K Means Clustering (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "1. CLustering is an Unsupervised ML that helps you identify the patterns from the data.\n",
    "\n",
    "2. There are no labels as such and hence it is not as it seem to be Supervised ML. Hence, there is no prediction and only Exploration of the data to understad the data better.\n",
    "\n",
    "3. The Clustering Algorithm that we are discussing right now is called K MEANS CLUSTERING...\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch the Data from Scikit Learn\n",
    "\n",
    "1. \"load_dataset(name)\" from sklearn\n",
    "\n",
    "2. save it in some object for e.g. i = load_iris()\n",
    "\n",
    "3. In order to fetch the data you will use i.data. (The data will be arrayed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the load iris in some object\n",
    "ir = load_iris() # call this as a function..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\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",
      "    :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",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "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",
      "\n"
     ]
    }
   ],
   "source": [
    "print(i.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "iris=pd.DataFrame(i.data, columns=i.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploying K Means Clustering\n",
    "\n",
    "1. K Means Clustering works on the concept of finding the nearest neighbors.\n",
    "\n",
    "2. Herein the nearest neighbors are found using Distance between the two points.\n",
    "\n",
    "3. The way it does is it creates Centroids/Central Points within the data to begin with. \n",
    "\n",
    "4. Identify the distance of all the points with centroid.\n",
    "\n",
    "5. If the distance is the least possible distance as it may appear to be, then the data points cling to that Cluster Centroid else the centroids keep changing their location so that we are doubly sure about the clusters getting formed...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = KMeans() # initiate the machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=k.fit(iris, i.target).predict(iris) # Default k value = 08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5, 0,\n",
       "       0, 5, 0, 5, 0, 5, 1, 1, 1, 7, 1, 7, 1, 2, 1, 7, 2, 7, 7, 4, 7, 1,\n",
       "       7, 7, 4, 7, 4, 7, 4, 4, 1, 1, 1, 1, 4, 7, 7, 7, 7, 4, 7, 1, 1, 4,\n",
       "       7, 7, 7, 1, 7, 2, 7, 7, 7, 1, 2, 7, 6, 4, 3, 6, 6, 3, 7, 3, 6, 3,\n",
       "       6, 6, 6, 4, 4, 6, 6, 3, 3, 4, 6, 4, 3, 4, 6, 3, 4, 4, 6, 3, 3, 3,\n",
       "       6, 4, 4, 3, 6, 6, 4, 6, 6, 6, 4, 6, 6, 6, 4, 6, 6, 4])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying the Optimal Cluster Size\n",
    "\n",
    "1. We will be calculating the Within Cluster Sum of Sq Error.\n",
    "\n",
    "2. If the WCSS is lowest for a certain point, that point will demonstrate the Optimal # Clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.25555556, 3.67037037, 1.5037037 , 0.28888889],\n",
       "       [6.52666667, 3.04666667, 4.6       , 1.45333333],\n",
       "       [5.        , 2.3       , 3.275     , 1.025     ],\n",
