Loan_approval.ipynb

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        "<a href=\"https://colab.research.google.com/github/Kshitijasharma/celebal_internship/blob/main/Loan_approval.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# import libraries\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n"
      ]
    },
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      "cell_type": "code",
      "source": [
        "# load the train and test dataset\n",
        "train = pd.read_csv(\"Training Dataset.csv\")\n",
        "test = pd.read_csv(\"Test Dataset.csv\")"
      ],
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      "execution_count": 2,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# load the train and test dataset\n",
        "train = pd.read_csv(\"Training Dataset.csv\")\n",
        "test = pd.read_csv(\"Test Dataset.csv\")"
      ],
      "metadata": {
        "id": "gOjLBknCcEqR"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# take a look at the top 5 rows of the train set, notice the column \"Loan_Status\"\n",
        "train.head()"
      ],
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      "execution_count": 5,
      "outputs": [
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          "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 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></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
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              "      <th>ApplicantIncome</th>\n",
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              "      <th>LoanAmount</th>\n",
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              "      <th>Credit_History</th>\n",
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              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
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              "      <td>Graduate</td>\n",
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              "      <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",
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              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
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              "      <td>1.0</td>\n",
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              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 614,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 614,\n        \"samples\": [\n          \"LP002139\",\n          \"LP002223\",\n          \"LP001570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6109,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 505,\n        \"samples\": [\n          8333,\n          4342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2926.2483692241885,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 287,\n        \"samples\": [\n          1840.0,\n          2042.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.58732523570545,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.12040985461256,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3648783192364048,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# take a look at the top 5 rows of the test set, notice the absense of \"Loan_Status\" that we will predict\n",
