Data_Mining_Academic_Project.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/LakshmanPalli/credit-approval/blob/main/Data_Mining_Academic_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Libararies"
      ],
      "metadata": {
        "id": "86elZ6lmj3fs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "lNN9cIET11EB"
      },
      "outputs": [],
      "source": [
        "# Python Standard Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Scikit-learn Classifiers\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# Scikit-learn Model Selection\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Scikit-learn Preprocessing\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "\n",
        "# Scikit-learn Metrics\n",
        "from sklearn.metrics import (classification_report, accuracy_score, confusion_matrix,\n",
        "                             roc_curve, auc, precision_recall_curve, average_precision_score)\n",
        "\n",
        "# Scikit-learn Feature Selection\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "\n",
        "# Visualization Libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import graphviz\n",
        "\n",
        "# Jupyter Magic Commands\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore') # Ignore warnings"
      ],
      "metadata": {
        "id": "os9NxvC8CW7N"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load data"
      ],
      "metadata": {
        "id": "enASH7GVj-Fk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset into a pandas DataFrame\n",
        "credit_df = pd.read_csv('/content/german-credit-card.csv')"
      ],
      "metadata": {
        "id": "hoBeWv1F13kh"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Show data information"
      ],
      "metadata": {
        "id": "ytB17Ya4drte"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display basic information about the DataFrame\n",
        "print(\"Basic Information:\")\n",
        "print(credit_df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5V1AEjQLiCTe",
        "outputId": "9724fba2-bc26-4b61-fc71-cf42419e14dc"
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      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Basic Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 21 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   checkin_acc        1000 non-null   object\n",
            " 1   duration           1000 non-null   int64 \n",
            " 2   credit_history     1000 non-null   object\n",
            " 3   purpose            1000 non-null   object\n",
            " 4   amount             1000 non-null   int64 \n",
            " 5   saving_acc         1000 non-null   object\n",
            " 6   present_emp_since  1000 non-null   object\n",
            " 7   inst_rate          1000 non-null   int64 \n",
            " 8   personal_status    1000 non-null   object\n",
            " 9   other_debtors      1000 non-null   object\n",
            " 10  residing_since     1000 non-null   int64 \n",
            " 11  property           1000 non-null   object\n",
            " 12  age                1000 non-null   int64 \n",
            " 13  inst_plans         1000 non-null   object\n",
            " 14  housing            1000 non-null   object\n",
            " 15  num_credits        1000 non-null   int64 \n",
            " 16  job                1000 non-null   object\n",
            " 17  dependents         1000 non-null   int64 \n",
            " 18  telephone          1000 non-null   object\n",
            " 19  foreign_worker     1000 non-null   object\n",
            " 20  status             1000 non-null   int64 \n",
            "dtypes: int64(8), object(13)\n",
            "memory usage: 164.2+ KB\n",
            "None\n"
          ]
        }
      ]
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      "execution_count": 23,
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First Few Rows:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  checkin_acc  duration credit_history purpose  amount saving_acc  \\\n",
              "0         A11         6            A34     A43    1169        A65   \n",
              "1         A12        48            A32     A43    5951        A61   \n",
              "2         A14        12            A34     A46    2096        A61   \n",
              "3         A11        42            A32     A42    7882        A61   \n",
              "4         A11        24            A33     A40    4870        A61   \n",
              "\n",
              "  present_emp_since  inst_rate personal_status other_debtors  ...  property  \\\n",
              "0               A75          4             A93          A101  ...      A121   \n",
              "1               A73          2             A92          A101  ...      A121   \n",
              "2               A74          2             A93          A101  ...      A121   \n",
              "3               A74          2             A93          A103  ...      A122   \n",
              "4               A73          3             A93          A101  ...      A124   \n",
              "\n",
              "  age  inst_plans housing num_credits   job dependents  telephone  \\\n",
              "0  67        A143    A152           2  A173          1       A192   \n",
              "1  22        A143    A152           1  A173          1       A191   \n",
              "2  49        A143    A152           1  A172          2       A191   \n",
              "3  45        A143    A153           1  A173          2       A191   \n",
              "4  53        A143    A153           2  A173          2       A191   \n",
              "\n",
              "  foreign_worker status  \n",
              "0           A201      1  \n",
              "1           A201      2  \n",
              "2           A201      1  \n",
              "3           A201      1  \n",
              "4           A201      2  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-33b3e52b-066f-4fd9-ae2a-077e931fa170 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Display the first few rows of the DataFrame\n",
        "print(\"\\nFirst Few Rows:\")\n",
        "credit_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "5doZLyO311ED"
      },
      "outputs": [],
      "source": [
        "# # Mapping the 'status' column to 'Good' and 'Bad'\n",
        "# credit_df['status'] = credit_df['status'].replace({1: 'Good', 2: 'Bad'})\n",
        "\n",
        "# # Display the first few rows to verify the changes\n",
        "# credit_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data preprocessing"
      ],
      "metadata": {
        "id": "1oVOtcIVm-DO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Remove target from model training"
      ],
      "metadata": {
        "id": "UQ9ivAgym1kF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Wf-J2juL11ED"
      },
      "outputs": [],
      "source": [
        "# Get the features without the target variable\n",
        "X_features = list(credit_df.columns)\n",
        "X_features.remove('status')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create dummy variables for categorical values."
