Kmeans-Basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np;\n",
    "import pandas as pd;\n",
    "import matplotlib.pyplot as plt;\n",
    "import seaborn as sns;\n",
    "sns.set()\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Satisfaction</th>\n",
       "      <th>Loyalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.620871</td>\n",
       "      <td>1.016476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.250000</td>\n",
       "      <td>-0.967500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.750000</td>\n",
       "      <td>0.947500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Satisfaction    Loyalty\n",
       "count     30.000000  30.000000\n",
       "mean       6.400000   0.001000\n",
       "std        2.620871   1.016476\n",
       "min        1.000000  -1.690000\n",
       "25%        4.250000  -0.967500\n",
       "50%        7.000000   0.150000\n",
       "75%        8.750000   0.947500\n",
       "max       10.000000   1.380000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"datasets/satisfactionloyalty.csv\");\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zGDduHGJjY/Hggw/askar47W2uhb2ovX64ADg4qTFU1EBsu3I1VDDH/G1IWara211KEhudebMGbz00ksoKCiA0WhEfHw8ZsyYgZ49e3bm4WTFIOla2Atg0fuH21zC1eDuirefGekwNZioYXSmRoqtR/JHR48eRXp6Ovbv34/77rsPTz/9NHx9fbF161bMnTsXn376aYeLJCJp1LA+uBpqAFqPjCpr6vHxnuZ1lRw1TGwdrBYHSWpqKrKysuDm5oaYmBhkZGTAx8fHfP+QIUMQGhpqtcKIyHIGd9fbjgbk0rObDnXXG9u8XU5phwpF02sAcKOhCWmHCh0ySOQIVouDpL6+HuvWrTMvcftHzs7O+M9//mOVooioYyaH+7V5fGJyuHxnVt5u7SJL1jSyJrWMjNRCjmC1OEiWLVsGoPlSKWVlZfDx8Wm1VjpPByZShmmHoORxgavXGjp0u62oYXSmJnIEq8VBcvnyZbzwwgv44YcfoNfrUV1djSFDhmDlypWiKS4iUoZpfXClTj5Qyw5cDaMzNZHj/8XiLyQmJycjICAA3377LXJzc/Htt98iMDAQycnJViuGiOzX5HA/uDiJdylK7MBHBPXBU1EBMLi7QoPmHaaSpyArTY7/F4tP/w0LC0Nubi6cnZ3Nt924cQOjRo3CsWPHrFaQHHj6b9fCXogp2Q+1fSGRr41mUs/astrpv3/6059QWFiIgIAA821nz56Fu7u7xcUQUddmml4jdbH1tKfFQfL0009j5syZmDJlCnx9fVFcXIy0tDQ8//zzVi+KiIjsh8VB8vjjj6Nv377IzMzEzz//DG9vb6xYscJ8aXkiInJMnb5ECtB8jGTIkCE4ffq0pCKuXbuGl19+GXl5edDpdFi8eDFGjx7dartjx45h3rx56N+/PwDAxcUFO3bs6PDz8RhJ18JeiLEfLdgLMdVcIuWPJOSQ2aZNm9CzZ0/897//xfnz5zFt2jTk5OS0ed0uPz8/pKWlSX5OIiKyDotP/70da3xrdc+ePZg6dSoAoH///rjvvvvw1VdfSX5cIiKyvTuOSI4ePXrb+27evGmVIoqLi3HXXXeZfzcajSgtLW1z2/PnzyMuLg5OTk5ITExEXFycVWogIqLOuWOQLF26tN37jUbjHZ8kLi4OxcXFbd535MiRO/69SVBQEA4dOgQ3NzcUFRVh1qxZ8PHxwUMPPWTxYwBod67Pnnh5uSldgmqwF2LsRwv2QswW/bhjkBw4cEDyk+zcubPd+319fXHp0iV4enoCAEpKShAWFtZqu169WgKgb9++iIyMxIkTJzocJDzY3rWwF2LsRwv2QsxWB9slHyOxhgkTJmD79u0AmqeufvzxR4waNarVduXl5eaD+9XV1Th8+LDoC5JERCQ/yWdtWcOcOXOQlJSEsWPHQqvV4vXXXzePPtasWQNvb28kJCQgJycHn332GZycnNDY2GheO56IiJQj6Xsk9opTW10LeyHGfrRgL8S69NQWERHZLwYJERFJwiAhIiJJGCRERCQJg4SIiCRhkBARkSQMEiIikoRBQkREkjBIiIhIEgYJERFJwiAhIiJJGCRERCQJg4SIiCRhkBARkSQMEiIikoRBQkREkjBIiIhIEgYJERFJwiAhIiJJGCRERCQJg4SIiCRxUroAsj9H80qRdqgQlTX1MLi7YnK4H0YE9VG6LCJSCIOEOuRoXik+3nMGNxqaAACVNfX4eM8ZAGCYEKmU6cPflZp6eNrgw58qgmT37t3YuHEjCgsLsWTJEkyfPv222/773//Ghg0bIAgC/vKXv+CVV16BVssZOrmkHSo0h4jJjYYmpB0qdNgg4QiNbkcNrw05PvypYg8cGBiIVatWITo6ut3tioqKsG7dOmzfvh05OTn47bffkJ6eLlOVBDS/CDtye1dnepOa/v2mN+nRvFKFKyOlqeW10d6HP2tRRZD4+/tj0KBBdxxZfPHFF4iMjISnpye0Wi3i4+ORnZ0tU5UEAAZ31w7d3tXJ8SYl+6SW14YcH/5UESSWKikpga+vr/l3X19flJSUKFiR45kc7gcXJ/HLxsVJi8nhfgpVpCyO0Oh21PLakOPDnyzHSOLi4lBcXNzmfUeOHIFOp5OjDDODoZesz2crXl5usj/npEfc4O7WDVv3nEZF1TX09uiOGVGBeGR4X9lruZUSvQAAL4/uuFx1rc3blaoJUK4fauTor42Z0UFYt+MU6m82mm9zddZhZnSQ1eqQJUh27txplccxGo2iQCouLobRaOzw41RWXkVTk2CVmpTi5eWGy5drFXnuoHv0SP2/EaLblKoFULYXsQ8PEB3IBJpHaLEPD1CsJiX7oTZ8bTS/X2dMGNzqrK2ge/QW16HVatr9AK6Ks7YsNX78eEybNg3PPfcc9Ho9duzYcccD9ES2ZDrrRekzc0h91PTaGBHUByOC+tgsWDWCICj+0TwzMxMrVqxATU0NnJ2d0b17d2zevBmDBg3CmjVr4O3tjYSEBADAv/71L2zcuBEAMHLkSCxbtqzDU2MckXQt7IUY+9GCvRDrbD/uNCJRRZDIjUHStbAXYuxHC/ZCzFZBYldnbRERkfowSIiISBIGCRERScIgISIiSRgkREQkCYOEiIgkYZAQEZEkDBIiIpKEQUJERJIwSIiISBIGCRERScIgISIiSRgkREQkiV2tR0J0q6N5pa0W6+E6IETyY5BQh5l24Eou1nM0r1S0+lxlTT0+3nMGABgmRDLj1BZ1iGkHXllTD6BlB340r1TWOtIOFYqWMAWAGw1NSDtUKGsdRMQgoQ5Syw7cFGSW3k5EtsMgoQ5Ryw7c4O7aoduJyHYYJNQhatmBTw73g4uT+OXr4qTF5HA/WesgIgYJdZBaduAjgvrgqagAGNxdoUFzkD0VFcAD7UQK4Flb1CGmHbXSZ22ZahkR1AdeXm64fLlW9ucnomYMEuow0w6ciAjg1BYREUmkiiDZvXs3Jk6ciHvvvRfbtm277XbHjh3DkCFDEBMTg5iYGMTHx8tYJRERtUUVU1uBgYFYtWoVPvrooztu6+fnh7S0NBmqIiIiS6giSPz9/QEAWq0qBkhERNQBqgiSjjh//jzi4uLg5OSExMRExMXFdfgxDIZeNqhMfl5ebkqXoBrshRj70YK9ELNFP2QJkri4OBQXF7d535EjR6DT6Sx6nKCgIBw6dAhubm4oKirCrFmz4OPjg4ceeqhD9VRWXkVTk9Chv1EbnvLagr0QYz9asBdine2HVqtp9wO4LEGyc+dOqzxOr14t/5C+ffsiMjISJ06c6HCQEBGR9djVQYny8nIIQvNIorq6GocPH0ZAQIDCVREROTZVHCPJzMzEihUrUFNTg/379+Ojjz7C5s2bMWjQIKxZswbe3t5ISEhATk4OPvvsMzg5OaGxsRExMTGIjIxUunwiIoemEUwf8R0Ij5F0LeyFGPvRgr0Qs9UxErua2iIiIvVhkBARkSSqOEZiD9SwTjkRkRoxSCxgWqfctMSsaZ1yAAwTInJ4DBILtLdOuSMGCUdnYuwHOToGiQXUsk65GnB0JsZ+EPFgu0XUsk65GrQ3OnNE7AcRg8QialmnXA04OhNjP4gYJBYZEdQHT0UFmEcgBndXPBUV4JBTFxydibEfRDxGYjGuU95scrif6JgA4LijM4D9IAIYJNRBpjDlWUrN2A8iBgl1AkdnYuwHOToeIyEiIkkYJEREJAmDhIiIJGGQEBGRJA55sF2r1ShdglV0lX+HNbAXYuxHC/ZCrDP9uNPfOOQKiUREZD2c2iIiIkkYJEREJAmDhIiIJGGQEBGRJAwSIiKShEFCRESSMEiIiEgSBgkREUnCICEiIkkYJHakqqoKc+fOxfjx4zFx4kQ899xzuHLlitJlKW7dunUYPHgwfvnlF6VLUVR9fT2Sk5Mxbtw4TJw4Ea+++qrSJSnm4MGDiI2NRUxMDCZOnIicnBylS5JVamoqIiIiWr0vzp07hyeeeALjx4/HE088gfPnz1vnCQWyG1VVVcI333xj/v2tt94SXn75ZQUrUt5PP/0kzJkzR3jkkUeEn3/+WelyFPXGG28I//jHP4SmpiZBEATh8uXLClekjKamJiEkJMT8ejh9+rQwdOhQobGxUeHK5HP8+HGhuLhYGD16tOh98eSTTwq7du0SBEEQdu3aJTz55JNWeT6OSOyIXq9HWFiY+fehQ4eiuLhYwYqUdePGDbz++utITk6GRuPYF+arq6vDrl278Pzzz5t70bt3b4WrUo5Wq0VtbS0AoLa2Ft7e3tBqHWd3FxISAqPRKLqtsrIS+fn5iI6OBgBER0cjPz/fKrMaDnn1366gqakJn332GSIiIpQuRTFr1qzBpEmT0LdvX6VLUVxRURH0ej3WrVuHY8eOoWfPnnj++ecREhKidGmy02g0WL16NZ555hn06NEDdXV1+PDDD5UuS3ElJSXw8fGBTqcDAOh0Onh7e6OkpASenp6SHttxIrqLeeONN9CjRw9Mnz5d6VIUcfLkSfz4449ITExUuhRVaGhoQFFREe69916kpaXhxRdfxIIFC3D16lWlS5NdQ0MDPvzwQ7z//vs4ePAgPvjgA7zwwguoq6tTurQui0Fih1JTU/Hbb79h9erVDjVcv9Xx48dx9uxZjBkzBhERESgtLcWcOXOQm5urdGmK8PX1hZOTk3naYsiQIfDw8MC5c+cUrkx+p0+fRnl5OYYPHw4AGD58OLp3747CwkKFK1OW0WhEWVkZGhsbAQCNjY0oLy9vNQXWGY65F7Jjq1atwk8//YT33nsPLi4uSpejmHnz5iE3NxcHDhzAgQMH0KdPH2zatAkPP/yw0qUpwtPTE2FhYTh8+DCA5rNzKisr0a9fP4Urk1+fPn1QWlqKs2fPAgAKCwtRUVGBe+65R+HKlGUwGBAYGIjMzEwAQGZmJgIDAyVPawFc2MquFBQUIDo6Gv3790e3bt0AAHfffTfee+89hStTXkREBNavXw9/f3+lS1FMUVERlixZgurqajg5OeFvf/sbwsPDlS5LEenp6diwYYP5xIOFCxciMjJS4arkk5KSgpycHFRUVMDDwwN6vR5ZWVkoLCxEUlISampq4O7ujtTUVAwcOFDy8zFIiIhIEk5tERGRJAwSIiKShEFCRESSMEiIiEgSBgkREUnCICGyouLiYgQHB5u/9NWeVatWISwsDCNHjrRqDenp6Zg9e7ZVH5OoPTz9lwjAd999h3feeQcFBQXQ6XQYOHAglixZggceeKDdv4uIiEBKSgoeeuihDj1fSUkJxo8fj4MHD8JgMHS67osXL2LMmDHIy8uDkxMvnUfK4CuPHN7Vq1cxf/58LF++HFFRUbh58ya+++47m1454NKlS9Dr9ZJChEgtOLVFDs90Paro6GjodDp069YNDz/8MAICAnDhwgXMmDEDYWFhCAsLw9///nfU1NQAABYtWoTi4mLMnz8fwcHB2LBhAy5evIjBgwejoaEBAJCWloYxY8YgODgYERERSE9Px5EjRzB79myUl5cjODgYSUlJAJq/fT1y5EgMHz4c06ZNQ0FBgbnG69ev46233sLo0aMxfPhwJCQk4Pr16+aLdj744IMIDg7GyZMnkZaWhoSEBPPfnjhxAo899hiGDx+Oxx57DCdOnDDf9+STT2L16tWYOnUqgoODMXv2bC6WRh1nlVVNiOxYbW2tEBoaKrz00kvCl19+KVRXV5vvO3/+vJCbmyvU19cLlZWVQmJiopCSkmK+f/To0cLhw4fNvxcVFQn+/v7CzZs3hbq6OiE4OFgoLCwUBEEQysrKhF9++UUQBEH45ptvhFGjRonq2LFjh1BbWyvU19cLKSkpwqRJk8z3LV++XJg+fbpQWloqNDQ0CN9//71QX18vej6Tzz//XJg6daogCM2LoYWEhAg7d+4Ubt68KWRkZAghISHClStXBEEQhOnTpwtjxowRzp49K1y7dk2YPn268Pbbb1urteQgOCIhh9erVy98+umn0Gg0ePXVVzFixAjMnz8fFRUV6NevH0aOHAkXFxd4enpi1qxZOH78uMWPrdVqUVBQgOvXr8Pb2xt//vOfb7vtlClT0KtXL7i4uGDBggU4c+YMamtr0dTUhM8//xxLly41rycxbJ/ya5UAAAK9SURBVNgwi6bevvzyS/Tr1w+xsbHmqwMPHDgQBw8eNG8zefJkDBgwAN26dcOECRNw+vRpi/99RACPkRABAPz8/PDWW28BaL5a7KJFi/Dmm29i6dKlSElJwXfffYe6ujoIggB3d3eLHrNHjx5YtWoVNm/ejKVLl2LYsGFYvHgx/Pz8Wm3b2NiIVatWYe/evbhy5Yp5eYCqqircuHED9fX1nVrAq7y8HL6+vqLbfH19UVZWZv7dy8vL/HP37t3x+++/d/h5yLFxREL0B35+fpg8eTIKCgrw7rvvQqPRID09HSdOnMDbb78NoQMnOo4aNQpbtmxBbm4uBg4ciFdffbXN7TIyMrB//35s2bIF33//PQ4cOAAAEAQBHh4ecHV1RVFRUau/u9MSw97e3q2WYzatlEdkLQwScniFhYXYvHkzSktLATTvaDMzMzFkyBDU1dWhR48ecHd3R1lZGTZu3Cj62969e7e5gweAiooK7N+/H7///jtcXFzQo0cP8zKnf1RXVwcXFxd4eHjg2rVrWLlypfk+rVaLxx57DP/85z/NCxOdPHkSN27cgKenJ7Ra7W1rCA8Px/nz55GRkYGGhgZkZ2fj119/xSOPPNKJThG1jUFCDq9Xr144deoU4uPjMXToUDz++OPw9/dHUlISnnvuOeTn5yMkJATz5s3DuHHjRH87b948fPDBBwgJCcGmTZtE9zU1NWHLli0YNWoUQkNDcfz4cSQnJ7dZQ2xsLHx9fTFq1Cg8+uijGDp0qOj+xYsXw9/fH1OmTEFoaCjeeecdNDU1oXv37pg/fz4SEhIQEhKCH374QfR3Hh4eWL9+PbZs2YKwsDBs3LgR69evt8piRkQm/EIiERFJwhEJERFJwiAhIiJJGCRERCQJg4SIiCRhkBARkSQMEiIikoRBQkREkjBIiIhIEgYJERFJ8v+AHnYFbiBGDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Satisfaction'], data['Loyalty'])\n",
