R4_InternalLab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data set has information about features of silhouette extracted from the images of different cars\n",
    "\n",
    "Four \"Corgie\" model vehicles were used for the experiment: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400 cars. This particular combination of vehicles was chosen with the expectation that the bus, van and either one of the cars would be readily distinguishable, but it would be more difficult to distinguish between the cars.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Read the dataset using function .dropna() - to avoid dealing with NAs as of now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.stats import zscore\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "plt.rcParams['figure.figsize'] = (16, 9)\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10</td>\n",
       "      <td>162.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>159</td>\n",
       "      <td>176.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>197</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>170.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>199</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>50.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10</td>\n",
       "      <td>207.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>158</td>\n",
       "      <td>223.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>196</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9</td>\n",
       "      <td>144.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>160.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>207</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>44.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>52</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>144</td>\n",
       "      <td>241.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>183</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0           95         48.0                  83.0         178.0   \n",
       "1           91         41.0                  84.0         141.0   \n",
       "2          104         50.0                 106.0         209.0   \n",
       "3           93         41.0                  82.0         159.0   \n",
       "4           85         44.0                  70.0         205.0   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0                  72.0                       10          162.0   \n",
       "1                  57.0                        9          149.0   \n",
       "2                  66.0                       10          207.0   \n",
       "3                  63.0                        9          144.0   \n",
       "4                 103.0                       52          149.0   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0           42.0                    20.0                        159   \n",
       "1           45.0                    19.0                        143   \n",
       "2           32.0                    23.0                        158   \n",
       "3           46.0                    19.0                        143   \n",
       "4           45.0                    19.0                        144   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0            176.0              379.0                      184.0   \n",
       "1            170.0              330.0                      158.0   \n",
       "2            223.0              635.0                      220.0   \n",
       "3            160.0              309.0                      127.0   \n",
       "4            241.0              325.0                      188.0   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                         70.0             6.0              16.0   \n",
       "1                         72.0             9.0              14.0   \n",
       "2                         73.0            14.0               9.0   \n",
       "3                         63.0             6.0              10.0   \n",
       "4                        127.0             9.0              11.0   \n",
       "\n",
       "   skewness_about.2  hollows_ratio class  \n",
       "0             187.0            197   van  \n",
       "1             189.0            199   van  \n",
       "2             188.0            196   car  \n",
       "3             199.0            207   van  \n",
       "4             180.0            183   bus  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "vehicle_data = pd.read_csv('vehicle.csv')\n",
    "vehicle_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_data.isnull().values.any() #check for missing data. True indicates that there are missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the missing row as of now\n",
    "vehicle_data_removedNA = vehicle_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_data_removedNA.isnull().values.any() #check for missing data. True indicates that there are missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Print/ Plot the dependent (categorical variable) - Class column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the variable is categorical, you can use value_counts function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bus</th>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car</th>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>van</th>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "class                                                                 \n",
       "bus            205          205                   205           205   \n",
       "car            413          413                   413           413   \n",
       "van            195          195                   195           195   \n",
       "\n",