        "test.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 243
        },
        "id": "xRYNiLqlcN8Z",
        "outputId": "bc16a6e4-9c7a-4fd4-cc4d-84b509a469a7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "0  LP001015   Male     Yes          0      Graduate            No   \n",
              "1  LP001022   Male     Yes          1      Graduate            No   \n",
              "2  LP001031   Male     Yes          2      Graduate            No   \n",
              "3  LP001035   Male     Yes          2      Graduate            No   \n",
              "4  LP001051   Male      No          0  Not Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5720                  0       110.0             360.0   \n",
              "1             3076               1500       126.0             360.0   \n",
              "2             5000               1800       208.0             360.0   \n",
              "3             2340               2546       100.0             360.0   \n",
              "4             3276                  0        78.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area  \n",
              "0             1.0         Urban  \n",
              "1             1.0         Urban  \n",
              "2             1.0         Urban  \n",
              "3             NaN         Urban  \n",
              "4             1.0         Urban  "
            ],
            "text/html": [
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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>LP001015</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
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              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001022</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>Graduate</td>\n",
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              "      <td>1500</td>\n",
              "      <td>126.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001031</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5000</td>\n",
              "      <td>1800</td>\n",
              "      <td>208.0</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>LP001035</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2340</td>\n",
              "      <td>2546</td>\n",
              "      <td>100.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001051</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3276</td>\n",
              "      <td>0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 367,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 367,\n        \"samples\": [\n          \"LP002070\",\n          \"LP001187\",\n          \"LP001099\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4910,\n        \"min\": 0,\n        \"max\": 72529,\n        \"num_unique_values\": 314,\n        \"samples\": [\n          6794,\n          2783\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2334,\n        \"min\": 0,\n        \"max\": 24000,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          3725,\n          833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.36665239301822,\n        \"min\": 28.0,\n        \"max\": 550.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          112.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.15664341399702,\n        \"min\": 6.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          120.0,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38014983549147024,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Semiurban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show the shape of the dataset i.e. no of rows, no of columns\n",
