10.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv('Social.csv')\nprint(df)\nX = df.iloc[:, 2:4]\ny = df.iloc[:, -1]",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "     User ID  Gender  Age  EstimatedSalary  Purchased\n0   15624510    Male   29            14500          1\n1   15810944    Male   30            20000          1\n2   15668575  Female   26            32100          0\n3   15603246  Female   27            57000          0\n4   15804002    Male   19            38700          1\n5   15728773    Male   27            58000          0\n6   15598044  Female   27            84000          0\n7   15694829  Female   32           187000          1\n8   15600575    Male   25            33000          0\n9   15727311  Female   35            65000          1\n10  15570769  Female   26            80000          0\n11  15606274  Female   26            65400          0\n12  15746139    Male   20            86000          0\n13  15704987    Male   32            18000          0\n14  15628972    Male   18            12000          1\n15  15697686    Male   29            80000          0\n16  15733883    Male   47            25000          1\n17  15617482    Male   45            26000          1\n18  15704583    Male   46            28000          1\n19  15621083  Female   48            29000          1\n20  15649487    Male   45            22000          1\n21  15736760  Female   47            49000          1\n22  15714658    Male   48            41000          1\n23  15599081  Female   45            22000          1\n24  15705113    Male   46            23000          1\n25  15631159    Male   47            20000          1\n26  15792818    Male   49            28000          1\n27  15633531  Female   47            30000          1\n28  15744529    Male   29            43000          0\n29  15669656    Male   31            18000          0\n30  15581198    Male   31            74000          0\n31  15729054  Female   27           137000          1\n32  15573452  Female   21            16000          0\n33  15776733  Female   28            44000          0\n34  15724858    Male   27            60000          0\n35  15713144    Male   35            27000          0\n36  15690188  Female   33            28000          0\n37  15689425    Male   30            49000          0\n38  15671766  Female   26            72000          0\n39  15782806  Female   27            31000          0\n40  15764419  Female   27            17000          1\n41  15591915  Female   33            51000          0\n42  15772798    Male   35           108000          0\n43  15792008    Male   30            15700          1\n44  15715541  Female   28            84000          0\n45  15639277    Male   23            20000          0\n46  15798850    Male   25            79000          0\n47  15776348  Female   27            54000          0\n48  15727696    Male   30           135000          0\n49  15744529    Male   29            43000          0\n50  15669656    Male   31            18000          0\n51  15581198    Male   31            74000          0\n52  15729054  Female   27           137000          1\n53  15573452  Female   21            16000          0\n54  15776733  Female   28            44000          0\n55  15724858    Male   27            60000          0\n56  15713144    Male   35            27000          0\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 2
    },
    {
      "cell_type": "code",
      "source": "from sklearn.model_selection import train_test_split\nX_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=0.3,random_state=0)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 3
    },
    {
      "cell_type": "code",
      "source": "from sklearn.tree import DecisionTreeClassifier\nclassifier = DecisionTreeClassifier()\nclassifier.fit(X_train,Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 4,
          "output_type": "execute_result",
          "data": {
            "text/plain": "DecisionTreeClassifier()",
            "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "source": "from sklearn import tree\ntree.plot_tree(classifier)\n\ny_pred = classifier.predict(X_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5
    },
    {
      "cell_type": "code",
      "source": "from sklearn.metrics import confusion_matrix, accuracy_score\ncm = confusion_matrix(Y_test, y_pred)\nprint(\"Confusion Matrix:\")\nprint(cm)\nprint(\"Accuracy: \", accuracy_score(Y_test, y_pred))\n\nnew_data_point = [[22, 27000]]    # age, estimated salary\npredicted_class = classifier.predict(new_data_point)\nprint(\"Predicted class for \", new_data_point, \"is: \", predicted_class)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Confusion Matrix:\n[[11  2]\n [ 2  3]]\nAccuracy:  0.7777777777777778\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/lib/python3.11/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Predicted class for  [[22, 27000]] is:  [0]\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 6
    },
    {
      "cell_type": "code",
      "source": "from sklearn.tree import export_graphviz\nexport_graphviz(classifier, out_file='10.dot', feature_names=X.columns, class_names=['Not Purchased', 'Purchased'])",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 7
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    }
  ]
}