international_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeRegressor, export_graphviz\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import combinations\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "import copy\n",
    "from six import StringIO\n",
    "from IPython.display import Image  \n",
    "import pydotplus\n",
    "import statsmodels.api as sm\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Location  Period  Monitor  Protect from tobacco smoke  \\\n",
      "0     Albania    2018        2                           5   \n",
      "1     Albania    2016        2                           5   \n",
      "2     Albania    2014        2                           5   \n",
      "3     Albania    2010        3                           5   \n",
      "4     Algeria    2018        3                           3   \n",
      "..        ...     ...      ...                         ...   \n",
      "587    Zambia    2010        1                           4   \n",
      "588  Zimbabwe    2018        2                           3   \n",
      "589  Zimbabwe    2016        2                           3   \n",
      "590  Zimbabwe    2014        2                           3   \n",
      "591  Zimbabwe    2010        2                           3   \n",
      "\n",
      "     Offer help to quit tobacco use  Warn about the dangers of tobacco  \\\n",
      "0                                 4                                  4   \n",
      "1                                 4                                  4   \n",
      "2                                 4                                  4   \n",
      "3                                 3                                  4   \n",
      "4                                 3                                  2   \n",
      "..                              ...                                ...   \n",
      "587                               3                                  2   \n",
      "588                               4                                  2   \n",
      "589                               4                                  2   \n",
      "590                               2                                  2   \n",
      "591                               3                                  2   \n",
      "\n",
      "     Enforce bans on tobacco advertising  Anti-tobacco mass media campaigns  \\\n",
      "0                                      5                                  2   \n",
      "1                                      5                                  3   \n",
      "2                                      5                                  2   \n",
      "3                                      5                                  5   \n",
      "4                                      4                                  1   \n",
      "..                                   ...                                ...   \n",
      "587                                    2                                  5   \n",
      "588                                    2                                  2   \n",
      "589                                    2                                  2   \n",
      "590                                    2                                  2   \n",
      "591                                    2                                  2   \n",
      "\n",
      "     Price  Usage ParentLocation  \n",
      "0     67.2   29.2         Europe  \n",
      "1     66.3   29.7         Europe  \n",
      "2     64.1   30.1         Europe  \n",
      "3     52.9   31.1         Europe  \n",
      "4     34.2   18.8         Africa  \n",
      "..     ...    ...            ...  \n",
      "587   36.3   16.7         Africa  \n",
      "588   35.9   13.9         Africa  \n",
      "589   35.9   14.3         Africa  \n",
      "590   36.1   14.7         Africa  \n",
      "591   51.7   15.6         Africa  \n",
      "\n",
      "[592 rows x 11 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Albania', 'Algeria', 'Andorra', 'Argentina', 'Armenia',\n",
       "       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n",
       "       'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Benin',\n",
