Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Call required libraries\n",
    "import time                   # To time processes\n",
    "import warnings               # To suppress warnings\n",
    "\n",
    "import numpy as np            # Data manipulation\n",
    "import pandas as pd           # Dataframe manipulatio \n",
    "import matplotlib.pyplot as plt                   # For graphics\n",
    "import seaborn as sns\n",
    "import plotly.plotly as py #For World Map\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler  # For scaling dataset\n",
    "from sklearn.cluster import KMeans, AgglomerativeClustering, AffinityPropagation #For clustering\n",
    "from sklearn.mixture import GaussianMixture #For GMM clustering\n",
    "\n",
    "import os                     # For os related operations\n",
    "import sys                    # For data size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/home/tapas/data/')"
   ]
  },
  {
   "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>Happiness.Rank</th>\n",
       "      <th>Happiness.Score</th>\n",
       "      <th>Whisker.high</th>\n",
       "      <th>Whisker.low</th>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>5.354019</td>\n",
       "      <td>5.452326</td>\n",
       "      <td>5.255713</td>\n",
       "      <td>0.984718</td>\n",
       "      <td>1.188898</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.408786</td>\n",
       "      <td>0.246883</td>\n",
       "      <td>0.123120</td>\n",
       "      <td>1.850238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44.888751</td>\n",
       "      <td>1.131230</td>\n",
       "      <td>1.118542</td>\n",
       "      <td>1.145030</td>\n",
       "      <td>0.420793</td>\n",
       "      <td>0.287263</td>\n",
       "      <td>0.237073</td>\n",
       "      <td>0.149997</td>\n",
       "      <td>0.134780</td>\n",
       "      <td>0.101661</td>\n",
       "      <td>0.500028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.693000</td>\n",
       "      <td>2.864884</td>\n",
       "      <td>2.521116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.377914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.500000</td>\n",
       "      <td>4.505500</td>\n",
       "      <td>4.608172</td>\n",
       "      <td>4.374955</td>\n",
       "      <td>0.663371</td>\n",
       "      <td>1.042635</td>\n",
       "      <td>0.369866</td>\n",
       "      <td>0.303677</td>\n",
       "      <td>0.154106</td>\n",
       "      <td>0.057271</td>\n",
       "      <td>1.591291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>5.279000</td>\n",
       "      <td>5.370032</td>\n",
       "      <td>5.193152</td>\n",
       "      <td>1.064578</td>\n",
       "      <td>1.253918</td>\n",
       "      <td>0.606042</td>\n",
       "      <td>0.437454</td>\n",
       "      <td>0.231538</td>\n",
       "      <td>0.089848</td>\n",
       "      <td>1.832910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>116.500000</td>\n",
       "      <td>6.101500</td>\n",
       "      <td>6.194600</td>\n",
       "      <td>6.006527</td>\n",
       "      <td>1.318027</td>\n",
       "      <td>1.414316</td>\n",
       "      <td>0.723008</td>\n",
       "      <td>0.516561</td>\n",
       "      <td>0.323762</td>\n",
       "      <td>0.153296</td>\n",
       "      <td>2.144654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>155.000000</td>\n",
       "      <td>7.537000</td>\n",
       "      <td>7.622030</td>\n",
       "      <td>7.479556</td>\n",
       "      <td>1.870766</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>0.949492</td>\n",
       "      <td>0.658249</td>\n",
       "      <td>0.838075</td>\n",
       "      <td>0.464308</td>\n",
       "      <td>3.117485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Happiness.Rank  Happiness.Score  Whisker.high  Whisker.low  \\\n",
       "count      155.000000       155.000000    155.000000   155.000000   \n",
       "mean        78.000000         5.354019      5.452326     5.255713   \n",
       "std         44.888751         1.131230      1.118542     1.145030   \n",
       "min          1.000000         2.693000      2.864884     2.521116   \n",
       "25%         39.500000         4.505500      4.608172     4.374955   \n",
       "50%         78.000000         5.279000      5.370032     5.193152   \n",
       "75%        116.500000         6.101500      6.194600     6.006527   \n",
       "max        155.000000         7.537000      7.622030     7.479556   \n",
