TASK 3/IRIS flower Classification - Task 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "434477f9",
   "metadata": {},
   "source": [
    "# IRIS Flower Classification\n",
    "\n",
    "# Problem Statement \n",
    "\n",
    "1. The Iris flower dataset consists of three species: setosa, versicolor, and virginica. These species can be distinguished based on their measurements.Now imagine that you have the measurements of Iris flowers categorized by their respective species. Your objective is to train a machine learning model that can learn from these measurements and accurately classify the Iris flowers into their respective species.\n",
    "\n",
    "2. Use the Iris dataset to develop a model that can classify iris flowers into different speccies based on their and petal measurements. This dataset is widely used for introductory classification tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "068042df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82756abf",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the dataset\n",
    "\n",
    "df=pd.read_csv('Downloads/Iris.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b2787c7",
   "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  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   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": [
    "# Getting information about data \n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d4b1036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09838a0c",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting statstical data \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70dca77d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to display number of samples on each class\n",
    "\n",
    "df['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3971739f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Visual representation\n",
    "\n",
    "df['sepal_length'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04535255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['sepal_width'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7cf2487a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['petal_length'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ef1f3bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['petal_width'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cdb1c479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a2ae33d640>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Scatter plot \n",
    "colors=['red','orange','blue']\n",
    "species=['Iris-setosa','Iris-versicolor','Iris-virginica']\n",
    "\n",
    "for i in range(3):\n",
    "    x=df[df['species']==species[i]]\n",
    "    plt.scatter(x['sepal_length'],x['sepal_width'],c=colors[i],label=species[i])\n",
    "plt.xlabel('sepal_length')\n",
    "plt.ylabel('sepal_width')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db21c042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a2ae4483a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "colors=['red','orange','blue']\n",
    "species=['Iris-setosa','Iris-versicolor','Iris-virginica']\n",
    "\n",
    "for i in range(3):\n",
    "    x=df[df['species']==species[i]]\n",
    "    plt.scatter(x['petal_length'],x['petal_width'],c=colors[i],label=species[i])\n",
    "plt.xlabel('petal_length')\n",
    "plt.ylabel('petal_width')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea7f1701",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.109369      0.871754     0.817954\n",
       "sepal_width      -0.109369     1.000000     -0.420516    -0.356544\n",
       "petal_length      0.871754    -0.420516      1.000000     0.962757\n",
       "petal_width       0.817954    -0.356544      0.962757     1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coorelation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81a532e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Corr=df.corr()\n",
    "fig, ax=plt.subplots(figsize=(5,4))\n",
    "sns.heatmap(Corr, annot=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb6f2782",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoder\n",
    "\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0687e44e",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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>0</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>0</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>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width  species\n",
       "0           5.1          3.5           1.4          0.2        0\n",
       "1           4.9          3.0           1.4          0.2        0\n",
       "2           4.7          3.2           1.3          0.2        0\n",
       "3           4.6          3.1           1.5          0.2        0\n",
       "4           5.0          3.6           1.4          0.2        0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species']=le.fit_transform(df['species'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3edcad04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model training \n",
    "\n",
    "X=df.drop(columns=['species'])\n",
    "Y=df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c368ef64",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9106fa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4) (120, 4) (30, 4)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de8c6bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "660aca74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "444db30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy on training data \n",
    "\n",
    "X_train_prediction=model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "12e58af4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 0 1 2 1 0 2 1 1 2 1 1 2 1 0 2 0 1 0 0 0 1 2 2 0 2 2 2 1 0 0 2 1 1 2 2 1\n",
      " 0 1 0 2 1 1 0 1 1 1 2 0 1 0 1 2 0 1 0 0 0 2 2 0 0 2 2 1 2 1 1 2 0 2 2 2 0\n",
      " 2 0 0 1 2 1 2 1 1 2 1 1 1 2 2 2 2 0 1 1 1 1 2 1 0 0 2 1 2 0 2 0 2 2 0 1 0\n",
      " 2 1 0 2 1 0 0 2 0]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0faf707f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data : 0.9583333333333334\n"
     ]
    }
   ],
   "source": [
    "training_data_accuracy =accuracy_score(Y_train, X_train_prediction)\n",
    "\n",
    "print('Accuracy score of training data :', training_data_accuracy )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72e0c0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy on test data \n",
    "\n",
    "X_test_prediction =model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9eb7b430",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 2 0 0 2 0 2 2 0 0 0 0 0 1 1 0 1 2 1 1 1 2 1 1 0 0 2 0 2]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cb5771ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data : 1.0\n"
     ]
    }
   ],
   "source": [
    "testing_data_accuracy=accuracy_score(Y_test,X_test_prediction)\n",
    "\n",
    "print('Accuracy score of testing data :', testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a59db0bf",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}