       "       'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Burundi',\n",
       "       'Cambodia', 'Cameroon', 'Canada', 'Chad', 'Chile', 'China',\n",
       "       'Colombia', 'Comoros', 'Congo', 'Cook Islands', 'Costa Rica',\n",
       "       'Croatia', 'Cuba', 'Cyprus', 'Czechia',\n",
       "       \"Democratic People's Republic of Korea\", 'Denmark',\n",
       "       'Dominican Republic', 'Egypt', 'El Salvador', 'Eritrea', 'Estonia',\n",
       "       'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gambia',\n",
       "       'Georgia', 'Germany', 'Ghana', 'Greece', 'Guyana', 'Haiti',\n",
       "       'Hungary', 'Iceland', 'India', 'Indonesia',\n",
       "       'Iran (Islamic Republic of)', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait',\n",
       "       'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia',\n",
       "       'Lithuania', 'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia',\n",
       "       'Mali', 'Malta', 'Mauritius', 'Mexico', 'Mongolia', 'Morocco',\n",
       "       'Mozambique', 'Myanmar', 'Namibia', 'Nauru', 'Nepal',\n",
       "       'Netherlands', 'New Zealand', 'Niger', 'Nigeria', 'Norway', 'Oman',\n",
       "       'Pakistan', 'Palau', 'Panama', 'Paraguay', 'Peru', 'Philippines',\n",
       "       'Poland', 'Portugal', 'Qatar', 'Republic of Korea',\n",
       "       'Republic of Moldova', 'Romania', 'Russian Federation', 'Rwanda',\n",
       "       'Samoa', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal',\n",
       "       'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia',\n",
       "       'Slovenia', 'Solomon Islands', 'South Africa', 'Spain',\n",
       "       'Sri Lanka', 'Sweden', 'Switzerland', 'Thailand', 'Timor-Leste',\n",
       "       'Togo', 'Tonga', 'Tunisia', 'Turkey', 'Tuvalu', 'Uganda',\n",
       "       'Ukraine', 'United Arab Emirates',\n",
       "       'United Kingdom of Great Britain and Northern Ireland',\n",
       "       'United Republic of Tanzania', 'United States of America',\n",
       "       'Uruguay', 'Uzbekistan', 'Vanuatu', 'Yemen', 'Zambia', 'Zimbabwe'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tobacco = pd.read_csv('tobacco_use_and_legislation_ww.csv', usecols=range(1, 12))\n",
    "print(tobacco)\n",
    "tobacco.Location.unique()"
   ]
  },
  {
   "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>Location</th>\n",
       "      <th>Monitor</th>\n",
       "      <th>Protect from tobacco smoke</th>\n",
       "      <th>Offer help to quit tobacco use</th>\n",
       "      <th>Warn about the dangers of tobacco</th>\n",
       "      <th>Enforce bans on tobacco advertising</th>\n",
       "      <th>Anti-tobacco mass media campaigns</th>\n",
       "      <th>Price</th>\n",
       "      <th>Usage</th>\n",
       "      <th>ParentLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>6.612803</td>\n",
       "      <td>0.805709</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.57735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57735</td>\n",
       "      <td>6.767754</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.57735</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.201087</td>\n",
       "      <td>0.403113</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.298427</td>\n",
       "      <td>3.571531</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.957427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>6.257262</td>\n",
       "      <td>1.410378</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.513576</td>\n",
       "      <td>0.65</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>0.57735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.996665</td>\n",
       "      <td>1.332604</td>\n",
       "      <td>Western Pacific</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.44453</td>\n",
       "      <td>1.547848</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.075169</td>\n",
       "      <td>0.853913</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.957427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.867232</td>\n",
       "      <td>0.727438</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location  Monitor Protect from tobacco smoke  \\\n",
