Task 3 Unsupervised Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task # 3 - To Explore Unsupervised Machine Learning\n",
    "From the given ‘Iris’ dataset, predict the optimum number of\n",
    "clusters and represent it visually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        [5.8, 2.7, 3.9, 1.2],\n",
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       "        [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",
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       "        [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",
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       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': 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, 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, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 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, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'C:\\\\Users\\\\shali\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\iris.csv'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris=datasets.load_iris()\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.DataFrame(iris.data, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'])\n",
    "y = pd.DataFrame(iris.target, columns=['Target'])\n",
    "c=iris.target_names\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal Length</th>\n",
       "      <th>Sepal Width</th>\n",
       "      <th>Petal Length</th>\n",
       "      <th>Petal Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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      ],
      "text/plain": [
       "   Sepal Length  Sepal Width  Petal Length  Petal Width\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Target\n",
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimension of Data\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns in data\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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  150 non-null    float64\n",
      " 1   Sepal Width   150 non-null    float64\n",
      " 2   Petal Length  150 non-null    float64\n",
      " 3   Petal Width   150 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 4.8 KB\n"
     ]
    }
   ],
   "source": [
    "#checking the presence of null values\n",
    "#x.isnull().sum()\n",
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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</th>\n",
       "      <th>Sepal Width</th>\n",
       "      <th>Petal Length</th>\n",
       "      <th>Petal Width</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  Sepal Width  Petal Length  Petal Width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of numberial variables\n",
    "x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x217a8adaee0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3))\n",
    "colors = np.array(['red', 'green', 'blue'])\n",
    "iris_targets_legend = np.array(iris.target_names)\n",
    "red_patch = mpatches.Patch(color='red', label='Setosa')\n",
    "green_patch = mpatches.Patch(color='green', label='Versicolor')\n",
    "blue_patch = mpatches.Patch(color='blue', label='Virginica')\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x['Sepal Length'], x['Sepal Width'], c=colors[y['Target']])\n",
    "plt.title('Sepal Length vs Sepal Width')\n",
    "plt.legend(handles=[red_patch, green_patch, blue_patch])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(x['Petal Length'], x['Petal Width'], c= colors[y['Target']])\n",
    "plt.title('Petal Length vs Petal Width')\n",
    "plt.legend(handles=[red_patch, green_patch, blue_patch])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the optimum number of clusters for k-means classification\n",
    "\n",
    "x = x.iloc[:, [0, 1, 2, 3]].values\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++',max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "# Plotting the results onto a line graph, \n",
    "# `allowing us to observe 'The elbow'\n",
    "plt.figure(figsize  =(10,8))\n",
    "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": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying kmeans to the dataset / Creating the kmeans classifier\n",
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++',max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_kmeans = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x217a95a7220>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the clusters - On the first two columns\n",
    "plt.figure(figsize  =(10,8))\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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}