       "\n",
       "       Economy..GDP.per.Capita.      Family  Health..Life.Expectancy.  \\\n",
       "count                155.000000  155.000000                155.000000   \n",
       "mean                   0.984718    1.188898                  0.551341   \n",
       "std                    0.420793    0.287263                  0.237073   \n",
       "min                    0.000000    0.000000                  0.000000   \n",
       "25%                    0.663371    1.042635                  0.369866   \n",
       "50%                    1.064578    1.253918                  0.606042   \n",
       "75%                    1.318027    1.414316                  0.723008   \n",
       "max                    1.870766    1.610574                  0.949492   \n",
       "\n",
       "          Freedom  Generosity  Trust..Government.Corruption.  \\\n",
       "count  155.000000  155.000000                     155.000000   \n",
       "mean     0.408786    0.246883                       0.123120   \n",
       "std      0.149997    0.134780                       0.101661   \n",
       "min      0.000000    0.000000                       0.000000   \n",
       "25%      0.303677    0.154106                       0.057271   \n",
       "50%      0.437454    0.231538                       0.089848   \n",
       "75%      0.516561    0.323762                       0.153296   \n",
       "max      0.658249    0.838075                       0.464308   \n",
       "\n",
       "       Dystopia.Residual  \n",
       "count         155.000000  \n",
       "mean            1.850238  \n",
       "std             0.500028  \n",
       "min             0.377914  \n",
       "25%             1.591291  \n",
       "50%             1.832910  \n",
       "75%             2.144654  \n",
       "max             3.117485  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wh = pd.read_csv(\"2017.csv\") #Read the dataset\n",
    "wh.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension of dataset: wh.shape\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Country                           object\n",
       "Happiness.Rank                     int64\n",
       "Happiness.Score                  float64\n",
       "Whisker.high                     float64\n",
       "Whisker.low                      float64\n",
       "Economy..GDP.per.Capita.         float64\n",
       "Family                           float64\n",
       "Health..Life.Expectancy.         float64\n",
       "Freedom                          float64\n",
       "Generosity                       float64\n",
       "Trust..Government.Corruption.    float64\n",
       "Dystopia.Residual                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Dimension of dataset: wh.shape\")\n",
    "wh.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fda85c9b990>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wh1 = wh[['Happiness.Score','Economy..GDP.per.Capita.','Family','Health..Life.Expectancy.', 'Freedom', \n",
    "          'Generosity','Trust..Government.Corruption.','Dystopia.Residual']] #Subsetting the data\n",
    "cor = wh1.corr() #Calculate the correlation of the above variables\n",
    "sns.heatmap(cor, square = True) #Plot the correlation as heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "colorbar": {
          "title": "Happiness"
         },
         "locationmode": "country names",
         "locations": [
          "Norway",
          "Denmark",
          "Iceland",
          "Switzerland",
          "Finland",
          "Netherlands",
          "Canada",
          "New Zealand",
          "Sweden",
          "Australia",
          "Israel",
          "Costa Rica",
          "Austria",
          "United States",
          "Ireland",
          "Germany",
          "Belgium",
          "Luxembourg",
          "United Kingdom",
          "Chile",
          "United Arab Emirates",
          "Brazil",
          "Czech Republic",
          "Argentina",
          "Mexico",
          "Singapore",
          "Malta",
          "Uruguay",
          "Guatemala",
          "Panama",
          "France",
          "Thailand",
          "Taiwan Province of China",
          "Spain",
          "Qatar",
          "Colombia",
          "Saudi Arabia",
          "Trinidad and Tobago",
          "Kuwait",
          "Slovakia",
          "Bahrain",
          "Malaysia",
          "Nicaragua",
          "Ecuador",
          "El Salvador",
          "Poland",
          "Uzbekistan",
          "Italy",
          "Russia",
