzG0amT4fXF/W2vgm56lcbAHLEmd2AOWRiS5huFtYaNeUFU39KhH+zcDWJI6cQhCkjoxgCWpEwNYkjoxgCWpk/8Popa4MWnxhAMAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "for col in num_cols:\n",
        "    sns.displot(X[col])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "5kuaWQZowEHE",
        "outputId": "685a52ef-6762-4a79-d205-90c81d12cd4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "32422          80          64000                  RENT                7.0   \n",
              "81            144         250000                  RENT                4.0   \n",
              "32416          94          24000                  RENT                1.0   \n",
              "747           123          78000                  RENT                7.0   \n",
              "183           144         200000              MORTGAGE                4.0   \n",
              "575           123          80004                  RENT                2.0   \n",
              "32506          84          94800              MORTGAGE                2.0   \n",
              "32297         144        6000000              MORTGAGE               12.0   \n",
              "\n",
              "      loan_intent loan_grade  loan_amnt  loan_int_rate  \\\n",
              "32422     MEDICAL          B       6800          11.71   \n",
              "81        VENTURE          C       4800          13.57   \n",
              "32416     MEDICAL          C       6500            NaN   \n",
              "747       VENTURE          B      20000            NaN   \n",
              "183     EDUCATION          B       6000          11.86   \n",
              "575     EDUCATION          B      20400          10.25   \n",
              "32506    PERSONAL          A      10000           7.51   \n",
              "32297    PERSONAL          C       5000          12.73   \n",
              "\n",
              "      cb_person_default_on_file  cb_person_cred_hist_length  \n",
              "32422                         N                          25  \n",
              "81                            N                           3  \n",
              "32416                         N                          27  \n",
              "747                           N                           4  \n",
              "183                           N                           2  \n",
              "575                           N                           3  \n",
              "32506                         N                          24  \n",
              "32297                         N                          25  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5f78907-79c3-4f7b-afe0-9147a83d6092\">\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>person_age</th>\n",
              "      <th>person_income</th>\n",
              "      <th>person_home_ownership</th>\n",
              "      <th>person_emp_length</th>\n",
              "      <th>loan_intent</th>\n",
              "      <th>loan_grade</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>loan_int_rate</th>\n",
              "      <th>cb_person_default_on_file</th>\n",
              "      <th>cb_person_cred_hist_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32422</th>\n",
              "      <td>80</td>\n",
              "      <td>64000</td>\n",
              "      <td>RENT</td>\n",
              "      <td>7.0</td>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>B</td>\n",
              "      <td>6800</td>\n",
              "      <td>11.71</td>\n",
              "      <td>N</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>144</td>\n",
              "      <td>250000</td>\n",
              "      <td>RENT</td>\n",
              "      <td>4.0</td>\n",
              "      <td>VENTURE</td>\n",
              "      <td>C</td>\n",
              "      <td>4800</td>\n",
              "      <td>13.57</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32416</th>\n",
              "      <td>94</td>\n",
              "      <td>24000</td>\n",
              "      <td>RENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>C</td>\n",
              "      <td>6500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>747</th>\n",
              "      <td>123</td>\n",
              "      <td>78000</td>\n",
              "      <td>RENT</td>\n",
              "      <td>7.0</td>\n",
              "      <td>VENTURE</td>\n",
              "      <td>B</td>\n",
              "      <td>20000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>144</td>\n",
              "      <td>200000</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>4.0</td>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>B</td>\n",
              "      <td>6000</td>\n",
              "      <td>11.86</td>\n",
              "      <td>N</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>123</td>\n",
              "      <td>80004</td>\n",
              "      <td>RENT</td>\n",
              "      <td>2.0</td>\n",
              "      <td>EDUCATION</td>\n",
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              "      <td>20400</td>\n",
              "      <td>10.25</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32506</th>\n",
              "      <td>84</td>\n",
              "      <td>94800</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>2.0</td>\n",
              "      <td>PERSONAL</td>\n",
              "      <td>A</td>\n",
              "      <td>10000</td>\n",
              "      <td>7.51</td>\n",
