Loan_prediction_svm.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data processing\n",
        "loan=pd.read_csv('/content/train_u6lujuX_CVtuZ9i (1).csv')"
      ],
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        "id": "9dIEV6Lo_rYA"
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        "loan.head()"
      ],
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              "    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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              "      <td>LP001008</td>\n",
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              "      <td>Graduate</td>\n",
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            "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",
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              "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",
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              "      <th>mean</th>\n",
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        "outputId": "eac5c3a8-ec2f-4510-bee8-f1f205b511bd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Wy73ulkB-ob",
        "outputId": "1acf6f3e-9739-4c20-c2ef-91c7988076f6"
      },
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan=loan.dropna()"
      ],
      "metadata": {
        "id": "AIrJymOhHIyR"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loan.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDkgK68gH4Is",
        "outputId": "22cdada0-6854-46e4-eab8-eaa9020df272"
      },
      "execution_count": 8,
      "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",
              "Loan_Status          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#label encodind\n",
        "loan.replace({'Loan_Status':{'N':0,'Y':1}},inplace=True)\n",
        "loan.replace({'Married':{'No':0,'Yes':1},'Self_Employed':{'No':0,'Yes':1}},inplace=True)\n",
        "loan.replace({'Gender':{'Male':0,'Female':1}},inplace=True)\n",
        "loan.replace({'Education':{'Not Graduate':0,'Graduate':1},'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2}},inplace=True)\n"
      ],
      "metadata": {
        "id": "HnYtyGr6H83U"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loan.head(30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1oMY-Fe1Ikm0",
        "outputId": "3d2dd054-f99d-4f6c-a2d3-511d4a47481b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
              "1   LP001003       0        1          1          1              0   \n",
              "2   LP001005       0        1          0          1              1   \n",
              "3   LP001006       0        1          0          0              0   \n",
              "4   LP001008       0        0          0          1              0   \n",
              "5   LP001011       0        1          2          1              1   \n",
              "6   LP001013       0        1          0          0              0   \n",
              "7   LP001014       0        1          4          1              0   \n",
              "8   LP001018       0        1          2          1              0   \n",
              "9   LP001020       0        1          1          1              0   \n",
              "10  LP001024       0        1          2          1              0   \n",
              "12  LP001028       0        1          2          1              0   \n",
              "13  LP001029       0        0          0          1              0   \n",
              "14  LP001030       0        1          2          1              0   \n",
              "15  LP001032       0        0          0          1              0   \n",
              "17  LP001036       1        0          0          1              0   \n",
              "18  LP001038       0        1          0          0              0   \n",
              "20  LP001043       0        1          0          0              0   \n",
              "21  LP001046       0        1          1          1              0   \n",
              "22  LP001047       0        1          0          0              0   \n",
              "25  LP001066       0        1          0          1              1   \n",
              "26  LP001068       0        1          0          1              0   \n",
              "27  LP001073       0        1          2          0              0   \n",
              "28  LP001086       0        0          0          0              0   \n",
              "31  LP001095       0        0          0          1              0   \n",
              "32  LP001097       0        0          1          1              1   \n",
              "33  LP001098       0        1          0          1              0   \n",
              "34  LP001100       0        0          4          1              0   \n",
              "37  LP001112       1        1          0          1              0   \n",
              "38  LP001114       0        0          0          1              0   \n",
              "39  LP001116       0        0          0          0              0   \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",
              "6              2333             1516.0        95.0             360.0   \n",
              "7              3036             2504.0       158.0             360.0   \n",
              "8              4006             1526.0       168.0             360.0   \n",
              "9             12841            10968.0       349.0             360.0   \n",
              "10             3200              700.0        70.0             360.0   \n",
              "12             3073             8106.0       200.0             360.0   \n",
              "13             1853             2840.0       114.0             360.0   \n",
              "14             1299             1086.0        17.0             120.0   \n",
              "15             4950                0.0       125.0             360.0   \n",
              "17             3510                0.0        76.0             360.0   \n",
              "18             4887                0.0       133.0             360.0   \n",
              "20             7660                0.0       104.0             360.0   \n",
              "21             5955             5625.0       315.0             360.0   \n",
              "22             2600             1911.0       116.0             360.0   \n",
              "25             9560                0.0       191.0             360.0   \n",
              "26             2799             2253.0       122.0             360.0   \n",
              "27             4226             1040.0       110.0             360.0   \n",
              "28             1442                0.0        35.0             360.0   \n",
              "31             3167                0.0        74.0             360.0   \n",
              "32             4692                0.0       106.0             360.0   \n",
              "33             3500             1667.0       114.0             360.0   \n",
