Gaussian Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e03e1bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gaussian Naive Bayes Algorithm\n",
    "# Supports continuous valued features and models each as conforming to a Gaussian (normal) distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1e0f224",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the packages\n",
    "from sklearn import tree\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "plt.rcParams['figure.figsize'] = [30,15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd20137a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "df = pd.read_csv(\"iris.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5196d44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78ce611a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into feature and target columns\n",
    "X = df[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']]\n",
    "y = df['Species']\n",
    "# Splitting the columns into training and testing dataset/\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "652091c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.97\n"
     ]
    }
   ],
   "source": [
    "# Creating a AdaBoost Classifier model\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train) # Fitting the data\n",
    "y_pred = gnb.predict(X_test) # Predicting the values of the test data\n",
    "print('Accuracy: {:.2f}'.format(gnb.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76c47d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the confusion matrix\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_size_inches(16,4)\n",
    "\n",
    "df[df.Species=='Iris-setosa'].plot(kind='scatter',x='SepalLengthCm',y='SepalWidthCm',color='orange', label='Setosa', ax = ax1)\n",
    "df[df.Species=='Iris-versicolor'].plot(kind='scatter',x='SepalLengthCm',y='SepalWidthCm',color='blue', label='versicolor',ax=ax1)\n",
    "df[df.Species=='Iris-virginica'].plot(kind='scatter',x='SepalLengthCm',y='SepalWidthCm',color='green', label='virginica', ax=ax1)\n",
    "\n",
    "df[df.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa', ax = ax2)\n",
    "df[df.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=ax2)\n",
    "df[df.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=ax2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66f6853f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/prabhatm/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianNB was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Iris-versicolor'], dtype='<U15')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of the output using input\n",
    "gnb.predict([[1,1, 5, 1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61268de2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        11\n",
      "Iris-versicolor       0.93      1.00      0.96        13\n",
      " Iris-virginica       1.00      0.83      0.91         6\n",
      "\n",
      "       accuracy                           0.97        30\n",
      "      macro avg       0.98      0.94      0.96        30\n",
      "   weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying the classification report\n",
    "from sklearn.metrics import classification_report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03f5daa9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}