       "       [7.475     , 3.125     , 6.3       , 2.05      ],\n",
       "       [6.03636364, 2.70909091, 4.90909091, 1.69545455],\n",
       "       [4.71304348, 3.12173913, 1.4173913 , 0.19130435],\n",
       "       [6.56086957, 3.06956522, 5.52608696, 2.15217391],\n",
       "       [5.62083333, 2.69166667, 4.075     , 1.2625    ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k.cluster_centers_ # Centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.290983238178892"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k.inertia_ # Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WCSS Calculation through Loop\n",
    "wcss = []\n",
    "cluster = range(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop\n",
    "\n",
    "for i in cluster:\n",
    "    k = KMeans(n_clusters=i) # running clustering in Loop\n",
    "    model = k.fit(iris) # Fiting the Model without target values\n",
    "    wcss.append(k.inertia_) # append the error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize this...\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(cluster, wcss, \"-o\", markerfacecolor=\"red\", \n",
    "         linestyle = \"dashed\", markersize = 10)\n",
    "plt.xlabel(\"# Clusters\")\n",
    "plt.ylabel(\"Error\")\n",
    "plt.title(\"Elbow Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[680.8244,\n",
       " 152.36870647733906,\n",
       " 78.94084142614602,\n",
       " 57.31787321428571,\n",
       " 46.53558205128205,\n",
       " 38.930963049671746,\n",
       " 34.320426739926745,\n",
       " 30.085307692307694,\n",
       " 28.297603741030212]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting the K Means with 03 Cluster Size\n",
    "k = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_kmeans = k.fit(iris).predict(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# centroids \n",
    "centroid = k.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = centroid[:,0] # Sepal Length Centroid\n",
    "c2 = centroid[:, 1] # Sepal Width Centroid..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "plt.scatter(iris[iris.columns[0]], iris[iris.columns[1]], \n",
    "            c = pred_kmeans)\n",
    "plt.scatter(c1,c2, marker = \"d\", s = 220, c= \"r\")\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.title(\"K Means Clustering - IRIS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "plt.scatter(iris[iris.columns[2]], iris[iris.columns[3]], \n",
    "            c = pred_kmeans)\n",
    "plt.scatter(centroid[:, 2],centroid[:, 3], marker = \"d\", \n",
    "            s = 220, c= \"r\")\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.title(\"K Means Clustering - IRIS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Diabetes Dataset\n",
    "from sklearn.datasets import load_diabetes\n",
    "d = load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diabetes dataset\n",
      "================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "\n",
      "Ten baseline variables, age, sex, body mass index, average blood\n",
      "pressure, and six blood serum measurements were obtained for each of n =\n",
      "442 diabetes patients, as well as the response of interest, a\n",