       "0       Albania      0.5                        0.0   \n",
       "1       Algeria  0.57735                        0.0   \n",
       "2       Andorra  0.57735                        0.5   \n",
       "3     Argentina      0.5                        1.5   \n",
       "4       Armenia      0.0                        0.0   \n",
       "..          ...      ...                        ...   \n",
       "143  Uzbekistan      0.0                        0.0   \n",
       "144     Vanuatu  0.57735                        0.0   \n",
       "145       Yemen      0.0                        1.0   \n",
       "146      Zambia      0.5                        0.0   \n",
       "147    Zimbabwe      0.0                        0.0   \n",
       "\n",
       "    Offer help to quit tobacco use Warn about the dangers of tobacco  \\\n",
       "0                              0.5                               0.0   \n",
       "1                              0.0                               0.0   \n",
       "2                              0.0                               0.0   \n",
       "3                              0.0                               1.5   \n",
       "4                              0.5                          0.957427   \n",
       "..                             ...                               ...   \n",
       "143                            0.5                               0.0   \n",
       "144                            0.0                               1.0   \n",
       "145                            0.0                               0.5   \n",
       "146                            0.5                               0.0   \n",
       "147                       0.957427                               0.0   \n",
       "\n",
       "    Enforce bans on tobacco advertising Anti-tobacco mass media campaigns  \\\n",
       "0                                   0.0                          1.414214   \n",
       "1                                   0.0                           0.57735   \n",
       "2                                   0.0                               0.5   \n",
       "3                                   1.0                               0.5   \n",
       "4                                   0.0                          1.414214   \n",
       "..                                  ...                               ...   \n",
       "143                                 1.0                               0.5   \n",
       "144                                 0.0                               0.5   \n",
       "145                                 0.5                               0.5   \n",
       "146                                 0.0                               1.5   \n",
       "147                                 0.0                               0.0   \n",
       "\n",
       "        Price     Usage         ParentLocation  \n",
       "0    6.612803  0.805709                 Europe  \n",
       "1    6.767754  0.512348                 Africa  \n",
       "2    9.201087  0.403113                 Europe  \n",
       "3    5.298427  3.571531               Americas  \n",
       "4    6.257262  1.410378                 Europe  \n",
       "..        ...       ...                    ...  \n",
       "143  6.513576      0.65                 Europe  \n",
       "144  2.996665  1.332604        Western Pacific  \n",
       "145   1.44453  1.547848  Eastern Mediterranean  \n",
       "146  3.075169  0.853913                 Africa  \n",
       "147  7.867232  0.727438                 Africa  \n",
       "\n",
       "[148 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtobacco = pd.DataFrame(index=range(tobacco.Location.unique().size), columns=tobacco.columns.drop(['Period']))\n",
    "count = 0\n",
    "for country in list(np.unique(tobacco.Location.to_numpy())):\n",
    "    parentcountry = tobacco[tobacco['Location'] == country].ParentLocation.tolist()[0]\n",
    "    temp = tobacco[tobacco['Location'] == country].drop(['Location', 'Period', 'ParentLocation'], axis = 1)\n",
    "    countrystd = temp.std(axis=0).to_list()\n",
    "    countrystd.insert(0, country)\n",
    "    countrystd.append(parentcountry)\n",
