DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "686e0625",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from Convert import convert_to_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "iris = convert_to_dataframe(load_iris())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepallength  sepalwidth  petallength  petalwidth      target\n46           5.1         3.8          1.6         0.2      setosa\n64           5.6         2.9          3.6         1.3  versicolor\n54           6.5         2.8          4.6         1.5  versicolor\n65           6.7         3.1          4.4         1.4  versicolor\n8            4.4         2.9          1.4         0.2      setosa\n117          7.7         3.8          6.7         2.2   virginica\n69           5.6         2.5          3.9         1.1  versicolor\n123          6.3         2.7          4.9         1.8   virginica\n47           4.6         3.2          1.4         0.2      setosa\n13           4.3         3.0          1.1         0.1      setosa\n37           4.9         3.6          1.4         0.1      setosa\n61           5.9         3.0          4.2         1.5  versicolor\n136          6.3         3.4          5.6         2.4   virginica\n120          6.9         3.2          5.7         2.3   virginica\n124          6.7         3.3          5.7         2.1   virginica\n103          6.3         2.9          5.6         1.8   virginica\n59           5.2         2.7          3.9         1.4  versicolor\n126          6.2         2.8          4.8         1.8   virginica\n5            5.4         3.9          1.7         0.4      setosa\n121          5.6         2.8          4.9         2.0   virginica",
      "text/html": "<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>sepallength</th>\n      <th>sepalwidth</th>\n      <th>petallength</th>\n      <th>petalwidth</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>46</th>\n      <td>5.1</td>\n      <td>3.8</td>\n      <td>1.6</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>5.6</td>\n      <td>2.9</td>\n      <td>3.6</td>\n      <td>1.3</td>\n      <td>versicolor</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>6.5</td>\n      <td>2.8</td>\n      <td>4.6</td>\n      <td>1.5</td>\n      <td>versicolor</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>6.7</td>\n      <td>3.1</td>\n      <td>4.4</td>\n      <td>1.4</td>\n      <td>versicolor</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>4.4</td>\n      <td>2.9</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>117</th>\n      <td>7.7</td>\n      <td>3.8</td>\n      <td>6.7</td>\n      <td>2.2</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>5.6</td>\n      <td>2.5</td>\n      <td>3.9</td>\n      <td>1.1</td>\n      <td>versicolor</td>\n    </tr>\n    <tr>\n      <th>123</th>\n      <td>6.3</td>\n      <td>2.7</td>\n      <td>4.9</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>4.6</td>\n      <td>3.2</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>4.3</td>\n      <td>3.0</td>\n      <td>1.1</td>\n      <td>0.1</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>4.9</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.1</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>5.9</td>\n      <td>3.0</td>\n      <td>4.2</td>\n      <td>1.5</td>\n      <td>versicolor</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>6.3</td>\n      <td>3.4</td>\n      <td>5.6</td>\n      <td>2.4</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>120</th>\n      <td>6.9</td>\n      <td>3.2</td>\n      <td>5.7</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>6.7</td>\n      <td>3.3</td>\n      <td>5.7</td>\n      <td>2.1</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>6.3</td>\n      <td>2.9</td>\n      <td>5.6</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>5.2</td>\n      <td>2.7</td>\n      <td>3.9</td>\n      <td>1.4</td>\n      <td>versicolor</td>\n    </tr>\n    <tr>\n      <th>126</th>\n      <td>6.2</td>\n      <td>2.8</td>\n      <td>4.8</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5.4</td>\n      <td>3.9</td>\n      <td>1.7</td>\n      <td>0.4</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>121</th>\n      <td>5.6</td>\n      <td>2.8</td>\n      <td>4.9</td>\n      <td>2.0</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sample(20)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepallength', 'sepalwidth', 'petallength', 'petalwidth']\n",
      "['setosa', 'versicolor', 'virginica']\n"
     ]
    }
   ],
   "source": [
    "X = iris.drop(['target'], axis=1)\n",
    "Y = iris['target']\n",
    "feature_names = iris.columns.values.tolist()[:-1]\n",
    "class_names = Y.unique().tolist()\n",
    "print(feature_names)\n",
    "print(class_names)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f6099f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepallength  sepalwidth  petallength  petalwidth\n131          7.9         3.8          6.4         2.0\n102          7.1         3.0          5.9         2.1\n113          5.7         2.5          5.0         2.0\n25           5.0         3.0          1.6         0.2\n3            4.6         3.1          1.5         0.2\n..           ...         ...          ...         ...\n44           5.1         3.8          1.9         0.4\n108          6.7         2.5          5.8         1.8\n141          6.9         3.1          5.1         2.3\n127          6.1         3.0          4.9         1.8\n132          6.4         2.8          5.6         2.2\n\n[120 rows x 4 columns]",