              "      <td>N</td>\n",
              "      <td>24</td>\n",
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              "    <tr>\n",
              "      <th>32297</th>\n",
              "      <td>144</td>\n",
              "      <td>6000000</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>12.0</td>\n",
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              "      <td>5000</td>\n",
              "      <td>12.73</td>\n",
              "      <td>N</td>\n",
              "      <td>25</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5f78907-79c3-4f7b-afe0-9147a83d6092')\"\n",
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              "                                                     [key], {});\n",
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              "          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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "X.loc[X['person_age']>=80, :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "1Jbg548QwEHF"
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      "outputs": [],
      "source": [
        "X = X.loc[X['person_age']<80, :]"
      ]
    },
    {
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      "execution_count": 25,
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          "output_type": "execute_result",
          "data": {
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              "(25924, 10)"
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        {
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          "data": {
            "text/plain": [
              "     person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "210          21         192000              MORTGAGE              123.0   \n",
              "0            22          59000                  RENT              123.0   \n",
              "\n",
              "    loan_intent loan_grade  loan_amnt  loan_int_rate  \\\n",
              "210     VENTURE          A      20000           6.54   \n",
              "0      PERSONAL          D      35000          16.02   \n",
              "\n",
              "    cb_person_default_on_file  cb_person_cred_hist_length  \n",
              "210                         N                           4  \n",
              "0                           Y                           3  "
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              "     person_age  person_income person_home_ownership  person_emp_length  \\\n",
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              "0      PERSONAL          D      35000          16.02            1   \n",
              "210     VENTURE          A      20000           6.54            0   \n",
              "\n",
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              "\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-534a6076-4926-4bc0-91ad-fb2faafd1cb1 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-534a6076-4926-4bc0-91ad-fb2faafd1cb1');\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": 27
        }
      ],
      "source": [
        "df.query(\"person_age<=person_emp_length+14\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "PjtjgN89wEHG"
      },
      "outputs": [],
      "source": [
        "X = X.loc[(X['person_emp_length']<66) | (X['person_emp_length'].isna()), :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "JIcSV_PowEHG"
      },
      "outputs": [],
      "source": [
        "# since we've removed some data from X, we need to pass on these updations to y as well,\n",
        "# as y doesn't know some of its corresponding X's have been deleted.\n",
        "y = y[X.index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "fSJLarKNwEHG",
        "outputId": "8a1ea0f8-0276-4c83-b4cf-2ef591ece0f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['person_home_ownership',\n",
              " 'loan_intent',\n",
              " 'loan_grade',\n",
              " 'cb_person_default_on_file']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "cat_cols = [col for col in X if X[col].dtypes == 'O']\n",
        "cat_cols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "qhNWl5jswEHG"
      },
      "outputs": [],
      "source": [
        "num_pipe = Pipeline([\n",
        "    ('impute', IterativeImputer()),\n",
        "    ('scale', StandardScaler()),\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "mUtbCLS1wEHG"
      },
      "outputs": [],
      "source": [
        "ct = ColumnTransformer([\n",
        "    ('num_pipe', num_pipe, num_cols),\n",
        "    ('cat_cols', OneHotEncoder(sparse=False, handle_unknown='ignore'), cat_cols)\n",
        "], remainder='passthrough')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "cxOMxaaMwEHH"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "8cYCPmGuwEHH"
      },
      "outputs": [],
      "source": [
        "grid = {\n",
        "    RandomForestClassifier(random_state=0, n_jobs=-1, class_weight='balanced'):\n",
        "    {'model__n_estimators':[300,400,500],\n",
        "     'coltf__num_pipe__impute__estimator': [LinearRegression(), RandomForestRegressor(random_state=0),\n",
        "                                        KNeighborsRegressor()]},\n",
        "    \n",
        "    LGBMClassifier(class_weight='balanced', random_state=0, n_jobs=-1):\n",
        "    {'model__n_estimators':[300,400,500],\n",
        "     'model__learning_rate':[0.001,0.01,0.1,1,10],\n",
