Loan_Prediction.ipynb

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      "source": [
        "# **LOAN PREDICTION**\n",
        "\n",
        "## **Data Source :**\n",
        "\n",
        "We use the dataset available at https://www.kaggle.com/datasets/altruistdelhite04/loan-prediction-problem-dataset"
      ],
      "metadata": {
        "id": "zXyZjMdZKNOp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Connect google drive to google colab."
      ],
      "metadata": {
        "id": "UGkU1cVSoY4z"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pZJXmT02nI_w"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Import Libraries**"
      ],
      "metadata": {
        "id": "8H_T_H9sGNDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "XZ_GRpBInUip"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Load Data**\n",
        "\n",
        "Load the dataset to a pandas DataFrame, i.e training data into **\"train\"** and testing data into **\"test\"**."
      ],
      "metadata": {
        "id": "xcb_gweMGTOV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv('/content/drive/MyDrive/train_loan_prediction.csv')\n",
        "test = pd.read_csv('/content/drive/MyDrive/test_loan_prediction.csv')"
      ],
      "metadata": {
        "id": "np5y4gydnOeT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
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      "execution_count": 4,
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            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "0  LP001002   Male      No          0      Graduate            No   \n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5849                0.0         NaN             360.0   \n",
              "1             4583             1508.0       128.0             360.0   \n",
              "2             3000                0.0        66.0             360.0   \n",
              "3             2583             2358.0       120.0             360.0   \n",
              "4             6000                0.0       141.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area Loan_Status  \n",
              "0             1.0         Urban           Y  \n",
              "1             1.0         Rural           N  \n",
              "2             1.0         Urban           Y  \n",
              "3             1.0         Urban           Y  \n",
              "4             1.0         Urban           Y  "
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              "      <th>0</th>\n",
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              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
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              "      <td>Graduate</td>\n",
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              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
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              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
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              "      <td>141.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
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      "source": [
        "Now, we map the values of the **\"Loan_Status\"** column in the DataFrame **\"train\"** to binary values. Specifically it replaces,\n",
        "\n",
        "**Y --> 1**\n",
        "\n",
        "**N --> 0**"
      ],
      "metadata": {
        "id": "AgwQB1h1pGQp"
      }
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    {
      "cell_type": "code",
      "source": [
        "train.Loan_Status = train.Loan_Status.map({'Y':1,'N':0})                # label encoding"
      ],
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        "id": "X3S8NQ3nGD0h"
      },
      "execution_count": 6,
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            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "0  LP001002   Male      No          0      Graduate            No   \n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5849                0.0         NaN             360.0   \n",
              "1             4583             1508.0       128.0             360.0   \n",
              "2             3000                0.0        66.0             360.0   \n",
              "3             2583             2358.0       120.0             360.0   \n",
              "4             6000                0.0       141.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area  Loan_Status  \n",
              "0             1.0         Urban            1  \n",
              "1             1.0         Rural            0  \n",
              "2             1.0         Urban            1  \n",
              "3             1.0         Urban            1  \n",
              "4             1.0         Urban            1  "
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              "      <th>Loan_ID</th>\n",
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              "      <th>Education</th>\n",
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              "      <td>LP001005</td>\n",
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              "      <td>LP001006</td>\n",
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              "      <td>LP001008</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-00975676-e431-4143-9d7f-9c202ecf4c86 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-00975676-e431-4143-9d7f-9c202ecf4c86');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see the descriptive statistics of the numerical columns in the DataFrame **\"train\"**."