    "    newtobacco.iloc[count, :] = countrystd\n",
    "    count += 1\n",
    "newtobacco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Monitor                                0.371703\n",
       "Protect from tobacco smoke             0.342358\n",
       "Offer help to quit tobacco use         0.293652\n",
       "Warn about the dangers of tobacco      0.596275\n",
       "Enforce bans on tobacco advertising    0.270773\n",
       "Anti-tobacco mass media campaigns      1.033693\n",
       "Price                                  4.926539\n",
       "Usage                                  1.438712\n",
       "dtype: float64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtobacco.drop(['Location','ParentLocation'], axis=1).mean(axis=0)\n",
    "# newtobacco.drop('Location', axis=1).plot(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtobaccofinal = pd.DataFrame(index=range(tobacco.Location.unique().size), columns=tobacco.columns.drop(['Period']))\n",
    "count = 0\n",
    "for country in list(np.unique(tobacco.Location.to_numpy())):\n",
    "    parentcountry = tobacco[tobacco['Location'] == country].ParentLocation.tolist()[0]\n",
    "    temp = tobacco[tobacco['Location'] == country]\n",
    "    countryrow = temp.drop(['Location', 'Period', 'Price','Usage','ParentLocation'], axis = 1).mean(axis=0).to_list()\n",
    "    price = temp.iloc[0].Price - temp.iloc[len(temp)-1].Price\n",
    "    usage = temp.iloc[0].Usage - temp.iloc[len(temp)-1].Usage\n",
    "    countryrow.insert(0, country)\n",
    "    countryrow.append(price)\n",
    "    countryrow.append(usage)\n",
    "    countryrow.append(parentcountry)\n",
    "    newtobaccofinal.iloc[count, :] = countryrow\n",
    "    count += 1\n",
    "newtobaccofinal\n",
    "newtobaccofinal.to_csv('save.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.028524774774775, 1.7476103603603605, 2.689863577863578, 3.1741179751179756, 3.3108107035607035, 3.9114790826665833, 3.9765716365091373, 4.1060117836367835, 4.259808876183877, 4.391550028050029, 4.532465405978564, 4.831305059523809, 4.831305059523809, 5.09738446528425, 5.097384465284249, 5.127598520503263, 5.127598520503263, 5.127598520503263, 5.127598520503263, 5.129170463875205, 5.139382005173704, 5.20448620819095, 5.20448620819095]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = newtobaccofinal[newtobaccofinal.columns.drop(['Location', 'Usage', 'ParentLocation'])]\n",
    "y = newtobaccofinal['Usage']\n",
    "criteria = range(2, 25)\n",
    "accuracies = []\n",
    "for i in range(len(criteria)):\n",
    "    reg = DecisionTreeRegressor(min_samples_leaf=criteria[i])\n",
    "    reg.fit(x, y)\n",
    "    ypred = reg.predict(x)\n",
    "    accuracies.append(mean_squared_error(y, ypred))\n",
    "plt.plot(criteria, accuracies)\n",
    "plt.xlabel('Minimum size of leaf')\n",
    "plt.ylabel('Mean squared error')\n",
    "print(accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regmse = DecisionTreeRegressor(min_samples_split=43, min_samples_leaf=2, criterion='squared_error')\n",
    "# regmse.fit(x, y)\n",
    "\n",
    "# dot_data = StringIO()\n",
    "# export_graphviz(regmse, out_file=dot_data,  \n",
    "#                 filled=True, rounded=True,\n",
    "#                 special_characters=True,feature_names = x.columns.to_list())\n",
    "# graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "# graph.write_png('smokingdecisiontree_se.png')\n",
    "# Image(graph.create_png())\n",
    "\n",
    "regmse2 = DecisionTreeRegressor(min_samples_leaf=14, criterion='squared_error')\n",
    "regmse2.fit(x, y)\n",
    "\n",
    "dot_data = StringIO()\n",
    "export_graphviz(regmse2, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True,feature_names = x.columns.to_list())\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "graph.write_png('smokingdecisiontree_se2.png')\n",
    "Image(graph.create_png())\n",
    "\n",
    "\n",
    "# reg2 = DecisionTreeRegressor(min_samples_split=43, min_samples_leaf=2, criterion='friedman_mse')\n",
    "# reg2.fit(x, y)\n",
    "\n",
    "# dot_data = StringIO()\n",
    "# export_graphviz(reg2, out_file=dot_data,  \n",
    "#                 filled=True, rounded=True,\n",