          "Belize",
          "Japan",
          "Lithuania",
          "Algeria",
          "Latvia",
          "South Korea",
          "Moldova",
          "Romania",
          "Bolivia",
          "Turkmenistan",
          "Kazakhstan",
          "North Cyprus",
          "Slovenia",
          "Peru",
          "Mauritius",
          "Cyprus",
          "Estonia",
          "Belarus",
          "Libya",
          "Turkey",
          "Paraguay",
          "Hong Kong S.A.R., China",
          "Philippines",
          "Serbia",
          "Jordan",
          "Hungary",
          "Jamaica",
          "Croatia",
          "Kosovo",
          "China",
          "Pakistan",
          "Indonesia",
          "Venezuela",
          "Montenegro",
          "Morocco",
          "Azerbaijan",
          "Dominican Republic",
          "Greece",
          "Lebanon",
          "Portugal",
          "Bosnia and Herzegovina",
          "Honduras",
          "Macedonia",
          "Somalia",
          "Vietnam",
          "Nigeria",
          "Tajikistan",
          "Bhutan",
          "Kyrgyzstan",
          "Nepal",
          "Mongolia",
          "South Africa",
          "Tunisia",
          "Palestinian Territories",
          "Egypt",
          "Bulgaria",
          "Sierra Leone",
          "Cameroon",
          "Iran",
          "Albania",
          "Bangladesh",
          "Namibia",
          "Kenya",
          "Mozambique",
          "Myanmar",
          "Senegal",
          "Zambia",
          "Iraq",
          "Gabon",
          "Ethiopia",
          "Sri Lanka",
          "Armenia",
          "India",
          "Mauritania",
          "Congo (Brazzaville)",
          "Georgia",
          "Congo (Kinshasa)",
          "Mali",
          "Ivory Coast",
          "Cambodia",
          "Sudan",
          "Ghana",
          "Ukraine",
          "Uganda",
          "Burkina Faso",
          "Niger",
          "Malawi",
          "Chad",
          "Zimbabwe",
          "Lesotho",
          "Angola",
          "Afghanistan",
          "Botswana",
          "Benin",
          "Madagascar",
          "Haiti",
          "Yemen",
          "South Sudan",
          "Liberia",
          "Guinea",
          "Togo",
          "Rwanda",
          "Syria",
          "Tanzania",
          "Burundi",
          "Central African Republic"
         ],
         "text": [
          "Norway",
          "Denmark",
          "Iceland",
          "Switzerland",
          "Finland",
          "Netherlands",
          "Canada",
          "New Zealand",
          "Sweden",
          "Australia",
          "Israel",
          "Costa Rica",
          "Austria",
          "United States",
          "Ireland",
          "Germany",
          "Belgium",
          "Luxembourg",
          "United Kingdom",
          "Chile",
          "United Arab Emirates",
          "Brazil",
          "Czech Republic",
          "Argentina",
          "Mexico",
          "Singapore",
          "Malta",
          "Uruguay",
          "Guatemala",
          "Panama",
          "France",
          "Thailand",
          "Taiwan Province of China",
          "Spain",
          "Qatar",
          "Colombia",
          "Saudi Arabia",
          "Trinidad and Tobago",
          "Kuwait",
          "Slovakia",
          "Bahrain",
          "Malaysia",
          "Nicaragua",
          "Ecuador",
          "El Salvador",
          "Poland",
          "Uzbekistan",
          "Italy",
          "Russia",
          "Belize",
          "Japan",
          "Lithuania",
          "Algeria",
          "Latvia",
          "South Korea",
          "Moldova",
          "Romania",
          "Bolivia",
          "Turkmenistan",
          "Kazakhstan",
          "North Cyprus",
          "Slovenia",
          "Peru",
          "Mauritius",
          "Cyprus",
          "Estonia",
          "Belarus",
          "Libya",
          "Turkey",
          "Paraguay",
          "Hong Kong S.A.R., China",
          "Philippines",
          "Serbia",
          "Jordan",
          "Hungary",
          "Jamaica",
          "Croatia",
          "Kosovo",
          "China",
          "Pakistan",
          "Indonesia",
          "Venezuela",
          "Montenegro",
          "Morocco",
          "Azerbaijan",
          "Dominican Republic",
          "Greece",
          "Lebanon",
          "Portugal",
          "Bosnia and Herzegovina",
          "Honduras",
          "Macedonia",
          "Somalia",
          "Vietnam",
          "Nigeria",
          "Tajikistan",