      ],
      "metadata": {
        "id": "_hjLKUFDq4MW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VYLy984vn5Pw",
        "outputId": "81c85e39-ecd5-4225-a92c-a8b27be6ac55"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "count       614.000000         614.000000  592.000000         600.00000   \n",
              "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
              "std        6109.041673        2926.248369   85.587325          65.12041   \n",
              "min         150.000000           0.000000    9.000000          12.00000   \n",
              "25%        2877.500000           0.000000  100.000000         360.00000   \n",
              "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
              "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
              "max       81000.000000       41667.000000  700.000000         480.00000   \n",
              "\n",
              "       Credit_History  Loan_Status  \n",
              "count      564.000000   614.000000  \n",
              "mean         0.842199     0.687296  \n",
              "std          0.364878     0.463973  \n",
              "min          0.000000     0.000000  \n",
              "25%          1.000000     0.000000  \n",
              "50%          1.000000     1.000000  \n",
              "75%          1.000000     1.000000  \n",
              "max          1.000000     1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-386ae1d6-cba7-4cd7-9df9-901b43de834b\">\n",
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>592.000000</td>\n",
              "      <td>600.00000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>614.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5403.459283</td>\n",
              "      <td>1621.245798</td>\n",
              "      <td>146.412162</td>\n",
              "      <td>342.00000</td>\n",
              "      <td>0.842199</td>\n",
              "      <td>0.687296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6109.041673</td>\n",
              "      <td>2926.248369</td>\n",
              "      <td>85.587325</td>\n",
              "      <td>65.12041</td>\n",
              "      <td>0.364878</td>\n",
              "      <td>0.463973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2877.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3812.500000</td>\n",
              "      <td>1188.500000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5795.000000</td>\n",
              "      <td>2297.250000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>81000.000000</td>\n",
              "      <td>41667.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>480.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-386ae1d6-cba7-4cd7-9df9-901b43de834b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-386ae1d6-cba7-4cd7-9df9-901b43de834b 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-386ae1d6-cba7-4cd7-9df9-901b43de834b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, first we store the \"Loan_Status\" of training data into **\"Loan_Status\"**. Then we remove or drop the **\"Loan_Status\"** column from training data.\n",
        "\n",
        "Now, we combine the training and testing data vertically i.e stack these two data sets on top of each other using the \"concat\" function and store the newly formed dataset into **\"data\"**."
      ],
      "metadata": {
        "id": "6QniahvGq-oL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Loan_Status = train.Loan_Status                            # store loan status in variable in Loan_status\n",
        "train.drop('Loan_Status',axis=1,inplace=True)              # drop loan status column from train data\n",
        "Loan_ID = test.Loan_ID                                     # store loan id in variable Loan_ID\n",
        "data = pd.concat([train, test])                            # combine train and test datframes into single as data"
      ],
      "metadata": {
        "id": "u39zcpgCJDlH"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "VYoZ3QMAJ2Ew",
        "outputId": "c8c5194e-5d00-4f75-ec55-8b145464f26d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "0  LP001002   Male      No          0      Graduate            No   \n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5849                0.0         NaN             360.0   \n",
              "1             4583             1508.0       128.0             360.0   \n",
              "2             3000                0.0        66.0             360.0   \n",
              "3             2583             2358.0       120.0             360.0   \n",
              "4             6000                0.0       141.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area  \n",
              "0             1.0         Urban  \n",
              "1             1.0         Rural  \n",
              "2             1.0         Urban  \n",
              "3             1.0         Urban  \n",
              "4             1.0         Urban  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f5162008-ac34-4dfb-9a00-94c76ad8d772\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5162008-ac34-4dfb-9a00-94c76ad8d772')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f5162008-ac34-4dfb-9a00-94c76ad8d772 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-f5162008-ac34-4dfb-9a00-94c76ad8d772');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0C7NxquMQ-u",
        "outputId": "48255c9d-a207-4827-db1e-2aa9ecafa3b7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(981, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check whether if there is any duplicate value in the dataset or not."
      ],
      "metadata": {
        "id": "fO7UBgW_stiH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate = data[data.duplicated()]              # check duplicate values"
      ],
      "metadata": {
        "id": "x1Cqep5rn691"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhM8FNmfoEuM",
        "outputId": "002686ba-a1ff-4709-bcf6-e7a15c42832b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To visualize missing values in a DataFrame **\"data\"**, create a heatmap that visually represents the presence of missing values in the DataFrame **\"data\"**."