        "train.shape, test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-4EeB3ZcZEC",
        "outputId": "faa197dd-9968-4c52-af03-6ade3797114a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((614, 13), (367, 12))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate train-test-split ratio\n",
        "train.shape[0]/(train.shape[0]+test.shape[0]), test.shape[0]/(train.shape[0]+test.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntxJXUVmcekl",
        "outputId": "21adffc0-bffa-4cc8-d20b-a616770d0643"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6258919469928644, 0.37410805300713557)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# take a look at the features (i.e. independent variables) in the dataset\n",
        "train.columns, test.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RaaKt-X2cf1U",
        "outputId": "79067071-e9ef-4da1-c624-b99401fc0943"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
              "        'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
              "        'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
              "       dtype='object'),\n",
              " Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
              "        'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
              "        'Loan_Amount_Term', 'Credit_History', 'Property_Area'],\n",
              "       dtype='object'))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show the data types for each column of the train set\n",
        "train.dtypes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rXAZq5brcil1",
        "outputId": "2ba284c1-c3e2-4f9d-d68c-9be7e0727b53"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               object\n",
              "Gender                object\n",
              "Married               object\n",
              "Dependents            object\n",
              "Education             object\n",
              "Self_Employed         object\n",
              "ApplicantIncome        int64\n",
              "CoapplicantIncome    float64\n",
              "LoanAmount           float64\n",
              "Loan_Amount_Term     float64\n",
              "Credit_History       float64\n",
              "Property_Area         object\n",
              "Loan_Status           object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# concise summary of the dataset, info about index dtype, column dtypes, non-null values and memory usage\n",
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eppdn8JfeVxI",
        "outputId": "f7daed0f-703f-4f06-de75-480d2db850f8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            614 non-null    object \n",
            " 1   Gender             601 non-null    object \n",
            " 2   Married            611 non-null    object \n",
            " 3   Dependents         599 non-null    object \n",
