Loan Classification project.ipynb

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      "source": [
        "## ML Classification: Loan predictions\n"
      ],
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        "id": "EkJ9T15ZhRwH"
      }
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    {
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      "source": [
        "# Loading libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import roc_auc_score, accuracy_score, classification_report\n",
        "from sklearn.model_selection import cross_val_score, RandomizedSearchCV, KFold\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, FunctionTransformer\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "import joblib\n",
        "import pickle\n",
        "\n",
        "from scipy.stats import skew\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "sXzxOKhnhX7V"
      },
      "execution_count": 1,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Data ingestion\n",
        "df = pd.read_csv(\"loan_data.csv\")"
      ],
      "metadata": {
        "id": "xQ66MtGJh5gA"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking first 5 rows of dataset\n",
        "df.head()"
      ],
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        "outputId": "84a3bad8-86ee-4d9a-b854-f46b9b250430"
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      "execution_count": 3,
      "outputs": [
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              "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
              "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
              "1              1         credit_card    0.1071       228.22       11.082143   \n",
              "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
              "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
              "4              1         credit_card    0.1426       102.92       11.299732   \n",
              "\n",
              "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
              "0  19.48   737        5639.958333      28854        52.1               0   \n",
              "1  14.29   707        2760.000000      33623        76.7               0   \n",
              "2  11.63   682        4710.000000       3511        25.6               1   \n",
              "3   8.10   712        2699.958333      33667        73.2               1   \n",
              "4  14.97   667        4066.000000       4740        39.5               0   \n",
              "\n",
              "   delinq.2yrs  pub.rec  not.fully.paid  \n",
              "0            0        0               0  \n",
              "1            0        0               0  \n",
              "2            0        0               0  \n",
              "3            0        0               0  \n",
              "4            1        0               0  "
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              "      <th>credit.policy</th>\n",
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              "      <td>0.1357</td>\n",
              "      <td>366.86</td>\n",
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              "      <td>0.1426</td>\n",
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         33,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delinq.2yrs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub.rec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"not.fully.paid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA\n",
        "- Conducting a statistical analysis of the dataset and make some visualizations so as to understand the data"
      ],
      "metadata": {
        "id": "VFm_pFouurIY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Schema of dataset\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHOgQ2eViRFI",
        "outputId": "386ea7df-aa4b-4dc9-ffe6-286c7c216d2c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9578 entries, 0 to 9577\n",
            "Data columns (total 14 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   credit.policy      9578 non-null   int64  \n",
            " 1   purpose            9578 non-null   object \n",
            " 2   int.rate           9578 non-null   float64\n",
            " 3   installment        9578 non-null   float64\n",
            " 4   log.annual.inc     9578 non-null   float64\n",
            " 5   dti                9578 non-null   float64\n",
            " 6   fico               9578 non-null   int64  \n",
            " 7   days.with.cr.line  9578 non-null   float64\n",
            " 8   revol.bal          9578 non-null   int64  \n",
            " 9   revol.util         9578 non-null   float64\n",
            " 10  inq.last.6mths     9578 non-null   int64  \n",
            " 11  delinq.2yrs        9578 non-null   int64  \n",
            " 12  pub.rec            9578 non-null   int64  \n",
            " 13  not.fully.paid     9578 non-null   int64  \n",
            "dtypes: float64(6), int64(7), object(1)\n",
            "memory usage: 1.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Notes\n",
        "- The dataset has 1 object, 7 int 64 and 6 float64 data types.\n",
        "- dataset has a total of 9578 entries with no null values"
      ],
      "metadata": {
        "id": "QhOzpc8sL7zk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of unique values in each column\n",
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yilan-UIrlug",