      ],
      "metadata": {
        "id": "QSb6KSyMeRtK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "-FLxRCjk11ED"
      },
      "outputs": [],
      "source": [
        "# insert the code to create dummy variable for categorical features. Hint: See MailingData_Preprocessing for example.\n",
        "# Select only the features without the target\n",
        "credit_df_X = credit_df[X_features]\n",
        "\n",
        "# Get dummy variables for categorical columns\n",
        "credit_df_complete = pd.get_dummies(credit_df_X, drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "NwUi8s6v11ED",
        "outputId": "c3b33e71-0c48-41c3-9684-42aa0331e160",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   duration  amount  inst_rate  residing_since  age  num_credits  dependents  \\\n",
              "0         6    1169          4               4   67            2           1   \n",
              "1        48    5951          2               2   22            1           1   \n",
              "2        12    2096          2               3   49            1           2   \n",
              "3        42    7882          2               4   45            1           2   \n",
              "4        24    4870          3               4   53            2           2   \n",
              "\n",
              "   checkin_acc_A12  checkin_acc_A13  checkin_acc_A14  ...  property_A124  \\\n",
              "0                0                0                0  ...              0   \n",
              "1                1                0                0  ...              0   \n",
              "2                0                0                1  ...              0   \n",
              "3                0                0                0  ...              0   \n",
              "4                0                0                0  ...              1   \n",
              "\n",
              "   inst_plans_A142  inst_plans_A143  housing_A152  housing_A153  job_A172  \\\n",
              "0                0                1             1             0         0   \n",
              "1                0                1             1             0         0   \n",
              "2                0                1             1             0         1   \n",
              "3                0                1             0             1         0   \n",
              "4                0                1             0             1         0   \n",
              "\n",
              "   job_A173  job_A174  telephone_A192  foreign_worker_A202  \n",
              "0         1         0               1                    0  \n",
              "1         1         0               0                    0  \n",
              "2         0         0               0                    0  \n",
              "3         1         0               0                    0  \n",
              "4         1         0               0                    0  \n",
              "\n",
              "[5 rows x 48 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-557cf2da-459a-4741-b781-b7f4c0377a82\" 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>duration</th>\n",
              "      <th>amount</th>\n",
              "      <th>inst_rate</th>\n",
              "      <th>residing_since</th>\n",
              "      <th>age</th>\n",
              "      <th>num_credits</th>\n",
              "      <th>dependents</th>\n",
              "      <th>checkin_acc_A12</th>\n",
              "      <th>checkin_acc_A13</th>\n",
              "      <th>checkin_acc_A14</th>\n",
              "      <th>...</th>\n",
              "      <th>property_A124</th>\n",
              "      <th>inst_plans_A142</th>\n",
              "      <th>inst_plans_A143</th>\n",
              "      <th>housing_A152</th>\n",
              "      <th>housing_A153</th>\n",
              "      <th>job_A172</th>\n",
              "      <th>job_A173</th>\n",
              "      <th>job_A174</th>\n",
              "      <th>telephone_A192</th>\n",
              "      <th>foreign_worker_A202</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>1169</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>67</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>48</td>\n",
              "      <td>5951</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12</td>\n",
              "      <td>2096</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42</td>\n",
              "      <td>7882</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24</td>\n",
              "      <td>4870</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 48 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-557cf2da-459a-4741-b781-b7f4c0377a82')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-557cf2da-459a-4741-b781-b7f4c0377a82 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-557cf2da-459a-4741-b781-b7f4c0377a82');\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",