              "34            12500             3000.0       320.0             360.0   \n",
              "37             3667             1459.0       144.0             360.0   \n",
              "38             4166             7210.0       184.0             360.0   \n",
              "39             3748             1668.0       110.0             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",
              "4              1.0              2            1  \n",
              "5              1.0              2            1  \n",
              "6              1.0              2            1  \n",
              "7              0.0              1            0  \n",
              "8              1.0              2            1  \n",
              "9              1.0              1            0  \n",
              "10             1.0              2            1  \n",
              "12             1.0              2            1  \n",
              "13             1.0              0            0  \n",
              "14             1.0              2            1  \n",
              "15             1.0              2            1  \n",
              "17             0.0              2            0  \n",
              "18             1.0              0            0  \n",
              "20             0.0              2            0  \n",
              "21             1.0              2            1  \n",
              "22             0.0              1            0  \n",
              "25             1.0              1            1  \n",
              "26             1.0              1            1  \n",
              "27             1.0              2            1  \n",
              "28             1.0              2            0  \n",
              "31             1.0              2            0  \n",
              "32             1.0              0            0  \n",
              "33             1.0              1            1  \n",
              "34             1.0              0            0  \n",
              "37             1.0              1            1  \n",
              "38             1.0              2            1  \n",
              "39             1.0              1            1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4e9a215a-0140-427c-9703-fc0949543133\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>0</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>0</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",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>0</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LP001011</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LP001013</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2333</td>\n",
              "      <td>1516.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LP001014</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3036</td>\n",
              "      <td>2504.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LP001018</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4006</td>\n",
              "      <td>1526.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LP001020</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12841</td>\n",
              "      <td>10968.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LP001024</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3200</td>\n",
              "      <td>700.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LP001028</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3073</td>\n",
              "      <td>8106.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>LP001029</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1853</td>\n",
              "      <td>2840.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>LP001030</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1299</td>\n",
              "      <td>1086.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>LP001032</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4950</td>\n",
              "      <td>0.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LP001036</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3510</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LP001038</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4887</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>LP001043</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7660</td>\n",
              "      <td>0.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>LP001046</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5955</td>\n",
              "      <td>5625.0</td>\n",
              "      <td>315.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>LP001047</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2600</td>\n",
              "      <td>1911.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>LP001066</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>LP001068</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2799</td>\n",
              "      <td>2253.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>LP001073</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4226</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>LP001086</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1442</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>LP001095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3167</td>\n",
              "      <td>0.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>LP001097</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4692</td>\n",
              "      <td>0.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>LP001098</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3500</td>\n",
              "      <td>1667.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>LP001100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12500</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>LP001112</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3667</td>\n",
              "      <td>1459.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>LP001114</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4166</td>\n",
              "      <td>7210.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>LP001116</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3748</td>\n",
              "      <td>1668.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e9a215a-0140-427c-9703-fc0949543133')\"\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",
              "       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-4e9a215a-0140-427c-9703-fc0949543133 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-4e9a215a-0140-427c-9703-fc0949543133');\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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan['Dependents']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-bBCJTthI1wO",
        "outputId": "d6cdcc06-0089-4a6b-81bd-31efd61d52e9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       1\n",
              "2       0\n",