        "outputId": "d0294e54-fdfc-40bd-c237-f21e4837c82f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "credit.policy           2\n",
              "purpose                 7\n",
              "int.rate              249\n",
              "installment          4788\n",
              "log.annual.inc       1987\n",
              "dti                  2529\n",
              "fico                   44\n",
              "days.with.cr.line    2687\n",
              "revol.bal            7869\n",
              "revol.util           1035\n",
              "inq.last.6mths         28\n",
              "delinq.2yrs            11\n",
              "pub.rec                 6\n",
              "not.fully.paid          2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- There are discrete and continuous variables"
      ],
      "metadata": {
        "id": "rCoInSILmeCe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "q2xSi-fSr87n"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsGiOmLnbYCL"
      },
      "source": [
        "### Statistical summary of numerical variables in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "qF5Z5MZCixaY",
        "outputId": "367cf69f-5be3-4e2e-fe3a-53a488bdc70d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    count          mean           std         min  \\\n",
              "credit.policy      9578.0      0.804970      0.396245    0.000000   \n",
              "int.rate           9578.0      0.122640      0.026847    0.060000   \n",
              "installment        9578.0    319.089413    207.071301   15.670000   \n",
              "log.annual.inc     9578.0     10.932117      0.614813    7.547502   \n",
              "dti                9578.0     12.606679      6.883970    0.000000   \n",
              "fico               9578.0    710.846314     37.970537  612.000000   \n",
              "days.with.cr.line  9578.0   4560.767197   2496.930377  178.958333   \n",
              "revol.bal          9578.0  16913.963876  33756.189557    0.000000   \n",
              "revol.util         9578.0     46.799236     29.014417    0.000000   \n",
              "inq.last.6mths     9578.0      1.577469      2.200245    0.000000   \n",
              "delinq.2yrs        9578.0      0.163708      0.546215    0.000000   \n",
              "pub.rec            9578.0      0.062122      0.262126    0.000000   \n",
              "not.fully.paid     9578.0      0.160054      0.366676    0.000000   \n",
              "\n",
              "                           25%          50%           75%           max  \n",
              "credit.policy         1.000000     1.000000      1.000000  1.000000e+00  \n",
              "int.rate              0.103900     0.122100      0.140700  2.164000e-01  \n",
              "installment         163.770000   268.950000    432.762500  9.401400e+02  \n",
              "log.annual.inc       10.558414    10.928884     11.291293  1.452835e+01  \n",
              "dti                   7.212500    12.665000     17.950000  2.996000e+01  \n",
              "fico                682.000000   707.000000    737.000000  8.270000e+02  \n",
              "days.with.cr.line  2820.000000  4139.958333   5730.000000  1.763996e+04  \n",
              "revol.bal          3187.000000  8596.000000  18249.500000  1.207359e+06  \n",
              "revol.util           22.600000    46.300000     70.900000  1.190000e+02  \n",
              "inq.last.6mths        0.000000     1.000000      2.000000  3.300000e+01  \n",
              "delinq.2yrs           0.000000     0.000000      0.000000  1.300000e+01  \n",
              "pub.rec               0.000000     0.000000      0.000000  5.000000e+00  \n",
              "not.fully.paid        0.000000     0.000000      0.000000  1.000000e+00  "
            ],
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              "      <th>min</th>\n",
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              "      <th>credit.policy</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>0.804970</td>\n",
              "      <td>0.396245</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>int.rate</th>\n",
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              "      <td>0.060000</td>\n",
              "      <td>0.103900</td>\n",
              "      <td>0.122100</td>\n",
              "      <td>0.140700</td>\n",
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              "      <td>207.071301</td>\n",
              "      <td>15.670000</td>\n",
              "      <td>163.770000</td>\n",
              "      <td>268.950000</td>\n",
              "      <td>432.762500</td>\n",
              "      <td>9.401400e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log.annual.inc</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>10.932117</td>\n",
              "      <td>0.614813</td>\n",
              "      <td>7.547502</td>\n",
              "      <td>10.558414</td>\n",
              "      <td>10.928884</td>\n",
              "      <td>11.291293</td>\n",
              "      <td>1.452835e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>12.606679</td>\n",
              "      <td>6.883970</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.212500</td>\n",
              "      <td>12.665000</td>\n",
              "      <td>17.950000</td>\n",
              "      <td>2.996000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fico</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>710.846314</td>\n",