    "plt.xlabel('Satisfaction')\n",
    "plt.ylabel('Loyalty')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Satisfaction</th>\n",
       "      <th>Loyalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>-1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>-0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Satisfaction  Loyalty\n",
       "0              4    -1.33\n",
       "1              6    -0.28\n",
       "2              5    -0.99\n",
       "3              7    -0.29\n",
       "4              4     1.06\n",
       "5              1    -1.66\n",
       "6             10    -0.97\n",
       "7              8    -0.32\n",
       "8              8     1.02\n",
       "9              8     0.68\n",
       "10            10    -0.34\n",
       "11             5     0.39\n",
       "12             5    -1.69\n",
       "13             2     0.67\n",
       "14             7     0.27\n",
       "15             9     1.36\n",
       "16             8     1.38\n",
       "17             7     1.36\n",
       "18             7    -0.34\n",
       "19             9     0.67\n",
       "20            10     1.18\n",
       "21             3    -1.69\n",
       "22             4     1.04\n",
       "23             3    -0.96\n",
       "24             6     1.03\n",
       "25             9    -0.99\n",
       "26            10     0.37\n",
       "27             9     0.03\n",
       "28             3    -1.36\n",
       "29             5     0.73"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=data.copy()\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans=KMeans(2)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters=x.copy()\n",
    "clusters['cluster_pred']=kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(clusters['Satisfaction'], clusters['Loyalty'], c=clusters['cluster_pred'],cmap='rainbow')\n",
    "plt.xlabel('Satisfaction')\n",
    "plt.ylabel('Loyalty')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardizing the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.93138063, -1.3318111 ],\n",
       "       [-0.15523011, -0.28117124],\n",
       "       [-0.54330537, -0.99160391],\n",
       "       [ 0.23284516, -0.29117733],\n",
       "       [-0.93138063,  1.05964534],\n",
       "       [-2.09560642, -1.6620122 ],\n",
       "       [ 1.39707095, -0.97159172],\n",
       "       [ 0.62092042, -0.32119561],\n",
       "       [ 0.62092042,  1.01962097],\n",
       "       [ 0.62092042,  0.67941378],\n",
       "       [ 1.39707095, -0.3412078 ],\n",
