TASK-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***The Sparks Foundation Task-2***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually.***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Import all the required libraries***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Read the CSV file***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\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>146</td>\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>147</td>\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>148</td>\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>149</td>\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>150</td>\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 × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"iris.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Dropping the unnecessary columns***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Id', 'Species'],axis='columns',inplace=True)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>cluster</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",
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       "      <th>...</th>\n",
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       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
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       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</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>2</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>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",
       "      <td>0</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  cluster\n",
       "0              5.1           3.5            1.4           0.2        1\n",
       "1              4.9           3.0            1.4           0.2        1\n",
       "2              4.7           3.2            1.3           0.2        1\n",
       "3              4.6           3.1            1.5           0.2        1\n",
       "4              5.0           3.6            1.4           0.2        1\n",
       "..             ...           ...            ...           ...      ...\n",
       "145            6.7           3.0            5.2           2.3        0\n",
       "146            6.3           2.5            5.0           1.9        2\n",
       "147            6.5           3.0            5.2           2.0        0\n",
       "148            6.2           3.4            5.4           2.3        0\n",
       "149            5.9           3.0            5.1           1.8        2\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***K-Means***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,[0,1,2,3]].values\n",
    "sse = []\n",
    "k_rng = range(1,10)\n",
    "for k in k_rng:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(x)\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Plotting an ELBOW GRAPH to find the correct number of cluster***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xe330d08>]"
      ]
     },
     "execution_count": 31,
     "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": [
    "plt.xlabel('K MEANS')\n",
    "plt.ylabel('SUM OF SQUARED ERROR')\n",
    "plt.title('THE ELBOW ANALYSIS')\n",
    "plt.plot(k_rng,sse,color='purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***This show that 3 is optimum number of cluster to form in iris dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=3)\n",
    "y_predict = km.fit_predict(x)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Adding a cluster column to show which cluster does the particular feature belong to***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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</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>0</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>2</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>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",
       "      <td>0</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  cluster\n",
       "0              5.1           3.5            1.4           0.2        1\n",
       "1              4.9           3.0            1.4           0.2        1\n",
       "2              4.7           3.2            1.3           0.2        1\n",
       "3              4.6           3.1            1.5           0.2        1\n",
       "4              5.0           3.6            1.4           0.2        1\n",
       "..             ...           ...            ...           ...      ...\n",
       "145            6.7           3.0            5.2           2.3        0\n",
       "146            6.3           2.5            5.0           1.9        2\n",
       "147            6.5           3.0            5.2           2.0        0\n",
       "148            6.2           3.4            5.4           2.3        0\n",
       "149            5.9           3.0            5.1           1.8        2\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster']=y_predict\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cluster.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df.cluster==0]\n",
    "df2 = df[df.cluster==1]\n",
    "df3 = df[df.cluster==2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Plotting a scatter plot showing the cluster***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xe19ae48>"
      ]
     },
     "execution_count": 55,
     "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": [
    "plt.title('K Means Clustering')\n",
    "plt.scatter(x[y_predict==0,0],x[y_predict==0,1],c='red',label='Iris-setosa')\n",
    "plt.scatter(x[y_predict==1,0],x[y_predict==1,1],c='green',label='Iris-virginica')\n",
    "plt.scatter(x[y_predict==2,0],x[y_predict==2,1],c='yellow',label='Iris-versicolor')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***THANK YOU FOR WATCHING***"
   ]
  }
 ],
 "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
}