       "       pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "class                                                                 \n",
       "bus                     205                      205            205   \n",
       "car                     413                      413            413   \n",
       "van                     195                      195            195   \n",
       "\n",
       "       elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "class                                                                     \n",
       "bus              205                     205                        205   \n",
       "car              413                     413                        413   \n",
       "van              195                     195                        195   \n",
       "\n",
       "       scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "class                                                                  \n",
       "bus                205                205                        205   \n",
       "car                413                413                        413   \n",
       "van                195                195                        195   \n",
       "\n",
       "       scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "class                                                                  \n",
       "bus                            205             205               205   \n",
       "car                            413             413               413   \n",
       "van                            195             195               195   \n",
       "\n",
       "       skewness_about.2  hollows_ratio  \n",
       "class                                   \n",
       "bus                 205            205  \n",
       "car                 413            413  \n",
       "van                 195            195  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing the count\n",
    "vehicle_data_removedNA.groupby('class').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# different types of vehicles\n",
    "\n",
    "plot = sns.countplot(x = \"class\", data = vehicle_data_removedNA)\n",
    "plot.set_xticklabels(plot.get_xticklabels(), rotation=40);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for any missing values in the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "compactness                    0\n",
       "circularity                    5\n",
       "distance_circularity           4\n",
       "radius_ratio                   6\n",
       "pr.axis_aspect_ratio           2\n",
       "max.length_aspect_ratio        0\n",
       "scatter_ratio                  1\n",
       "elongatedness                  1\n",
       "pr.axis_rectangularity         3\n",
       "max.length_rectangularity      0\n",
       "scaled_variance                3\n",
       "scaled_variance.1              2\n",
       "scaled_radius_of_gyration      2\n",
       "scaled_radius_of_gyration.1    4\n",
       "skewness_about                 6\n",
       "skewness_about.1               1\n",
       "skewness_about.2               1\n",
       "hollows_ratio                  0\n",
       "class                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Standardize the data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the dimensions of the data are not really known to us, it would be wise to standardize the data using z scores before we go for any clustering methods.\n",
    "You can use zscore function to do this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependentVar_df = vehicle_data_removedNA.iloc[:,-1]\n",
    "independentVar_df = vehicle_data_removedNA.iloc[:,0:18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.163231</td>\n",
       "      <td>0.520408</td>\n",
       "      <td>0.060669</td>\n",
       "      <td>0.264970</td>\n",
       "      <td>1.283254</td>\n",
       "      <td>0.299721</td>\n",
       "      <td>-0.198517</td>\n",
       "      <td>0.129648</td>\n",
       "      <td>-0.217151</td>\n",
       "      <td>0.766312</td>\n",
       "      <td>-0.397397</td>\n",
       "      <td>-0.339014</td>\n",
       "      <td>0.301676</td>\n",
       "      <td>-0.321192</td>\n",
       "      <td>-0.071523</td>\n",
       "      <td>0.371287</td>\n",
       "      <td>-0.321809</td>\n",
       "      <td>0.171837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.322874</td>\n",
       "      <td>-0.619123</td>\n",
       "      <td>0.124067</td>\n",
       "      <td>-0.836393</td>\n",
       "      <td>-0.599253</td>\n",
       "      <td>0.085785</td>\n",
       "      <td>-0.591720</td>\n",
       "      <td>0.514333</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.337462</td>\n",
       "      <td>-0.590034</td>\n",
       "      <td>-0.618754</td>\n",
       "      <td>-0.502972</td>\n",
       "      <td>-0.053505</td>\n",
       "      <td>0.538425</td>\n",
       "      <td>0.147109</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.442318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.256966</td>\n",
       "      <td>0.845988</td>\n",
       "      <td>1.518823</td>\n",
       "      <td>1.187734</td>\n",
       "      <td>0.530251</td>\n",
       "      <td>0.299721</td>\n",
       "      <td>1.162569</td>\n",
       "      <td>-1.152637</td>\n",
       "      <td>0.949438</td>\n",
       "      <td>0.697326</td>\n",
       "      <td>1.111591</td>\n",
       "      <td>1.122486</td>\n",
       "      <td>1.415804</td>\n",
       "      <td>0.080339</td>\n",
       "      <td>1.555006</td>\n",
       "      <td>-0.413338</td>\n",
       "      <td>-0.159204</td>\n",
       "      <td>0.036596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.079822</td>\n",
       "      <td>-0.619123</td>\n",
       "      <td>-0.002729</td>\n",
       "      <td>-0.300595</td>\n",