       "       [-0.54330537,  0.38923705],\n",
       "       [-0.54330537, -1.69203048],\n",
       "       [-1.70753116,  0.66940768],\n",
       "       [ 0.23284516,  0.26916393],\n",
       "       [ 1.00899568,  1.35982816],\n",
       "       [ 0.62092042,  1.37984035],\n",
       "       [ 0.23284516,  1.35982816],\n",
       "       [ 0.23284516, -0.3412078 ],\n",
       "       [ 1.00899568,  0.66940768],\n",
       "       [ 1.39707095,  1.17971847],\n",
       "       [-1.31945589, -1.69203048],\n",
       "       [-0.93138063,  1.03963316],\n",
       "       [-1.31945589, -0.96158562],\n",
       "       [-0.15523011,  1.02962706],\n",
       "       [ 1.00899568, -0.99160391],\n",
       "       [ 1.39707095,  0.36922486],\n",
       "       [ 1.00899568,  0.02901767],\n",
       "       [-1.31945589, -1.36182938],\n",
       "       [-0.54330537,  0.72944425]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "x_scaled=preprocessing.scale(x)\n",
    "x_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elbow Method\n",
    "\n",
    "inertia_: Sum of squared distances of samples to their closest cluster center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[60.0,\n",
       " 29.818973034723147,\n",
       " 17.913349527387965,\n",
       " 10.247181805928422,\n",
       " 7.792695153937187,\n",
       " 6.54983679159933,\n",
       " 5.4602053116458205,\n",
       " 4.352037765292862,\n",
       " 3.816324896488448,\n",
       " 3.3881513928614355,\n",
       " 2.711433831430747,\n",
       " 2.3181875479438374,\n",
       " 2.0355316571134074,\n",
       " 1.8395507829632274,\n",
       " 1.5724205073309323,\n",
       " 1.3602105207952933,\n",
       " 1.2302940301324312,\n",
       " 0.9062963379251507,\n",
       " 0.7706518173776857,\n",
       " 0.6366791702988839,\n",
       " 0.549713762368285,\n",
       " 0.4239312504600061,\n",
       " 0.3227119817275011,\n",
       " 0.24721053307798668,\n",
       " 0.17170908442847233,\n",
       " 0.11383861748989679,\n",
       " 0.0559681505513213,\n",
       " 0.0014517677692203244,\n",
       " 0.00020024383023728806]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss = []\n",
    "for i in range(1,30):\n",
    "    kmeans=KMeans(i)\n",
    "    kmeans.fit(x_scaled)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "wcss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,30),wcss)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Satisfaction</th>\n",
       "      <th>Loyalty</th>\n",
       "      <th>cluster_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.06</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>0.67</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>0.27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9</td>\n",