              "      <td>37.970537</td>\n",
              "      <td>612.000000</td>\n",
              "      <td>682.000000</td>\n",
              "      <td>707.000000</td>\n",
              "      <td>737.000000</td>\n",
              "      <td>8.270000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>days.with.cr.line</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>4560.767197</td>\n",
              "      <td>2496.930377</td>\n",
              "      <td>178.958333</td>\n",
              "      <td>2820.000000</td>\n",
              "      <td>4139.958333</td>\n",
              "      <td>5730.000000</td>\n",
              "      <td>1.763996e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol.bal</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>16913.963876</td>\n",
              "      <td>33756.189557</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3187.000000</td>\n",
              "      <td>8596.000000</td>\n",
              "      <td>18249.500000</td>\n",
              "      <td>1.207359e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol.util</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>46.799236</td>\n",
              "      <td>29.014417</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>46.300000</td>\n",
              "      <td>70.900000</td>\n",
              "      <td>1.190000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inq.last.6mths</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>1.577469</td>\n",
              "      <td>2.200245</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.300000e+01</td>\n",
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              "      <th>delinq.2yrs</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>0.163708</td>\n",
              "      <td>0.546215</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>9578.0</td>\n",
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              "\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-fee0185d-8310-403e-9f19-078dde0d56a6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 9578.0,\n        \"max\": 9578.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          9578.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4728.2832510329035,\n        \"min\": 0.06212152850281896,\n        \"max\": 16913.963875548132,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.06212152850281896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9323.193678969577,\n        \"min\": 0.026846987213382245,\n        \"max\": 33756.18955725052,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.26212632637427247\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 172.2096932007591,\n        \"min\": 0.0,\n        \"max\": 612.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1116.0117978611133,\n        \"min\": 0.0,\n        \"max\": 3187.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          22.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2532.9488564606177,\n        \"min\": 0.0,\n        \"max\": 8596.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          46.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5144.251889664989,\n        \"min\": 0.0,\n        \"max\": 18249.5,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          737.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 334442.5499377867,\n        \"min\": 0.2164,\n        \"max\": 1207359.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          13.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Notes\n",
        "- The numerical variables have varying scales hence scaling can be performed\n",
        "- Differences between mean and median indicate some skewness on some features e.g. revol.bal, instalment etc\n",
        "- Log transformation can also be appplied to continuous variables\n",
        "- High std indicates variability in a feature, as indicated by the revol.bal etc\n",
        "- No null values in data\n",
        "- Outliers can be observed since there are some large difrences between mean and median on features e.g. revol.bal"
      ],
      "metadata": {
        "id": "64Uv6knKMjSc"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAYSzViQbYCN"
      },
      "source": [
        "### Graphical visualization of the distribution of features"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Categorical features\n",
        "df['purpose'].value_counts().plot(kind=\"pie\",\n",
        "                              autopct =\"%1.1f%%\", title =\"Purpose of loan\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "b6hQSnQol-0p",
        "outputId": "d943ca08-4894-40d0-d62b-4af926a92743"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTQOK-JqbYCN"
      },
      "source": [
        "### Observation\n",
        "- Debt_consolidation is the most frequent purpose for borrowing loan\n",
        "- Educational is the least popular purpose"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of numerical columns\n",
        "df.hist(figsize=(12,12))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cECCWmvU8sKR",
        "outputId": "2fb85d1d-e466-44bd-82c5-e1d56431d1e6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZXj-QjSbYCN"
      },
      "source": [
        "### Notes\n",
        "-The histograms showed the distribution of features\n",
        "- Fico, int.rate and log.annual.inc are normally distributed\n",
        "- Logarithmic transformation can be done on the features which are skewed e.g\n",