          "Bhutan",
          "Kyrgyzstan",
          "Nepal",
          "Mongolia",
          "South Africa",
          "Tunisia",
          "Palestinian Territories",
          "Egypt",
          "Bulgaria",
          "Sierra Leone",
          "Cameroon",
          "Iran",
          "Albania",
          "Bangladesh",
          "Namibia",
          "Kenya",
          "Mozambique",
          "Myanmar",
          "Senegal",
          "Zambia",
          "Iraq",
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4.53499984741211, 4.513999938964839, 4.49700021743774, 4.46500015258789, 4.46000003814697, 4.44000005722046, 4.37599992752075, 4.31500005722046, 4.29199981689453, 4.29099988937378, 4.28599977493286, 4.2800002098083505, 4.19000005722046, 4.17999982833862, 4.1680002212524405, 4.138999938964839, 4.11999988555908, 4.09600019454956, 4.08099985122681, 4.03200006484985, 4.02799987792969, 3.97000002861023, 3.9360001087188703, 3.875, 3.80800008773804, 3.79500007629395, 3.7939999103546103, 3.7660000324249303, 3.65700006484985, 3.64400005340576, 3.6029999256133998, 3.59299993515015, 3.5910000801086404, 3.5329999923706095, 3.50699996948242, 3.4949998855590803, 3.47099995613098, 3.46199989318848, 3.34899997711182, 2.90499997138977, 2.69300007820129], \"locationmode\": \"country names\"}], {\"geo\": {\"projection\": {\"type\": \"Mercator\"}, \"showframe\": false}, \"title\": \"Happiness Index 2017\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ref: https://plot.ly/python/choropleth-maps/\n",
    "data = dict(type = 'choropleth', \n",
    "           locations = wh['Country'],\n",
    "           locationmode = 'country names',\n",
    "           z = wh['Happiness.Score'], \n",
    "           text = wh['Country'],\n",
    "           colorbar = {'title':'Happiness'})\n",
    "layout = dict(title = 'Happiness Index 2017', \n",
    "             geo = dict(showframe = False, \n",
    "                       projection = {'type': 'Mercator'}))\n",
    "choromap3 = go.Figure(data = [data], layout=layout)\n",
    "iplot(choromap3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.93599602,  1.50618765,  1.20357658, ...,  0.8569643 ,\n",
       "         1.90308437,  0.85629599],\n",
       "       [ 1.92269283,  1.18651768,  1.26503623, ...,  0.80685634,\n",
       "         2.73999784,  0.92989102],\n",
       "       [ 1.90672969,  1.1823454 ,  1.47266877, ...,  1.70201314,\n",
       "         0.30006609,  0.94796425],\n",
       "       ...,\n",
       "       [-1.77816933, -1.12910094, -0.51306362, ...,  0.79923322,\n",
       "        -0.56334657, -2.4660431 ],\n",
       "       [-2.17193469, -2.12929212, -1.95262416, ..., -0.31596505,\n",
       "        -0.38459935, -0.33549229],\n",
       "       [-2.35994869, -2.34773594, -4.15212515, ...,  0.253028  ,\n",
       "        -0.65680192,  0.43290816]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scaling of data\n",
    "ss = StandardScaler()\n",
    "ss.fit_transform(wh1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#K means Clustering \n",
    "def doKmeans(X, nclust=2):\n",
    "    model = KMeans(nclust)\n",
    "    model.fit(X)\n",
    "    clust_labels = model.predict(X)\n",
    "    cent = model.cluster_centers_\n",
    "    return (clust_labels, cent)\n",
    "\n",
    "clust_labels, cent = doKmeans(wh1, 2)\n",
    "kmeans = pd.DataFrame(clust_labels)\n",
    "wh1.insert((wh1.shape[1]),'kmeans',kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fda7c83b750>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the clusters obtained using k means\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "scatter = ax.scatter(wh1['Economy..GDP.per.Capita.'],wh1['Trust..Government.Corruption.'],\n",
    "                     c=kmeans[0],s=50)\n",
    "ax.set_title('K-Means Clustering')\n",
    "ax.set_xlabel('GDP per Capita')\n",
    "ax.set_ylabel('Corruption')\n",
    "plt.colorbar(scatter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def doAgglomerative(X, nclust=2):\n",
    "    model = AgglomerativeClustering(n_clusters=nclust, affinity = 'euclidean', linkage = 'ward')\n",
    "    clust_labels1 = model.fit_predict(X)\n",
    "    return (clust_labels1)\n",
    "\n",
    "clust_labels1 = doAgglomerative(wh1, 2)\n",
    "agglomerative = pd.DataFrame(clust_labels1)\n",
    "wh1.insert((wh1.shape[1]),'agglomerative',agglomerative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fda7c7f2fd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the clusters obtained using Agglomerative clustering or Hierarchical clustering\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "scatter = ax.scatter(wh1['Economy..GDP.per.Capita.'],wh1['Trust..Government.Corruption.'],\n",