    "#                 special_characters=True,feature_names = x.columns.to_list())\n",
    "# graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "# graph.write_png('smokingdecisiontree_fmse.png')\n",
    "# Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{7: [0, 5, 24, 28, 40, 60, 62, 83, 94, 99, 103, 118, 125, 129, 134, 139],\n",
       " 2: [1,\n",
       "  7,\n",
       "  8,\n",
       "  16,\n",
       "  27,\n",
       "  29,\n",
       "  34,\n",
       "  44,\n",
       "  54,\n",
       "  61,\n",
       "  63,\n",
       "  66,\n",
       "  69,\n",
       "  80,\n",
       "  87,\n",
       "  89,\n",
       "  100,\n",
       "  102,\n",
       "  117,\n",
       "  124,\n",
       "  130,\n",
       "  133,\n",
       "  135,\n",
       "  140],\n",
       " 1: [2, 9, 15, 30, 37, 45, 52, 75, 82, 88, 90, 96, 143, 146, 147],\n",
       " 6: [3, 17, 22, 26, 31, 32, 35, 58, 72, 79, 84, 92, 111, 115, 137],\n",
       " 4: [4,\n",
       "  23,\n",
       "  33,\n",
       "  36,\n",
       "  46,\n",
       "  50,\n",
       "  56,\n",
       "  59,\n",
       "  67,\n",
       "  70,\n",
       "  71,\n",
       "  73,\n",
       "  77,\n",
       "  86,\n",
       "  104,\n",
       "  105,\n",
       "  106,\n",
       "  109,\n",
       "  110,\n",
       "  116,\n",
       "  120,\n",
       "  121,\n",
       "  123,\n",
       "  131,\n",
       "  132],\n",
       " 3: [6,\n",
       "  10,\n",
       "  12,\n",
       "  13,\n",
       "  38,\n",
       "  43,\n",
       "  47,\n",
       "  48,\n",
       "  51,\n",
       "  57,\n",
       "  64,\n",
       "  68,\n",
       "  78,\n",
       "  81,\n",
       "  85,\n",
       "  93,\n",
       "  98,\n",
       "  107,\n",
       "  108,\n",
       "  122,\n",
       "  127,\n",
       "  128,\n",
       "  138,\n",
       "  141,\n",
       "  144],\n",
       " 5: [11, 14, 18, 19, 41, 53, 65, 74, 97, 101, 113, 126, 136, 142],\n",
       " 0: [20, 21, 25, 39, 42, 49, 55, 76, 91, 95, 112, 114, 119, 145]}"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsdict = {}\n",
    "leaves = regmse2.apply(x)\n",
    "leavesuniq = list(np.unique(leaves))\n",
    "for i in range(len(newtobaccofinal)):\n",
    "    indexleaf = leavesuniq.index(leaves[i])\n",
    "    if(indexleaf not in resultsdict):\n",
    "        resultsdict[indexleaf] = [i]\n",
    "    else:\n",
    "        resultsdict[indexleaf].append(i)\n",
    "resultsdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>106766.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>AFE</td>\n",
       "      <td>677243299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38928341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Western and Central</td>\n",
       "      <td>AFW</td>\n",
       "      <td>458803476.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>32866268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>XKX</td>\n",
       "      <td>1775378.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YEM</td>\n",
       "      <td>29825968.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>59308690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>18383956.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14862927.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Country Name Country Code   Population\n",
       "0                          Aruba          ABW     106766.0\n",
       "1    Africa Eastern and Southern          AFE  677243299.0\n",
       "2                    Afghanistan          AFG   38928341.0\n",
       "3     Africa Western and Central          AFW  458803476.0\n",
       "4                         Angola          AGO   32866268.0\n",
       "..                           ...          ...          ...\n",
       "261                       Kosovo          XKX    1775378.0\n",
       "262                        Yemen          YEM   29825968.0\n",
       "263                 South Africa          ZAF   59308690.0\n",
       "264                       Zambia          ZMB   18383956.0\n",
       "265                     Zimbabwe          ZWE   14862927.0\n",
       "\n",
       "[266 rows x 3 columns]"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = pd.read_csv('country_population.csv')\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country Name       Pakistan\n",
