loan.ipynb

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        "<a href=\"https://colab.research.google.com/github/arssite/MachineLearning/blob/main/loan.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 numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ld=pd.read_csv('loan_data.csv')"
      ],
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        "id": "IbnpssGI9B1d"
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      "execution_count": 70,
      "outputs": []
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      "source": [
        "ld.head()"
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      "execution_count": 71,
      "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",
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              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
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              "      <td>LP001005</td>\n",
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              "      <td>66.0</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
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              "      <td>LP001006</td>\n",
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              "      <td>Not Graduate</td>\n",
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              "      <td>2358.0</td>\n",
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              "      <td>360.0</td>\n",
              "      <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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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ld",
              "summary": "{\n  \"name\": \"ld\",\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}"
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          "execution_count": 71
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          "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": [
        "ld.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QaZNXIen9inM",
        "outputId": "a7086638-dbae-4f0a-e1bd-d0521e0be5d5"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ld.describe()"
      ],
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        "colab": {
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          "height": 300
        },
        "id": "lRf9CDtH9jm8",
        "outputId": "2adbe08b-5f6f-47ad-abe5-db035f0c9b92"
      },
      "execution_count": 74,
      "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  \n",
              "count      564.000000  \n",
              "mean         0.842199  \n",
              "std          0.364878  \n",
              "min          0.000000  \n",
              "25%          1.000000  \n",
              "50%          1.000000  \n",
              "75%          1.000000  \n",
              "max          1.000000  "
            ],
            "text/html": [
              "\n",
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              "  <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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ld\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27480.19432327756,\n        \"min\": 150.0,\n        \"max\": 81000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5403.459283387622,\n          3812.5,\n          614.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14332.564053846478,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          614.0,\n          1621.2457980271008,\n          2297.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 256.0091610169859,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          146.41216216216216,\n          128.0,\n          592.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 196.05608042946525,\n        \"min\": 12.0,\n        \"max\": 600.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          600.0,\n          342.0,\n          480.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 199.14146277938593,\n        \"min\": 0.0,\n        \"max\": 564.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8421985815602837,\n          1.0,\n          0.3648783192364048\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ld.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lfd7aQvh-5Hd",
        "outputId": "231efedf-bed3-4bcd-bbd3-d558013c1880"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               13\n",
              "Married               3\n",
              "Dependents           15\n",
              "Education             0\n",
              "Self_Employed        32\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           22\n",
              "Loan_Amount_Term     14\n",
              "Credit_History       50\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ld.replace({'Loan_Status':{'N':0,'Y':1}},inplace=True)"
      ],
      "metadata": {
        "id": "29YIOA8aAB-G"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ld['Dependents'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "peGoQftnAfnP",
        "outputId": "457222a5-719d-40ef-acc3-c6ea645f42f9"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dependents\n",
              "0     345\n",
              "1     102\n",
              "2     101\n",
              "3+     51\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ld=ld.replace(to_replace='3+',value=4)"
      ],
      "metadata": {
        "id": "Qc_UNS5hAlS_"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ld['Dependents'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6ypM_LeBsI-",
        "outputId": "86ff2b2a-89f8-44d0-de52-7b28aaa6d254"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dependents\n",
              "0    345\n",
              "1    102\n",
              "2    101\n",
              "4     51\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Education',hue='Loan_Status',data=ld)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "2ME58FVJBv8l",
        "outputId": "928b44aa-a766-49c5-aba5-348fb6616627"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Education', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "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": [
        "ld = ld.dropna()"
      ],
      "metadata": {