        "- Features have different ranges of distribution hence feature scaling is a necesity  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9rHO-BdbYCO"
      },
      "source": [
        "### Visualizing correlations on heatmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "scrolled": true,
        "id": "TgHTQa98bYCO",
        "outputId": "987e1298-b50e-43ed-d589-c4e78110495f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "figure = plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df.corr(), annot = True)\n",
        "plt.tight_layout()\n",
        "plt.title(\"Correlations of features in the Loan data dataset\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1Y_7A4_bYCO"
      },
      "source": [
        "### Inference\n",
        "- Fico and interset rate recorded the highest correlation of 71%\n",
        "- There are no extreme cases of correlation hence using all the features for modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXQPRuQXbYCO"
      },
      "source": [
        "### Checking for outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "p_1fXonybYCP",
        "outputId": "4ea3b99f-a7a4-4e24-e9b3-fc04fc472929"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Checking for outliers\n",
        "figure = plt.figure(figsize=(12,12))\n",
        "plt.subplot(2,3,1)\n",
        "sns.boxplot(df[\"int.rate\"])\n",
        "plt.title(\"int.rate\")\n",
        "\n",
        "plt.subplot(2,3,2)\n",
        "sns.boxplot(df[\"installment\"])\n",
        "plt.title(\"installment\")\n",
        "\n",
        "plt.subplot(2,3,3)\n",
        "sns.boxplot(df[\"days.with.cr.line\"])\n",
        "plt.title(\"days.with.cr.line\")\n",
        "\n",
        "plt.subplot(2,3,4)\n",
        "sns.boxplot(df[\"log.annual.inc\"])\n",
        "\n",
        "plt.subplot(2,3,5)\n",
        "sns.boxplot(df[\"fico\"])\n",
        "\n",
        "plt.subplot(2,3,6)\n",
        "sns.boxplot(df[\"inq.last.6mths\"])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Notes\n",
        "- There are outliers in the nuemrical features which may need to be handled  "
      ],
      "metadata": {
        "id": "NZ-E-xcJNUpL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analyzing the target column"
      ],
      "metadata": {
        "id": "qtwZz6720Wa_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "7OaiX3eIbYCV",
        "outputId": "71f7b3a7-d340-427f-a875-6c7bf79323b8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Distribution of not.fully .paid column\n",
        "sns.countplot(x='not.fully.paid', data=df)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2dCJ3h1EbYCW"
      },
      "source": [
        "### Notes\n",
        "- The target variable [\"not.fully.paid\"] is biased\n",
        "- Oversampling to be done using SMOTE algorithm inorder to balance the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data wrangling\n",
        "- Cleaning data and transforming the features in preparation for data modeling"
      ],
      "metadata": {
        "id": "dG7JQocjmU0W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Checking for missing numbers"
      ],
      "metadata": {
        "id": "dPmMFgaoklsd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAmYUfyQbYCL",
        "outputId": "2771f3ca-8279-4faf-a2f1-721843c27538"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df.isna().sum().any()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flUgUuAnbYCL"
      },
      "source": [
        "There are no misssing values in the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Checking for duplicates"
      ],
      "metadata": {
        "id": "cdriE7sIlLtW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYDMFQFSbYCM",
        "outputId": "ac4b185c-89b6-4792-a21b-adebdc460a0b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.duplicated().any()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyhG6I0LbYCM"
      },
      "source": [
        "The dataset has no duplicates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unJVnqxjbYCM"
      },
      "source": [
        "### Checking for variablitity in features"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()==1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZjKKykM2dT_",
        "outputId": "b0259647-466c-4522-dfb6-b86acd410d7a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "credit.policy        False\n",
              "purpose              False\n",
              "int.rate             False\n",
              "installment          False\n",
              "log.annual.inc       False\n",
              "dti                  False\n",
              "fico                 False\n",
              "days.with.cr.line    False\n",
              "revol.bal            False\n",
              "revol.util           False\n",
              "inq.last.6mths       False\n",
              "delinq.2yrs          False\n",
              "pub.rec              False\n",
              "not.fully.paid       False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iMkltFCbYCM"
      },
      "source": [
        "### Notes:\n",
        "- There is some variablity in all columns hence all columns are informative"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Handling outliers\n",
        "- Outliers should not be automatically removed because they may be useful and bring important information to prediction models.\n",
        "- However since this project is for learning purposes, I'll expore how to remove the outliers."