       "      <td>0.153750</td>\n",
       "      <td>0.085785</td>\n",
       "      <td>-0.742952</td>\n",
       "      <td>0.642562</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.337462</td>\n",
       "      <td>-0.911095</td>\n",
       "      <td>-0.738643</td>\n",
       "      <td>-1.462359</td>\n",
       "      <td>-1.258099</td>\n",
       "      <td>-0.071523</td>\n",
       "      <td>-0.301249</td>\n",
       "      <td>1.629444</td>\n",
       "      <td>1.524243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.052030</td>\n",
       "      <td>-0.130753</td>\n",
       "      <td>-0.763506</td>\n",
       "      <td>1.068668</td>\n",
       "      <td>5.173770</td>\n",
       "      <td>9.285029</td>\n",
       "      <td>-0.591720</td>\n",
       "      <td>0.514333</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.268476</td>\n",
       "      <td>1.689501</td>\n",
       "      <td>-0.647299</td>\n",
       "      <td>0.425468</td>\n",
       "      <td>7.307905</td>\n",
       "      <td>0.538425</td>\n",
       "      <td>-0.189159</td>\n",
       "      <td>-1.460039</td>\n",
       "      <td>-1.721531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0     0.163231     0.520408              0.060669      0.264970   \n",
       "1    -0.322874    -0.619123              0.124067     -0.836393   \n",
       "2     1.256966     0.845988              1.518823      1.187734   \n",
       "3    -0.079822    -0.619123             -0.002729     -0.300595   \n",
       "4    -1.052030    -0.130753             -0.763506      1.068668   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0              1.283254                 0.299721      -0.198517   \n",
       "1             -0.599253                 0.085785      -0.591720   \n",
       "2              0.530251                 0.299721       1.162569   \n",
       "3              0.153750                 0.085785      -0.742952   \n",
       "4              5.173770                 9.285029      -0.591720   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0       0.129648               -0.217151                   0.766312   \n",
       "1       0.514333               -0.606014                  -0.337462   \n",
       "2      -1.152637                0.949438                   0.697326   \n",
       "3       0.642562               -0.606014                  -0.337462   \n",
       "4       0.514333               -0.606014                  -0.268476   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0        -0.397397          -0.339014                   0.301676   \n",
       "1        -0.590034          -0.618754                  -0.502972   \n",
       "2         1.111591           1.122486                   1.415804   \n",
       "3        -0.911095          -0.738643                  -1.462359   \n",
       "4         1.689501          -0.647299                   0.425468   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                    -0.321192       -0.071523          0.371287   \n",
       "1                    -0.053505        0.538425          0.147109   \n",
       "2                     0.080339        1.555006         -0.413338   \n",
       "3                    -1.258099       -0.071523         -0.301249   \n",
       "4                     7.307905        0.538425         -0.189159   \n",
       "\n",
       "   skewness_about.2  hollows_ratio  \n",
       "0         -0.321809       0.171837  \n",
       "1          0.003400       0.442318  \n",
       "2         -0.159204       0.036596  \n",
       "3          1.629444       1.524243  \n",
       "4         -1.460039      -1.721531  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "independentVar_df_z = independentVar_df.apply(zscore)\n",
    "independentVar_df_z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vehicle_data_removedNA_z = independentVar_df_z\n",
    "#vehicle_data_removedNA_z['class'] = dependentVar_df\n",
    "#vehicle_data_removedNA_z.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K - Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Assign a dummy array called distortion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "distortion = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can later use this array to plot the elbow plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Calculating distortion - C distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iterating values of k from 1 to 10 fit K means model\n",
    "Using c distance - Get the measure for Sum of squares error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>num_clusters</th>\n",
       "      <th>cluster_errors</th>\n",
       "      <th>Avg Sil Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>8623.136976</td>\n",
       "      <td>0.389788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>7037.467395</td>\n",
       "      <td>0.290415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5741.186965</td>\n",
       "      <td>0.305264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>5215.990806</td>\n",
       "      <td>0.232440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4717.289831</td>\n",
       "      <td>0.221005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>4350.784822</td>\n",
       "      <td>0.223034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>4149.435989</td>\n",
       "      <td>0.188538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>3846.982451</td>\n",
       "      <td>0.208055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_clusters  cluster_errors  Avg Sil Score\n",
       "0             2     8623.136976       0.389788\n",
       "1             3     7037.467395       0.290415\n",
       "2             4     5741.186965       0.305264\n",
       "3             5     5215.990806       0.232440\n",