      "quantitative measure of disease progression one year after baseline.\n",
      "\n",
      "Data Set Characteristics:\n",
      "\n",
      "  :Number of Instances: 442\n",
      "\n",
      "  :Number of Attributes: First 10 columns are numeric predictive values\n",
      "\n",
      "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
      "\n",
      "  :Attributes:\n",
      "    :Age:\n",
      "    :Sex:\n",
      "    :Body mass index:\n",
      "    :Average blood pressure:\n",
      "    :S1:\n",
      "    :S2:\n",
      "    :S3:\n",
      "    :S4:\n",
      "    :S5:\n",
      "    :S6:\n",
      "\n",
      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n",
      "\n",
      "Source URL:\n",
      "http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
      "\n",
      "For more information see:\n",
      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
      "(http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(d.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = pd.read_csv(\"./Downloads/diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = diabetes.drop(\"Outcome\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = KMeans()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = k.fit(dataset).predict(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.097458</td>\n",
       "      <td>99.546610</td>\n",
       "      <td>71.686441</td>\n",
       "      <td>14.457627</td>\n",
       "      <td>1.978814</td>\n",
       "      <td>30.424153</td>\n",
       "      <td>0.396356</td>\n",
       "      <td>32.953390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.187500</td>\n",
       "      <td>149.583333</td>\n",
       "      <td>75.041667</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>282.520833</td>\n",
       "      <td>35.952083</td>\n",
       "      <td>0.567854</td>\n",
       "      <td>34.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.736842</td>\n",
       "      <td>159.684211</td>\n",
       "      <td>71.052632</td>\n",
       "      <td>33.684211</td>\n",
       "      <td>484.473684</td>\n",
       "      <td>35.284211</td>\n",
       "      <td>0.554211</td>\n",
       "      <td>32.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.975207</td>\n",
       "      <td>154.595041</td>\n",
       "      <td>79.338843</td>\n",
       "      <td>10.735537</td>\n",
       "      <td>0.355372</td>\n",
       "      <td>33.438017</td>\n",
       "      <td>0.472504</td>\n",
       "      <td>41.280992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.805882</td>\n",
       "      <td>103.976471</td>\n",
       "      <td>68.623529</td>\n",
       "      <td>26.994118</td>\n",
       "      <td>80.435294</td>\n",
       "      <td>31.121765</td>\n",
       "      <td>0.475676</td>\n",
       "      <td>28.282353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.555556</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>25.763889</td>\n",
       "      <td>0.393167</td>\n",
       "      <td>30.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.718519</td>\n",
       "      <td>133.296296</td>\n",
       "      <td>71.800000</td>\n",
       "      <td>30.762963</td>\n",