              "3       0\n",
              "4       0\n",
              "5       2\n",
              "       ..\n",
              "609     0\n",
              "610    3+\n",
              "611     1\n",
              "612     2\n",
              "613     0\n",
              "Name: Dependents, Length: 480, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan=loan.replace(to_replace='3+',value=4)"
      ],
      "metadata": {
        "id": "72lXaw7wJgva"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loan.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "FLoD2aP9KD5Q",
        "outputId": "58ca44d5-bbdb-4fbb-ed6b-8171381b8b5f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\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",
              "5  LP001011   Male     Yes          2      Graduate           Yes   \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         Rural            0  \n",
              "2             1.0         Urban            1  \n",
              "3             1.0         Urban            1  \n",
              "4             1.0         Urban            1  \n",
              "5             1.0         Urban            1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b4571cf7-0ceb-4ce4-939b-05953009e4da\">\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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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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",
              "      <td>0</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",
              "      <td>1</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",
              "      <td>1</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LP001011</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4571cf7-0ceb-4ce4-939b-05953009e4da')\"\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b4571cf7-0ceb-4ce4-939b-05953009e4da');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan['Dependents'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TVStzkDkKK3V",
        "outputId": "1a35498c-61a9-45e3-c25b-a5c5c5e69af0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    274\n",
              "2     85\n",
              "1     80\n",
              "4     41\n",
              "Name: Dependents, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#education vs loan status\n",
        "sns.countplot(x='Education',hue='Loan_Status',data=loan)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "44wMCR-9KSxE",
        "outputId": "3ae90070-9dad-40c5-c92d-2384b575b19f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f399d276450>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Married',hue='Loan_Status',data=loan)"
      ],
      "metadata": {
        "id": "HHr3TKVYK_iT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "2b6caab6-926b-4a71-ab56-8711f5ff986d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f399be95e90>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan=loan.drop('Loan_ID',axis=1)"
      ],
      "metadata": {
        "id": "PswVkR0yD3Jm"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loan.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "pHf5C6tzGXst",
        "outputId": "dfa97cfd-4e12-41d1-a165-d2666b9a78bf"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "1       0        1          1          1              0             4583   \n",
              "2       0        1          0          1              1             3000   \n",
              "3       0        1          0          0              0             2583   \n",
              "4       0        0          0          1              0             6000   \n",
              "5       0        1          2          1              1             5417   \n",
              "\n",
              "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "1             1508.0       128.0             360.0             1.0   \n",
              "2                0.0        66.0             360.0             1.0   \n",
              "3             2358.0       120.0             360.0             1.0   \n",
              "4                0.0       141.0             360.0             1.0   \n",
              "5             4196.0       267.0             360.0             1.0   \n",
              "\n",
              "   Property_Area  Loan_Status  \n",
              "1              0            0  \n",
              "2              2            1  \n",
              "3              2            1  \n",
              "4              2            1  \n",
              "5              2            1  "
            ],
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              "\n",
              "  <div id=\"df-be06205e-bd59-44aa-9cb5-f7236001171b\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th>Gender</th>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
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              "      <td>1.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=loan.drop('Loan_Status',axis=1)#axis=1 removes column,axis=0 removes row\n",
        "y=loan['Loan_Status']"
      ],
      "metadata": {
        "id": "_4H_MQvVGbxf"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "x,y"
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        "colab": {
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        "id": "gSXxeQyYGtEs",
        "outputId": "d5f6025e-3272-4590-b9a1-8c0b2ea53eb0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              " 1         0        1          1          1              0             4583   \n",
              " 2         0        1          0          1              1             3000   \n",
              " 3         0        1          0          0              0             2583   \n",
              " 4         0        0          0          1              0             6000   \n",
              " 5         0        1          2          1              1             5417   \n",
              " ..      ...      ...        ...        ...            ...              ...   \n",
              " 609       1        0          0          1              0             2900   \n",
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              " 612       0        1          2          1              0             7583   \n",
              " 613       1        0          0          1              1             4583   \n",
              " \n",
              "      CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              " 1               1508.0       128.0             360.0             1.0   \n",
              " 2                  0.0        66.0             360.0             1.0   \n",
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              " ..                 ...         ...               ...             ...   \n",
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              " 611              240.0       253.0             360.0             1.0   \n",