       "4             6     4717.289831       0.221005\n",
       "5             7     4350.784822       0.223034\n",
       "6             8     4149.435989       0.188538\n",
       "7             9     3846.982451       0.208055"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_range = range( 2, 10) #no point in having 1 cluster, hence taking it from 2.\n",
    "cluster_sil_scores = []\n",
    "for num_clusters in cluster_range:\n",
    "  clusters = KMeans( num_clusters, n_init = 5)\n",
    "  clusters.fit(independentVar_df_z)\n",
    "  labels = clusters.labels_                     # capture the cluster lables\n",
    "  centroids = clusters.cluster_centers_         # capture the centroids\n",
    "  distortion.append( clusters.inertia_ )    # capture the intertia\n",
    "  cluster_sil_scores.append(metrics.silhouette_score(independentVar_df_z, labels, metric='euclidean'))\n",
    "\n",
    "\n",
    "# combine the cluster_range and cluster_errors into a dataframe by combining them\n",
    "clusters_df = pd.DataFrame( { \"num_clusters\":cluster_range, \"cluster_errors\": distortion , \"Avg Sil Score\": cluster_sil_scores } )\n",
    "clusters_df[0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Plotting Elbow/ Scree Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xfd91fd0>]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot( clusters_df.num_clusters, clusters_df.cluster_errors, marker = \"o\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Matplotlib to plot the scree plot - Note: Scree plot plots distortion vs the no of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find out the optimal value of K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The elbow plot shows that 3 or 5 might be a good choice for K and it seems logical to go with 3 given we know that there are 3 qualities of wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using optimal value of K - Cluster the data. \n",
    "Note: Since the data has more than 2 dimension we cannot visualize the data. As an alternative, we can observe the centroids and note how they are distributed across different dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=1, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = KMeans( n_clusters = 3, random_state = 1 )\n",
    "cluster.fit(independentVar_df_z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use kmeans.cluster_centers_ function to pull the centroid information from the instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.91987072, -0.52009251, -0.89320752, -1.06435572, -0.50042171,\n",
       "        -0.2190738 , -0.7791213 ,  0.86589322, -0.74535635, -0.484632  ,\n",
       "        -0.78240328, -0.78341064, -0.38498232,  0.83878747, -0.09524349,\n",
       "        -0.30171562, -1.05420601, -1.11069466],\n",
       "       [ 1.13076532,  1.17094237,  1.19500584,  1.01909505,  0.21505188,\n",
       "         0.34250798,  1.27180461, -1.19061224,  1.27548416,  1.09266768,\n",
       "         1.22038121,  1.28132727,  1.07954232, -0.02879497,  0.16229031,\n",
       "         0.26566456, -0.00535553,  0.18380067],\n",
       "       [-0.2339617 , -0.57387481, -0.30776905, -0.03041322,  0.2050726 ,\n",
       "        -0.11653151, -0.45904181,  0.32506329, -0.48798836, -0.53604446,\n",
       "        -0.41380935, -0.46366305, -0.60155031, -0.61924057, -0.06177969,\n",
       "         0.01066762,  0.81278556,  0.69897299]])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids = cluster.cluster_centers_\n",
    "centroids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Store the centroids in a dataframe with column names from the original dataset given "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.919871</td>\n",
       "      <td>-0.520093</td>\n",
       "      <td>-0.893208</td>\n",
       "      <td>-1.064356</td>\n",
       "      <td>-0.500422</td>\n",
       "      <td>-0.219074</td>\n",
       "      <td>-0.779121</td>\n",
       "      <td>0.865893</td>\n",
       "      <td>-0.745356</td>\n",
       "      <td>-0.484632</td>\n",
       "      <td>-0.782403</td>\n",
       "      <td>-0.783411</td>\n",
       "      <td>-0.384982</td>\n",
       "      <td>0.838787</td>\n",
       "      <td>-0.095243</td>\n",
       "      <td>-0.301716</td>\n",
       "      <td>-1.054206</td>\n",
       "      <td>-1.110695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.130765</td>\n",
       "      <td>1.170942</td>\n",
       "      <td>1.195006</td>\n",
       "      <td>1.019095</td>\n",
       "      <td>0.215052</td>\n",
       "      <td>0.342508</td>\n",
       "      <td>1.271805</td>\n",
       "      <td>-1.190612</td>\n",
       "      <td>1.275484</td>\n",
       "      <td>1.092668</td>\n",
       "      <td>1.220381</td>\n",
       "      <td>1.281327</td>\n",
       "      <td>1.079542</td>\n",
       "      <td>-0.028795</td>\n",
       "      <td>0.162290</td>\n",
       "      <td>0.265665</td>\n",
       "      <td>-0.005356</td>\n",
       "      <td>0.183801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.233962</td>\n",
       "      <td>-0.573875</td>\n",
       "      <td>-0.307769</td>\n",
       "      <td>-0.030413</td>\n",
       "      <td>0.205073</td>\n",
       "      <td>-0.116532</td>\n",
       "      <td>-0.459042</td>\n",
       "      <td>0.325063</td>\n",
       "      <td>-0.487988</td>\n",
       "      <td>-0.536044</td>\n",
       "      <td>-0.413809</td>\n",
       "      <td>-0.463663</td>\n",
       "      <td>-0.601550</td>\n",
       "      <td>-0.619241</td>\n",
       "      <td>-0.061780</td>\n",
       "      <td>0.010668</td>\n",
       "      <td>0.812786</td>\n",
       "      <td>0.698973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0    -0.919871    -0.520093             -0.893208     -1.064356   \n",