       "      <td>163.266667</td>\n",
       "      <td>34.154074</td>\n",
       "      <td>0.560948</td>\n",
       "      <td>33.096296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>183.666667</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>31.666667</td>\n",
       "      <td>756.666667</td>\n",
       "      <td>39.700000</td>\n",
       "      <td>1.051333</td>\n",
       "      <td>37.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0           1          2          3           4          5  \\\n",
       "0  4.097458   99.546610  71.686441  14.457627    1.978814  30.424153   \n",
       "1  4.187500  149.583333  75.041667  31.500000  282.520833  35.952083   \n",
       "2  3.736842  159.684211  71.052632  33.684211  484.473684  35.284211   \n",
       "3  4.975207  154.595041  79.338843  10.735537    0.355372  33.438017   \n",
       "4  2.805882  103.976471  68.623529  26.994118   80.435294  31.121765   \n",
       "5  3.555556  117.000000   0.666667   2.000000    0.694444  25.763889   \n",
       "6  3.718519  133.296296  71.800000  30.762963  163.266667  34.154074   \n",
       "7  1.666667  183.666667  73.333333  31.666667  756.666667  39.700000   \n",
       "\n",
       "          6          7  \n",
       "0  0.396356  32.953390  \n",
       "1  0.567854  34.375000  \n",
       "2  0.554211  32.736842  \n",
       "3  0.472504  41.280992  \n",
       "4  0.475676  28.282353  \n",
       "5  0.393167  30.444444  \n",
       "6  0.560948  33.096296  \n",
       "7  1.051333  37.666667  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(k.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the Optimal Cluster Size\n",
    "cluster = range(1,10)\n",
    "error = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cluster_size in cluster:\n",
    "    k = KMeans(n_clusters=cluster_size)\n",
    "    k.fit(dataset)\n",
    "    error.append(k.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tPAA2bYK+faGn/rpFpHtoWKar1ddDWRnMmKHlCUSk26jcu1pBAfz7v4chmiuuiJ1GRHKExgm6w5VXhrs1XXcdFBfDpZfGTiQiWU7l3h3M4De/CQX/wx/C0KHw1a/GTiUiWUzl3l169IC77w5n7uPHx04jIllOY+7dKT8fbrwRDjoIdu6EV1+NnUhEspTKPZbLLw93cVq6NHYSEclCKvdYvv99KCoKV7G++WbsNCKSZVTusQwbBo8+Ctu3w6mnah0aEUkplXtMI0fCQw/B+vXw5S/rIicRSRnNlolt3Di47z7Yd98wZVJEJAV05p4OTj01lDzAggVhJo2ISCckVe5mNtHMXjOzlWa2x2vozezLZuZmVpq6iDnk+efhjDO0Do2IdFq75W5mPYCbgEnASKDCzEa2clwR8H+Av6c6ZM449lj4yU+0Do2IdFoyZ+5jgZXu/qa7bwfuAaa0ctzPgeuArSnMl3uuugrOPTesQ/PLX8ZOIyIZKplyHwy83ez52sS2T5nZ0cBQd3+wrR9kZjPMrMbMamprazscNieYhatYzz4bLrsMli+PnUhEMlAy5d7aFI5PB4TNLA/4NXBJez/I3We5e6m7lxYXFyefMtf06AG//z08+CCMGhU7jYhkoGTKfS0wtNnzIcA7zZ4XAaOBRWb2FjAOmK8PVTspPx9OPz18//TT4SEikqRkyv15YLiZHWJmvYBzgPmNO939I3cf4O4l7l4CPAtMdveaLkmcaxoawlIFZ52ldWhEJGntlru77wQuAB4FVgBz3X25mV1tZpO7OmDOy8uDefO0Do2IdIh5pPnUpaWlXlOjk/ukrVgR7sXarx9UV8OgQbETiUgEZrbY3dsd9tYVqplixIhw9er69XDTTbHTiEia09oymeS44+C550LRi4i0QWfumWbUqDAOv2YNXHSR1qERkVap3DPV44/D9dfD976ndWhEZDcalslU06eHs/errw433b7mmtiJRCSNqNwz2ZVXQm0tXHttKPhL2r1IWERyhIZlMpkZ3HADfOUrMGdOuGWfiAg6c898PXrA3XfD1q3Qq1fsNCKSJnTmng3y82G//aC+PpzFax0akZyncs8m9fXw0kthHZqXXoqdRkQiUrlnkwED4LHHoG9fOO00rUMjksNU7tlm2LBQ8Nu3w6mn0nDzb9lcMpqGvB7h65yq2AlFpBuo3LPRyJHw0EM0fPQxGy65hsmrbyDftzJ59Q1smFGpghfJASr3bDVuHHUFxVRsvZNFnMxO9mERJ1NRP