            " 4   Education          614 non-null    object \n",
            " 5   Self_Employed      582 non-null    object \n",
            " 6   ApplicantIncome    614 non-null    int64  \n",
            " 7   CoapplicantIncome  614 non-null    float64\n",
            " 8   LoanAmount         592 non-null    float64\n",
            " 9   Loan_Amount_Term   600 non-null    float64\n",
            " 10  Credit_History     564 non-null    float64\n",
            " 11  Property_Area      614 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 62.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# frequency table of a variable will give us the count of each category in that variable\n",
        "train['Loan_Status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6L9uCgrneXia",
        "outputId": "b259c04d-6c17-4aad-d9a7-c72122612126"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_Status\n",
              "Y    422\n",
              "N    192\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# percentage distribution can be calculated by setting the normalize=True to show proportions instead of number\n",
        "train['Loan_Status'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MD6ovNaqed9T",
        "outputId": "84231c60-50d1-4a1c-d013-d546f2254367"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_Status\n",
              "Y    0.687296\n",
              "N    0.312704\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bar plot to visualize the frequency\n",
        "train['Loan_Status'].value_counts().plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "EUDmViTyejAx",
        "outputId": "e3e7d76c-515b-4b07-a9b0-88152a7e2248"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Loan_Status'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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o6NGj13wer9crj8fjtwAAgM4roCso39Xa2qo5c+bogQce0MCBA631P/3pT9W7d28lJyfr8OHDWrhwoWpqarRt2zZJktvt9osTSdZjt9t9zecqLi7WsmXL2jpVAADQwbQ5UPLz83XkyBH94Q9/8Fs/Y8YM6+tBgwYpKSlJY8aM0YkTJ3TPPfe06bkKCwtVUFBgPfZ4PEpJSWnbxAEAgPHa9BLPrFmzVFpaqg8++EA9e/b83rEjR46UJB0/flyS5HQ6VV9f7zfmyuPr3bdis9lkt9v9FgAA0HkFFCg+n0+zZs3S9u3b9f7776tv37433KeqqkqSlJSUJElyuVyqrq5WQ0ODNaasrEx2u11paWmBTAcAAHRSAb3Ek5+fr5KSEr3zzjuKiYmx7hlxOByKjIzUiRMnVFJSovHjxys+Pl6HDx/W3Llz9dBDDyk9PV2SNHbsWKWlpWnKlClauXKl3G63ioqKlJ+fL5vN1v5nCAAAOpyArqCsX79ejY2NGj16tJKSkqzlzTfflCRFRERoz549Gjt2rPr3769f/vKXysnJ0Y4dO6xjhIWFqbS0VGFhYXK5XPqHf/gHTZ061e9zUwAAwJ0toCsoPp/ve7enpKSovLz8hsfp3bu3/uM//iOQpwYAAHcQfhcPAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIwTUKAUFxdrxIgRiomJUUJCgh577DHV1NT4jbl06ZLy8/MVHx+v6Oho5eTkqL6+3m9MbW2tsrOzFRUVpYSEBM2fP1/Nzc03fzYAAKBTCChQysvLlZ+frwMHDqisrEyXL1/W2LFjdfHiRWvM3LlztWPHDr311lsqLy9XXV2dJk2aZG1vaWlRdna2mpqatH//fm3evFmbNm3S4sWL2++sAABAhxbi8/l8bd35m2++UUJCgsrLy/XQQw+psbFRPXr0UElJiR5//HFJ0ueff67U1FRVVFRo1KhR2rlzpx599FHV1dUpMTFRkrRhwwYtXLhQ33zzjSIiIm74vB6PRw6HQ42NjbLb7W2dfofUZ9G7wZ4CbqMvV2QHewoA0G4C+fl9U/egNDY2SpLi4uIkSZWVlbp8+bIyMjKsMf3791evXr1UUVEhSaqoqNCgQYOsOJGkzMxMeTweHT169GamAwAAOonwtu7Y2tqqOXPm6IEHHtDAgQMlSW63WxEREYqNjfUbm5iYKLfbbY35bpxc2X5l27V4vV55vV7rscfjaeu0AQBAB9DmKyj5+fk6cuSItm7d2p7zuabi4mI5HA5rSUlJueXPCQAAgqdNgTJr1iyVlpbqgw8+UM+ePa31TqdTTU1NOnfunN/4+vp6OZ1Oa8xfvqvnyuMrY/5SYWGhGhsbreXUqVNtmTYAAOggAgoUn8+nWbNmafv27Xr//ffVt29fv+3Dhg1Tly5dtHfvXmtdTU2Namtr5XK5JEkul0vV1dVqaGiwxpSVlclutystLe2az2uz2WS32/0WAADQeQV0D0p+fr5KSkr0zjvvKCYmxrpnxOFwKDIyUg6HQ9OmTVNBQYHi4uJkt9s1e/ZsuVwujRo1SpI0duxYpaWlacqUKVq5cqXcbreKioqUn58vm83W/mcIAAA6nIACZf369ZKk0aNH+63fuHGjnnrqKUnSqlWrFBoaqpycHHm9XmVmZmrdunXW2LCwMJWWlmrmzJlyuVzq1q2b8vLytHz58ps7EwAA0Gnc1OegBAufg4I7BZ+DAqAzCeTnd5vfZgwAaF/8B+TOwn9Avh+/LBAAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxgk4UD788ENNmDBBycnJCgkJ0dtvv+23/amnnlJISIjfMm7cOL8xZ8+eVW5urux2u2JjYzVt2jRduHDhpk4EAAB0HgEHysWLFzV48GCtXbv2umPGjRun06dPW8u//uu/+m3Pzc3V0aNHVVZWptLSUn344YeaMWNG4LMHAACdUnigO2RlZSkrK+t7x9hsNjmdzmtu++yzz7Rr1y4dOnRIw4cPlyS99NJLGj9+vF588UUlJycHOiUAANDJ3JJ7UPbt26eEhATdf//9mjlzps6cOWNtq6ioUGxsrBUnkpSRkaHQ0FAdPHjwmsfzer3yeDx+CwAA6LzaPVDGjRun119/XXv37tWvf/1rlZeXKysrSy0tLZIkt9uthIQEv33Cw8MVFxcnt9t9zWMWFxfL4XBYS0pKSntPGwAAGCTgl3hu5Mknn7S+HjRokNLT03XPPfdo3759GjNmTJuOWVhYqIKCAuuxx+MhUgAA6MRu+duM7777bnXv3l3Hjx+XJDmdTjU0NPiNaW5u1tmzZ69734rNZpPdbvdbAABA53XLA+Xrr7/WmTNnlJSUJElyuVw6d+6cKisrrTHvv/++WltbNXLkyFs9HQAA0AEE/BLPhQsXrKshknTy5ElVVVUpLi5OcXFxWrZsmXJycuR0OnXixAktWLBA9957rzIzMyVJqampGjdunKZPn64NGzbo8uXLmjVrlp588knewQMAACS14QrKxx9/rKFDh2ro0KGSpIKCAg0dOlSLFy9WWFiYDh8+rL/5m7/Rfffdp2nTpmnYsGH6z//8T9lsNusYW7ZsUf/+/TVmzBiNHz9eDz74oF555ZX2OysAANChBXwFZfTo0fL5fNfdvnv37hseIy4uTiUlJYE+NQAAuEPwu3gAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGCfgQPnwww81YcIEJScnKyQkRG+//bbfdp/Pp8WLFyspKUmRkZHKyMjQF1984Tfm7Nmzys3Nld1uV2xsrKZNm6YLFy7c1IkAAIDOI+BAuXjxogYPHqy1a9dec/vKlSu1Zs0abdiwQQcPHlS3bt2UmZmpS5cuWWNyc3N19OhRlZWVqbS0VB9++KFmzJjR9rMAAACdSnigO2RlZSkrK+ua23w+n1avXq2ioiJNnDhRkvT6668rMTFRb7/9tp588kl99tln2rVrlw4dOqThw4dLkl566SWNHz9eL774opKTk2/idAAAQGfQrvegnDx5Um63WxkZGdY6h8OhkSNHqqKiQpJUUVGh2NhYK04kKSMjQ6GhoTp48OA1j+v1euXxePwWAADQebVroLjdbklSYmKi3/rExERrm9vtVkJCgt/28PBwxcXFWWP+UnFxsRwOh7WkpKS057QBAIBhOsS7eAoLC9XY2Ggtp06dCvaUAADALdSugeJ0OiVJ9fX1fuvr6+utbU6nUw0NDX7bm5ubdfbsWWvMX7LZbLLb7X4LAADovNo1UPr27Sun06m9e/da6zwejw4ePCiXyyVJcrlcOnfunCorK60x77//vlpbWzVy5Mj2nA4AAOigAn4Xz4ULF3T8+HHr8cmTJ1VVVaW4uDj16tVLc+bM0a9+9Sv169dPffv21bPPPqvk5GQ99thjkqTU1FSNGzdO06dP14YNG3T58mXNmjVLTz75JO/gAQAAktoQKB9//LF+8pOfWI8LCgokSXl5edq0aZMWLFigixcvasaMGTp37pwefPBB7dq1S127drX22bJli2bNmqUxY8YoNDRUOTk5WrNmTTucDgAA6AxCfD6fL9iTCJTH45HD4VBjY+Mddz9Kn0XvBnsKuI2+XJEd7CngNuL7+85yJ35/B/Lzu0O8iwcAANxZCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYJx2D5SlS5cqJCTEb+nfv7+1/dKlS8rPz1d8fLyio6OVk5Oj+vr69p4GAADowG7JFZQBAwbo9OnT1vKHP/zB2jZ37lzt2LFDb731lsrLy1VXV6dJkybdimkAAIAOKvyWHDQ8XE6n86r1jY2N+t3vfqeSkhI9/PDDkqSNGzcqNTVVBw4c0KhRo27FdAAAQAdzS66gfPHFF0pOTtbdd9+t3Nxc1dbWSpIqKyt1+fJlZWRkWGP79++vXr16qaKi4lZMBQAAdEDtfgVl5MiR2rRpk+6//36dPn1ay5Yt049//GMdOXJEbrdbERERio2N9dsnMTFRbrf7usf0er3yer3WY4/H097TBgAABmn3QMnKyrK+Tk9P18iRI9W7d2/927/9myIjI9t0zOLiYi1btqy9pggAAAx3y99mHBsbq/vuu0/Hjx+X0+lUU1OTzp075zemvr7+mvesXFFYWKjGxkZrOXXq1C2eNQAACKZbHigXLlzQiRMnlJSUpGHDhqlLly7au3evtb2mpka1tbVyuVzXPYbNZpPdbvdbAABA59XuL/HMmzdPEyZMUO/evVVXV6clS5YoLCxMkydPlsPh0LRp01RQUKC4uDjZ7XbNnj1bLpeLd/AAAABLuwfK119/rcmTJ+vMmTPq0aOHHnzwQR04cEA9evSQJK1atUqhoaHKycmR1+tVZmam1q1b197TAAAAHVi7B8rWrVu/d3vXrl21du1arV27tr2fGgAAdBL8Lh4AAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxglqoKxdu1Z9+vRR165dNXLkSH300UfBnA4AADBE0ALlzTffVEFBgZYsWaJPPvlEgwcPVmZmphoaGoI1JQAAYIigBcpvfvMbTZ8+XT/72c+UlpamDRs2KCoqSq+99lqwpgQAAAwRHownbWpqUmVlpQoLC611oaGhysjIUEVFxVXjvV6vvF6v9bixsVGS5PF4bv1kDdPq/VOwp4Db6E78O34n4/v7znInfn9fOWefz3fDsUEJlP/93/9VS0uLEhMT/dYnJibq888/v2p8cXGxli1bdtX6lJSUWzZHwASO1cGeAYBb5U7+/j5//rwcDsf3jglKoASqsLBQBQUF1uPW1ladPXtW8fHxCgkJCeLMcDt4PB6lpKTo1KlTstvtwZ4OgHbE9/edxefz6fz580pOTr7h2KAESvfu3RUWFqb6+nq/9fX19XI6nVeNt9lsstlsfutiY2Nv5RRhILvdzj9gQCfF9/ed40ZXTq4Iyk2yERERGjZsmPbu3Wuta21t1d69e+VyuYIxJQAAYJCgvcRTUFCgvLw8DR8+XD/60Y+0evVqXbx4UT/72c+CNSUAAGCIoAXKE088oW+++UaLFy+W2+3WkCFDtGvXrqtunAVsNpuWLFly1ct8ADo+vr9xPSG+H/JeHwAAgNuI38UDAACMQ6AAAADjECgAAMA4BAqMUVdXF+wpAAAMQaDAGAMGDFBJSUmwpwEAMACBAmM8//zz+sd//Ef93d/9nc6ePRvs6QAAgohAgTF+/vOf6/Dhwzpz5ozS0tK0Y8eOYE8JABAkfA4KjPTyyy9r7ty5Sk1NVXi4/+cJfvLJJ0GaFYCbERoaesNf8BoSEqLm5ubbNCOYrEP8NmPcWb766itt27ZNd911lyZOnHhVoADomLZv337dbRUVFVqzZo1aW1tv44xgMv7lh1FeffVV/fKXv1RGRoaOHj2qHj16BHtKANrJxIkTr1pXU1OjRYsWaceOHcrNzdXy5cuDMDOYiECBMcaNG6ePPvpIL7/8sqZOnRrs6QC4herq6rRkyRJt3rxZmZmZqqqq0sCBA4M9LRiEQIExWlpadPjwYfXs2TPYUwFwizQ2NuqFF17QSy+9pCFDhmjv3r368Y9/HOxpwUDcJAsAuC1WrlypX//613I6nXrhhReu+ZIPcAWBAgC4LUJDQxUZGamMjAyFhYVdd9y2bdtu46xgKl7iAQDcFlOnTr3h24yBK7iCAgAAjMMnyQIAAOMQKAAAwDgECgAAMA6BAgAAjEOgAHeYp556So899liwp3Fdr776qgYPHqzo6GjFxsZq6NChKi4utra3df5Lly7VkCFD2m+iAG4p3mYMwBivvfaa5syZozVr1uiv//qv5fV6dfjwYR05ciTYUwNwm3EFBYClvLxcP/rRj2Sz2ZSUlKRFixapubnZ2r5r1y49+OCDio2NVXx8vB599FGdOHHC2v7ll18qJCRE27Zt009+8hNFRUVp8ODBqqio+EHP/+///u/6+7//e02bNk333nuvBgwYoMmTJ+v555+X9OerIJs3b9Y777yjkJAQhYSEaN++fZKkhQsX6r777lNUVJTuvvtuPfvss7p8+bIkadOmTVq2bJn++7//29pv06ZN1nyrqqqsOZw7d87vuN9++61yc3PVo0cPRUZGql+/ftq4ceNN/CkD+CG4ggJAkvTHP/5R48eP11NPPaXXX39dn3/+uaZPn66uXbtq6dKlkqSLFy+qoKBA6enpunDhghYvXqy//du/VVVVlUJD////d5555hm9+OKL6tevn5555hlNnjxZx48fV3j49/+T43Q6VV5erq+++kq9e/e+avu8efP02WefyePxWJEQFxcnSYqJidGmTZuUnJys6upqTZ8+XTExMVqwYIGeeOIJHTlyRLt27dKePXskSQ6HQ/X19Tf8c3n22Wd17Ngx7dy5U927d9fx48f1f//3fz/ozxRA2xEoACRJ69atU0pKil5++WWFhISof//+qqur08KFC7V48WKFhoYqJyfHb5/XXntNPXr00LFjx/x+E+28efOUnZ0tSVq2bJkGDBig48ePq3///t87hyVLlmjSpEnq06eP7rvvPrlcLo0fP16PP/64QkNDFR0drcjISHm9XjmdTr99i4qKrK/79OmjefPmaevWrVqwYIEiIyMVHR2t8PDwq/a7kdraWg0dOlTDhw+3jg3g1uMlHgCSpM8++0wul8vvo8gfeOABXbhwQV9//bUk6YsvvtDkyZN19913y263Wz+sa2tr/Y6Vnp5ufZ2UlCRJamhouOEckpKSVFFRoerqav3iF79Qc3Oz8vLyNG7cOLW2tn7vvm+++aYeeOABOZ1ORUdHq6io6Kp5tcXMmTO1detWDRkyRAsWLND+/ftv+pgAboxAAfCDTZgwQWfPntWrr76qgwcP6uDBg5KkpqYmv3FdunSxvr4SPDcKjO8aOHCgfv7zn+uNN95QWVmZysrKVF5eft3xFRUVys3N1fjx41VaWqpPP/1UzzzzzFXz+ktXXpb67m/8uHLfyhVZWVn66quvNHfuXNXV1WnMmDGaN2/eDz4XAG1DoACQJKWmpqqiosLvh/V//dd/KSYmRj179tSZM2dUU1OjoqIijRkzRqmpqfr2229v+bzS0tIk/fn+F0mKiIhQS0uL35j9+/erd+/eeuaZZzR8+HD169dPX331ld+Ya+3Xo0cPSdLp06etdd+9Yfa74/Ly8vTGG29o9erVeuWVV276vAB8P+5BAe5AjY2NV/0gnjFjhlavXq3Zs2dr1qxZqqmp0ZIlS1RQUKDQ0FDdddddio+P1yuvvKKkpCTV1tZq0aJF7TqvmTNnKjk5WQ8//LB69uyp06dP61e/+pV69Oghl8sl6c/3gOzevVs1NTWKj4+Xw+FQv379VFtbq61bt2rEiBF69913tX37dr9j9+nTRydPnlRVVZV69uypmJgYRUZGatSoUVqxYoX69u2rhoYGv3tZJGnx4sUaNmyYBgwYIK/Xq9LSUqWmprbreQO4Bh+AO0peXp5P0lXLtGnTfPv27fONGDHCFxER4XM6nb6FCxf6Ll++bO1bVlbmS01N9dlsNl96erpv3759Pkm+7du3+3w+n+/kyZM+Sb5PP/3U2ufbb7/1SfJ98MEHN5zb73//e9/48eN9SUlJvoiICF9ycrIvJyfHd/jwYWtMQ0OD75FHHvFFR0f7HXf+/Pm++Ph4X3R0tO+JJ57wrVq1yudwOKz9Ll265MvJyfHFxsb6JPk2btzo8/l8vmPHjvlcLpcvMjLSN2TIEN97773nd9znnnvOl5qa6ouMjPTFxcX5Jk6c6Puf//mftvzRAwhAiM/3neu5AAAABuAeFAAAYBwCBcBtk5WVpejo6GsuL7zwQrCnB8AgvMQD4Lb54x//eN1PYY2Li7M+FRYACBQAAGAcXuIBAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGOf/AZ+C1FO4YEyQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.figure(1)\n",