       "      <td>0.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Satisfaction  Loyalty  cluster_pred\n",
       "0              4    -1.33             0\n",
       "1              6    -0.28             3\n",
       "2              5    -0.99             0\n",
       "3              7    -0.29             3\n",
       "4              4     1.06             2\n",
       "5              1    -1.66             0\n",
       "6             10    -0.97             3\n",
       "7              8    -0.32             3\n",
       "8              8     1.02             1\n",
       "9              8     0.68             1\n",
       "10            10    -0.34             3\n",
       "11             5     0.39             2\n",
       "12             5    -1.69             0\n",
       "13             2     0.67             2\n",
       "14             7     0.27             3\n",
       "15             9     1.36             1\n",
       "16             8     1.38             1\n",
       "17             7     1.36             1\n",
       "18             7    -0.34             3\n",
       "19             9     0.67             1\n",
       "20            10     1.18             1\n",
       "21             3    -1.69             0\n",
       "22             4     1.04             2\n",
       "23             3    -0.96             0\n",
       "24             6     1.03             2\n",
       "25             9    -0.99             3\n",
       "26            10     0.37             1\n",
       "27             9     0.03             3\n",
       "28             3    -1.36             0\n",
       "29             5     0.73             2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_new=KMeans(4)\n",
    "kmeans.fit(x)\n",
    "cluster_new=x.copy()\n",
    "cluster_new['cluster_pred']=kmeans_new.fit_predict(x_scaled)\n",
    "cluster_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the new clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cluster_new['Satisfaction'], cluster_new['Loyalty'], c=cluster_new['cluster_pred'],cmap='rainbow')\n",
    "plt.xlabel('Satisfaction')\n",
    "plt.ylabel('Loyalty')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "1. The xxx dots are the people who are less satisfied and less loyal and therefore can be termed as alienated.\n",
    "2. The yyy dots are people with high loyalty and less satisfaction.\n",
    "3. The zzz dots are the people with high loyalty and high satisfaction and they are the fans.\n",
    "4. The www blue dots are the people who are in the midst of things."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}