        "     'model__boosting_type': ['gbdt', 'goss', 'dart'],\n",
        "     'coltf__num_pipe__impute__estimator':[LinearRegression(), RandomForestRegressor(random_state=0),\n",
        "                                        KNeighborsRegressor()]},\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "scrolled": true,
        "id": "e1q0n3IIwEHH",
        "outputId": "725124a1-f1f3-455d-be99-264a21afc517",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForestClassifier(class_weight='balanced', n_jobs=-1, random_state=0)\n",
            "--------------------------------------------------\n",
            "{'model__n_estimators': [300, 400, 500], 'coltf__num_pipe__impute__estimator': [LinearRegression(), RandomForestRegressor(random_state=0), KNeighborsRegressor()]}\n",
            "\n",
            "\n",
            "LGBMClassifier(class_weight='balanced', random_state=0)\n",
            "--------------------------------------------------\n",
            "{'model__n_estimators': [300, 400, 500], 'model__learning_rate': [0.001, 0.01, 0.1, 1, 10], 'model__boosting_type': ['gbdt', 'goss', 'dart'], 'coltf__num_pipe__impute__estimator': [LinearRegression(), RandomForestRegressor(random_state=0), KNeighborsRegressor()]}\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for clf, param in grid.items():\n",
        "    print(clf)\n",
        "    print('-'*50)\n",
        "    print(param)\n",
        "    print('\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "scrolled": false,
        "id": "Gpind2v5wEHH",
        "outputId": "c7a2d39f-e3a6-439d-be2d-0d3e6fc9ecaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py:700: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
          ]
        }
      ],
      "source": [
        "full_df = pd.DataFrame()\n",
        "best_algos = {}\n",
        "\n",
        "for clf, param in grid.items():\n",
        "    pipe = Pipeline([\n",
        "    ('coltf', ct),\n",
        "    ('model', clf)\n",
        "])\n",
        "\n",
        "    gs = RandomizedSearchCV(estimator=pipe, param_distributions=param, scoring='accuracy',\n",
        "                            n_jobs=-1, verbose=3, n_iter=4, random_state=0)\n",
        "    \n",
        "    gs.fit(X, y)\n",
        "    \n",
        "    all_res = pd.DataFrame(gs.cv_results_)\n",
        "\n",
        "    temp = all_res.loc[:, ['params', 'mean_test_score']]\n",
        "    algo_name = str(clf).split('(')[0]\n",
        "    temp['algo'] = algo_name\n",
        "    \n",
        "    full_df = pd.concat([full_df, temp], ignore_index=True)\n",
        "    best_algos[algo_name] = gs.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "scrolled": true,
        "id": "evr_Jx7GwEHI",
        "outputId": "f5e2fab7-bd99-4cb6-dd17-4798b42b2cbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              params  mean_test_score  \\\n",
              "3  {'model__n_estimators': 400, 'coltf__num_pipe_...         0.922730   \n",
              "0  {'model__n_estimators': 400, 'coltf__num_pipe_...         0.922537   \n",
              "2  {'model__n_estimators': 400, 'coltf__num_pipe_...         0.922537   \n",
              "1  {'model__n_estimators': 500, 'coltf__num_pipe_...         0.922537   \n",
              "7  {'model__n_estimators': 300, 'model__learning_...         0.908379   \n",
              "4  {'model__n_estimators': 300, 'model__learning_...         0.869339   \n",
              "5  {'model__n_estimators': 300, 'model__learning_...         0.868606   \n",
              "6  {'model__n_estimators': 300, 'model__learning_...         0.864478   \n",
              "\n",
              "                     algo  \n",
              "3  RandomForestClassifier  \n",
              "0  RandomForestClassifier  \n",
              "2  RandomForestClassifier  \n",
              "1  RandomForestClassifier  \n",
              "7          LGBMClassifier  \n",
              "4          LGBMClassifier  \n",
              "5          LGBMClassifier  \n",
              "6          LGBMClassifier  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc3090e9-f709-44fe-83bb-c6cc3339224f\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>params</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>algo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>{'model__n_estimators': 400, 'coltf__num_pipe_...</td>\n",
              "      <td>0.922730</td>\n",
              "      <td>RandomForestClassifier</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>{'model__n_estimators': 400, 'coltf__num_pipe_...</td>\n",
              "      <td>0.922537</td>\n",
              "      <td>RandomForestClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>{'model__n_estimators': 400, 'coltf__num_pipe_...</td>\n",
              "      <td>0.922537</td>\n",
              "      <td>RandomForestClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{'model__n_estimators': 500, 'coltf__num_pipe_...</td>\n",
              "      <td>0.922537</td>\n",
              "      <td>RandomForestClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>{'model__n_estimators': 300, 'model__learning_...</td>\n",
              "      <td>0.908379</td>\n",
              "      <td>LGBMClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>{'model__n_estimators': 300, 'model__learning_...</td>\n",
              "      <td>0.869339</td>\n",
              "      <td>LGBMClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>{'model__n_estimators': 300, 'model__learning_...</td>\n",
              "      <td>0.868606</td>\n",