      ],
      "metadata": {
        "id": "2MvlkhO_tIQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data.isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "6XtmEAaSoI2U",
        "outputId": "2171fd5e-15df-4112-d44a-c8e7aa92f053"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To calculate the total number of missing values in each column of a DataFrame \"data\", we calculate the sum of True values in each column of the DataFrame, which corresponds to the number of missing values in each column.\n",
        "\n",
        "As a result, a Series object that displays the total count of missing values for each column in the DataFrame **\"data\"**."
      ],
      "metadata": {
        "id": "2nrWHzhmtiXu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGHcLKadoM4u",
        "outputId": "cb6fb186-9c7a-4460-cb05-b0270a07c5de"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               24\n",
              "Married               3\n",
              "Dependents           25\n",
              "Education             0\n",
              "Self_Employed        55\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           27\n",
              "Loan_Amount_Term     20\n",
              "Credit_History       79\n",
              "Property_Area         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, to fill the missing values in numeric columns of the DataFrame \"data\" we use the **\"mean\"** value of each respective column."
      ],
      "metadata": {
        "id": "0RTrbD002lzG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.fillna(data.mean(numeric_only=True).round(1), inplace=True)                # fill null numeric values with mean"
      ],
      "metadata": {
        "id": "WjvvKGpmoXJt"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to fill the missing values in columns such as, **\"Gender\", \"Married\", \"Dependents\"** and **\"Self_Employed\"** we use the **mode** of each column respectively. Then replace the missing values with **\"mode\"** of that specific column."
      ],
      "metadata": {
        "id": "oHnb06on28LD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Gender'].fillna(data['Gender'].mode()[0], inplace=True)                   # replace missing values with the mode\n",
        "data['Married'].fillna(data['Married'].mode()[0], inplace=True)\n",
        "data['Dependents'].fillna(data['Dependents'].mode()[0], inplace=True)\n",
        "data['Self_Employed'].fillna(data['Self_Employed'].mode()[0], inplace=True)"
      ],
      "metadata": {
        "id": "XW4FRk3tSePi"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()                # check null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDFiJRlkS8fu",
        "outputId": "f76af630-5be2-4296-c427-e4d0d5165f57"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID              0\n",
              "Gender               0\n",
              "Married              0\n",
              "Dependents           0\n",
              "Education            0\n",
              "Self_Employed        0\n",
              "ApplicantIncome      0\n",
              "CoapplicantIncome    0\n",
              "LoanAmount           0\n",
              "Loan_Amount_Term     0\n",
              "Credit_History       0\n",
              "Property_Area        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For understanding the distribution and proportion of categories within a categorical variables, create a count plot for specific column using the Seaborn library."
      ],
      "metadata": {
        "id": "aCqk_iZ754kg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count Plot for column - \"Gender\"**"
      ],
      "metadata": {
        "id": "ucsvsUUQtlXc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(4, 3))\n",
        "sns.countplot(x='Gender', data=data)                      # countplot for gender column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "3aUf71J-bW56",
        "outputId": "cf2cfc11-c248-4a53-c3eb-cdf02eb015bc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Gender', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count Plot for column - \"Married\"**"
      ],
      "metadata": {
        "id": "_IdD4843txdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(4, 3))\n",
        "sns.countplot(x='Married', data=data)                             # countplot for married column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "dvkI0986ngFt",
        "outputId": "bdeb01b3-0ae9-4a56-b65c-fdc2bf788b47"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Married', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count Plot for column - \"Dependents\"**"
      ],
      "metadata": {
        "id": "dWPpUFFJt6N8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(4, 3))\n",
        "sns.countplot(x='Dependents', data=data)                        # count plot for dependents column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "Di8EC6n9q1-F",
        "outputId": "c1afd62c-3dc2-43d7-fc72-d64eb78cc25e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Dependents', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count Plot for column - \"Education\"**"
      ],
      "metadata": {
        "id": "w0f5vgIMuLmS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(4, 3))\n",
        "sns.countplot(x='Education', data=data)                           # count plot for education column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "vGCXPmWLq6oe",
        "outputId": "ed0fe30a-91af-4695-ec4f-548f23e7dda1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Education', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count Plot for column - \"Self_Employed\"**"
      ],
      "metadata": {
        "id": "84Jc0bl3uQMY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(4, 3))\n",
        "sns.countplot(x='Self_Employed', data=data)                         # count plot for self employed column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "fCIk_X54q9mc",
        "outputId": "e0152009-a402-47a2-c42f-c4c90f7c9a10"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Self_Employed', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count Plot for column - \"Property_Area\"**"
      ],
      "metadata": {
        "id": "HsJnSCZCuVrX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(4, 3))\n",
        "sns.countplot(x='Property_Area', data=data)                          # count plot for property area column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "-BGHUBKnrFTC",
        "outputId": "bdccae20-0b89-4291-adae-9200362fd62a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Property_Area', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, count the occurrences of each unique value in the \"Dependents\" column of the DataFrame \"data\" using **\"value_counts\"** function."