       "1     1.130765     1.170942              1.195006      1.019095   \n",
       "2    -0.233962    -0.573875             -0.307769     -0.030413   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0             -0.500422                -0.219074      -0.779121   \n",
       "1              0.215052                 0.342508       1.271805   \n",
       "2              0.205073                -0.116532      -0.459042   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0       0.865893               -0.745356                  -0.484632   \n",
       "1      -1.190612                1.275484                   1.092668   \n",
       "2       0.325063               -0.487988                  -0.536044   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0        -0.782403          -0.783411                  -0.384982   \n",
       "1         1.220381           1.281327                   1.079542   \n",
       "2        -0.413809          -0.463663                  -0.601550   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                     0.838787       -0.095243         -0.301716   \n",
       "1                    -0.028795        0.162290          0.265665   \n",
       "2                    -0.619241       -0.061780          0.010668   \n",
       "\n",
       "   skewness_about.2  hollows_ratio  \n",
       "0         -1.054206      -1.110695  \n",
       "1         -0.005356       0.183801  \n",
       "2          0.812786       0.698973  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroid_df = pd.DataFrame(centroids, columns = list(independentVar_df_z) )\n",
    "centroid_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hint: Use pd.Dataframe function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use kmeans.labels_ function to print out the labels of the classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=cluster.predict(independentVar_df_z)\n",
    "independentVar_df_z[\"GROUP\"] = prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "GROUP                                                                 \n",
       "0              240          240                   240           240   \n",
       "1              260          260                   260           260   \n",
       "2              313          313                   313           313   \n",
       "\n",
       "       pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "GROUP                                                                 \n",
       "0                       240                      240            240   \n",
       "1                       260                      260            260   \n",
       "2                       313                      313            313   \n",
       "\n",
       "       elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "GROUP                                                                     \n",
       "0                240                     240                        240   \n",
       "1                260                     260                        260   \n",
       "2                313                     313                        313   \n",
       "\n",
       "       scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "GROUP                                                                  \n",
       "0                  240                240                        240   \n",
       "1                  260                260                        260   \n",
       "2                  313                313                        313   \n",
       "\n",
       "       scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "GROUP                                                                  \n",
       "0                              240             240               240   \n",
       "1                              260             260               260   \n",
       "2                              313             313               313   \n",
       "\n",
       "       skewness_about.2  hollows_ratio  \n",
       "GROUP                                   \n",
       "0                   240            240  \n",
       "1                   260            260  \n",
       "2                   313            313  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "independentVar_df_z.groupby(\"GROUP\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       2, 1, 0, 2, 2, 2, 0, 0, 0, 2, 1, 2, 1, 0, 2, 2, 2, 2, 1, 2, 0, 1,\n",
       "       2, 1, 2, 2, 1, 0, 1, 0, 2, 2, 2, 0, 1, 2, 0, 2, 1, 0, 1, 2, 2, 1,\n",
       "       0, 2, 0, 2, 2, 0, 2, 1, 1, 2, 2, 1, 1, 2, 2, 0, 2, 1, 1, 1, 1, 2,\n",
       "       1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 0, 1, 2, 0, 1, 1, 1, 2, 1, 0, 0, 1,\n",
       "       1, 1, 2, 1, 2, 2, 1, 2, 0, 2, 0, 2, 1, 2, 0, 2, 2, 2, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 2, 2, 1, 2, 0, 1, 1, 0, 2, 2, 1, 1, 1, 0, 1,\n",
       "       2, 1, 1, 0, 0, 1, 0, 1, 2, 0, 2, 1, 1, 2, 0, 2, 1, 1, 2, 2, 0, 2,\n",
       "       2, 1, 0, 2, 1, 0, 0, 1, 0, 2, 0, 0, 0, 2, 1, 1, 2, 0, 1, 2, 1, 1,\n",
       "       0, 2, 1, 0, 0, 2, 2, 1, 0, 0, 1, 0, 2, 2, 2, 2, 2, 2, 1, 2, 0])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarchical Clustering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Variable creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Hierarchical clustering, we will create datasets using multivariate normal distribution to visually observe how the clusters are formed at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.random.multivariate_normal([10, 0], [[3, 1], [1, 4]], size=[100,])\n",
    "b = np.random.multivariate_normal([0, 20], [[3, 1], [1, 4]], size=[50,])\n",
    "c = np.random.multivariate_normal([10, 20], [[3, 1], [1, 4]], size=[100,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_df = pd.DataFrame(a, columns=list('xy'))\n",