              "      <td>LGBMClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>{'model__n_estimators': 300, 'model__learning_...</td>\n",
              "      <td>0.864478</td>\n",
              "      <td>LGBMClassifier</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-dc3090e9-f709-44fe-83bb-c6cc3339224f 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-dc3090e9-f709-44fe-83bb-c6cc3339224f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "full_df.sort_values('mean_test_score', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "scrolled": true,
        "id": "Pjd4UW2qwEHI",
        "outputId": "da6763a9-17d8-47b3-ba1b-2f5ea2e63d76",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model__n_estimators': 400,\n",
              " 'coltf__num_pipe__impute__estimator': RandomForestRegressor(random_state=0)}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "full_df.sort_values('mean_test_score', ascending=False).iloc[0, 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "scrolled": true,
        "id": "Bh-lCWP_wEHI",
        "outputId": "44cffaf7-edf2-40ca-bab8-2e359c8a6cab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('coltf',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('num_pipe',\n",
              "                                                  Pipeline(steps=[('impute',\n",
              "                                                                   IterativeImputer(estimator=RandomForestRegressor(random_state=0))),\n",
              "                                                                  ('scale',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['person_age',\n",
              "                                                   'person_income',\n",
              "                                                   'person_emp_length',\n",
              "                                                   'loan_amnt', 'loan_int_rate',\n",
              "                                                   'cb_person_cred_hist_length']),\n",
              "                                                 ('cat_cols',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore',\n",
              "                                                                sparse=False),\n",
              "                                                  ['person_home_ownership',\n",
              "                                                   'loan_intent', 'loan_grade',\n",
              "                                                   'cb_person_default_on_file'])])),\n",
              "                ('model',\n",
              "                 RandomForestClassifier(class_weight='balanced',\n",
              "                                        n_estimators=400, n_jobs=-1,\n",
              "                                        random_state=0))])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "be = best_algos['RandomForestClassifier']\n",
        "be"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "scrolled": true,
        "id": "pFHV23_0wEHI",
        "outputId": "81960591-48d3-4ec1-972f-64e7a37740d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('coltf',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('num_pipe',\n",
              "                                                  Pipeline(steps=[('impute',\n",
              "                                                                   IterativeImputer(estimator=RandomForestRegressor(random_state=0))),\n",
              "                                                                  ('scale',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['person_age',\n",
              "                                                   'person_income',\n",
              "                                                   'person_emp_length',\n",
              "                                                   'loan_amnt', 'loan_int_rate',\n",
              "                                                   'cb_person_cred_hist_length']),\n",
              "                                                 ('cat_cols',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore',\n",
              "                                                                sparse=False),\n",
              "                                                  ['person_home_ownership',\n",
              "                                                   'loan_intent', 'loan_grade',\n",
              "                                                   'cb_person_default_on_file'])])),\n",
              "                ('model',\n",
              "                 RandomForestClassifier(class_weight='balanced',\n",
              "                                        n_estimators=400, n_jobs=-1,\n",
              "                                        random_state=0))])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "be.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "a7434sXHwEHJ"
      },
      "outputs": [],
      "source": [
        "preds = be.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "B9K_ULbnwEHJ",
        "outputId": "84cfd9b6-3259-4f17-c253-c69978f1602e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5007,   59],\n",
              "       [ 436,  982]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "confusion_matrix(y_test, preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "scrolled": true,
        "id": "QES61qIEwEHJ",