      "Country Code            PAK\n",
      "Population      220892331.0\n",
      "Name: 184, dtype: object\n",
      "Country Name           China\n",
      "Country Code             CHN\n",
      "Population      1402112000.0\n",
      "Name: 40, dtype: object\n",
      "Country Name        Nigeria\n",
      "Country Code            NGA\n",
      "Population      206139587.0\n",
      "Name: 174, dtype: object\n",
      "Country Name           India\n",
      "Country Code             IND\n",
      "Population      1380004385.0\n",
      "Name: 109, dtype: object\n",
      "Country Name      Indonesia\n",
      "Country Code            IDN\n",
      "Population      273523621.0\n",
      "Name: 106, dtype: object\n",
      "Country Name    United States of America\n",
      "Country Code                         USA\n",
      "Population                   329484123.0\n",
      "Name: 251, dtype: object\n",
      "Country Name        Uganda\n",
      "Country Code           UGA\n",
      "Population      45741000.0\n",
      "Name: 247, dtype: object\n",
      "Country Name         Yemen\n",
      "Country Code           YEM\n",
      "Population      29825968.0\n",
      "Name: 262, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for key in resultsdict.keys():\n",
    "    poptemp = population[population['Country Name'].isin(newtobaccofinal.iloc[resultsdict[key]].Location)]\n",
    "    print(population.iloc[poptemp['Population'].idxmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13185349092422927\n"
     ]
    }
   ],
   "source": [
    "X = newtobaccofinal.drop([\"Usage\", \"Location\", \"ParentLocation\"], axis=1)\n",
    "y = newtobaccofinal[\"Usage\"]\n",
    "regr = LinearRegression()\n",
    "regr.fit(X, y)\n",
    "print(regr.score(X, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimal_cluster(data=None, config=None, ns=None, features=None, target=None, niter=1):\n",
    "    x = data[features]\n",
    "    y = data[target]\n",
    "    finalscore = 0\n",
    "    tempscore = 0\n",
    "    for nit in range(niter):\n",
    "\n",
    "        if not config:\n",
    "            for n in ns:\n",
    "                #initial configuration\n",
    "                conf = list(range(len(data)))\n",
    "                random.shuffle(conf)\n",
    "                config = {i: conf[i::n] for i in range(n)}\n",
    "        \n",
    "        change = True\n",
    "        while(change):\n",
    "            change = False\n",
    "            models = []\n",
    "            for key in config:\n",
    "                xtemp = x.iloc[config[key]]\n",
    "                ytemp = y.iloc[config[key]]\n",
    "                regr = LinearRegression()\n",
    "                regr.fit(xtemp, ytemp)\n",
    "                tempscore += regr.score(xtemp, ytemp)*len(config[key])\n",
    "                models.append(regr)\n",
    "            config2 = copy.deepcopy(config)\n",
    "            for key in config:\n",
    "                for r in config[key]:\n",
    "                    xrow = x.iloc[[r]]\n",
    "                    yrow = y.iloc[[r]]\n",
    "                    results = []\n",
    "                    for model in models:\n",
    "                        results.append(abs(float(model.predict(xrow)[0])-float(yrow)))\n",
    "                    minr = results.index(min(results))\n",
    "                    if(list(config)[minr] != key):\n",
    "                        if(not change):\n",
    "                            change = True\n",
    "                        config2[key].remove(r)\n",
    "                        config2[list(config)[minr]].append(r)\n",
    "            config = config2\n",
    "        \n",
    "        if tempscore > finalscore:\n",
    "            finalscore = tempscore\n",
    "            finalresults = config2\n",
    "\n",