5u6ypmx04lIF1O5Z7HCt1+lmrJdtlVTRuGaFZESiUh3UblnsbphIyijepdtZVRTRyF861vwySeRkolIV1O5Z7HCmZVUFUxnAgvpyQ4msJCq/G9ReFIpbNwYLoAC+MMf4I034oYVkZTSFapZLG9aBQOB+ZUXUrhmBXXDRlA48xfkTatoOuijj8IiZNu3w/jx8I1vwNlnw777RsstIp2n2+wJrF0bzt7vugteew369Alr1ZSXx04mIi3oNnuSvCFD4Iorwn1an302nL0fc0zY98gj8OMfh9IXkYyhcpcmZuFWfrfcAgcfHLY991xYUvjII+H44+HWW2HTprg5RaRdKndp209/GoZt/vM/YfNmOPdcOOmkpv26C5RIWlK5S/sOPBAuvRRefhlqauC668L2rVvhqKPg8svhlVfiZhSRXajcJXlmMGZMWJQM4IMP4NBD4b/+K9y4e+xYuOmmMM1SRKJSucveGzwY7r8f3nkHfv3rMJ3ygguaPnzduBF27IibUSRHqdyl8wYOhB/8AF58MQzdHHdc2P6jH4WZOBdfrPXlRbqZyl1Sa/ToMHwDYZ58WRnceCN87nNheuUddwDQMKdKSxGLdCGVu3Sd006Dv/wlDNvccAPk5UFNDQ1zqtgwo1JLEYt0oaSuUDWzicB/Az2A2939mhb7Lwa+A+wEaoFvu/vqtn6mrlDNUdu3s/nwY5i8+gYWcfKnmyewkPn7f4OiF56CkpJ4+UTSXMquUDWzHsBNwCRgJFBhZiNbHPYCUOru/wL8Gbiu45ElJ/TqReGaFa0vRbxpLaxcGTY89RR87Wvwq1+F7zdvjhBWJHMlMywzFljp7m+6+3bgHmBK8wPcfaG71yeePgsMSW1MySZ7XIp4yJFh8TKA9euhuhouuSRcNLXffjBiBKxbF/bX1kJdXTcnF8kcyZT7YODtZs/XJrbtyXTg4dZ2mNkMM6sxs5ra2trkU0pWaXUp4oLpFF7zE8jPDwd99auwZg289x4sWABXXRVuH/iZz4T9V14ZVq4cPRq++c0wpv+3v+mKWZGEZJb8tVa2tfp/kJl9HSgFTmptv7vPAmZBGHNPMqNkmdaXIp6561LEjQYOhEmTwqO5igooLg5XzD78MPzud+Hm4KsTH/XcfHOYtVNaGq6i7d27y/+7RNJJMuW+Fhja7PkQ4J2WB5nZF4BK4CR335aaeJKt8qZVUJQo86K9+QFlZeEB4Wx93bqmIRuA22+HF14I3/fsGQr+K18Jq19CuAtV481KmmmYU0Vd5cxmbzqVrb/piKS5ZMr9eWC4mR0CrAPOAb7W/AAzOxq4FZjo7htSnlKkLWbhYqkhzT7qWbw4DOvU1ITva2rCjUkgFHtxMRx2WFhOobQUSktpWPoSG877GRX1s6mmjLLV1VTNmM5AUMFLxkl2KuTpwPWEqZB3uPtMM7saqHH3+Wb2/4CjgPWJP7LG3Se39TM1FVKi2bIFfv7zpuJPlP7m/YYw+aO7d5+iOfQCitYsj5VWZBfJToXUnZgktzU0hPvH1tTQMO3r5PtWdrLPp7t7soNt5JN34CC4885wYdaaNbBoEXz2s+ExaFC4QEukG+hOTCLJyMuD4cOhomLPUzT3GwwTJ4aF0gCefjrcrWr8+LCtsDDM5Fm2LOx//XWYPz8813RNiUTlLpKwxymaN10X1sQZPTocePbZYeXLhx8Os3IuuCDMwT/ggLD/3nthypTwIW7fvmH65vHHw/vvh/3LlsGTT8Lbb4ffHJKgtXiko5L5QFUkJyQ9RbNXLzj88PBozXnnwSmnwJtvNj1WrYL99w/7b7klvCk0/qySkvDh7gMPhN8kXn45lP5nPwtFRZ+uxaMPeqUjNOYu0t3WrQt3rmpe/lu2hN8EACZPDkUPMGAAmz92Jm//0+4f9B74PYqeXxhm/vTqFeE/RGLQB6oimWr58qbyX7WKhltnkc+21j/obbye8ItfhMceC9+ffz7U14fSb3wceSSMGxf219dDnz5NSzN3gq4L6H7JlruGZUTSzahR4ZFQ90g1ZaurdzlzL6OaugElFP38sjCWP2hQ059//XVYsSKsv7N9e9h29tkwd274fvBg+Oc/Q+kPGBC+lpeH4SSA226D/v13fXPo33+3GUEaLkpvKneRNFc4s5KqGdObSpTq8EHv9TOhtRJ9/PHw1T0M99TWNhWzO1RWwoYN4U2htjY8Nm0K++vrYcaM3X/mZZfBtdfCxx/DmWdCcTF1j/2Nivo5n77pLOJkKupnM//y8yiaOhkKClLy24HsHZW7SJrr0Fo8zZlBUVF4NN926aV7/jO9e8PataHwm5f/MceE/fX14Y3i1Vcp3PJu60s3r3s9zBLq3Tv8ZnDAAeGisbPOCtcIzJ4dtjd/HHbYrjk7ScNFKneRjNDptXiSfqG8MGwzeA8Lvw4aFC7gAupKRrc+XNR/GEWXnQcffBDeIN5/v6m433gjFH3Lz/oeeCD8RvDoo/Cd7zSV/gEHhK8XXQSHHhqmj772WtP2AQPC5wfNaLgowd2jPMaMGeMikrk++cMffX3BIT6BJ7wn230CT/j6gkP8kz/8se0/uHOne22t+4oV7k8/7X7ffe7vvhv2Pf+8+ze/6X7mme7HH+8+fLh7v37uixeH/bfe6h7eGpoeBQXur74a9s+b5x8XfMYn8MQuh0zgCf/44FHuH34YXnvHjq77i+lihGVf2u1YnbmLyF7Z6+GiHj2azrpbKi0NyzzsSXl5uBq48TeCxsfAgWH/hg0U1m9ofbhozYrwucG114aN++0XPiju3x+eeSZMJ/3Tn8Jqoo3b+/cPvyU03kRm586wyuhe6s7hIpW7iOy1bhsuatQ4e2dPvvtd6mb+d+vDRcNGUFReHoacPvyw6fHxx03XCTz5JNx6ayjxRvvvDxs3hu+nTYP7728q/n79wucFjW9Ic+eGN5vmbw4DB8KwYd0+XKR57iKSVXYr0cTsooGzkvitAppmGTWWf309nHBC2Pc//wNLljTt27gxlP+994b948eH20M2d/TRsGQJm0tGt35j+IMvpOitZUn/9+kiJhHJWdFmy2zbFgp/48amN4D8fDj1VBryerS+6qj1Jq/hk6RfQhcxiUjO6vbhokb5+WFGUfOLyhLqho3Y83BRF0TRqpAiIt1gj6uOzqzsktfTmbuISDfY69lFe0nlLiLSTbpzuEjDMiIiWUjlLiKShVTuIiJZSOUuIpKFVO4iIlko2hWqZlYLrN7LPz4AeD+FcVIlXXNB+mZTro5Rro7JxlwHu3sbC+wE0cq9M8ysJpnLb7tbuuaC9M2mXB2jXB2Ty7k0LCMikoVU7iIiWShTy31W7AB7kK65IH2zKVfHKFfH5GyujBxzFxGRtmXqmbuIiLRB5S4ikoUyqtzN7A4z22Bmyd+TqhuY2VAzW2hmK8xsuZl9P3YmADPrbWbPmdnSRK6rYmdqzsx6mNkLZvZg7CyNzOwtM3vZzF40s7S5VZiZ7W9mfzazVxP/zo5Pg0xHJP6eGh8fm9kPYucCMLOLEv/ml5lZlZn1jp0JwMy+n8i0vKv/rjJqzN3MTgS2AHe7++jYeRqZ2YHAge6+xMyKgMXAVHd/JXIuAwrdfYuZ7QNUA99392dj5mpkZhcDpcC+7n5m7DwQyh0odfe0uvDFzH4HPO3ut5tZL6DA3TfFztXIzHoA64Dj3H1vL05MVZbBhH/rI939n2Y2F1jg7ndFzjUauAcYC2wHHgHOc/d/dMXrZdSZu7s/BXwYO0dL7r7e3Zckvt8MrAAGx00FHmxJPN0n8UiLd3MzGwKcAdweO0u6M7N9gROB2QDuvj2dij3hFOCN2MXeTE+gj5n1BAqAdyLnARgBPOvu9e6+E3gSKO+qF8uocs8EZlYCHA38PW6SIDH08SKwAXjc3dMiF3A9cBnQEDtICw48ZmaLzWxG7DAJnwVqgTsTw1i3m1lh7FAtnANUxQ4B4O7rgF8Ca4D1wEfu/ljcVAAsA040swPMrAA4HRjaVS+mck8hM+sL/AX4gbt/HDsPgLt/4u6fB4YAYxO/GkZlZmcCG9x9cewsrTjB3Y8BJgHnJ4YCY+sJHAPc4u5HA3XAFXEjNUkME00G/hQ7C4CZ9QOmAIcABwGFZvb1uKnA3VcA1wKPE4ZklgI7u+r1VO4pkhjT/gswx93vjZ2npcSv8YuAiZGjAJwATE6Mb98D/JuZ/SFupMDd30l83QDcRxgfjW0tsLbZb11/JpR9upgELHH392IHSfgCsMrda919B3Av8K+RMwHg7rPd/Rh3P5EwxNwl4+2gck+JxAeXs4EV7v6r2HkamVmxme2f+L4P4R/9q3FTgbv/yN2HuHsJ4df5J9w9+pmVmRUmPhAnMexxKuFX6ajc/V3gbTM7IrHpFCDqh/UtVJAmQzIJa4BxZlaQ+H/zFMLnYNGZ2cDE12HAl+jCv7eMukG2mVUBE4ABZrYW+Jm7z46bCghnov8BvJwY3wb4sbsviJgJ4EDgd4mZDHnAXHdPm2mHaegzwH2hD+gJ/NHdH4kb6VMXAnMSQyBvAt+KnAeAxNjxF4Hvxc7SyN3/bmZ/BpYQhj1eIH2WIfiLmR0A7ADOd/eNXfVCGTUVUkREkqNhGRGRLKRyFxHJQip3EZEspHIXEclCKncRkSykchcRyUIqdxGRLPT/Ac1SOLeVdFayAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cluster, error, \"-o\", c = \"r\", \n",
    "         markerfacecolor=\"blue\", linestyle = \"dashed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[11615812.918327227,\n",
       " 5142376.455984383,\n",
       " 2913322.58008178,\n",
       " 2151126.9425235316,\n",
       " 1737626.3385149883,\n",
       " 1466591.4021123825,\n",
       " 1301095.9853839797,\n",
       " 1143409.4096190135,\n",
       " 1001679.0317468981]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "413995.9642019407"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2151320.938003855-1737324.9738019141"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pred_clusters' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-1ff639835e11>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGlucose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBMI\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mpred_clusters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m plt.scatter(centres[\"Glucose\"], centres[\"BMI\"], marker = \"d\",c = \"r\",\n\u001b[0;32m      3\u001b[0m            s = 120)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pred_clusters' is not defined"
     ]
    }
   ],
   "source": [
    "plt.scatter(dataset.Glucose, dataset.BMI, c =pred_clusters)\n",
    "plt.scatter(centres[\"Glucose\"], centres[\"BMI\"], marker = \"d\",c = \"r\",\n",
    "           s = 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting the No of Clusters = 4\n",
    "k = KMeans(n_clusters=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_clusters = k.fit(dataset).predict(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "centres = pd.DataFrame(k.cluster_centers_, columns=dataset.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "centres[\"Glucose\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Findings\n",