        "outputId": "d2adead9-deeb-4bc1-8a5d-ac4c2a528569",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f3aa5772b20>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_confusion_matrix(be, X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "yHsmWlFZwEHJ",
        "outputId": "68b1fc3c-a5e7-4713-fc25-f709765eab50",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.99      0.95      5066\n",
            "           1       0.94      0.69      0.80      1418\n",
            "\n",
            "    accuracy                           0.92      6484\n",
            "   macro avg       0.93      0.84      0.88      6484\n",
            "weighted avg       0.93      0.92      0.92      6484\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, preds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "k6mh4dWawEHJ",
        "outputId": "a71b2779-1f13-4f4a-c4bb-7c044385e7d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9236582356570019"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "be.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7np3AOmmwEHK"
      },
      "source": [
        "###### precision recall curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "pfdEOIE5wEHK",
        "outputId": "25fb5987-fd5f-49c4-95ce-6c86b93ea6ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_precision_recall_curve is deprecated; Function `plot_precision_recall_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: PrecisionRecallDisplay.from_predictions or PrecisionRecallDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3aa482b7c0>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_precision_recall_curve(estimator=be, X=X_test, y=y_test, name='model AUC')\n",
        "baseline = y_test.sum() / len(y_test)\n",
        "plt.axhline(baseline, ls='--', color='r', label=f'Baseline model ({round(baseline,2)})')\n",
        "plt.legend(loc='best')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRZ3PmW2wEHK"
      },
      "source": [
        "###### learning curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "CXZy3AR_wEHK"
      },
      "outputs": [],
      "source": [
        "a, b, c = learning_curve(be, X, y, n_jobs=-1, scoring='accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "vzxYfWVKwEHK",
        "outputId": "66cff2eb-f3dd-4bdd-8b10-fbabed8c150f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3aa4596c40>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(a, b.mean(axis=1), label='training accuracy')\n",
        "plt.plot(a, c.mean(axis=1),  label='validation accuracy')\n",
        "plt.xlabel('training sample sizes')\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PM6QreewEHM"
      },
      "source": [
        "##### Overfitting:\n",
        "\n",
        "1. High training accuracy (--- low bias)\n",
        "2. Low testing/ validation accuracy (--- high variance)\n",
        "3. Big gap between training and validation curves (--- high variance)\n",
        "4. Overfitting makes a very complex model and learns even the \"noise\" in the data, which is undesirable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "qoldLMqMwEHN"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4HRE-kCnwEHN"
      },
      "source": [
        "### Part 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2maIeHy-wEHN"
      },
      "source": [
        "Remedial measures:\n",
        "\n",
        "1. Add more training samples, if possible, to allow the model to learn better\n",
        "\n",
        "\n",
        "2. Working with data at hand:\n",
        "\n",
        "    Make a simpler model / reduce complexity of model:\n",
        "- try reducing number of features\n",
        "- try increasing regularization (lambda)\n",
        "- try pruning the decision trees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "y05YSpKiwEHN"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "54olC1jQwEHO"
      },
      "outputs": [],
      "source": [
        "grid = {\n",
        "    \n",
        "    RandomForestClassifier(random_state=0, n_jobs=-1, class_weight='balanced'):\n",
        "    {'model__n_estimators':[100,200,300],\n",
        "     'model__max_depth':[5, 9, 13],\n",
        "     'model__min_samples_split':[4,6,8],\n",
        "     'coltf__num_pipe__impute__estimator': [LinearRegression(), RandomForestRegressor(random_state=0),\n",
        "                                        KNeighborsRegressor()]},\n",
        "    \n",
        "#     LGBMClassifier(class_weight='balanced', random_state=0, n_jobs=-1):\n",
        "#     {'model__n_estimators':[100,200,300],\n",
        "#      'model__max_depth':[5, 9, 13],\n",
        "#      'model__num_leaves': [7,15,31],\n",
        "#      'model__learning_rate':[0.0001,0.001,0.01,0.1,],\n",
        "#      'model__boosting_type': ['gbdt', 'goss', 'dart'],\n",
        "#      'coltf__num_pipe__impute__estimator':[LinearRegression(), RandomForestRegressor(random_state=0),\n",
        "#                                         KNeighborsRegressor()]} \n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "scrolled": true,
        "id": "YSxRXg8DwEHO",
        "outputId": "6224bc73-d80e-48f3-afb7-dc620f2c620a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForestClassifier(class_weight='balanced', n_jobs=-1, random_state=0)\n",
            "--------------------------------------------------\n",
            "{'model__n_estimators': [100, 200, 300], 'model__max_depth': [5, 9, 13], 'model__min_samples_split': [4, 6, 8], 'coltf__num_pipe__impute__estimator': [LinearRegression(), RandomForestRegressor(random_state=0), KNeighborsRegressor()]}\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for clf, param in grid.items():\n",
