Task-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK - PREDICTION OF OPTIMUM NUMBER OF CLUSTERS FROM THE GIVEN IRIS DATSET\n",
    "\n",
    "NAME - PRIYA ISSAR\n",
    "\n",
    "BATCH - GRIP JUNE 2021\n",
    "\n",
    "Data Science & Buisness Analytics Internship at The Sparks Foundation\n",
    "\n",
    "Dataset - https://bit.ly/3kXTdox\n",
    "\n",
    "Language used- Python 3 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 (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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset = datasets.load_iris()\n",
    "iris_dataframe = pd.DataFrame(iris_dataset.data, columns = iris_dataset.feature_names)\n",
    "iris_dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing values as an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = iris_dataframe.iloc[:,[0,1,2,3]].values\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding optimum number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'KMeans' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-f51d81fc13a7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     kmeans= KMeans(n_clusters = i, init ='k-means++',\n\u001b[0m\u001b[0;32m      5\u001b[0m                   max_iter = 300,n_init=10,random_state =0)\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'KMeans' object is not callable"
     ]
    }
   ],
   "source": [
    "s = []\n",
    "\n",
    "for i in range(1,11):\n",
    "    kmeans= KMeans(n_clusters = i, init ='k-means++',\n",
    "                  max_iter = 300,n_init=10,random_state =0)\n",
    "    \n",
    "    kmeans.fit(x)\n",
    "    s.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', \n",
    "                    max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the graph to observe the pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "## Creating K-Means Classifier\n",
    "\n",
    "\n",
    "Now,we assume the no. of clusters to be 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying kmeans to the dataset \n",
    "# Creating the kmeans classifier\n",
    "\n",
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++',\n",
    "                max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_kmeans = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the cluster data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25f505315b0>"
      ]
     },
     "execution_count": 22,
     "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-On the first two columns\n",
    "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], \n",
    "            s = 100, c = 'red', label = 'Iris-setosa')\n",
    "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], \n",
    "            s = 100, c = 'blue', label = 'Iris-versicolour')\n",
    "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1],\n",
    "            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], \n",
    "            s = 100, c = 'yellow', label = 'Centroids')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}