    "\n",
    "1. BMI and Glucose are very much linked together\n",
    "\n",
    "2. Although we have used these variables but the role of cluster is not identified. If I assume that people are diabetic vs non diabetic then the rest of the 02 cluster centroids have some meaningful information that is needed to be decoded.\n",
    "\n",
    "3. We get the cluster size as 4 and hence we need to find out more information about these groups.\n",
    "\n",
    "## OPTIMAL # of CLUSTERS is Dependant on the Business/ Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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, 0, 0, 2, 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
       "       2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IRIS Labels\n",
    "pred_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append the Pred_kmeans in IRIS Dataset\n",
    "pred = pd.DataFrame(pred_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred.columns=[\"Species\"] # Renaming the column of Pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append the pred df into IRIS\n",
    "newiris = pd.concat([iris,pred], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Silhoutte Score\n",
    "\n",
    "Higher the Silhoutte Score, the better the clustering would be...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5525919445213676"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(iris, newiris.Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_size = [2,3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Cluster  2 The Silhoutte is  0.6808136202713507\n",
      "On Cluster  3 The Silhoutte is  0.5525919445213676\n",
      "On Cluster  4 The Silhoutte is  0.49722797262968016\n",
      "On Cluster  5 The Silhoutte is  0.4885175508538632\n",
      "On Cluster  6 The Silhoutte is  0.36951627458890624\n"
     ]
    }
   ],
   "source": [
    "sil = []\n",
    "silhoutte = []\n",
    "for s in cluster_size:\n",
    "    k = KMeans(n_clusters=s)\n",
    "    model = k.fit(iris).predict(iris)\n",
    "    sil = silhouette_score(iris, model) # calc the silhoutte score\n",
    "    silhoutte.append(sil)\n",
    "    print(\"On Cluster \", s, \"The Silhoutte is \", sil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Silhoutte Plot')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,8])\n",
    "plt.subplot(221)\n",
    "plt.plot(cluster, wcss, \"-o\", markerfacecolor = \"red\", markersize = 8)\n",
    "plt.xlabel(\"# Cluster\")\n",
    "plt.ylabel(\"WCSS\")\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(cluster_size, silhoutte, \"-o\", markerfacecolor = \"red\", \n",
    "         markersize = 8, linestyle=\"dashed\")\n",
    "plt.xlabel(\"# Cluster\")\n",
    "plt.ylabel(\"Silhoutte\")\n",
    "plt.title(\"Silhoutte Plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that Silhoutte Value has to be considered with WCSS. If the WCSS is high and Silhoutte is also high then such Cluster value will not be considered. We will be considering Silhoutte only in case where the WCSS is lowest..."
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}