Task-2/Task2-Prediction+using+Unsupervised+learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Prediction using Unsupervised Learning\n",
    "\n",
    "### Objective: To predict the optimum number of clusters in the famous 'Iris' dataset. \n",
    "\n",
    "#### Author: Preethi Evelyn Sadanandan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the relevant libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the dataset\n",
    "raw_data = pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\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</td>\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": [
       "   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\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               150\n",
       "SepalLengthCm     35\n",
       "SepalWidthCm      23\n",
       "PetalLengthCm     43\n",
       "PetalWidthCm      22\n",
       "Species            3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Petal Length vs Petal Width')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the data points based on features on a scatter diagram\n",
    "fig, (ax1, ax2)  = plt.subplots(1, 2, figsize = (15, 8))\n",
    "ax1.scatter(data['SepalLengthCm'], data['SepalWidthCm'], color = 'green')\n",
    "ax1.set_title('Sepal Length vs Sepal Width')\n",
    "\n",
    "ax2.scatter(data['PetalLengthCm'], data['PetalWidthCm'], color = 'red')\n",
    "ax2.set_title('Petal Length vs Petal Width')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#selecting the features\n",
    "x = data.iloc[:, 1:5]\n",
    "x_scaled = preprocessing.scale(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the optimum number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "for i in range (1,7):\n",
    "    kmeans = KMeans(i).fit(x_scaled)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Within-cluster sum of squares')"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to plot the Elbow method graph\n",
    "num_clusters = range(1,7)\n",
    "plt.plot(num_clusters, wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('No of clusters')\n",
    "plt.ylabel('Within-cluster sum of squares')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from the above graph, using Elbow method we can observe that 3 would be the optimum number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_new = KMeans(3)\n",
    "kmeans_new.fit(x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Cluster Predicted</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>1</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>1</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>1</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>1</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>1</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>2</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>0</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>2</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>2</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\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",
       "     Cluster Predicted  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  \n",
       "..                 ...  \n",
       "145                  2  \n",
       "146                  0  \n",
       "147                  2  \n",
       "148                  2  \n",
       "149                  0  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_new = x.copy()\n",
    "clusters_new['Cluster Predicted'] = kmeans_new.fit_predict(x_scaled)\n",
    "clusters_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the resulting clusters predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Petal Length vs Petal Width')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2)  = plt.subplots(1, 2, figsize = (15, 8))\n",
    "ax1.scatter(data['SepalLengthCm'], data['SepalWidthCm'], c = clusters_new['Cluster Predicted'], cmap = 'rainbow')\n",
    "ax1.set_title('Sepal Length vs Sepal Width')\n",
    "\n",
    "ax2.scatter(data['PetalLengthCm'], data['PetalWidthCm'], c = clusters_new['Cluster Predicted'], cmap = 'rainbow')\n",
    "ax2.set_title('Petal Length vs Petal Width')\n"
   ]
  }
 ],
 "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
}