        "    print(clf)\n",
        "    print('-'*50)\n",
        "    print(param)\n",
        "    print('\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "scrolled": true,
        "id": "xIOmRdXSwEHO",
        "outputId": "4cf80481-82af-459f-920e-eae2bd01e2b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
          ]
        }
      ],
      "source": [
        "full_df = pd.DataFrame()\n",
        "best_algos = {}\n",
        "\n",
        "for clf, param in grid.items():\n",
        "    pipe = Pipeline([\n",
        "    ('coltf', ct),\n",
        "    ('model', clf)\n",
        "])\n",
        "\n",
        "    gs = RandomizedSearchCV(estimator=pipe, param_distributions=param, scoring='accuracy',\n",
        "                            n_jobs=-1, verbose=3, n_iter=4)\n",
        "    \n",
        "    gs.fit(X, y)\n",
        "    \n",
        "    all_res = pd.DataFrame(gs.cv_results_)\n",
        "\n",
        "    temp = all_res.loc[:, ['params', 'mean_test_score']]\n",
        "    algo_name = str(clf).split('(')[0]\n",
        "    temp['algo'] = algo_name\n",
        "    \n",
        "    full_df = pd.concat([full_df, temp])\n",
        "    best_algos[algo_name] = gs.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "scrolled": true,
        "id": "SmgCRq7pwEHO",
        "outputId": "0e25cafe-46ea-4c07-b6f8-02a18a13265d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              params  mean_test_score  \\\n",
              "2  {'model__n_estimators': 100, 'model__min_sampl...         0.910655   \n",
              "0  {'model__n_estimators': 100, 'model__min_sampl...         0.907492   \n",
              "1  {'model__n_estimators': 200, 'model__min_sampl...         0.813710   \n",
              "3  {'model__n_estimators': 300, 'model__min_sampl...         0.811434   \n",
              "\n",
              "                     algo  \n",
              "2  RandomForestClassifier  \n",
              "0  RandomForestClassifier  \n",
              "1  RandomForestClassifier  \n",
              "3  RandomForestClassifier  "
            ],
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              "    <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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>params</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>algo</th>\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",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "full_df.sort_values('mean_test_score', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "scrolled": true,
        "id": "A6t3Fhw3wEHP",
        "outputId": "514d8aec-3e0a-420c-d388-1642cc614c84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('coltf',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('num_pipe',\n",
              "                                                  Pipeline(steps=[('impute',\n",
              "                                                                   IterativeImputer(estimator=RandomForestRegressor(random_state=0))),\n",
              "                                                                  ('scale',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['person_age',\n",
              "                                                   'person_income',\n",
              "                                                   'person_emp_length',\n",
              "                                                   'loan_amnt', 'loan_int_rate',\n",
              "                                                   'cb_person_cred_hist_length']),\n",
              "                                                 ('cat_cols',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore',\n",
              "                                                                sparse=False),\n",
              "                                                  ['person_home_ownership',\n",
              "                                                   'loan_intent', 'loan_grade',\n",
              "                                                   'cb_person_default_on_file'])])),\n",
              "                ('model',\n",
              "                 RandomForestClassifier(class_weight='balanced', max_depth=13,\n",
              "                                        min_samples_split=4, n_jobs=-1,\n",
              "                                        random_state=0))])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "be = best_algos['RandomForestClassifier']\n",
        "be"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "scrolled": true,
        "id": "KlXBYYFFwEHP",
        "outputId": "d2f809ae-e499-4586-a688-20ff962fb32f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('coltf',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('num_pipe',\n",
              "                                                  Pipeline(steps=[('impute',\n",
              "                                                                   IterativeImputer(estimator=RandomForestRegressor(random_state=0))),\n",
              "                                                                  ('scale',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['person_age',\n",
              "                                                   'person_income',\n",
              "                                                   'person_emp_length',\n",