    "                     c=agglomerative[0],s=50)\n",
    "ax.set_title('Agglomerative Clustering')\n",
    "ax.set_xlabel('GDP per Capita')\n",
    "ax.set_ylabel('Corruption')\n",
    "plt.colorbar(scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def doAffinity(X):\n",
    "    model = AffinityPropagation(damping = 0.5, max_iter = 250, affinity = 'euclidean')\n",
    "    model.fit(X)\n",
    "    clust_labels2 = model.predict(X)\n",
    "    cent2 = model.cluster_centers_\n",
    "    return (clust_labels2, cent2)\n",
    "\n",
    "clust_labels2, cent2 = doAffinity(wh1)\n",
    "affinity = pd.DataFrame(clust_labels2)\n",
    "wh1.insert((wh1.shape[1]),'affinity',affinity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fda7c7328d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the cluster obtained using Affinity algorithm\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "scatter = ax.scatter(wh1['Economy..GDP.per.Capita.'],wh1['Trust..Government.Corruption.'],\n",
    "                     c=affinity[0],s=50)\n",
    "ax.set_title('Affinity Clustering')\n",
    "ax.set_xlabel('GDP per Capita')\n",
    "ax.set_ylabel('Corruption')\n",
    "plt.colorbar(scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def doGMM(X, nclust=2):\n",
    "    model = GaussianMixture(n_components=nclust,init_params='kmeans')\n",
    "    model.fit(X)\n",
    "    clust_labels3 = model.predict(X)\n",
    "    return (clust_labels3)\n",
    "\n",
    "clust_labels3 = doGMM(wh1,2)\n",
    "gmm = pd.DataFrame(clust_labels3)\n",
    "wh1.insert((wh1.shape[1]),'gmm',gmm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fda7c5fb650>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the cluster obtained using GMM\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "scatter = ax.scatter(wh1['Economy..GDP.per.Capita.'],wh1['Trust..Government.Corruption.'],\n",
    "                     c=gmm[0],s=50)\n",
    "ax.set_title('Affinity Clustering')\n",
    "ax.set_xlabel('GDP per Capita')\n",
    "ax.set_ylabel('Corruption')\n",
    "plt.colorbar(scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "wh1.insert(0,'Country',wh.iloc[:,0])\n",
    "wh1.iloc[:,[0,9,10,11,12]]\n",
    "data = [dict(type='choropleth',\n",
    "             locations = wh1['Country'],\n",
    "             locationmode = 'country names',\n",
    "             z = wh1['kmeans'],\n",
    "             text = wh1['Country'],\n",
    "             colorbar = {'title':'Cluster Group'})]\n",
    "layout = dict(title='Clustering of Countries based on K-Means',\n",
    "              geo=dict(showframe = False,\n",
    "                       projection = {'type':'Mercator'}))\n",
    "map1 = go.Figure(data = data, layout=layout)\n",
    "iplot(map1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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    "             z = wh1['agglomerative'],\n",
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   "metadata": {},
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     },
     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "data = [dict(type='choropleth',\n",
    "             locations = wh1['Country'],\n",
    "             locationmode = 'country names',\n",
    "             z = wh1['affinity'],\n",
    "             text = wh1['Country'],\n",
    "             colorbar = {'title':'Cluster Group'})]\n",
    "layout = dict(title='Grouping of Countries based on Affinity Clustering',\n",
    "              geo=dict(showframe = False, projection = {'type':'Mercator'}))\n",
    "map3 = dict(data=data, layout=layout)\n",
    "iplot(map3)"
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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          "title": "Cluster Group"
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    "data = [dict(type='choropleth',\n",
    "             locations = wh1['Country'],\n",
    "             locationmode = 'country names',\n",
    "             z = wh1['gmm'],\n",
    "             text = wh1['Country'],\n",
    "             colorbar = {'title':'Cluster Group'})]\n",
    "layout = dict(title='Grouping of Countries based on GMM clustering',\n",
    "              geo=dict(showframe = False, projection = {'type':'Mercator'}))\n",
    "map4 = dict(data=data, layout=layout)\n",
    "iplot(map4)"
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