    "b_df = pd.DataFrame(b, columns=list('xy'))\n",
    "c_df = pd.DataFrame(c, columns=list('xy'))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Combine all three arrays a,b,c into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 2)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.concat([a_df,b_df,c_df],axis=0)\n",
    "dataset.head()\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Use scatter matrix to print all the 3 distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000CA77198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000CB059E8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FE180B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000CAD2160>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(dataset, alpha=0.2, figsize=(6, 6), diagonal='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Find out the linkage matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9603509410578206"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import cophenet, dendrogram, linkage,fcluster\n",
    "from scipy.spatial.distance import pdist  #Pairwise distribution between data points\n",
    "Z = linkage(dataset, 'ward')\n",
    "c, coph_dists = cophenet(Z , pdist(dataset))\n",
    "\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use ward as linkage metric and distance as Eucledian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Plot the dendrogram for the consolidated dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 15))\n",
    "plt.title('Agglomerative Hierarchical Clustering Dendogram')\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('Distance')\n",
    "dendrogram(Z, leaf_rotation=90.,color_threshold=30,  leaf_font_size=8. )\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13. Recreate the dendrogram for last 12 merged clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AYLNKF3fFL774IoYPHx4vvfRSlC5dOkaPHh2vvvpqLFq0KLp27VqSMwIAAAD/4Yp9BMYf/vCHqFChQtx3331RuvQ33aNx48Yxffr0EhsOAAAAIGI7jsCYO3du3H///bl4ERFRuXLl+PTTT0tkMAAAAICNin0ERoUKFeLzzz/PW7Z8+fKoVq3aDh8KAAAA4NuKHTCOPfbYGDhwYLzxxhuRZVksXLgw7r333jjuuONKcj4AAACA4r+FpEuXLlGmTJl48MEHY/369TFs2LDo0KFDdOrUqSTnAwAAACh+wCgoKIgTTzwxTjzxxJKcBwAAAKCIYr+FZNy4cbFo0aK8ZYsWLYonn3xyhw8FAAAA8G3FDhjPPPNM7LnnnnnL9txzz3jmmWd2+FAAAAAA31bsgPH111/nfYRqRETp0qXjq6++2uFDAQAAAHxbsQPGPvvsE3/729/ylj377LOxzz777PChAAAAAL6t2Cfx7NmzZ9x8880xbdq0qFOnTnz44YfxySefxIABA0pyPgAAAIDiB4wGDRrE0KFD49VXX42VK1fGIYccEq1bt45y5cqV5HwAAAAAxQ8YERHlypWLI444oqRmAQAAANisYgeMjz76KP70pz/F4sWLY+3atXnXDRs2bIcPBgAAALBRsQPG0KFDo06dOnHWWWfF7rvvXpIzAQAAAOQpdsBYsmRJ3HTTTbHbbsX+4BIAAACAHaLYNaJZs2axePHiEhwFAAAAYPOKfQRGrVq14pZbbol27dpF1apV8647/fTTd/hgAAAAABsVO2B8+eWX0bp161i/fn2sWLGiJGcCAAAAyFPsgNGnT5+SnAMAAABgi4odMDZas2ZNfP7555FlWW5ZnTp1duhQAAAAAN+2XZ9Cctddd8W7775b5LoxY8bs0KEAAAAAvq3Yn0IyfPjwaNGiRTz00ENRoUKFGDFiRBx33HFx4YUXluR8AAAAAMUPGO+++25069YtKlasGFmWRYUKFaJ79+6OvgAAAABKXLEDRpkyZWL9+vUREVGpUqVYvnx5ZFkWq1evLrHhAAAAACK24xwYTZs2jX/84x9x1FFHxY9//OO49dZbo0yZMtGiRYuSnA8AAACg+AHjsssuy319xhlnRIMGDWLt2rXRvn37EhkMAAAAYKNiv4Xkqaee+r8b7bZbHHnkkfHTn/40nnvuuRIZDAAAAGCjYgeMv/zlL9u1HAAAAGBH2eZbSN54442IiNiwYUPu640+/PDDKF++fMlMBgAAAPD/bDNgDBs2LCIivvrqq9zXEREFBQVRpUqVOPvss0tuOgAAAIAoRsC49957IyLinnvuiYsuuqjEBwIAAADYVLHPgbFpvHjjjTdiwYIFO3wgAAAAgE0VO2Bcd9118c9//jMiIsaNGxdDhw6NIUOGxBNPPFFiwwEAAABEbEfAeP/996Nx48YREfH3v/89rrvuurjlllt8jCoAAABQ4rZ5DoyNsiyLiIhly5ZFRMSee+4ZERFffPFFCYwFAAAA8H+KHTCaNGkSDz30UKxatSratm0bEd/EjEqVKpXYcAAAAAAR2/EWkgsvvDAqVKgQe+21V/ziF7+IiIgPPvggOnXqVGLDAQAAAERsxxEYlSpVijPPPDNv2cEHH7zDBwIAAADY1FYDxhNPPBGnnnpqRESMGTNmi+udfvrpO3YqAAAAgG/ZasBYsWLFZr8GAAAA2Jm2GjDOO++83NedO3eOBQsWxOrVq6OwsDCaNm0aDRo0KPEBAQAAALZ5Dowsy2LYsGExbdq0qF69elSrVi1WrlwZq1atiiOPPDL+//buNcquuj4f+DOTmQwJ5DoTkoabEuSSaYIo4KXIpcbLsraNrKh0NV5aFUVbWkFfVJeAC6lUa1EQcVElLY2r0tJ2qqVqiXZB66VGAZGBxbWhuXDJ5EYgFyZk/i/yz2gkkJCZOeebzOfzKnNmztlPzt777D3P/PZvn3feeWlpaWlEVgAAAGCU2mOBsWTJktx999351Kc+lWOOOWbw8QceeCBf+MIXcvPNN+f1r3/9iIYEAAAARrc93kb11ltvzR/8wR/sUl4kyTHHHJN3v/vd+a//+q8RCwcAAACQ7EWBsWLFisyePXu335s9e3ZWrFgx7KEAAAAAftkeC4zt27dn3Lhxu/3euHHjsn379mEPBQAAAPDL9jgHxjPPPJO77rrrOb+vwAAAAABG2h4LjEmTJuWaa655zu9PnDhxWAMBAAAA/Ko9FhhXX311I3IAAAAAPKc9zoEBAAAA0GwKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDy2hqxkC996Uu57bbbMmnSpHzuc59Lkjz55JO54oorsnr16kybNi0f/vCHc8ghh2RgYCCLFi3K7bffno6Ojnzwgx/M0Ucf3YiYAAAAQFENGYFx5pln5mMf+9guj/X09GTOnDm58sorM2fOnPT09CRJbr/99jz66KO58sorc+655+YrX/lKIyICAAAAhTWkwJg9e3YOOeSQXR5bunRpzjjjjCTJGWeckaVLlyZJfvKTn+T0009PS0tLjj322Dz11FNZt25dI2ICAAAARTXkEpLd2bBhQ6ZMmZIkmTJlSp544okkydq1a9PV1TX4c52dnVm7du3gz/6yJUuWZMmSJUmSyy+/fJfn7Yu2trYhv8ZwkKNWhheSo719xy41UpkrvB8VMshRM0eFDHLUzFEhgxw1MvzqcbLCeyFHvQxy1MxRIYMcNXM0MkPTCoznMjAw8KzHWlpadvuz8+bNy7x58wa/7uvrG9Kyu7q6hvwaw0GOWhleSI7+/s4kSV/fmqbmGEkVMshRM0eFDHLUzFEhgxw1MvzqcbLCeyFHvQxy1MxRIYMcNXMMR4aZM2fu1c817S4kkyZNGrw0ZN26dZk4cWKSHSMufvk/v2bNmt2OvgAAAABGj6YVGCeffHJuueWWJMktt9ySU045ZfDxW2+9NQMDA7nvvvsyfvx4BQYAAACMcg25hOTzn/987r777mzcuDEf+MAH8ra3vS3z58/PFVdcke9973vp6urKBRdckCQ56aSTctttt+X888/P2LFj88EPfrAREQEAAIDCGlJg/Omf/uluH7/oooue9VhLS0ve+973jnQkAAAAYD/StEtIAAAAAPaWAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5bU1OwAAsP9YvHh8enrGvaDntLe3pb+/c4QSybE/ZejtbU+SLFjQ2dQcv0qOWhmeL8f8+ZuzcOGmJiQCKjACAwDYaz094wZ/CYUXqru7P93d/c2OwX6qt7f9BReowIHFCAwA4AXp7u7PjTeu2euf7+rqSl/f3v/8SJGjVgY5auaokOG5cuwcuQOMXkZgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUF5bswN86EMfykEHHZTW1taMGTMml19+eZ588slcccUVWb16daZNm5YPf/jDOeSQQ5odFQAAAGiSphcYSXLxxRdn4sSJg1/39PRkzpw5mT9/fnp6etLT05OFCxc2MSEAAADQTCUvIVm6dGnOOOOMJMkZZ5yRpUuXNjkRAAAA0EwlRmBcdtllSZLXve51mTdvXjZs2JApU6YkSaZMmZInnnhit89bsmRJlixZkiS5/PLL09XVNaQcbW1tQ36N4SBHrQwvJEd7+45daqQyV3g/KmSQo2aOChnkGPkc+/I5d6C+F/tzjgoZ5KiZo0KG58ox0udZe5ujGSrkqJBBjpo5Gpmh6QXGpZdemqlTp2bDhg351Kc+lZkzZ+71c+fNm5d58+YNft3X1zekLF1dXUN+jeEgR60MLyRHf39nkqSvb01Tc4ykChnkqJmjQgY5Rj7HvnzOHajvxf6co0IGOWrmqJDhuXKM9HnW3uZohgo5KmSQo2aO4ciwtz1A0y8hmTp1apJk0qRJOeWUU/LAAw9k0qRJWbduXZJk3bp1u8yPAQAAAIw+TS0wtmzZks2bNw/++84778yRRx6Zk08+ObfcckuS5JZbbskpp5zSzJgAAABAkzX1EpINGzbkL//yL5MkzzzzTE477bS89KUvzaxZs3LFFVfke9/7Xrq6unLBBRc0MyYAAADQZE0tMKZPn57Pfvazz3p8woQJueiii5qQCAAAAKio6XNgAAAAAOyJAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAMMbCzoAABtOSURBVAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHkKDAAAAKA8BQYAAABQngIDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5bc0OwMhbfM/i9DzY84Kf197env7+/hFItH9leCE5etd8MUmy4N/+qKk5RlKFDEmy8MSFmX/E/GbHAAAAGsQIjFGg58Ge9K7pbXaMUaH7wj9K94UjU17wC71renPD3Tc0OwYAANBARmCMEt2d3bnxzTe+oOd0dXWlr69vhBLtPxnkqJdhwb8taOryAQCAxjMCAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHltzQ4ANM7iexan58GeIb1Ge3t7+vv7hynRvuld05uWlpYs+LcFTc2RDM/7MX/W/Cw8YeEwJQIAgAOTAgNGkZ4He9K7pjfdnd3NjjIk3Z3dJYqU4dC7pjdJFBgADbJ4/fr0bNy4y2Ptjz5a4phSIUeFDM+Vo3fLIUmSBcuXNzVHM1TIMRwZ5k+YkIWTJw9TIkYjBQaMMt2d3bnxzTfu8/O7urrS19c3jIlGd44Ko0gARpOejRvTu3Vrujs6mh2FF6j7mnuaHYEh6N26NUkUGAyJAgMAgFGlu6MjNx5xxODXB0opfqBkkKNmjiH/0aaBI2c4cJnEEwAAAChPgQEAAACUp8AAAAAAyjMHBgC8AOMXL864nj3fjritvT2dBWauH+4cbb1fTJJ0LvijpmXYV8ORY/P8+dm00F2DAKAZFBgA8AKM6+lJe29v+rv379sR76ubu/e+uDjQtPfuuO2xAgMAmkOBAQAvUH93d9bc+Py3I+7q6sqaIjPXNztHhQzDkaNzgdseA0AzmQMDAAAAKE+BAQAAAJSnwAAAAADKU2AAAAAA5SkwAAAAgPIUGAAAAEB5bqMKAAAwAhavX5+ejRuH/Drtjz6a/v7+YUjUvAy9W7cmSRYsX97UHMNlOHLMnzAhCydPHqZEo4MRGAAAACOgZ+PGwV/cR7vujo50d3Q0O0YZvVu3Dku5NdoYgQEAADBCujs6cuMRRwzpNbq6utLX1zdMifbfDAdSjqGORBmtjMAAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAoT4EBAAAAlKfAAAAAAMpTYAAAAADlKTAAAACA8hQYAAAAQHltzQ5wIFl8z+L0PNgz5Ndpb29Pf3//MCTaoXdNb5Jkwb8taGqO3Zk/a34WnrBwRJcBAADA/s8IjGHU82DPYFlQSXdnd7o7u5sd41l61/QOS+EDAADAgc8IjGHW3dmdG99845Beo6urK319fcOUqG6OFzoiBAAAgNFLgQEAAMCosHj9+vRs3Djk12l/9NEhXW7fu3VrkmTB8uVNzTF/woQsnDx5SBkaySUkAAAAjAo9GzcOlgfN1N3Rke6OjqZm6N26dVjKnEYyAgMYlapMuruvk+wOd47EpLrUNX7x4ozrGfr+2tbens4h7CftvTv2184FQ9tfh5ojSTbPn59NC+2vAPuiu6MjNx5xxJBe40C47H+ooz+awQgMYFSqMululUl2TapLZeN6egbLg2bq7+5Of3fz99f23t5hKXQAYH9jBAYwapl09xdMqkt1/d3dWXPj0PfXNUX216HkGOoIEADYXykwAABouMXr1+emIU4+ty92N3HeUCfB2xv720R5ABW5hAQAgIbr2bgxP3vqqYYvtxkT5+2PE+UBVGQEBgAATXHiwQfn72fMaHaMEb8ccH+cKA+gIiMwAAAAgPIUGAAAAEB5LiGB/cTiexbnpv+7aUiTjO28behQ7jix8MSFmX/E/H1+PgAAwL4wAgP2Ez0P9uRnj/1sSK/R3dmd7s7ufX5+75re3HD3DUPKAAAAsC+MwID9yInTT8zfv+Hvm7b8oYzcAAAAGAojMAAAAIDyFBgAAABAeS4hAeCAMX7x4rTddFM6hzDZ7Z609+6YDLdzwfNfUtXW3j7kHJvnz8+mhQuH9BoAo9Hi9evTs3HjkF+n/dFHhzaB+tatSZIFy5c3Ncf8CROycPLkIWWACozAAOCAMa6nJy0/G9pkt3vS392d/u59nwx3b7X39mZcT8+ILwfgQNSzceNgedBM3R0d6e7oaGqG3q1bh6XMgQqMwADYz7nF7q4GTjwxa/6+eZPd7tTV1ZU1fX37/Pw9jfAA4Pl1d3TkxiOOGNJrdHV1pW8In+XDZSg5hjr6AypRYLBbw/EL0Z7szS9M7e3tQ84wf9b8LDzBEGwOXD0P9uTutXdn9tTZ+/waQ7m9bvKLW+xWKDAAADgwKTDYreH4hWhPhvoL097YWZIoMDjQucUuAAAHOgUGz6nZvxAlQx+255cqAOD5LF6/PjcNcYLEPdnbiRyHOlFjYrJGGE2G+vk1XJPMLty2LfPbGlMtKDAAYJgNx91Q9vZuJ3vSunBhMt+lPfBcejZuzN1PP53ZY8eO2DIaNYnjzl9GFBgwOgz182s4Ppt6t27NDX19mT9jxpBfa28oMID9TpVJK5M6E1dSy7ienrTcfXcye98vwxuOO5209/YmN9ygwIA9OPHgg/P3DTr5fj5DHnlqskYYdZr9+dXozx0Fxv/nFyLYf1SYtDIxcSXPr8LdUDoXLEh7UxMAAAwfBcb/5xci2L9UmKPFHCvAC1Xl8iKXFgH7o+GYs2Z/nPeBX/CO/xK/EAEAI6nC5UUuLQL2V8MxZ83+OO8Dv6DAAABGTJURB0mdUQfNvrzIpUU1VfnLsr8q1zMa7zTxfJo950Nivplmam12AADgwDWupyctP/vZkF6jv7t7WEYdtN5ww5BeA0ZSz8aN+dlTTw3pNbo7Oob01+Wdf1WmlqFuG0PdLhLbBnU0v0KD52BiVYADQ7NHHCRGHbB/aPZflqv8VbnKaJTEqIOdqmwbYAQGZfU82JOfPTa0v9p1d3YPeXLVnROrAgAw8iqMRkmMOoCKml8nwvMwsSoAwOjT7BEHiVEHUJERGAAAAEB5CgwAAACgPAUGAAAAUJ4CAwAAAChPgQEAAACUp8AAAAAAylNgAAAAAOUpMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAor63ZAZ7PHXfckUWLFmX79u157Wtfm/nz5zc7EgAAANAEZUdgbN++PV/96lfzsY99LFdccUW+//3vZ8WKFc2OBQAAADRB2QLjgQceyIwZMzJ9+vS0tbXl1a9+dZYuXdrsWAAAAEATtAwMDAw0O8Tu/OhHP8odd9yRD3zgA0mSW2+9Nffff3/e8573DP7MkiVLsmTJkiTJ5Zdf3pScAAAAwMgrOwfG7nqVlpaWXb6eN29e5s2b16hIAAAAQJOUvYSks7Mza9asGfx6zZo1mTJlShMTAQAAAM1StsCYNWtWHnnkkTz++OPZtm1bfvCDH+Tkk09udiwAAACgCcrOgZEkt912W/72b/8227dvz1lnnZWzzz672ZEAAACAJihdYAAAAAAkhS8hAQAAANhJgQEAAACUp8AAAAAAylNgAAAAAOW1NTtAsy1btiwrV65MS0tLZs6cmRe96EVyNDlHhQxVcmzZsiV33nlnVq1alSSZOXNm5syZk3HjxjU8S4X3o0IGOWqq8F7YX+tlqLJO5KiVQY6aOSpk2KnC51eVHBUyVMpRRbPfj2bur6P2LiTf+MY3ct999+XXfu3Xcuihh2ZgYCCPP/54HnnkkRx33HH5nd/5nabmWLVqVY4//vhRlaP6Oml0juuvvz5PPPFEjj322Bx66KFJkscffzz33ntvJk2alHe+850NyVHh/aiQ4flyVNlfG/1+PProo/nJT36SVatWDR5AX/7yl2fGjBkNWX5SZ53YX2tlSOqsEzlqZZCjZo4KGZI6n18Vjm3ei2erfN7TyPXS7P111BYYDz/8cI466qgX/D05DuwMlXJs2LAhkyZN2u33nnjiiUycOLEhOSq8HxUyyLGra665JhMmTEh3d3emT58+eAC966678uSTT+a8884b8QxJjfcisb9Wy5DUWSdy1MogR80cFTIkdT6/KuSokKFSDuc9v9Ds/XXUFhjr1q3LlClTMjAwkKVLl2blypU59NBD88pXvjJjxoxpdrw88MADOeaYY5odo+E5HnrooXR2dmbChAn56U9/mrFjx+bEE09s2PKr5fi///u/3HfffXnyySczefLkzJ07N1OnTm14jl/17W9/O2984xsbuswK62Tbtm254447MmHChBx33HG59dZbs2nTprzmNa/JwQcf3NAsDz30UO6777489dRTOfjgg/OSl7wks2bNatjyt23blra23V+F+HzfG4kcVdZJhf216rGtGZ8ZSbJ8+fK0trbmsMMOG3zs/vvvz0te8pJRmaPCNlohQ2KdVMxRZZ1UON94Lo08L69yPKlynK9y3rM7zTrGJjs+O5YvX57p06c3bNsctQXGJz/5yVx88cW57rrr0tHRkV//9V/PsmXL8uCDD+aCCy5oWI7t27fv9vHLLrssn/jEJ0ZVjmuuuSYDAwNpb2/PE088kSlTpmT8+PHZsGFD3v/+9zckQ6UcX/va1/L000/nqKOOSm9vb9rb29Pa2prjjjsuZ5xxRsNyXHTRRWlpaUmS7Py4WL58eY488sh88pOfbEiGKuvks5/9bGbNmpVNmzbloYceykknnZQJEybk+9//fj7+8Y83LMff/M3fZNu2bZkzZ07Gjx+fzZs3584770xra2v+8A//sCEZBgYGcvvtt6e1tTVz585Na+uOOaGXLl2aU045pSEZkjrrpMr+WuHYVuEzI9kxxHX9+vUZM2ZMNm7cmA9+8IOZOHHi4Hs02nJU2EYrZEisk4o5qqyTKucbFc7LKxxPkjrH+SrnPRWOsZdddlk+/vGP56abbsrPf/7zvOxlL8u9996bqVOn5vd///dHfPmjdhLPnRvdypUrBz8ITjzxxIaeXCXJO9/5zt02yw8//PCoy/Hoo48Ovv8XXnhhLrzwwiRp+DqpkuPBBx/MRRddlCT5zd/8zVx66aX5xCc+kUsvvbShJzannnpqHn744Zx55pnp7u5Okvz5n/95PvaxjzUsQ5V1smnTppx99tmDOX77t387SXLLLbc0NMf//u//Puv/fuqppzb0JO+qq67KoYcemtbW1vzzP/9zPvCBD2TmzJn593//94YeyKuskyr7a4VjW4XPjGTHOtn5/3744YfzV3/1V1m4cGFDM1TL0exttEKGnTmsk1o5qqyTKucbFc7LKxxPkjrH+SrnPRWOsdu2bUuS/PjHP87FF1+c1tbWvP71r29YuTZqC4wzzjgjX/7yl9PZ2Zkrr7wys2fPzsMPP5yjjz66oTkOO+ywfPSjH8348eN3efzSSy8ddTl+uW3+vd/7vcF/N3qQUJUcEydOTE9Pz+BfRA4//PBn5WuEN7/5zdm2bVu++93v5uabb85pp53W0OUnddZJR0dH/umf/in9/f2ZPHlyvvnNb+bggw9u+LDBo48+Otdee23mzp2bcePGZfPmzfn5z3+eF7/4xQ3LsHbt2px//vlJknnz5uVLX/pS3vCGNzRs+TtVWSdV9tcKx7YKnxnJjvd+57Deo446Kh/5yEdy1VVXZcWKFaMyR4VttEKGncuzTmrlqLJOqpxvVDgvr3A8Seoc56uc91Q4xq5YsSJXXXVVHnvssWzbti1jx45NkvT39zdk+WMuueSSSxqypGIGBgbyspe9LM8880wOOuigtLW15dRTT83pp5+eZcuWZfLkyQ3JcfLJJ2f8+PGDLedOp5122rMeO9BzzJw5M1OnTh2c1TfZ0fBNnDgxTz/9dMPWSZUc06ZNS2trax555JG8+MUvzpve9Ka0tLRk7ty5eeyxxxqWI9nRwh9zzDE55ZRTcv/992fMmDGZO3duw5ZfaZ10dHTkhBNOyBvf+MasWLEi27dvz9vf/vasXLmyYTle+tKXZurUqVm1alUee+yxbN++PaecckrOOuushiw/Sf7zP/8zr3jFK9Le3p5x48bl1a9+db7xjW/krrvuylve8paG5aiyTqrsrxWObcuWLcvUqVN3+cxoa2vLnDlzGnp8PfLII9Pe3p6DDjooSTJ27Nj8xm/8Rrq6unLkkUc2JMOecmzfvn1UbaMVMiR1to0q70eFHFX2kyrnGxXOyyscT5I6x/kq5z0VjrFz587Ny1/+8px11lmD2+mWLVsyffr0bN26dcQzjNo5ML75zW/m3nvvTWdnZ6ZNm5Zkx+1f1q5dm+OPPz5vfvObm5pvNE7i+VzrZM2aNTnhhBMatk6q5xiN26h1sncauU4ef/zxHHLIIc/669B9992XY489tiEZkjrrRI5aGZ5Po4+vu3s/Vq9enb6+vlH3+VUhw/OpsG343Ki5nzT6fOO5VDj3Go3b585lOu95/gyN2k9GbYGR7GgWH3744TzyyCMZGBjIzJkzc9RRRw1OjNIIFSbpqZSjwjqRY1e2jXo5qqyTKjPGV1gnctTLUGU/SWq8H1VyVMhg26iZo0KGKjmqbKMV3otKOZz31MgwqguMChYuXPick/Rcd911oy4H9dg26qmwTqrMGA/PpcJ+Qk22DaqzjdbjvKeOUTuJZxWHHXZYPvKRjzzrPsbNmMSzQg7qsW3UU2GdVJkxHp5Lhf2EmmwbVGcbrcd5Tx1GYDTZvffem1mzZj1rJt1nnnkmy5cvz4te9KJRlYN6bBv1VFgnn/jEJ3LxxRcPZnjyySdz1VVX5aGHHspf//Vfj/jyYU8q7CfUZNugOttoPc576lBgNFmFiVgq5aAe20Y9FdbJAw88kGnTpmXSpEmDj23fvj0//OEPc9hhhzm5oukq7CfUZNugOttoPc576lBgFFBhIpZKOajHtlFPs9dJlRnj4fk0ez+hLtsG1dlGa3HeU4cCA4B94uQKABgtnPfUoMAAAAAAymttdgAAAACAPVFgAAAAAOUpMACAMq6++up8/etf36fnvuMd78hjjz22T8+95JJL8t3vfnefngsANEbbnn8EAKC+v/u7v2t2BABgBBmBAQAAAJRnBAYAkCTp6enJt771rWzevDlTpkzJe9/73syZMycPPPBAFi1alJUrV2bs2LF5xStekXe9611pa9txGvG2t70t73nPe3LTTTdl/fr1edOb3pQzzzwzV111VVasWJETTzwx559/ftra2tLb25urrroqr3/963PTTTfloIMOyjnnnJPXvOY1u83005/+NF//+tezevXqHH744Xnf+96Xo446arc/+7a3vS1XXnllZsyYkauvvjodHR1ZvXp17rnnnhx++OE5//zzM2PGjCTJnXfemeuuuy7r1q3L6aefnl+9Kdv3vve9fPOb38z69etzzDHH5Nxzz820adNy77335jOf+Uz+4i/+Il1dXVm2bFkuueSSXHbZZTnssMOGcW0AAL/KCAwAIKtWrcp3vvOdfPrTn87111+fj3/845k2bVqSpLW1Ne9617vy1a9+NZ/61Kdy11135Tvf+c4uz7/jjjty+eWX57LLLss3vvGNXHvttTn//PNzzTXXZPny5fnv//7vwZ9dv359Nm7cmC9/+cv50Ic+lGuvvTarVq16VqaHHnoo11xzTc4999xcd911mTdvXj7zmc+kv79/r/5P3//+9/PWt741ixYtyowZMwbn1njiiSfyuc99Luecc06++tWvZvr06bn33nsHn/fjH/84//Iv/5ILL7wwX/nKV3L88cfnC1/4QpLkuOOOy7x583L11Vfn6aefzhe/+MWcc845ygsAaAAFBgCQ1tbW9Pf3Z8WKFdm2bVsOPfTQwdEKRx99dI499tiMGTMmhx56aObNm5e77757l+f/7u/+bsaPH58jjjgiRxxxRObOnZvp06dn/PjxOemkk7Js2bJdfv7tb3972tvbM3v27Jx00kn5wQ9+8KxM3/3udzNv3ry85CUvSWtra84888y0tbXl/vvv36v/0yte8Yocc8wxGTNmTE477bTBDLfffnsOP/zwvPKVr0xbW1t+67d+K5MnTx583pIlS/KWt7wlhx9+eMaMGZO3vOUtWbZsWVavXp0keetb35pNmzblz/7szzJlypS84Q1v2Nu3GQAYApeQAACZMWNG3v3ud+cf//EfBy/7eOc735mpU6dm1apVuf766/Pggw/m6aefzjPPPJOjjz56l+f/cgEwduzYZ329fv36wa8PPvjgHHTQQYNfT5s2LevWrXtWpr6+vtxyyy359re/PfjYtm3bsnbt2r36P/1yho6OjmzZsiVJsm7dunR2dg5+r6WlZZevV69enUWLFuX6668ffGxgYCBr167NtGnT0tbWljPPPDOLFi3Ku971rrS0tOxVHgBgaBQYAECS5LTTTstpp52WTZs25dprr83Xvva1/PEf/3G+8pWv5EUvelH+5E/+JOPGjctNN92UH/3oR/u8nKeeeipbtmwZLDH6+vpyxBFHPOvnOjs7c/bZZ+fss8/e52XtzuTJk7NmzZrBrwcGBnb5uqurK2efffZzzsuxdu3a3HjjjTnzzDNz/fXX59Of/nTa29uHNSMA8GwuIQEAsmrVqtx1113p7+/P2LFjM3bs2LS27jhN2Lx5c8aPH5+DDjooK1euzH/8x38MeXn/8A//kG3btuWee+7Jbbfdlle96lXP+pnXvva1ufnmm3P//fdnYGAgW7ZsyW233ZbNmzcPadkve9nLsnz58vzP//xPnnnmmXzrW9/aZYTI6173uvT09GT58uVJkk2bNuWHP/xhkh1lx9VXX52zzjor5513XqZMmZIbbrhhSHkAgL1jBAYAkP7+/nzta1/LypUrM2bMmBx33HE599xzkyTveMc7cu211+Zf//Vf8+IXvzivfvWrc9ddd+3zsiZPnpxDDjkk73//+zN27Ni8733v2+0kmLNmzcr73//+XHfddXnkkUcyduzYHH/88TnhhBP2edlJMnHixFxwwQVZtGhRvvSlL+X000/PcccdN/j9U089NVu2bMnnP//59PX1Zfz48ZkzZ05e9apX5Vvf+lY2bNiQc845Jy0tLTnvvPPy0Y9+NC9/+cuHnAsAeH4tA7963zAAgBGy8zaqX/7yl5sdBQDYz7iEBAAAAChPgQEAAACU5xISAAAAoDwjMAAAAIDyFBgAAABAeQoMAAAAoDwFBgAAAFCeAgMAAAAo7/8BOlybFO/TAOsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 15))\n",
    "plt.title('Agglomerative Hierarchical Clustering Dendogram')\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('Distance')\n",
    "dendrogram(Z,truncate_mode='lastp', leaf_rotation=90.,color_threshold=30,  leaf_font_size=8.)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hint: Use truncate_mode='lastp' attribute in dendrogram function to arrive at dendrogram "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. From the truncated dendrogram, find out the optimal distance between clusters which u want to use an input for clustering data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above dendogram, we can visually see that from 25 to 80 we get 3 clusters which is largely seperated and tighiest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15. Using this distance measure and fcluster function to cluster the data into 3 different groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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 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 1 1 1 1 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 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]\n"
     ]
    }
   ],
   "source": [
    "fcluster_labels = fcluster(Z, t=50, criterion='distance')\n",
    "print(fcluster_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"Group\"] = fcluster_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use matplotlib to visually observe the clusters in 2D space "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'g', 'b', 'y', 'c', 'm']\n",
    "fig, ax = plt.subplots()\n",
    "c = np.array([2,250])\n",
    "\n",
    "for i in fcluster_labels:\n",
    "        points = np.array(dataset.loc[dataset['Group'] == i])\n",
    "        #c[i] = np.mean(points, axis=0)\n",
    "        ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12099748>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the values and plotting it\n",
    "\n",
    "f1 = dataset['x'].values\n",
    "\n",
    "f2 = dataset['y'].values\n",
    "\n",
    "X = np.array(list(zip(f1, f2)))\n",
    "\n",
    "plt.scatter(f1, f2, c='black', s=7)"
   ]
  }
 ],
 "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
}