OEA_DesicionTree.ipynb

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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
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        "data = pd.read_csv('/content/outputfinal (1).csv')\n",
        "data.head()"
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            "text/plain": [
              "    income_annum   loan_amount   loan_term   cibil_score   loan_status\n",
              "0        9600000      29900000          12           778             1\n",
              "1        4100000      12200000           8           417             0\n",
              "2        9100000      29700000          20           506             0\n",
              "3        8200000      30700000           8           467             0\n",
              "4        9800000      24200000          20           382             0"
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4269,\n  \"fields\": [\n    {\n      \"column\": \" income_annum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2806839,\n        \"min\": 200000,\n        \"max\": 9900000,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          6200000,\n          9300000,\n          7600000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" loan_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9043362,\n        \"min\": 300000,\n        \"max\": 39500000,\n        \"num_unique_values\": 378,\n        \"samples\": [\n          25800000,\n          26100000,\n          21700000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" loan_term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 2,\n        \"max\": 20,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          14,\n          8,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" cibil_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 172,\n        \"min\": 300,\n        \"max\": 900,\n        \"num_unique_values\": 601,\n        \"samples\": [\n          859,\n          414,\n          500\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          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.iloc[:, 0:4]\n",
        "Y = data.iloc[:, 4]\n",
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKTFxXPRbKYg",
        "outputId": "3a6c9c39-c5f9-4228-a872-192407a8e69c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       income_annum   loan_amount   loan_term   cibil_score\n",
            "0           9600000      29900000          12           778\n",
            "1           4100000      12200000           8           417\n",
            "2           9100000      29700000          20           506\n",
            "3           8200000      30700000           8           467\n",
            "4           9800000      24200000          20           382\n",
            "...             ...           ...         ...           ...\n",
            "4264        1000000       2300000          12           317\n",
            "4265        3300000      11300000          20           559\n",
            "4266        6500000      23900000          18           457\n",
            "4267        4100000      12800000           8           780\n",
            "4268        9200000      29700000          10           607\n",
            "\n",
            "[4269 rows x 4 columns]\n",
            "0       1\n",
            "1       0\n",
            "2       0\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "4264    0\n",
            "4265    1\n",
            "4266    0\n",
            "4267    1\n",
            "4268    1\n",
            "Name:  loan_status, Length: 4269, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.3, random_state=1)"
      ],
      "metadata": {
        "id": "XW2cRMZLbeZ-"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNibTU-AbnUf",
        "outputId": "9c4581d9-f0f3-483f-ea31-9163c58aea91"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1398    1\n",
            "1519    0\n",
            "4192    1\n",
            "434     1\n",
            "925     0\n",
            "       ..\n",
            "2895    1\n",
            "2763    1\n",
            "905     0\n",
            "3980    1\n",
            "235     1\n",
            "Name:  loan_status, Length: 2988, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "Xtrain = sc_X.fit_transform(Xtrain)\n",
        "Xtest = sc_X.fit_transform(Xtest)\n",
        "print(Xtrain)\n",
        "print(Xtest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iVXeXXPbqAE",
        "outputId": "00f22707-4a50-4e9d-e07c-51d38f9684fd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.55378947  2.41719228  1.57665864  1.67833654]\n",
            " [ 1.02113499  0.72307099  0.88114583 -0.61293642]\n",
            " [-0.64784903 -0.76066922 -0.16212338  0.39313013]\n",
            " ...\n",
            " [ 1.66032036  2.43933766  0.53338943 -0.83392214]\n",
            " [-1.64213738 -1.55790276 -0.50987979  1.31196549]\n",
            " [ 0.66603201  0.93345207 -1.2053926  -0.70598304]]\n",
            "[[ 1.53102472  2.40540596  0.56608215 -1.21845854]\n",
            " [ 1.42320292  0.30748629  0.56608215 -0.78575098]\n",
            " [ 0.63250969  0.32956966 -0.86067876 -1.35115552]\n",
            " ...\n",
            " [-0.05036173 -0.09001428  0.20939192  1.40086454]\n",
            " [-1.48798578 -1.3377244   1.2794626  -0.09341889]\n",
            " [ 1.31538111  1.62144651  1.2794626   1.74703058]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier = classifier.fit(Xtrain, Ytrain)"
      ],
      "metadata": {
        "id": "F3iFCUl3bze4"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Ypred = classifier.predict(Xtest)\n",
        "print(Ypred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaMAnrWwb1dW",
        "outputId": "1d5b3ac9-977c-4b50-f007-a53638f1c6ea"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 ... 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print('Accuracy Score: ', metrics.accuracy_score(Ytest, Ypred))"
      ],
      "metadata": {
        "id": "4ZkLR-sgzClJ",
        "outputId": "501726d7-be9b-456e-9eb2-0e33d4e53170",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.9578454332552693\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix = confusion_matrix(Ytest, Ypred)\n",
        "print(confusion_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIyl-9Uzb89g",
        "outputId": "afecc571-ab37-4f1e-b2bc-a37495e58eee"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[448  35]\n",
            " [ 19 779]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Ytest, Ypred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5epczvfHV6i",
        "outputId": "0b3aa49a-2cf9-4e7c-ca9b-7f1fd1c62961"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.93      0.94       483\n",
            "           1       0.96      0.98      0.97       798\n",
            "\n",
            "    accuracy                           0.96      1281\n",
            "   macro avg       0.96      0.95      0.95      1281\n",
            "weighted avg       0.96      0.96      0.96      1281\n",
            "\n"
          ]
        }
      ]
    }
  ]
}