Task2 - Prediction Iris Data Using Unsupervised Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Science and Business Analytics Internship-Dec20\n",
    "#### GRIP @ The Sparks Foundation\n",
    "### K- Means Clustering\n",
    "### Task-2 : Prediction Iris Data Using Unsupervised Machine Learning\n",
    "### Author: Abu Bakkar Siddikk\n",
    "##### Batch: December-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:31:47.252139Z",
     "start_time": "2020-12-10T16:31:47.245136Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import Neccessary Dependancy \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Import Dataset from Sklearn\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:31:47.990590Z",
     "start_time": "2020-12-10T16:31:47.977594Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load iris Dataset\n",
    "dataset = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:31:48.851467Z",
     "start_time": "2020-12-10T16:31:48.842462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to view Feature Name\n",
    "dataset.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:31:49.833729Z",
     "start_time": "2020-12-10T16:31:49.810730Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to view iris Data\n",
    "dataset.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:51:23.384626Z",
     "start_time": "2020-12-10T16:51:23.376626Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 4)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To Find the Shape of Data \n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:31:51.060357Z",
     "start_time": "2020-12-10T16:31:51.053356Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create dataframe to include the data and feature name\n",
    "dataset = pd.DataFrame(dataset.data, columns = dataset.feature_names )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:31:53.855882Z",
     "start_time": "2020-12-10T16:31:53.834874Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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",
       "    </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",
       "    </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",
       "    </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>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</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"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to show top five Row\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:33:42.687738Z",
     "start_time": "2020-12-10T16:33:42.622714Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</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 (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.199333  \n",
       "std            0.762238  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To Describe Dataset\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:34:44.612241Z",
     "start_time": "2020-12-10T16:34:44.592238Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 4 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",
      "dtypes: float64(4)\n",
      "memory usage: 4.8 KB\n"
     ]
    }
   ],
   "source": [
    "# To see the informaton of dataset\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:35:46.397741Z",
     "start_time": "2020-12-10T16:35:46.384756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "145    False\n",
       "146    False\n",
       "147    False\n",
       "148    False\n",
       "149    False\n",
       "Length: 150, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check duplicates Data\n",
    "dataset.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:36:02.813927Z",
     "start_time": "2020-12-10T16:36:02.803924Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see number of Duplicates data\n",
    "dataset.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:38:40.889451Z",
     "start_time": "2020-12-10T16:38:40.881455Z"
    }
   },
   "outputs": [],
   "source": [
    "# TO Drop Duplicates Data \n",
    "dataset.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:38:46.486444Z",
     "start_time": "2020-12-10T16:38:46.474444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Again to see is there have any duplicates data\n",
    "dataset.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:41:35.763090Z",
     "start_time": "2020-12-10T16:41:35.126630Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='count'>"
      ]
     },
     "execution_count": 45,
     "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": [
    "# To Count the sepal length (cm) value\n",
    "sns.countplot(dataset.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:42:17.942914Z",
     "start_time": "2020-12-10T16:42:17.519912Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal width (cm)', ylabel='count'>"
      ]
     },
     "execution_count": 46,
     "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": [
    "# To Count the sepal width (cm) value\n",
    "sns.countplot(dataset.iloc[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:43:00.000409Z",
     "start_time": "2020-12-10T16:42:59.000444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='petal length (cm)', ylabel='count'>"
      ]
     },
     "execution_count": 48,
     "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": [
    "# To Count the petal length (cm) value\n",
    "sns.countplot(dataset.iloc[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:43:19.770699Z",
     "start_time": "2020-12-10T16:43:19.132694Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='petal width (cm)', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "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": [
    "# To Count the petal width (cm) value\n",
    "sns.countplot(dataset.iloc[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:44:25.991016Z",
     "start_time": "2020-12-10T16:44:23.054500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ac7aaf37f0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To see the pair of All features values\n",
    "sns.pairplot(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:53:44.192623Z",
     "start_time": "2020-12-10T16:53:42.610621Z"
    }
   },
   "outputs": [],
   "source": [
    "# To Finding the optimum number of clusters for k-means classification Algorithm\n",
    "X = dataset.iloc[:,0:].values\n",
    "\n",
    "DataList = []\n",
    "\n",
    "for i in range(1, 15):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(X)\n",
    "    DataList.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T16:57:23.745800Z",
     "start_time": "2020-12-10T16:57:23.462798Z"
    }
   },
   "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": [
    "# Plotting the results onto a line graph \n",
    "plt.plot([1,2,3,4,5,6,7,8,9,10,11,12,13,14], DataList)\n",
    "plt.title('Clustering method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Within cluster sum of squares')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T17:00:36.714187Z",
     "start_time": "2020-12-10T17:00:36.644150Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0,\n",
       "       0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0,\n",
       "       0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying kmeans to the dataset / Creating the kmeans classifier\n",
    "Kmeans = KMeans(n_clusters = 3, init = 'k-means++',max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_kmeans = Kmeans.fit_predict(X)\n",
    "y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-10T17:07:40.850948Z",
     "start_time": "2020-12-10T17:07:40.412916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ac7fdcdaf0>"
      ]
     },
     "execution_count": 79,
     "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": [
    "# To Visualising the clusters On the first two columns\n",
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Iris-setosa')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Iris-versicolour')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Iris-virginica')\n",
    "\n",
    "# Plotting the centroids of the clusters\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], s = 100, c = 'yellow', label = 'Centroids')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thank You"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.6"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
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     "library": "var_list.r",
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    }
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 },
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 "nbformat_minor": 4
}