      ],
      "metadata": {
        "id": "g1O6poRD9imR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Dependents'].value_counts()                # Dependent column values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0Y4bb44UgS0",
        "outputId": "74f095e7-3fa5-4e95-a483-80e35c643895"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     570\n",
              "1     160\n",
              "2     160\n",
              "3+     91\n",
              "Name: Dependents, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then replace the value **\"3+\"** with **\"4\"** in dependents column."
      ],
      "metadata": {
        "id": "viwJSAc79vSd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.replace(to_replace='3+', value=4, inplace=True)            # replacing the value of 3+ to 4"
      ],
      "metadata": {
        "id": "9_S5j1_iUiZm"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Dependents'].value_counts()                # Dependent column values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ab8LjvQUt-4",
        "outputId": "947ae44c-3f8a-4ae0-9e35-1dc051a67442"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    570\n",
              "1    160\n",
              "2    160\n",
              "4     91\n",
              "Name: Dependents, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In our dataset \"data\" there are multiple columns which stores categorical values. Now we convert categorical variables into a numerical format. Specifically for each column as-\n",
        "\n",
        "**Married : Yes --> 1, No --> 0**\n",
        "\n",
        "**Gender : Male --> 1, Female --> 0**\n",
        "\n",
        "**Self_Employed : Yes --> 1, No --> 0**\n",
        "\n",
        "**Property_Area : Rural --> 0, Semiurban --> 1, Urban --> 2**\n",
        "\n",
        "**Education : Graduate --> 1, Not Graduate --> 0**"
      ],
      "metadata": {
        "id": "VvSgk3nK_OeM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# convert categorical columns to numerical values\n",
        "\n",
        "data.replace({'Married':{'Yes':1, 'No':0},'Gender':{'Male':1,'Female':0},'Self_Employed':{'Yes':1, 'No':0},\n",
        "                      'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2},'Education':{'Graduate':1,'Not Graduate':0}},inplace=True)"
      ],
      "metadata": {
        "id": "qyKX4ddarJvx"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "fGupEf9Zvai0",
        "outputId": "48ce4023-a683-4b31-801f-ff34dd56f60e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
              "0  LP001002       1        0          0          1              0   \n",
              "1  LP001003       1        1          1          1              0   \n",
              "2  LP001005       1        1          0          1              1   \n",
              "3  LP001006       1        1          0          0              0   \n",
              "4  LP001008       1        0          0          1              0   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5849                0.0       142.5             360.0   \n",
              "1             4583             1508.0       128.0             360.0   \n",
              "2             3000                0.0        66.0             360.0   \n",
              "3             2583             2358.0       120.0             360.0   \n",
              "4             6000                0.0       141.0             360.0   \n",
              "\n",
              "   Credit_History  Property_Area  \n",
              "0             1.0              2  \n",
              "1             1.0              0  \n",
              "2             1.0              2  \n",
              "3             1.0              2  \n",
              "4             1.0              2  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
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              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
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              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0d416a2-052d-49f3-842d-0f92151b2d40')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c0d416a2-052d-49f3-842d-0f92151b2d40 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-c0d416a2-052d-49f3-842d-0f92151b2d40');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, remove or drop the **Loan_ID** column from dataframe **\"data\"**."