    "    return finalresults, finalscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "({0: [143, 96, 84, 139, 72, 52, 88, 120, 48, 69, 122, 116, 64, 71, 144, 101, 15, 60, 128, 56, 57, 41, 123, 77], 1: [125, 75, 134, 25, 55, 1, 90, 19, 117, 30, 82, 43, 74, 50, 103, 83, 113], 2: [108, 23, 3, 95, 98, 107, 70, 66, 138, 44, 114, 91, 141, 87, 112, 61], 3: [38, 135, 89, 6, 31, 21, 80, 127, 102, 110, 118, 0, 146, 65, 49, 54, 46, 10], 4: [40, 73, 13, 132, 147, 106, 18, 126, 39, 111, 124, 121, 119, 26, 4, 104, 45, 11, 140, 20, 12, 85], 5: [109, 92, 24, 58, 33, 34, 81, 9, 68, 36, 86, 62, 22], 6: [78, 5, 142, 79, 17, 59, 32, 37, 42, 145, 63, 93, 51, 94, 2, 131, 8, 129, 53, 14, 27, 28], 7: [99, 100, 97, 47, 7, 105, 76, 16, 35, 137, 136, 29, 115, 133, 67, 130]}, 3220.057913903866)\n",
      "({7: [5, 28, 62, 103, 125, 89, 6, 38, 0, 60, 118, 129, 26, 49, 86, 31, 101, 84, 134], 2: [7, 16, 34, 87, 124, 130, 3, 58, 72, 138, 97, 104, 100, 102, 27, 12, 78, 64, 80, 29], 1: [37, 45, 88, 90, 147, 139, 35, 136, 10, 77, 83, 57, 137, 32, 56, 59, 55, 15, 146, 52, 116], 6: [24, 99, 54, 63, 96, 143, 94, 46, 47, 66, 79, 67, 123, 91, 140, 2, 113, 144, 93, 76, 4, 133, 69, 110], 4: [23, 33, 70, 106, 109, 121, 131, 132, 44, 117, 30, 111, 107, 141, 126, 81, 95, 36, 13, 71, 11, 50, 115, 122, 18], 3: [43, 48, 85, 1, 61, 9, 75, 22, 92, 135, 108, 128, 8], 5: [14, 19, 41, 53, 142, 40, 120, 98, 105, 51, 74, 73], 0: [20, 21, 39, 42, 112, 114, 119, 82, 17, 68, 127, 25, 65, 145]}, 1507.0038672397295)\n"
     ]
    }
   ],
   "source": [
    "clustersrand = optimal_cluster(data=newtobaccofinal, ns=[8], features=newtobaccofinal.columns.drop([\"Usage\", \"Location\", \"ParentLocation\"]), target=\"Usage\", niter=10)\n",
    "clustersnew = optimal_cluster(data=newtobaccofinal, config=resultsdict, features=newtobaccofinal.columns.drop([\"Usage\", \"Location\", \"ParentLocation\"]), target=\"Usage\")\n",
    "print(clustersrand)\n",
    "print(clustersnew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9955447818713915, 0.9857414513390416, 0.99009790668601, 0.9864343108521781, 0.9203396224098466, 0.9981156982496142, 0.9871481551539502, 0.9965356400620113]\n",
      "[0.9588964442907612, 0.9852430156020537, 0.9330195454376637, 0.9895616257582731, 0.4792646637977236, 0.9882487573449287, 0.999264842087745, 0.992004651471023]\n"
     ]
    }
   ],
   "source": [
    "x = newtobaccofinal.drop([\"Usage\", \"Location\", \"ParentLocation\"], axis=1)\n",
    "y = newtobaccofinal[\"Usage\"]\n",
    "randresult = []\n",
    "for key in clustersrand[0].keys():\n",
    "    xtemp = x.iloc[clustersrand[0][key]]\n",
    "    ytemp = y.iloc[clustersrand[0][key]]\n",
    "    regr = LinearRegression()\n",
    "    regr.fit(xtemp, ytemp)\n",
    "    randresult.append(regr.score(xtemp, ytemp))\n",
    "newresult = []\n",
    "for key in clustersnew[0].keys():\n",
    "    xtemp = x.iloc[clustersnew[0][key]]\n",
    "    ytemp = y.iloc[clustersnew[0][key]]\n",
    "    regr = LinearRegression()\n",
    "    regr.fit(xtemp, ytemp)\n",
    "    newresult.append(regr.score(xtemp, ytemp))\n",
    "print(randresult)\n",
    "print(newresult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "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>Monitor</th>\n",
       "      <th>Protect from tobacco smoke</th>\n",
       "      <th>Offer help to quit tobacco use</th>\n",
       "      <th>Warn about the dangers of tobacco</th>\n",
       "      <th>Enforce bans on tobacco advertising</th>\n",
       "      <th>Anti-tobacco mass media campaigns</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.947926</td>\n",
       "      <td>-1.44817</td>\n",
       "      <td>3.129177</td>\n",
       "      <td>1.425648</td>\n",
       "      <td>-0.516958</td>\n",
       "      <td>-0.297462</td>\n",
       "      <td>0.035238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.427503</td>\n",
       "      <td>0.257456</td>\n",
       "      <td>2.313892</td>\n",
       "      <td>-2.235649</td>\n",
       "      <td>0.274949</td>\n",
       "      <td>0.180475</td>\n",
       "      <td>0.072309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.317259</td>\n",
       "      <td>-3.001583</td>\n",
       "      <td>-0.122312</td>\n",
       "      <td>0.472146</td>\n",
       "      <td>1.419743</td>\n",
       "      <td>-0.093594</td>\n",
       "      <td>-0.099224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.836352</td>\n",
       "      <td>0.820773</td>\n",
       "      <td>-0.633025</td>\n",
       "      <td>0.802125</td>\n",
       "      <td>-0.559128</td>\n",
       "      <td>0.93194</td>\n",