              "\n",
              "\n",
              "<div id=\"df-a12a41dd-2cae-4ac9-8a02-db6a81b50603\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a12a41dd-2cae-4ac9-8a02-db6a81b50603')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a12a41dd-2cae-4ac9-8a02-db6a81b50603 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Display the first few rows to verify the changes\n",
        "credit_df_complete.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "HavOhSub11EE",
        "outputId": "c9b597c8-6828-400d-8cd0-9d8b33043d1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Print the number of columns after creating dummy variables\n",
        "len(credit_df_complete.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Convert the credit rating (1 = Good, 2 = Bad) to value 0 or 1 for binary prediction labels"
      ],
      "metadata": {
        "id": "72B6ELjXejYQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "dg_8gAWp11EE"
      },
      "outputs": [],
      "source": [
        "#Change the reponse variable to 0 = Good and 1 = Bad\n",
        "Y = credit_df.status - 1\n",
        "X = credit_df_complete"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split the data for training and testing"
      ],
      "metadata": {
        "id": "QQsB0p96f3Ae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the data into a training set and a test set\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=42)\n",
        "\n",
        "# Printing the shapes to verify the split\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"Y_train shape:\", Y_train.shape)\n",
        "print(\"Y_test shape:\", Y_test.shape)"
      ],
      "metadata": {
        "id": "athAGnnzF_uX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b180ba1-e971-4cee-9d7a-b8a31687d68b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (900, 48)\n",
            "X_test shape: (100, 48)\n",
            "Y_train shape: (900,)\n",
            "Y_test shape: (100,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Standardize the data"
      ],
      "metadata": {
        "id": "u8CNxrpDgF1f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a StandardScaler object\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fitting the scaler to the training data and transforming it\n",
        "X_train_standardized = scaler.fit_transform(X_train)\n",
        "\n",
        "# Using the same scaler to transform the test data\n",
        "X_test_standardized = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "7fi_wn43MMA6"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Selection"
      ],
      "metadata": {
        "id": "L0I9TvwH-LDw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit a RandomForestClassifier to the data to compute feature importances\n",
        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "clf.fit(X_train_standardized, Y_train)\n",
        "\n",
        "# Output feature importances\n",
        "feature_importances = pd.DataFrame(clf.feature_importances_,\n",
        "                                   index=X_train.columns,\n",
        "                                   columns=['importance']).sort_values('importance', ascending=False)\n",
        "\n",
        "print(\"Feature Importances:\")\n",
        "print(feature_importances)\n",
        "\n",
        "# Select features based on importance\n",
        "selector = SelectFromModel(clf, prefit=True, threshold=0.01) # You can modify the threshold\n",
        "X_train_selected = selector.transform(X_train_standardized)\n",
        "X_test_selected = selector.transform(X_test_standardized)\n",
        "\n",
        "# Output the selected features\n",
        "selected_features = X_train.columns[selector.get_support()]\n",
        "print(\"\\nSelected Features:\")\n",
        "print(selected_features)\n",
        "print(\"\\nNumber of Selected Features:\")\n",
        "len(selected_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0KrnY6g0ksZ",
        "outputId": "06500b0b-e01c-47eb-9ab0-df7bffde3379"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Importances:\n",
            "                       importance\n",
            "amount                   0.121735\n",
            "age                      0.094451\n",
            "duration                 0.082279\n",
            "checkin_acc_A14          0.065759\n",
            "inst_rate                0.040798\n",
            "residing_since           0.038508\n",
            "credit_history_A34       0.025556\n",
            "inst_plans_A143          0.021696\n",
            "num_credits              0.020947\n",
            "housing_A152             0.020492\n",
            "telephone_A192           0.019871\n",
            "personal_status_A93      0.019463\n",
            "saving_acc_A65           0.019010\n",