              "                                                   'loan_amnt', 'loan_int_rate',\n",
              "                                                   'cb_person_cred_hist_length']),\n",
              "                                                 ('cat_cols',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore',\n",
              "                                                                sparse=False),\n",
              "                                                  ['person_home_ownership',\n",
              "                                                   'loan_intent', 'loan_grade',\n",
              "                                                   'cb_person_default_on_file'])])),\n",
              "                ('model',\n",
              "                 RandomForestClassifier(class_weight='balanced', max_depth=13,\n",
              "                                        min_samples_split=4, n_jobs=-1,\n",
              "                                        random_state=0))])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "be.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "GnyVDAMSwEHP"
      },
      "outputs": [],
      "source": [
        "preds = be.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "5Li-amxhwEHP",
        "outputId": "58e9a646-a339-4182-b5f0-e11f12db8b51",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4860,  206],\n",
              "       [ 376, 1042]])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "confusion_matrix(y_test, preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "scrolled": true,
        "id": "spiaEpN5wEHQ",
        "outputId": "c3d8d836-f1f0-4b0f-a59f-40334f374dea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f3aa5b1eca0>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_confusion_matrix(be, X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "33Y5NSwHwEHQ",
        "outputId": "efefa95d-01e3-4e3e-f844-de53572255a5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.96      0.94      5066\n",
            "           1       0.83      0.73      0.78      1418\n",
            "\n",
            "    accuracy                           0.91      6484\n",
            "   macro avg       0.88      0.85      0.86      6484\n",
            "weighted avg       0.91      0.91      0.91      6484\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, preds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "54mLmplzwEHQ",
        "outputId": "90b08abc-830f-44ed-851c-e98133c5975c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9102405922270204"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "be.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "MzGtkuynwEHQ",
        "outputId": "519fd780-ae7a-49d2-d867-27386cf5b35f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_precision_recall_curve is deprecated; Function `plot_precision_recall_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: PrecisionRecallDisplay.from_predictions or PrecisionRecallDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3aa4471be0>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_precision_recall_curve(be, X_test, y_test)\n",
        "baseline = y_test.sum() / len(y_test)\n",
        "plt.axhline(baseline, ls='--', color='r', label=f'Baseline model ({round(baseline,2)})')\n",
        "plt.legend(loc='best')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "oITP3lDOwEHR"
      },
      "outputs": [],
      "source": [
        "a, b, c = learning_curve(be, X, y, n_jobs=-1, cv=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "weDztlsCwEHR",
        "outputId": "4b0c2826-40fa-4f9a-e11d-e598d3509e34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2073,  6739, 11405, 16071, 20737])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "scrolled": true,
        "id": "ytPljbcpwEHR",
        "outputId": "f47a3a27-b813-40ac-d154-754b60db05e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.98552822, 0.98456343, 0.98456343, 0.98456343, 0.98456343],\n",
              "       [0.96928328, 0.96883811, 0.97165752, 0.97165752, 0.97165752],\n",
              "       [0.96089434, 0.96168347, 0.95966681, 0.95992986, 0.95992986],\n",
              "       [0.95582104, 0.95582104, 0.95519881, 0.95470101, 0.95662995],\n",
              "       [0.95225925, 0.94989632, 0.95148768, 0.95143946, 0.95066789]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "IMsFl2bWwEHS",
        "outputId": "dba12904-abf0-4fb1-f351-41acbcc56c36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.87868852, 0.88621022, 0.88503086, 0.88348765, 0.88560957],\n",
              "       [0.9024108 , 0.90221794, 0.90779321, 0.90046296, 0.90489969],\n",
              "       [0.90433944, 0.90568949, 0.91203704, 0.9058642 , 0.91126543],\n",
              "       [0.90742527, 0.90568949, 0.91705247, 0.90856481, 0.91184414],\n",
              "       [0.90742527, 0.90858245, 0.91454475, 0.91203704, 0.91087963]])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "c84Kbwu0wEHS",
        "outputId": "5c15e324-42da-4d50-ca11-ab22125d3022",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3aa44732e0>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(a, b.mean(axis=1), label='training accuracy')\n",
        "plt.plot(a, c.mean(axis=1),  label='validation accuracy')\n",
        "plt.xlabel('training sample sizes')\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8jHz1ECG_fY1"
      },
      "execution_count": 64,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}