        "plt.subplot(231)\n",
        "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Gender')\n",
        "\n",
        "plt.subplot(232)\n",
        "train['Married'].value_counts(normalize=True).plot.bar(title= 'Married')\n",
        "\n",
        "plt.subplot(233)\n",
        "train['Self_Employed'].value_counts(normalize=True).plot.bar(title= 'Self_Employed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "c_ohUCuWekq0",
        "outputId": "b5e22c0c-13f2-4b66-eee1-dfd9f6ce57a6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Self_Employed'}, xlabel='Self_Employed'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing remaining categorical features\n",
        "# plt.figure(1)\n",
        "plt.subplot(121)\n",
        "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(12,4), title= 'Dependents')\n",
        "\n",
        "plt.subplot(122)\n",
        "train['Property_Area'].value_counts(normalize=True).plot.bar(title= 'Property_Area')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "YG1NOwtAeqos",
        "outputId": "21df3ae1-ab34-40f8-81f7-edabe9419036"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing ApplicantIncome\n",
        "# plt.figure(1)\n",
        "plt.subplot(121)\n",
        "sns.distplot(train['ApplicantIncome']);\n",
        "\n",
        "plt.subplot(122)\n",
        "train['ApplicantIncome'].plot.box(figsize=(16,5))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "K4YfJslEesBL",
        "outputId": "e3fb10b2-bf8d-48c7-8562-9a2478076340"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-a033195d8add>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(train['ApplicantIncome']);\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see a fairly normal distribution (albeit still slightly right-skewed) for LoanAmount but there are lot of outliers in this variable"
      ],
      "metadata": {
        "id": "wrF9zfZlfCqn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(train.groupby('Loan_Status')['ApplicantIncome'].mean())\n",
        "\n",
        "train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "8Fdnnmste1Yt",
        "outputId": "e99b41f4-60ef-4005-a130-ff40d86d86a0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loan_Status\n",
            "N    5446.078125\n",
            "Y    5384.068720\n",
            "Name: ApplicantIncome, dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Loan_Status'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.crosstab(train['Credit_History'],train['Loan_Status']))\n",
        "\n",
        "Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status'])\n",
        "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
        "plt.xlabel('Credit_History')\n",
        "p = plt.ylabel('Percentage')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "nuORfy9RfFIo",
        "outputId": "7c6c8c75-e515-4255-8d61-d3d78b74e211"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loan_Status      N    Y\n",
            "Credit_History         \n",
            "0.0             82    7\n",
            "1.0             97  378\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}