        "id": "mWMfAlxBGKnf"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ld.replace({'Married':{'No':0,'Yes':1},'Gender':{'Male':1,'Female':0},'Self_Employed':{'No':0,'Yes':1},'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2},'Education':{'Graduate':1,'Not Graduate':0}},inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5xTME2gCOgW",
        "outputId": "a6674da3-8f38-44e9-eb4c-9f25a74b5a1e"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-82-03c25bdeb5a8>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  ld.replace({'Married':{'No':0,'Yes':1},'Gender':{'Male':1,'Female':0},'Self_Employed':{'No':0,'Yes':1},'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2},'Education':{'Graduate':1,'Not Graduate':0}},inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ld.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "eQhMa3TYDKI9",
        "outputId": "bf0c5af7-59d0-44c7-9002-343144eddbc7"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\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",
              "5  LP001011       1        1          2          1              1   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\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",
              "5             5417             4196.0       267.0             360.0   \n",
              "\n",
              "   Credit_History  Property_Area  Loan_Status  \n",
              "1             1.0              0            0  \n",
              "2             1.0              2            1  \n",
              "3             1.0              2            1  \n",
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              "      <td>1</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #666;\n",
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              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ld",
              "summary": "{\n  \"name\": \"ld\",\n  \"rows\": 480,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 480,\n        \"samples\": [\n          \"LP001319\",\n          \"LP002716\",\n          \"LP002622\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\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          \"0\",\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5668,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 405,\n        \"samples\": [\n          2484,\n          3717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2617.6922669225105,\n        \"min\": 0.0,\n        \"max\": 33837.0,\n        \"num_unique_values\": 232,\n        \"samples\": [\n          2064.0,\n          2083.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.50816421360777,\n        \"min\": 9.0,\n        \"max\": 600.0,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          192.0,\n          208.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.21240068043208,\n        \"min\": 36.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          36.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3533072691637982,\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\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = ld.drop(columns=['Loan_ID','Loan_Status'],axis=1)\n",
        "y = ld['Loan_Status']"
      ],
      "metadata": {
        "id": "2ZL_S6h39k19"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.1,stratify=y,random_state=1)"
      ],
      "metadata": {
        "id": "_SVcP-uh-BRs"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(ld.shape,\"\\n\",xtrain.shape,\"\\n\",xtest.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-wZxHUQEMOH",
        "outputId": "f7b5815a-7f1b-4034-f472-f19cfa6b14d2"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(480, 13) \n",
            " (432, 11) \n",
            " (48, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ld.shape,\"\\n\",ytrain.shape,\"\\n\",ytest.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXMeg-sQEuEO",
        "outputId": "96437b2b-904d-4477-8e8a-0fed85105b47"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(480, 13) \n",
            " (432,) \n",
            " (48,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=svm.SVC(kernel='linear')"
      ],
      "metadata": {
        "id": "c-nYZeta-GAs"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "OAAhavyY-IIs",
        "outputId": "6035065a-2db9-434e-a33a-68983377fead"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating model accuracy on training data\n",
        "training_prediction = model.predict(xtrain)\n",
        "training_accuracy = accuracy_score(training_prediction,ytrain)"
      ],
      "metadata": {
        "id": "peSWNfQ8-J0d"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on training data is :\", training_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3QfOSBcHIy3",
        "outputId": "f5b0b0a3-d3a0-4609-cac0-9df83ee799c1"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data is : 0.7800925925925926\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating model accuracy on testing data\n",
        "testing_prediction = model.predict(xtest)\n",
        "testing_accuracy = accuracy_score(testing_prediction,ytest)"
      ],
      "metadata": {
        "id": "bcYBQFiiHP_f"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on training data is :\", training_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKczmSexHRzo",
        "outputId": "fd1f3cf9-dcfe-422f-e478-5f0f08b15977"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data is : 0.7800925925925926\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input= [1,\t1,\t0,\t0,\t0,\t2583,\t2358.0,\t120.0,\t360.0,\t1.0,\t2\t\t]\n",
        "np_input = np.asarray(input)\n",
        "np_input_reshaped = np_input.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(np_input_reshaped)\n",
        "if (prediction[0]== 1):\n",
        "  print(\"The person is eligible for loan\")\n",
        "else:\n",
        "  print(\"The person is not eligible for loan\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1SkmNWTAHUK_",
        "outputId": "03760c0c-436f-42d4-c088-6954dda8925b"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The person is eligible for loan\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RB4hmbAgIzy6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}