            "checkin_acc_A12          0.018845\n",
            "property_A123            0.018764\n",
            "property_A122            0.017532\n",
            "present_emp_since_A73    0.017403\n",
            "personal_status_A92      0.017393\n",
            "purpose_A43              0.017251\n",
            "credit_history_A32       0.016615\n",
            "job_A173                 0.015836\n",
            "present_emp_since_A72    0.015611\n",
            "purpose_A42              0.015265\n",
            "dependents               0.014569\n",
            "present_emp_since_A75    0.014355\n",
            "present_emp_since_A74    0.014321\n",
            "credit_history_A31       0.014092\n",
            "job_A174                 0.013921\n",
            "purpose_A49              0.013325\n",
            "property_A124            0.012921\n",
            "job_A172                 0.012849\n",
            "other_debtors_A103       0.012548\n",
            "credit_history_A33       0.011793\n",
            "purpose_A41              0.011073\n",
            "saving_acc_A62           0.010456\n",
            "purpose_A46              0.009899\n",
            "checkin_acc_A13          0.009679\n",
            "personal_status_A94      0.009473\n",
            "saving_acc_A63           0.008369\n",
            "other_debtors_A102       0.007546\n",
            "inst_plans_A142          0.007492\n",
            "housing_A153             0.007274\n",
            "saving_acc_A64           0.007251\n",
            "purpose_A45              0.005800\n",
            "foreign_worker_A202      0.004459\n",
            "purpose_A410             0.002217\n",
            "purpose_A44              0.001912\n",
            "purpose_A48              0.001327\n",
            "\n",
            "Selected Features:\n",
            "Index(['duration', 'amount', 'inst_rate', 'residing_since', 'age',\n",
            "       'num_credits', 'dependents', 'checkin_acc_A12', 'checkin_acc_A14',\n",
            "       'credit_history_A31', 'credit_history_A32', 'credit_history_A33',\n",
            "       'credit_history_A34', 'purpose_A41', 'purpose_A42', 'purpose_A43',\n",
            "       'purpose_A49', 'saving_acc_A62', 'saving_acc_A65',\n",
            "       'present_emp_since_A72', 'present_emp_since_A73',\n",
            "       'present_emp_since_A74', 'present_emp_since_A75', 'personal_status_A92',\n",
            "       'personal_status_A93', 'other_debtors_A103', 'property_A122',\n",
            "       'property_A123', 'property_A124', 'inst_plans_A143', 'housing_A152',\n",
            "       'job_A172', 'job_A173', 'job_A174', 'telephone_A192'],\n",
            "      dtype='object')\n",
            "\n",
            "Number of Selected Features:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build Machine learning models (see Module 4 and 5 examples)"
      ],
      "metadata": {
        "id": "pogAWU-QgObT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to plot confusion matrix\n",
        "def plot_confusion_matrix(cm, classes, title='Confusion Matrix', cmap=plt.cm.Blues):\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()\n",
        "\n",
        "# Function to plot ROC curve\n",
        "def plot_roc_curve(fpr, tpr, roc_auc, label='ROC curve'):\n",
        "    plt.figure()\n",
        "    plt.plot(fpr, tpr, color='darkorange', lw=1, label=f'{label} (area = {roc_auc:0.2f})')\n",
        "    plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.title('Receiver Operating Characteristic')\n",
        "    plt.legend(loc=\"lower right\")\n",
        "    plt.show()\n",
        "\n",
        "# Models to train\n",
        "models = [\n",
        "    (\"Logistic Regression\", LogisticRegression(max_iter=1000, random_state=42)),\n",
        "    (\"Random Forest\", RandomForestClassifier(n_estimators=100, random_state=42))\n",
        "]\n",
        "\n",
        "# Training and evaluating each model\n",
        "for name, model in models:\n",
        "    # Training the model\n",
        "    model.fit(X_train_selected, Y_train)\n",
        "\n",
        "    # Predicting on the test set\n",
        "    Y_pred = model.predict(X_test_selected)\n",
        "\n",
        "    # Calculating accuracy\n",
        "    accuracy = accuracy_score(Y_test, Y_pred)\n",
        "    print(f\"\\n{name} - Accuracy: {accuracy:.4f}\")\n",
        "\n",
        "    # Printing classification report\n",
        "    print(\"Classification Report:\")\n",
        "    print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Rsm3ZFu2aKn",
        "outputId": "fac1a25d-8a23-43ec-f784-f3b846266950"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Logistic Regression - Accuracy: 0.7800\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.85      0.85        71\n",
            "           1       0.62      0.62      0.62        29\n",
