Task2 Data Science and Business Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "467cb57f",
   "metadata": {},
   "source": [
    "# Author :Vikas Shahu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055d029d",
   "metadata": {},
   "source": [
    "## Task2 Prediction using unsupervised machine learning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24a31ec",
   "metadata": {},
   "source": [
    "# GRIP @ The Sparks Foundation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83739174",
   "metadata": {},
   "source": [
    "## step1 reading data from source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e52f96cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbc640ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1f5698f3",
   "metadata": {},
   "outputs": [
    {
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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>0</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>0</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>0</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>0</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>0</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        0\n",
       "1   2            4.9           3.0            1.4           0.2        0\n",
       "2   3            4.7           3.2            1.3           0.2        0\n",
       "3   4            4.6           3.1            1.5           0.2        0\n",
       "4   5            5.0           3.6            1.4           0.2        0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "189fd874",
   "metadata": {},
   "source": [
    "## step2 EDA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e093beb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d01415c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <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",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\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>38.250000</td>\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>75.500000</td>\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>112.750000</td>\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>150.000000</td>\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",
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      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe730cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe13257f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Species'] = data['Species'].replace(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'],[0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "765de387",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    int64  \n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 7.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef7a8d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
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       "  <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",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000   \n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667   \n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161   \n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000   \n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000   \n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000   \n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000   \n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000   \n",
       "\n",
       "          Species  \n",
       "count  150.000000  \n",
       "mean     1.000000  \n",
       "std      0.819232  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      2.000000  \n",
       "max      2.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "57835950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91ce0087",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </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>0</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>0</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>0</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>0</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>0</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        0\n",
       "1   2            4.9           3.0            1.4           0.2        0\n",
       "2   3            4.7           3.2            1.3           0.2        0\n",
       "3   4            4.6           3.1            1.5           0.2        0\n",
       "4   5            5.0           3.6            1.4           0.2        0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8871e389",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c37309b",
   "metadata": {},
   "source": [
    "## step3 finding best k value using elbow technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "759e5b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3b7deeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "wcss = [] \n",
    "for i in range(1,11):\n",
    "    kmeans= KMeans(n_clusters = i,init='k-means++',random_state = 10)\n",
    "    kmeans.fit(data.iloc[:,:-1])\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bcb0d6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[281918.32439999987,\n",
       " 70616.29893333331,\n",
       " 31365.589670588226,\n",
       " 17793.07118503118,\n",
       " 11442.86805561735,\n",
       " 7917.101758974359,\n",
       " 5935.63121836211,\n",
       " 4552.590809941517,\n",
       " 3581.078914215687,\n",
       " 2963.9310521708694]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4c276b3e",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.xlabel('Number of clusters ')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d431219e",
   "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>0</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>0</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>0</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>0</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>0</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>2</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>2</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>2</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>2</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
       "0      1            5.1           3.5            1.4           0.2        0\n",
       "1      2            4.9           3.0            1.4           0.2        0\n",
       "2      3            4.7           3.2            1.3           0.2        0\n",
       "3      4            4.6           3.1            1.5           0.2        0\n",
       "4      5            5.0           3.6            1.4           0.2        0\n",
       "..   ...            ...           ...            ...           ...      ...\n",
       "145  146            6.7           3.0            5.2           2.3        2\n",
       "146  147            6.3           2.5            5.0           1.9        2\n",
       "147  148            6.5           3.0            5.2           2.0        2\n",
       "148  149            6.2           3.4            5.4           2.3        2\n",
       "149  150            5.9           3.0            5.1           1.8        2\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e3756c3",
   "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",
       "    </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": [
       "     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",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = data.iloc[:,:-1]\n",
    "train_data.drop(columns=['Id'],axis=1,inplace=True)\n",
    "train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a104a7c4",
   "metadata": {},
   "source": [
    "## step 4 clustering data into k clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "335017d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_model = KMeans(n_clusters=3,init='k-means++',random_state=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5c2ff1cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_model.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a14d968e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 2, 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, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2,\n",
       "       2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_labels = cluster_model.fit_predict(train_data)\n",
    "cluster_labels "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42bdc9ea",
   "metadata": {},
   "source": [
    "## step 5 visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ecd63396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After Classification')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "colors = np.array(['red', 'green', 'blue'])\n",
    "\n",
    "predictedY = np.choose(cluster_labels, [0, 1, 2]).astype(np.int64)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(data['PetalLengthCm'], data['PetalWidthCm'], c=colors[data['Species']])\n",
    "plt.title('Before Classification')\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(data['PetalLengthCm'], data['PetalWidthCm'], c=colors[predictedY])\n",
    "plt.title(\"After Classification\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "307575d3",
   "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": [
    "\n",
    "K = 3\n",
    "km = KMeans(K)\n",
    "clusts = km.fit_predict(train_data)\n",
    "\n",
    "#Plot the clusters obtained using k means\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "scatter = ax.scatter(data['PetalWidthCm'],data['SepalWidthCm'], data['PetalLengthCm'],\n",
    "                     c=clusts,s=20, cmap='winter')\n",
    "scatter = ax.scatter(km.cluster_centers_[:, 3],\n",
    "            km.cluster_centers_[:, 1],\n",
    "            km.cluster_centers_[:, 2],\n",
    "            s = 200,\n",
    "            marker='o',\n",
    "            c='red',\n",
    "            label='centroids')\n",
    "\n",
    "ax.set_title('K-Means Clustering')\n",
    "ax.set_xlabel('Petal Width')\n",
    "ax.set_ylabel('Sepal Length')\n",
    "ax.set_zlabel('Petal Length')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd37dcf",
   "metadata": {},
   "source": [
    "## step 6 Evaluating Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "eece041e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8933333333333333"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.metrics as sm\n",
    "sm.accuracy_score(data.Species, cluster_model.labels_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c6ed6d",
   "metadata": {},
   "source": [
    "#### using confusion metric "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "72fd54a2",
   "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>col_0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0     0   1   2\n",
       "Species            \n",
       "0        50   0   0\n",
       "1         0  48   2\n",
       "2         0  14  36"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data.Species, cluster_model.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd192fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        50\n",
      "           1       0.77      0.96      0.86        50\n",
      "           2       0.95      0.72      0.82        50\n",
      "\n",
      "    accuracy                           0.89       150\n",
      "   macro avg       0.91      0.89      0.89       150\n",
      "weighted avg       0.91      0.89      0.89       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "print(classification_report(data.Species, cluster_model.labels_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f2c253",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbca6fc",
   "metadata": {},
   "source": [
    "### for unlabeled we use clustering algorithm. Since we don't know the class value we group the data according the similarity( how close data points are). I was able to cluster the data into three clusters using Kmeans algorithm with accuracy 89.33%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76fee04e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
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