      ],
      "metadata": {
        "id": "jnZYWyFTyy0_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "etK9bBBKbYCP"
      },
      "outputs": [],
      "source": [
        "#Removing outliers\n",
        "df = df[(df['int.rate']<0.19)& (df[\"installment\"]<835)&\n",
        "                       (df[\"days.with.cr.line\"]<9450)&(df[\"fico\"]<815)&\n",
        "                       (df[\"inq.last.6mths\"]<5)&(df[\"log.annual.inc\"]<12.3)&\n",
        "                       (df[\"log.annual.inc\"]>9.4)&(df[\"dti\"]<34)]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNMucYqNNyUF",
        "outputId": "ac6b1b05-ffcc-4baa-ea3c-037c3dbffd93"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7948, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        },
        "id": "6lBJMBVNbYCP",
        "outputId": "41a4cb75-5cfc-420e-c3ee-0e90ef754cff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Rechecking to see if all outliers are handled\n",
        "figure = plt.figure(figsize=(10,10))\n",
        "plt.subplot(2,3,1)\n",
        "sns.boxplot(df[\"int.rate\"])\n",
        "plt.title(\"int.rate\")\n",
        "\n",
        "plt.subplot(2,3,2)\n",
        "sns.boxplot(df[\"installment\"])\n",
        "plt.title(\"installment\")\n",
        "\n",
        "plt.subplot(2,3,3)\n",
        "sns.boxplot(df[\"days.with.cr.line\"])\n",
        "plt.title(\"days.with.cr.line\")\n",
        "\n",
        "plt.subplot(2,3,4)\n",
        "sns.boxplot(df[\"log.annual.inc\"])\n",
        "\n",
        "plt.subplot(2,3,5)\n",
        "sns.boxplot(df[\"fico\"])\n",
        "\n",
        "plt.subplot(2,3,6)\n",
        "sns.boxplot(df[\"inq.last.6mths\"])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAdU7Sv0bYCP"
      },
      "source": [
        "### Notes\n",
        "- There are some outliers remaining even after removing the majority of outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature engineering\n",
        "- Log transformation on skewed numerical columns, Encoding and scaling data"
      ],
      "metadata": {
        "id": "T9qYTmoC4A9v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Separating features and target\n",
        "X = df.drop(columns=['not.fully.paid'],axis=1)\n",
        "y = df['not.fully.paid']"
      ],
      "metadata": {
        "id": "qTa4KeeVuIhD"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = [i for i in X.columns if X[i].dtype=='O']\n",
        "categorical_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8Qn1RFnuoF9",
        "outputId": "fc9ffc85-4b31-4976-9f04-458008270d6d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['purpose']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_columns = [i for i in X.columns if i not in categorical_columns]\n",
        "numerical_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7-TxylivGBm",
        "outputId": "cdc28225-4468-4f4e-a3b0-574bf47ddde0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['credit.policy',\n",
              " 'int.rate',\n",
              " 'installment',\n",
              " 'log.annual.inc',\n",
              " 'dti',\n",
              " 'fico',\n",
              " 'days.with.cr.line',\n",
              " 'revol.bal',\n",
              " 'revol.util',\n",
              " 'inq.last.6mths',\n",
              " 'delinq.2yrs',\n",
              " 'pub.rec']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "skewness = X[numerical_columns].apply(lambda x: skew(x))\n",
        "print(skewness)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_c4b8t1CqU7O",
        "outputId": "2d3d4281-06c3-40a4-892b-125400e1fb43"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "credit.policy       -2.202446\n",
            "int.rate             0.020404\n",
            "installment          0.811865\n",
            "log.annual.inc      -0.058821\n",
            "dti                  0.009017\n",
            "fico                 0.433146\n",
            "days.with.cr.line    0.459441\n",
            "revol.bal            5.115431\n",
            "revol.util           0.051541\n",
            "inq.last.6mths       0.893526\n",
            "delinq.2yrs          4.499062\n",
            "pub.rec              5.080534\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Selecting columns with skewness of greater than 1 for log transformation\n",
        "threshold = 1\n",
        "skewed_columns = skewness[skewness > threshold].index.tolist()\n",
        "skewed_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwzDNUlkrUaQ",
        "outputId": "db7e980b-4d40-49ba-a235-3a9bb8c85d42"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['revol.bal', 'delinq.2yrs', 'pub.rec']"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessor\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('log_transform', FunctionTransformer(np.log1p,validate = True),skewed_columns),\n",
        "        ('num', MinMaxScaler(), numerical_columns),\n",
        "        ('cat', OneHotEncoder(drop='first'), categorical_columns)\n",
        "    ])"
      ],
      "metadata": {
        "id": "8No651EjWp6w"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the preprocessor\n",
        "X_preprocessed = preprocessor.fit_transform(X)"
      ],
      "metadata": {
        "id": "nNzYcNXt6--W"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_preprocessed, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "SWDlKxYJ7Ddx"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# balancing the dataset\n",
        "smote = SMOTE()\n",
        "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "M3xErVx-7M_f"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking if dataset is balanced\n",