            "\n",
            "    accuracy                           0.78       100\n",
            "   macro avg       0.73      0.73      0.73       100\n",
            "weighted avg       0.78      0.78      0.78       100\n",
            "\n",
            "\n",
            "Random Forest - Accuracy: 0.7200\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.86      0.81        71\n",
            "           1       0.52      0.38      0.44        29\n",
            "\n",
            "    accuracy                           0.72       100\n",
            "   macro avg       0.65      0.62      0.63       100\n",
            "weighted avg       0.70      0.72      0.71       100\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning of the best model using grid search CV"
      ],
      "metadata": {
        "id": "bUOcj5NY9VdN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining the hyperparameters and their values\n",
        "param_grid = {\n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
        "    'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
        "    'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV\n",
        "grid_search = GridSearchCV(\n",
        "    LogisticRegression(max_iter=5000, random_state=42),\n",
        "    param_grid,\n",
        "    cv=5,  # 5-fold cross-validation\n",
        "    scoring='accuracy',\n",
        "    verbose=1,\n",
        "    n_jobs=-1  # Use all processors\n",
        ")\n",
        "\n",
        "# Fit the model\n",
        "grid_search.fit(X_train_selected, Y_train)\n",
        "\n",
        "# Get the best parameters\n",
        "best_params = grid_search.best_params_\n",
        "print(f\"Best Parameters: {best_params}\")\n",
        "\n",
        "# Evaluate the model with the best parameters on the test set\n",
        "best_model = grid_search.best_estimator_\n",
        "Y_pred_best = best_model.predict(X_test_selected)\n",
        "accuracy_best = accuracy_score(Y_test, Y_pred_best)\n",
        "print(f\"Accuracy with Best Parameters: {accuracy_best:.4f}\")\n",
        "\n",
        "# Classification report\n",
        "print(\"Classification Report with Best Parameters:\")\n",
        "print(classification_report(Y_test, Y_pred_best))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaRNM7Bq3EsM",
        "outputId": "d59f22bd-4e3a-4811-d10d-64eb3e9357e9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 120 candidates, totalling 600 fits\n",
            "Best Parameters: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "Accuracy with Best Parameters: 0.7800\n",
            "Classification Report with Best Parameters:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.85      0.85        71\n",
            "           1       0.62      0.62      0.62        29\n",
            "\n",
            "    accuracy                           0.78       100\n",
            "   macro avg       0.73      0.73      0.73       100\n",
            "weighted avg       0.78      0.78      0.78       100\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In conclusion, after tuning, the Logistic Regression model's best hyperparameters resulted in an accuracy of 78% with a macro average F1-score of 0.73"
      ],
      "metadata": {
        "id": "BJfkz2b75Hwg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plots and performance results of best model"
      ],
      "metadata": {
        "id": "T1p9E1zl9QJY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "cm = confusion_matrix(Y_test, Y_pred_best)\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"True\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "bqIIss416YwW",
        "outputId": "dd958c86-ed56-4389-9484-53f5e964030b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC Curve\n",
        "fpr, tpr, thresholds = roc_curve(Y_test, best_model.predict_proba(X_test_selected)[:,1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=1, label=f'ROC curve (area = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=1, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "wRKcoou56aqz",
        "outputId": "8015e91f-2c2d-45d8-8fb5-b27e2cb052b0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Precision-Recall Curve\n",
        "precision, recall, _ = precision_recall_curve(Y_test, best_model.predict_proba(X_test_selected)[:,1])\n",
        "average_precision = average_precision_score(Y_test, best_model.predict_proba(X_test_selected)[:,1])\n",
        "\n",
        "plt.figure()\n",
        "plt.step(recall, precision, where='post')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.title(f'Precision-Recall curve: AP={average_precision:.2f}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Na0QjT0Y6fzf",
        "outputId": "6195b321-93cb-45c2-ae7b-ac9fbff2565e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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