      ],
      "metadata": {
        "id": "SYCHaxfCCJKu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop('Loan_ID',inplace=True,axis=1)"
      ],
      "metadata": {
        "id": "BjxVxQkl0Icx"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Split data into features and targets-**\n",
        "\n",
        "Now, we split the dataframe into features and targets as-\n",
        "\n",
        "Here, \"x\" consists of independent variables of initial 614 tuples from dataframe \"data\".\n",
        "\n",
        "And \"y\" consists of dependent variable of those 614 tuples.\n",
        "\n"
      ],
      "metadata": {
        "id": "DXU8C7LGCSRh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.iloc[:614,]                #all the data in x (training set)\n",
        "y = Loan_Status                     #loan status will be our y"
      ],
      "metadata": {
        "id": "mLWsfJVu1FoQ"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Split data into training and testing data-**\n",
        "\n",
        "The data i.e x and y are now splitted into training data and testing data by using **\"train_test_split\"** function into **\"x_train\", \"x_test\", \"y_train\"** and **\"y_test\"**."
      ],
      "metadata": {
        "id": "omXRqe-TD8qT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)              # splitting into train and test data"
      ],
      "metadata": {
        "id": "uCyNKeV71_OG"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "0JiLAMk_2Sli",
        "outputId": "ab4dbb33-b035-4612-df6d-93173a203ac2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "46        1        1          1          1              0             5649   \n",
              "272       1        0          0          1              0             2833   \n",
              "474       1        0          2          1              0             5532   \n",
              "382       0        0          0          1              0             6000   \n",
              "283       0        0          0          1              0             1811   \n",
              "\n",
              "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "46                 0.0        44.0             360.0             1.0   \n",
              "272             1857.0       126.0             360.0             1.0   \n",
              "474             4648.0       162.0             360.0             1.0   \n",
              "382                0.0       156.0             360.0             1.0   \n",
              "283             1666.0        54.0             360.0             1.0   \n",
              "\n",
              "     Property_Area  \n",
              "46               2  \n",
              "272              0  \n",
              "474              0  \n",
              "382              2  \n",
              "283              2  "
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "4_6sV1vR2aNs",
        "outputId": "e6ef6fc0-ff21-4da7-ed80-e224b2f10743"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "454       1        0          0          1              1             7085   \n",
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              "\n",
              "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "454                0.0        84.0             360.0             1.0   \n",
              "52                 0.0       112.0             360.0             1.0   \n",
              "536             3906.0       324.0             360.0             1.0   \n",
              "469             2451.0       110.0             360.0             1.0   \n",
              "55              1167.0        97.0             360.0             1.0   \n",
              "\n",
              "     Property_Area  \n",
              "454              1  \n",
              "52               1  \n",
              "536              2  \n",
              "469              2  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The training data consists of 460 tuples and testing data consists of 154 tuples."