      "text/html": "<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>sepallength</th>\n      <th>sepalwidth</th>\n      <th>petallength</th>\n      <th>petalwidth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>131</th>\n      <td>7.9</td>\n      <td>3.8</td>\n      <td>6.4</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>7.1</td>\n      <td>3.0</td>\n      <td>5.9</td>\n      <td>2.1</td>\n    </tr>\n    <tr>\n      <th>113</th>\n      <td>5.7</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>1.6</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>5.1</td>\n      <td>3.8</td>\n      <td>1.9</td>\n      <td>0.4</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>6.7</td>\n      <td>2.5</td>\n      <td>5.8</td>\n      <td>1.8</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>6.9</td>\n      <td>3.1</td>\n      <td>5.1</td>\n      <td>2.3</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td>6.1</td>\n      <td>3.0</td>\n      <td>4.9</td>\n      <td>1.8</td>\n    </tr>\n    <tr>\n      <th>132</th>\n      <td>6.4</td>\n      <td>2.8</td>\n      <td>5.6</td>\n      <td>2.2</td>\n    </tr>\n  </tbody>\n</table>\n<p>120 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "clf = clf.fit(x_train, y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(clf, feature_names=feature_names, class_names=class_names, filled=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    index      target      y_pred\n",
      "0      36      setosa      setosa\n",
      "1      84  versicolor  versicolor\n",
      "2     137   virginica   virginica\n",
      "3      82  versicolor  versicolor\n",
      "4       2      setosa      setosa\n",
      "5      86  versicolor  versicolor\n",
      "6      58  versicolor  versicolor\n",
      "7     125   virginica   virginica\n",
      "8      27      setosa      setosa\n",
      "9      21      setosa      setosa\n",
      "10     31      setosa      setosa\n",
      "11     90  versicolor  versicolor\n",
      "12    134   virginica   virginica\n",
      "13     52  versicolor   virginica\n",
      "14    138   virginica   virginica\n",
      "15    101   virginica   virginica\n",
      "16      1      setosa      setosa\n",
      "17     30      setosa      setosa\n",
      "18     98  versicolor  versicolor\n",
      "19     39      setosa      setosa\n",
      "20     54  versicolor  versicolor\n",
      "21     20      setosa      setosa\n",
      "22    123   virginica   virginica\n",
      "23     81  versicolor  versicolor\n",
      "24     38      setosa      setosa\n",
      "25     72  versicolor   virginica\n",
      "26     93  versicolor  versicolor\n",
      "27      9      setosa      setosa\n",
      "28    142   virginica   virginica\n",
      "29     24      setosa      setosa\n"
     ]
    }
   ],
   "source": [
    "y_test_ind = y_test.reset_index()\n",
    "print(pd.concat([y_test_ind, pd.DataFrame({\"y_pred\": y_pred})], axis=1))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        12\n",
      "  versicolor       1.00      0.82      0.90        11\n",
      "   virginica       0.78      1.00      0.88         7\n",
      "\n",
      "    accuracy                           0.93        30\n",
      "   macro avg       0.93      0.94      0.92        30\n",
      "weighted avg       0.95      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[12  0  0]\n",
      " [ 0  9  2]\n",
      " [ 0  0  7]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion matrix:\")\n",
    "print(metrics.confusion_matrix(y_test, y_pred, labels=class_names))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.33% on Test Data\n",
      "Accuracy: 100.0% on Training Data\n"
     ]
    }
   ],
   "source": [
    "accuracy_test = metrics.accuracy_score(y_test, y_pred) * 100\n",
    "accuracy_train = metrics.accuracy_score(y_train, clf.predict(x_train)) * 100\n",
    "\n",
    "print(f\"Accuracy: {round(accuracy_test, 2)}% on Test Data\")\n",
    "print(f\"Accuracy: {round(accuracy_train, 2)}% on Training Data\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "992ba193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\attri\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "my_features = [[5.5, 3.5, 1.3, 0.2]]\n",
    "prediction = clf.predict(my_features)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9fe262b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "sepallength    0.000000\nsepalwidth     0.016691\npetallength    0.921442\npetalwidth     0.061867\ndtype: float64"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp = pd.Series(clf.feature_importances_, index=feature_names)\n",
    "feature_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9333333333333333"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test, y_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
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