        "print('Y_train\\n', y_train.value_counts(), '\\n')\n",
        "print('Y_train_resampled\\n', y_train_resampled.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoZfe-4u_UBv",
        "outputId": "dd0e7b53-2041-4141-e2a1-7a0db0376318"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y_train\n",
            " 0    5438\n",
            "1     920\n",
            "Name: not.fully.paid, dtype: int64 \n",
            "\n",
            "Y_train_resampled\n",
            " 0    5438\n",
            "1    5438\n",
            "Name: not.fully.paid, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Notes\n",
        "- The dataset was successfully balanced\n",
        "- Sampling techniques like smote are only applied to the training data to avoid data leakages\n",
        "- That way the model evaluation will also be realistic on the unseen test data"
      ],
      "metadata": {
        "id": "clqCXHSfV2R3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Traditional Machine Classifiers"
      ],
      "metadata": {
        "id": "vTCeWzMqykl5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "scrolled": false,
        "id": "79ccb8e7",
        "outputId": "fdcb598c-96c6-4409-8653-e5645e7ecf80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression \n",
            "\n",
            "Training accuracy is 0.6204486943729313 \n",
            "\n",
            "Test accuracy is 0.6050314465408805 \n",
            "\n",
            "****************************************\n",
            "\n",
            "SVC \n",
            "\n",
            "Training accuracy is 0.6237587348289813 \n",
            "\n",
            "Test accuracy is 0.5773584905660377 \n",
            "\n",
            "****************************************\n",
            "\n",
            "Random Forest Classifier \n",
            "\n",
            "Training accuracy is 1.0 \n",
            "\n",
            "Test accuracy is 0.8333333333333334 \n",
            "\n",
            "****************************************\n",
            "\n",
            "XGBClassifier \n",
            "\n",
            "Training accuracy is 0.9794961382861346 \n",
            "\n",
            "Test accuracy is 0.8471698113207548 \n",
            "\n",
            "****************************************\n",
            "\n",
            "Decision Tree Classifier \n",
            "\n",
            "Training accuracy is 1.0 \n",
            "\n",
            "Test accuracy is 0.7264150943396226 \n",
            "\n",
            "****************************************\n",
            "\n",
            "KNeighbors Classifier \n",
            "\n",
            "Training accuracy is 0.8618977565281354 \n",
            "\n",
            "Test accuracy is 0.6125786163522012 \n",
            "\n",
            "****************************************\n",
            "\n",
            "Ada Boost Classifier \n",
            "\n",
            "Training accuracy is 0.7442074292019125 \n",
            "\n",
            "Test accuracy is 0.7238993710691823 \n",
            "\n",
            "****************************************\n",
            "\n"
          ]
        }
      ],
      "source": [
        "models = {\n",
        "    \"Logistic Regression\": LogisticRegression(),\n",
        "    \"SVC\": SVC(),\n",
        "    \"Random Forest Classifier\": RandomForestClassifier(),\n",
        "    \"XGBClassifier\": XGBClassifier(),\n",
        "    \"Decision Tree Classifier\": DecisionTreeClassifier(),\n",
        "    \"KNeighbors Classifier\": KNeighborsClassifier(),\n",
        "    \"Ada Boost Classifier\": AdaBoostClassifier()\n",
        "}\n",
        "\n",
        "model_list = []\n",
        "accuracy_list =[]\n",
        "\n",
        "for i in range(len(list(models))):\n",
        "    model = list(models.values())[i]\n",
        "    model.fit(X_train_resampled, y_train_resampled)\n",
        "\n",
        "    # Predictions\n",
        "    y_train_pred = model.predict(X_train_resampled)\n",
        "    y_test_pred = model.predict(X_test)\n",
        "\n",
        "    # Evaluate Train and Test dataset\n",
        "    train_accuracy  = accuracy_score(y_train_resampled, y_train_pred)\n",
        "\n",
        "    test_accuracy  = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "\n",
        "    print(list(models.keys())[i],'\\n')\n",
        "    model_list.append(list(models.keys())[i])\n",
        "\n",
        "    print('Training accuracy is', train_accuracy,'\\n')\n",
        "    print('Test accuracy is', test_accuracy, '\\n')\n",
        "\n",
        "    accuracy_list.append(test_accuracy)\n",
        "    print('*'*40)\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(list(zip(model_list,accuracy_list)), columns=['Model Name', 'Accuracy score']).sort_values(by=[\"Accuracy score\"],ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "q9yUIaIkDFEj",
        "outputId": "546aa2c8-d91c-4b23-a596-85d4a0ba49c8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model Name  Accuracy score\n",
              "3             XGBClassifier        0.847170\n",
              "2  Random Forest Classifier        0.833333\n",
              "4  Decision Tree Classifier        0.726415\n",
              "6      Ada Boost Classifier        0.723899\n",
              "5     KNeighbors Classifier        0.612579\n",
              "0       Logistic Regression        0.605031\n",
              "1                       SVC        0.577358"
            ],
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              "      <th></th>\n",
              "      <th>Model Name</th>\n",