      ],
      "metadata": {
        "id": "SnWrCWFZFR5B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZmeA3al2cui",
        "outputId": "bf83b124-2db6-44db-f64c-12c3a50b8233"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((460, 11), (154, 11), (460,), (154,))"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Implementation**"
      ],
      "metadata": {
        "id": "F7897Ppi3xRR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For model implementation,  a list called \"models\" is created and initialized as an empty list. Now we add tuples in this list, each tuple consists of a string representing a machine learning model's name and an instance of the corresponding model class. The models added in the list are -\n",
        "\n",
        "\n",
        "*   Logistic Regression\n",
        "*   Decision Tree\n",
        "*   Linear Discriminant Analysis\n",
        "*   Random Forest\n",
        "*   Support Vector Classifier\n",
        "*   K- Nearest Neighbours\n",
        "*   Naive Bayes\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "mq7eyXp07gDN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models=[]                                 # empty list created\n",
        "\n",
        "models.append((\"Logistic Regression\",LogisticRegression()))                     # append various models\n",
        "models.append((\"Decision Tree\",DecisionTreeClassifier()))\n",
        "models.append((\"Linear Discriminant Analysis\",LinearDiscriminantAnalysis()))\n",
        "models.append((\"Random Forest\",RandomForestClassifier()))\n",
        "models.append((\"Support Vector Classifier\",SVC()))\n",
        "models.append((\"K- Nearest Neighbour\",KNeighborsClassifier()))\n",
        "models.append((\"Naive Bayes\",GaussianNB()))"
      ],
      "metadata": {
        "id": "1urtW_3W2r1y"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The term **\"scoring\"** refers to the evaluation metric used to assess the performance of a model. Here, scoring is set to **\"accuracy\"**."
      ],
      "metadata": {
        "id": "Wqy9KXXy-Ri0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scoring = 'accuracy'"
      ],
      "metadata": {
        "id": "zv49r_Q04Y3y"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now import two modules from **scikit-learn** which are : \n",
        "\n",
        "**\"KFold\"** and **\"cross_val_score\"**, these modules are used for performing cross-validation, which is a technique for evaluating and selecting models in machine learning."
      ],
      "metadata": {
        "id": "pOwxMyKc_RB8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold \n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "vWbWKGgz_To3"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, two empty lists are created as **\"result\"** and **\"names\"**.\n",
        "\n",
        "The list \"result\" is used to store the evaluation scores obtained during cross-validation.\n",
        "\n",
        "And the list \"names\" is  used to store the names of the models being evaluated."
      ],
      "metadata": {
        "id": "uhpTzeLI_sWU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = []\n",
        "names = []"
      ],
      "metadata": {
        "id": "kbvrhfrr4XFI"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we perform cross-validation for each model in the **\"models\"** list using the specified scoring metric. Then the evaluation scores for each model is stored\n",
        "in **\"cv_result\"** which is then appended into into the \"result\" list\n",
        "\n",
        "The name of the model is appended into the **\"names\"** list. Then in output it prints the mean score for each model with their names respectively."
      ],
      "metadata": {
        "id": "2WJwMQPwA_n5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for name, model in models:\n",
        "    kfold = KFold(n_splits=10, random_state=0, shuffle = True)\n",
        "    cv_result = cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)             \n",
        "    result.append(cv_result)\n",
        "    names.append(name)\n",
        "    print(\"%s %f\" % (name, cv_result.mean()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tk_UCidh4auD",
        "outputId": "ac5ad89a-c64b-40e8-c641-4b5fd9a70d38"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression 0.793478\n",
            "Decision Tree 0.697826\n",
            "Linear Discriminant Analysis 0.802174\n",
            "Random Forest 0.776087\n",
            "Support Vector Classifier 0.671739\n",
            "K- Nearest Neighbour 0.641304\n",
            "Naive Bayes 0.791304\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Build Model**\n",
        "\n",
        "Now we create an instance of the **\"Linear Discriminant Analysis\"** class and assigns it to the variable **\"LDA\"**. Then we train the model by using \"fit\" function on training data i.e, \"x_train\" and \"y_train\"."
      ],
      "metadata": {
        "id": "VIl-ABfCD4nP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LDA = LinearDiscriminantAnalysis()                        # create model\n",
        "LDA.fit(x_train, y_train)                                 # train model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "UUOlsog74vRs",
        "outputId": "38d36bee-7be5-4851-8d70-f80926d4bff4"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearDiscriminantAnalysis()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Test Model**\n",
        "\n",
        "Now, we test our model on the **\"x_test\"** data, then the predictions are stored in **\"pred\"**."