              " 612                0.0       187.0             360.0             1.0   \n",
              " 613                0.0       133.0             360.0             0.0   \n",
              " \n",
              "      Property_Area  \n",
              " 1                0  \n",
              " 2                2  \n",
              " 3                2  \n",
              " 4                2  \n",
              " 5                2  \n",
              " ..             ...  \n",
              " 609              0  \n",
              " 610              0  \n",
              " 611              2  \n",
              " 612              2  \n",
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              " \n",
              " [480 rows x 11 columns], 1      0\n",
              " 2      1\n",
              " 3      1\n",
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              "       ..\n",
              " 609    1\n",
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              " 611    1\n",
              " 612    1\n",
              " 613    0\n",
              " Name: Loan_Status, Length: 480, dtype: int64)"
            ]
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          "metadata": {},
          "execution_count": 35
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    {
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        "y"
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rNqMCnmGujU",
        "outputId": "82ba4037-33d5-4551-e870-b03d16241b9e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1      0\n",
              "2      1\n",
              "3      1\n",
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              "      ..\n",
              "609    1\n",
              "610    1\n",
              "611    1\n",
              "612    1\n",
              "613    0\n",
              "Name: Loan_Status, Length: 480, dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 36
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    {
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      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,stratify=y,random_state=2,train_size=0.78)"
      ],
      "metadata": {
        "id": "8rrcxW8vGyEq"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
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        "x_train\n"
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      "execution_count": 111,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "312       1        0          0          1              0             2507   \n",
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              "..      ...      ...        ...        ...            ...              ...   \n",
              "459       0        1          0          1              0             8334   \n",
              "194       0        0          0          1              0             4191   \n",
              "76        0        0          0          1              0             3750   \n",
              "286       1        0          0          1              1             2600   \n",
              "144       0        1          2          1              0            11757   \n",
              "\n",
              "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "312                0.0        56.0             360.0             1.0   \n",
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              "..                 ...         ...               ...             ...   \n",
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              "286             1717.0        99.0             300.0             1.0   \n",
              "144                0.0       187.0             180.0             1.0   \n",
              "\n",
              "     Property_Area  \n",
              "312              0  \n",
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              "[374 rows x 11 columns]"
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 111
        }
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      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2KT_WeHHKkt",
        "outputId": "184e9754-8db0-4492-934a-4f694651334e"
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      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(106, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
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    {
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      "source": [
        "classifier=svm.SVC(kernel='linear')"
      ],
      "metadata": {
        "id": "-rP5AWvjHM1W"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
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        "outputId": "c89a1f77-8218-490f-e104-95f8d6831588"
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      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "x_train_pred=classifier.predict(x_train)"
      ],
      "metadata": {
        "id": "UM4w3tc8H03h"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data_acc=accuracy_score(x_train_pred,y_train)"
      ],
      "metadata": {
        "id": "x2hM3xrKIZFm"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data_acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjG-mGjJIcvc",
        "outputId": "534a45eb-0173-4e53-b5c4-290afac469ed"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7914438502673797"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_pred=classifier.predict(x_test)"
      ],
      "metadata": {
        "id": "L_zV_vOMJCp-"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_acc=accuracy_score(x_test_pred,y_test)"
      ],
      "metadata": {
        "id": "2BfYprkTLKWn"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-xojSA7LVLM",
        "outputId": "6ada540b-fb57-4e81-d3b7-9be7686dc23d"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8207547169811321"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Enter which row\")\n",
        "n=int(input(\"Start :\"))\n",
        "m=int(input('End(will be excluded) :'))\n",
        "s1=x_test[n:m]\n",
        "op=classifier.predict(s1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqEn0-ikLXuj",
        "outputId": "c4a833c8-bef1-4608-ec80-c714c1eab770"
      },
      "execution_count": 127,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter which row\n",
            "Start :32\n",
            "End(will be excluded) :33\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "op"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqCD64DXOS1T",
        "outputId": "b52f7ce6-f8e0-4316-e1ea-aab05d267a42"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if op ==1:\n",
        "  print('loan approved')\n",
        "else:\n",
        "  print(\"loan not approved\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5Ntl-E2OUR8",
        "outputId": "427a08fd-429d-4aac-eb21-894cda77aa9d"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loan approved\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y0FzEmtQOlfU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}