              "      <th>Accuracy score</th>\n",
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              "      <th>3</th>\n",
              "      <td>XGBClassifier</td>\n",
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              "      <th>2</th>\n",
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              "      <th>4</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.726415</td>\n",
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              "      <th>6</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.723899</td>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Model Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"XGBClassifier\",\n          \"Random Forest Classifier\",\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10978246909865277,\n        \"min\": 0.5773584905660377,\n        \"max\": 0.8471698113207548,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.8471698113207548,\n          0.8333333333333334,\n          0.6050314465408805\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Notes\n",
        "- XGBClassifier performed best\n",
        "- A good fit is observed in models e.g. Logistic regression and Ada boost classifier\n",
        "- A large gap between the training and testing accuracies in models e.g Random forest classifier, Decision tree classifier and KNN classifier does suggest a possibility of overfitting"
      ],
      "metadata": {
        "id": "ThhrfBmjxxsF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hyper parameter tuning on the best performing model\n"
      ],
      "metadata": {
        "id": "ovXsHyPdvDoO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Notes\n",
        "- Cross-validation is used in hyperparameter tuning to estimate the performance of each hyperparameter configuration.\n",
        "- It helps prevent overfitting hyperparameters to a specific train-test split, ensuring the chosen hyperparameters generalize well to new data."
      ],
      "metadata": {
        "id": "BdOemB-TwEW3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model = XGBClassifier()"
      ],
      "metadata": {
        "id": "O-hrnKWwqWEM"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_scores = cross_val_score(xgb_model, X_train_resampled, y_train_resampled, cv=5)"
      ],
      "metadata": {
        "id": "d9iyzXxUuBap"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Cross-Validation Scores:\", cv_scores,'\\n')\n",
        "print(f\"Mean Accuracy: {cv_scores.mean():.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTAzAqgPuTJs",
        "outputId": "b6270525-ad8b-4b89-87fe-92481fa06070"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Scores: [0.61856618 0.94574713 0.94344828 0.93517241 0.94482759] \n",
            "\n",
            "Mean Accuracy: 0.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'classifier__n_estimators': [100, 200, 250, 300,500],\n",
        "    'classifier__max_depth': [1, 3, 5, 7],\n",
        "    'classifier__learning_rate': [0.01, 0.001, 0.1, 0.2]\n",
        "}"
      ],
      "metadata": {
        "id": "k4qej9yjvMmG"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_search = RandomizedSearchCV(\n",
        "    estimator=xgb_model,\n",
        "    param_distributions=param_grid,\n",
        "    n_iter=10,\n",
        "    scoring='accuracy',\n",
        "    cv=5\n",
        "    )"
      ],
      "metadata": {
        "id": "TBenV_hAjQu3"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_search.fit(X_train_resampled, y_train_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "iYXg-qoz6pGC",
        "outputId": "de9abb4d-459a-4f71-b7a0-16737c5c93ea"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5,\n",
              "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None, device=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           multi_strategy=None,\n",
              "                                           n_estimators=None, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           random_state=None, ...),\n",
              "                   param_distributions={'classifier__learning_rate': [0.01,\n",
              "                                                                      0.001,\n",
              "                                                                      0.1,\n",
              "                                                                      0.2],\n",
              "                                        'classifier__max_depth': [1, 3, 5, 7],\n",
              "                                        'classifier__n_estimators': [100, 200,\n",
              "                                                                     250, 300,\n",
              "                                                                     500]},\n",
              "                   scoring='accuracy')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
              "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None, device=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           multi_strategy=None,\n",
              "                                           n_estimators=None, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           random_state=None, ...),\n",
              "                   param_distributions={&#x27;classifier__learning_rate&#x27;: [0.01,\n",
              "                                                                      0.001,\n",
              "                                                                      0.1,\n",
              "                                                                      0.2],\n",
