Iris-Kmeans-Grip-Task2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2: K mean Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author: Vibhuti Mayekar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "os.chdir(\"F:\\\\Vibhuti\\\\business analytics and intelligence\\\\Internshala\\\\internshipwork_grip\")\n",
    "df = pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "  </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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Id',axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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>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",
       "    <tr>\n",
       "      <th>...</th>\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>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>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>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>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>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 × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         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\n",
       "..             ...           ...            ...           ...             ...\n",
       "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
       "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
       "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
       "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
       "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Visualizing the NULL or NA pr NAN values\n",
    "fig, ax = plt.subplots(figsize = (15,5))\n",
    "x = df.isna().sum().index\n",
    "y = df.isna().sum()\n",
    "ax.bar(x=x, height = y)\n",
    "ax.set_xticklabels(x, rotation = 45)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = np.percentile(df['SepalLengthCm'], 25, interpolation = 'midpoint')  \n",
    "Q2 = np.percentile(df['SepalLengthCm'], 50, interpolation = 'midpoint')  \n",
    "Q3 = np.percentile(df['SepalLengthCm'], 75, interpolation = 'midpoint') \n",
    "IQR = Q3-Q1\n",
    "print('Interquartile range is', IQR) \n",
    "low_lim = Q1 - 1.5 * IQR \n",
    "up_lim = Q3 + 1.5 * IQR \n",
    "print('low_limit is', low_lim) \n",
    "print('up_limit is', up_lim)\n",
    "outlier =[] \n",
    "for x in df['SepalLengthCm']: \n",
    "    if ((x> up_lim) or (x<low_lim)): \n",
    "         outlier.append(x) \n",
    "print(' outlier in the dataset is', outlier) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = np.percentile(df['SepalWidthCm'], 25, interpolation = 'midpoint')  \n",
    "Q2 = np.percentile(df['SepalWidthCm'], 50, interpolation = 'midpoint')  \n",
    "Q3 = np.percentile(df['SepalWidthCm'], 75, interpolation = 'midpoint') \n",
    "IQR = Q3-Q1\n",
    "print('Interquartile range is', IQR) \n",
    "low_lim = Q1 - 1.5 * IQR \n",
    "up_lim = Q3 + 1.5 * IQR \n",
    "print('low_limit is', low_lim) \n",
    "print('up_limit is', up_lim)\n",
    "outlier =[] \n",
    "for x in df['SepalWidthCm']: \n",
    "    if ((x> up_lim) or (x<low_lim)): \n",
    "         outlier.append(x) \n",
    "print(' outlier in the dataset is', outlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = np.percentile(df['PetalLengthCm'], 25, interpolation = 'midpoint')  \n",
    "Q2 = np.percentile(df['PetalLengthCm'], 50, interpolation = 'midpoint')  \n",
    "Q3 = np.percentile(df['PetalLengthCm'], 75, interpolation = 'midpoint') \n",
    "IQR = Q3-Q1\n",
    "print('Interquartile range is', IQR) \n",
    "low_lim = Q1 - 1.5 * IQR \n",
    "up_lim = Q3 + 1.5 * IQR \n",
    "print('low_limit is', low_lim) \n",
    "print('up_limit is', up_lim)\n",
    "outlier =[] \n",
    "for x in df['PetalLengthCm']: \n",
    "    if ((x> up_lim) or (x<low_lim)): \n",
    "         outlier.append(x) \n",
    "print(' outlier in the dataset is', outlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:, [0,1, 2, 3]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "#Finding the optimum number of clusters for k-means classification\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "#Plotting the results onto a line graph, allowing us to observe 'The elbow'\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The elbow method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS') #within cluster sum of squares\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=0, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x148a6881e48>"
      ]
     },
     "execution_count": 36,
     "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": [
    "#Visualising the clusters\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",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thank You..."
   ]
  }
 ],
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