DSBDA_A6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f44f464e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 6)\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "df=pd.read_csv('Iris.csv')\n",
    "print(df.shape)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa7b17dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The DataFrame Contains 150 Rows and 6 Columns \n",
      "Id                 int64\n",
      "SepalLengthCm    float64\n",
      "SepalWidthCm     float64\n",
      "PetalLengthCm    float64\n",
      "PetalWidthCm     float64\n",
      "Species           object\n",
      "dtype: object\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n",
      "None\n",
      "   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\n"
     ]
    }
   ],
   "source": [
    "print('The DataFrame Contains %d Rows and %d Columns '%(df.shape))\n",
    "print(df.dtypes)\n",
    "print(df.info())\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89eba0c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm\n",
      "0   1            5.1           3.5            1.4\n",
      "1   2            4.9           3.0            1.4\n",
      "2   3            4.7           3.2            1.3\n",
      "3   4            4.6           3.1            1.5\n",
      "4   5            5.0           3.6            1.4\n"
     ]
    }
   ],
   "source": [
    "features = df.iloc[:,0:4]\n",
    "print(features.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b44a3063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         Iris-setosa\n",
      "1         Iris-setosa\n",
      "2         Iris-setosa\n",
      "3         Iris-setosa\n",
      "4         Iris-setosa\n",
      "            ...      \n",
      "145    Iris-virginica\n",
      "146    Iris-virginica\n",
      "147    Iris-virginica\n",
      "148    Iris-virginica\n",
      "149    Iris-virginica\n",
      "Name: Species, Length: 150, dtype: object\n"
     ]
    }
   ],
   "source": [
    "target=df.iloc[:,5]\n",
    "print(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39ba4914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Initial DataFrame Contained 150 Rows and 6 Columns\n",
      "The Features Matrix Contains 150 Rows And 4 Columns\n",
      "The Target Vector Contains 150 Rows And 1 Columns\n"
     ]
    }
   ],
   "source": [
    "print('The Initial DataFrame Contained %d Rows and %d Columns'%(df.shape))\n",
    "print('The Features Matrix Contains %d Rows And %d Columns'%(features.shape))\n",
    "print('The Target Vector Contains %d Rows And %d Columns'%(np.array(target).reshape(-1,1).shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88012e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "algorithm=GaussianNB(priors=None, var_smoothing=1e-9)\n",
    "algorithm.fit(features,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32252998",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "print(algorithm.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fac7942c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Gaussian Model Has Achieved 0.99 Percent Accuracy\n"
     ]
    }
   ],
   "source": [
    "print('The Gaussian Model Has Achieved %.2f Percent Accuracy'%(algorithm.score(features,target)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c444e3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-setosa']\n"
     ]
    }
   ],
   "source": [
    "observation=[[5.0,3.7,1.6,0.1]]\n",
    "predictions=algorithm.predict(observation)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "378aa413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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