DT Classifier - Iris DataSet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To Explore Decision Tree Algorithm\n",
    "For the given ‘Iris’ dataset, create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import sklearn.datasets as datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve\n",
    "from sklearn.tree import plot_tree\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</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",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "       Species  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the iris dataset\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "df = pd.read_csv(url, header=None, names=['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
    "                                          'petal width (cm)', 'Species'])\n",
    "\n",
    "df.head() # To view first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To know number of rows and collumns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      " 4   Species            150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the dataframe information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 4 - Numerical Features and one categorical column\n",
    "- There are totally 150 rows or observations are in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "Species              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find if any null value is present\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>150.0</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.80</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.35</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>150.0</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean       std  min  25%   50%  75%  max\n",
       "sepal length (cm)  150.0  5.843333  0.828066  4.3  5.1  5.80  6.4  7.9\n",
       "sepal width (cm)   150.0  3.054000  0.433594  2.0  2.8  3.00  3.3  4.4\n",
       "petal length (cm)  150.0  3.758667  1.764420  1.0  1.6  4.35  5.1  6.9\n",
       "petal width (cm)   150.0  1.198667  0.763161  0.1  0.3  1.30  1.8  2.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see summary statistics\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TDiEt47FVNbPcF1NV8NK0SjK/0tWC0rRyikaSGmXBS1KjLHipm32TDiCtlXPwktQoz+AlqVEWvCQ1yoKXpEZZ8JLUKAtekhr1/wrrdpC/F+auAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To find outliers\n",
    "cols = df.columns[0:-1]\n",
    "for i in cols:\n",
    "    sns.boxplot(y=df[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the above boxplot we can say that there are outliers in the column 'sepal width (cm)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To remove outliers from 'sepal width (cm)'\n",
    "q1 = df['sepal width (cm)'].quantile(0.25)\n",
    "q3 = df['sepal width (cm)'].quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "df = df[(df['sepal width (cm)'] >= q1-1.5*iqr) & (df['sepal width (cm)'] <= q3+1.5*iqr)]\n",
    "df.shape # To find out the number of rows and column after outlier treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that after outlier treatment the number of rows are reduced to 146 from 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Blocplot for sepal width (cm) after outlier treatment\n",
    "sns.boxplot(y=df['sepal width (cm)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into train and test sets\n",
    "X = df.drop(\"Species\",axis=1)\n",
    "y = df[\"Species\"]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3, random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining an object for DTC and fitting for whole dataset\n",
    "dt = DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1 )\n",
    "dt.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting of decission tree\n",
    "from IPython.display import Image\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "\n",
    "features = X.columns\n",
    "dot_data = export_graphviz(dt, out_file=None, feature_names=features)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining an object for DTC and fitting for train dataset\n",
    "dt = DecisionTreeClassifier(random_state=1)\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = dt.predict(X_train)\n",
    "y_pred = dt.predict(X_test)\n",
    "y_prob = dt.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree-Train:  1.0\n",
      "Accuracy of Decision Tree-Test:  0.9545454545454546\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of Decision Tree-Train: ', accuracy_score(y_pred_train, y_train))\n",
    "print('Accuracy of Decision Tree-Test: ', accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The accuracy of the DT has slightly overfit\n",
    "- Shall try hyperparameter tuning to reduce overfitting if possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        15\n",
      "Iris-versicolor       1.00      0.87      0.93        15\n",
      " Iris-virginica       0.88      1.00      0.93        14\n",
      "\n",
      "       accuracy                           0.95        44\n",
      "      macro avg       0.96      0.96      0.95        44\n",
      "   weighted avg       0.96      0.95      0.95        44\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification for test before hyperparameter tuning\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'min_samples_leaf': 3, 'min_samples_split': 2}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hyperparameter Tuning of DTC\n",
    "\n",
    "dt = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "params = {'max_depth' : [2,3,4,5],\n",
    "        'min_samples_split': [2,3,4,5],\n",
    "        'min_samples_leaf': [1,2,3,4,5]}\n",
    "\n",
    "gsearch = GridSearchCV(dt, param_grid=params, cv=3)\n",
    "\n",
    "gsearch.fit(X,y)\n",
    "\n",
    "gsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passing best parameter for the Hyperparameter Tuning\n",
    "dt = DecisionTreeClassifier(**gsearch.best_params_, random_state=1)\n",
    "\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = dt.predict(X_train)\n",
    "y_prob_train = dt.predict_proba(X_train)[:,1]\n",
    "\n",
    "y_pred = dt.predict(X_test)\n",
    "y_prob = dt.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Train: \n",
      " [[32  0  0]\n",
      " [ 0 33  1]\n",
      " [ 0  0 36]]\n",
      "\n",
      " Confusion Matrix - Test: \n",
      " [[15  0  0]\n",
      " [ 0 13  2]\n",
      " [ 0  0 14]]\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix - Train:','\\n',confusion_matrix(y_train,y_pred_train))\n",
    "print('\\n','Confusion Matrix - Test:','\\n',confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        15\n",
      "Iris-versicolor       1.00      0.87      0.93        15\n",
      " Iris-virginica       0.88      1.00      0.93        14\n",
      "\n",
      "       accuracy                           0.95        44\n",
      "      macro avg       0.96      0.96      0.95        44\n",
      "   weighted avg       0.96      0.95      0.95        44\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification for test after hyperparameter tuning\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree-Train:  0.9901960784313726\n",
      "Accuracy of Decision Tree-Test:  0.9545454545454546\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of Decision Tree-Train: ', accuracy_score(y_pred_train, y_train))\n",
    "print('Accuracy of Decision Tree-Test: ', accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- After hyperparameter tuning we can say the overfitting has been reduced slightly"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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