      ],
      "metadata": {
        "id": "rAQjTzV6FQHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = LDA.predict(x_test)                                              # test model\n",
        "print(\"Model Accuracy:- \", accuracy_score(y_test, pred))                # print accuracy of the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPX-leu4AF9h",
        "outputId": "fead12e9-2976-4b0d-e30b-bb8665d125a8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy:-  0.8376623376623377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix**"
      ],
      "metadata": {
        "id": "CseZ0j5_Fn8a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, pred))                                # print confusion matrix "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVy-ITBYAq_H",
        "outputId": "2be471e5-9557-4357-9622-427e68825222"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 20  23]\n",
            " [  2 109]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Report**"
      ],
      "metadata": {
        "id": "sptOrhJkFs5M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, pred))                           # print classification report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTO0Y6-yA24u",
        "outputId": "bd9d4dc9-abce-4678-828d-5d4397e58590"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.47      0.62        43\n",
            "           1       0.83      0.98      0.90       111\n",
            "\n",
            "    accuracy                           0.84       154\n",
            "   macro avg       0.87      0.72      0.76       154\n",
            "weighted avg       0.85      0.84      0.82       154\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Final Testing**"
      ],
      "metadata": {
        "id": "-Kfq8gLYImgq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we extract that testing data from index 614 and onwards from the dataframe **\"data\"** and store it into **\"final_test\"**."
      ],
      "metadata": {
        "id": "-pBpv5pMFzJ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_test = data.iloc[614:,] \n",
        "final_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "lPKRx27uA4DR",
        "outputId": "cabc25ad-cecc-4f21-911e-802ea691aa92"
      },
      "execution_count": 45,
      "outputs": [
        {
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              "   Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
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              "0                0.0       110.0             360.0             1.0   \n",
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              "3             2546.0       100.0             360.0             0.8   \n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "**Predictions of Final testing**"
      ],
      "metadata": {
        "id": "eXSAOO6qKHwS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = LDA.predict(final_test)\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBY0xyT6FLBZ",
        "outputId": "e3eb29c4-d763-416c-d7ca-00716614985e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1\n",
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            " 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1]\n"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Create Dataframe **\"predicted_data\"** for the final predictions that are mapped with their respective **\"Loan_ID\"**."
      ],
      "metadata": {
        "id": "4Or1o5ooQYKE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_data = pd.DataFrame({\"Loan_ID\": Loan_ID, \"Predicted_Status\" : prediction})"
      ],
      "metadata": {
        "id": "zzQ-FgJ6K98b"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mc9rfPvgM_FS",
        "outputId": "1c703cd5-5a27-44f9-d946-4eb35456e96e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(367, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_data.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "zX3RqEgoMx6q",
        "outputId": "2a23c4b5-ea2d-450d-848c-9df1c8553d14"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID  Predicted_Status\n",
              "0  LP001015                 1\n",
              "1  LP001022                 1\n",
              "2  LP001031                 1\n",
              "3  LP001035                 1\n",
              "4  LP001051                 1\n",
              "5  LP001054                 1\n",
              "6  LP001055                 1\n",
              "7  LP001056                 0\n",
              "8  LP001059                 1\n",
              "9  LP001067                 1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8fe088b3-a0f4-4d3c-a9ab-b2e883285af3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <td>LP001015</td>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>LP001022</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>LP001031</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>LP001035</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001051</td>\n",
              "      <td>1</td>\n",
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              "      <th>5</th>\n",
              "      <td>LP001054</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LP001055</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LP001056</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LP001059</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LP001067</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8fe088b3-a0f4-4d3c-a9ab-b2e883285af3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-8fe088b3-a0f4-4d3c-a9ab-b2e883285af3 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-8fe088b3-a0f4-4d3c-a9ab-b2e883285af3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "These are the predictions generated by the trained model on the **\"final_test\"** data."
      ],
      "metadata": {
        "id": "sOIBNwOqKPaB"
      }
    }
  ]
}