Task #2 Prediction using Unsupervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRIP @ The Sparks Foundation - Data Science and Business Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2: Prediction using Unsupervised Machine Learning\n",
    "\n",
    "### Author: DANIEL JOSEPH RAJ I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective\n",
    "\n",
    "From the given \"Iris\" dataset, predict the optimum number of clusters (i.e K) and represent it visually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing all the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 (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",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\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\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "iris = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above output, we can see that there is no missing values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting scatter plot between Sepal length and Sepal width\n",
    "\n",
    "plt.figure(dpi=80)\n",
    "plt.style.use(\"default\")\n",
    "plt.scatter(iris.iloc[:,0], iris.iloc[:,1], color = \"firebrick\", alpha = 0.8)\n",
    "plt.title(\"Sepal Length Vs Sepal Width\")\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 512x384 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting scatter plot between Petal length and Petal width\n",
    "\n",
    "plt.figure(dpi=80)\n",
    "plt.style.use(\"default\")\n",
    "plt.scatter(iris.iloc[:,2], iris.iloc[:,3], color = \"teal\", alpha = 0.8)\n",
    "plt.title(\"Petal Length Vs Petal Width\")\n",
    "plt.xlabel(\"Petal Length\")\n",
    "plt.ylabel(\"Petal Width\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Converting attributs into 2D-array\n",
    "\n",
    "iris = iris.iloc[:,0:4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining the Optimum number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Finding the optimum number of clusters\n",
    "\n",
    "wcss = [] #assingning an empty list for \"within-cluster sum of square\"\n",
    "\n",
    "for i in range(1,16):\n",
    "    km = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300,\n",
    "               n_init = 10, random_state = 0)\n",
    "    km.fit(iris)\n",
    "    wcss.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 512x384 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting the elbow graph\n",
    "\n",
    "plt.figure(dpi=80)\n",
    "plt.plot(range(1, 16), wcss, color = \"orangered\")\n",
    "plt.title('The elbow Graph')\n",
    "plt.xlabel('Number of Cluster(K)')\n",
    "plt.ylabel('WCSS') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph, we get to know that the elbow occurs at k=3. Since, the WCSS does not decreases significanty from 3. Therefore, we can conclude that the optimum number of clusters is \"3\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the Classifier model and Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Building the classifier model with K=3\n",
    "\n",
    "km = KMeans(n_clusters = 3, init = \"k-means++\", max_iter = 300,\n",
    "            n_init = 10, random_state = 0)\n",
    "y_km = km.fit_predict(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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, 2, 0, 0, 0, 2, 0, 0, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Prediction class(labels) for each obseravtions in iris dataset\n",
    "\n",
    "y_km"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualizing the clusters on the Sepal length and Sepal width\n",
    "\n",
    "plt.figure(dpi=90)\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.scatter(iris[y_km == 1, 0], iris[y_km == 1, 1], \n",
    "            s = 30, c = 'orchid', label = 'Iris-setosa', alpha = 0.8)\n",
    "plt.scatter(iris[y_km == 2, 0], iris[y_km == 2, 1],\n",
    "            s = 30, c = 'orange', label = 'Iris-versicolour', alpha = 0.8)\n",
    "plt.scatter(iris[y_km == 0, 0], iris[y_km == 0, 1], \n",
    "            s = 30, c = 'turquoise', label = 'Iris-virginica', alpha = 0.8)\n",
    "plt.xlabel(\"Sepal length\")\n",
    "plt.ylabel(\"Sepal width\")\n",
    "plt.title(\"Cluster based on Sepal length & width\")\n",
    "\n",
    "\n",
    "## Plotting the centroids of the clusters\n",
    "\n",
    "plt.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:,1], \n",
    "            s = 30, c = 'black', label = 'Centroids')\n",
    "plt.legend(prop={\"size\":10})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualizing the clusters on the Petal length and Petal width\n",
    "\n",
    "plt.figure(dpi=90)\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.scatter(iris[y_km == 1, 2], iris[y_km == 1, 3], \n",
    "            s = 30, c = 'orchid', label = 'Iris-setosa', alpha = 0.8)\n",
    "plt.scatter(iris[y_km == 2, 2], iris[y_km == 2, 3],\n",
    "            s = 30, c = 'orange', label = 'Iris-versicolour', alpha = 0.8)\n",
    "plt.scatter(iris[y_km == 0, 2], iris[y_km == 0, 3], \n",
    "            s = 30, c = 'turquoise', label = 'Iris-virginica', alpha = 0.8)\n",
    "plt.xlabel(\"Petal length\")\n",
    "plt.ylabel(\"Petal width\")\n",
    "plt.title(\"Cluster based on Petal length & width\")\n",
    "\n",
    "\n",
    "## Plotting the centroids of the clusters\n",
    "\n",
    "plt.scatter(km.cluster_centers_[:, 2], km.cluster_centers_[:,3], \n",
    "            s = 30, c = 'black', label = 'Centroids')\n",
    "plt.legend(prop={\"size\":11})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "1. We have successfully predicted the optimum number of clusters, that is k = '3' and also represented it visually.\n",
    "2. Moreover, we can also visualize the clusters between Sepal length & Petal length, Sepal length & Petal width as well as petal    length and Sepal width and Petal width & Sepal width. "
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}