              "                                        &#x27;classifier__max_depth&#x27;: [1, 3, 5, 7],\n",
              "                                        &#x27;classifier__n_estimators&#x27;: [100, 200,\n",
              "                                                                     250, 300,\n",
              "                                                                     500]},\n",
              "                   scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
              "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None, device=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           multi_strategy=None,\n",
              "                                           n_estimators=None, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           random_state=None, ...),\n",
              "                   param_distributions={&#x27;classifier__learning_rate&#x27;: [0.01,\n",
              "                                                                      0.001,\n",
              "                                                                      0.1,\n",
              "                                                                      0.2],\n",
              "                                        &#x27;classifier__max_depth&#x27;: [1, 3, 5, 7],\n",
              "                                        &#x27;classifier__n_estimators&#x27;: [100, 200,\n",
              "                                                                     250, 300,\n",
              "                                                                     500]},\n",
              "                   scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best Parameters:\", random_search.best_params_,'\\n')\n",
        "print(\"Best Score:\", random_search.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7XIxYtAwmy0",
        "outputId": "163183fb-566c-474d-c172-68f7f50115f4"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'classifier__n_estimators': 100, 'classifier__max_depth': 7, 'classifier__learning_rate': 0.001} \n",
            "\n",
            "Best Score: 0.8775523157538878\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking the accuracy of the best parameters"
      ],
      "metadata": {
        "id": "oSkstH1x1cS8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_xgb_model =XGBClassifier(n_estimators=300, max_depth =1, learning_rate= 0.01)"
      ],
      "metadata": {
        "id": "DsoRDkqH1ng9"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_xgb_model.fit(X_train_resampled, y_train_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "4216be3b-a404-45a9-ac36-554c09b9fe69",
        "id": "EiO1Mpp72gOw"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=1, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=300, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=1, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=300, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=1, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=300, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_xgb_predictions = best_xgb_model.predict(X_test)"
      ],
      "metadata": {
        "id": "WTi4eQxu2qGz"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('XG boost with hyper parameter tuning accuracy score is : ', accuracy_score(y_test,best_xgb_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1616e0d4-cdac-44ec-8668-6772d08859a8",
        "id": "LXGE2yf42yeC"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XG boost with hyper parameter tuning accuracy score is :  0.5578616352201258\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Notes\n",
        "- A decrease in accuracy after hyperparameter tuning can be due to many factor which may need further investigation.\n",
        "- Randomness, model complexity, tuning could have introduced regularization or other measures to reduce overfitting or even underfitting: it's possible that the hyperparameters were not tuned effectively, leading to underfitting."
      ],
      "metadata": {
        "id": "n4gtf8XS3AnH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model persistence"
      ],
      "metadata": {
        "id": "7lYnkbXzYN06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(best_xgb_model, 'loan_classifier.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aJugXZqvV5l",
        "outputId": "ae1ab8c2-0bdc-4289-e692-1499f51755d0"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loan_classifier.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading model later\n",
        "model = joblib.load('loan_classifier.joblib')"
      ],
      "metadata": {
        "id": "_Rs8igBoYL0-"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions using the loaded model\n",
        "predictions = model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "3mIfPr9XZD71"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pickling"
      ],
      "metadata": {
        "id": "9d-XAPC_gcLH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('best_xgb_model.pkl', 'wb') as file:\n",
        "    pickle.dump(model, file)"
      ],
      "metadata": {
        "id": "tGyS5dVYfUip"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('best_xgb_model.pkl', 'rb') as file:\n",
        "    loan_model = pickle.load(file)\n"
      ],
      "metadata": {
        "id": "mEbNyarVfG-A"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions using the loaded model\n",
        "predictions = loan_model.predict(X_test)"
      ],
      "metadata": {
        "id": "Up91wwpDfRi8"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U3e1-F8W5TCb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}