       "      <td>0.026614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.838343</td>\n",
       "      <td>0.41282</td>\n",
       "      <td>1.393615</td>\n",
       "      <td>-0.524988</td>\n",
       "      <td>0.160787</td>\n",
       "      <td>0.746522</td>\n",
       "      <td>-0.101978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.642761</td>\n",
       "      <td>-3.270379</td>\n",
       "      <td>3.659919</td>\n",
       "      <td>-4.653756</td>\n",
       "      <td>1.432152</td>\n",
       "      <td>0.205638</td>\n",
       "      <td>0.084356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.171841</td>\n",
       "      <td>-0.129997</td>\n",
       "      <td>-1.051269</td>\n",
       "      <td>-1.107101</td>\n",
       "      <td>-0.806404</td>\n",
       "      <td>0.7066</td>\n",
       "      <td>0.021743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.873184</td>\n",
       "      <td>-0.48565</td>\n",
       "      <td>2.119797</td>\n",
       "      <td>0.16779</td>\n",
       "      <td>1.569835</td>\n",
       "      <td>-1.66606</td>\n",
       "      <td>0.086466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Monitor Protect from tobacco smoke Offer help to quit tobacco use  \\\n",
       "0 -0.947926                   -1.44817                       3.129177   \n",
       "1 -1.427503                   0.257456                       2.313892   \n",
       "2 -1.317259                  -3.001583                      -0.122312   \n",
       "3 -1.836352                   0.820773                      -0.633025   \n",
       "4  0.838343                    0.41282                       1.393615   \n",
       "5  4.642761                  -3.270379                       3.659919   \n",
       "6  0.171841                  -0.129997                      -1.051269   \n",
       "7 -1.873184                   -0.48565                       2.119797   \n",
       "\n",
       "  Warn about the dangers of tobacco Enforce bans on tobacco advertising  \\\n",
       "0                          1.425648                           -0.516958   \n",
       "1                         -2.235649                            0.274949   \n",
       "2                          0.472146                            1.419743   \n",
       "3                          0.802125                           -0.559128   \n",
       "4                         -0.524988                            0.160787   \n",
       "5                         -4.653756                            1.432152   \n",
       "6                         -1.107101                           -0.806404   \n",
       "7                           0.16779                            1.569835   \n",
       "\n",
       "  Anti-tobacco mass media campaigns     Price  \n",
       "0                         -0.297462  0.035238  \n",
       "1                          0.180475  0.072309  \n",
       "2                         -0.093594 -0.099224  \n",
       "3                           0.93194  0.026614  \n",
       "4                          0.746522 -0.101978  \n",
       "5                          0.205638  0.084356  \n",
       "6                            0.7066  0.021743  \n",
       "7                          -1.66606  0.086466  "
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs = pd.DataFrame(columns=newtobaccofinal.columns.drop([\"Usage\", \"Location\", \"ParentLocation\"]), index=range(len(clustersrand[0])))\n",
    "x = newtobaccofinal.drop([\"Usage\", \"Location\", \"ParentLocation\"], axis=1)\n",
    "y = newtobaccofinal[\"Usage\"]\n",
    "count = 0\n",
    "for key in clustersrand[0].keys():\n",
    "    xtemp = x.iloc[clustersrand[0][key]]\n",
    "    ytemp = y.iloc[clustersrand[0][key]]\n",
    "    regr = LinearRegression()\n",
    "    regr.fit(xtemp, ytemp)\n",
    "    coeffs.iloc[count] = regr.coef_\n",
    "    count += 1\n",
    "coeffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Usage   R-squared:                       0.996\n",
      "Model:                            OLS   Adj. R-squared:                  0.994\n",
      "Method:                 Least Squares   F-statistic:                     510.8\n",
      "Date:                Sun, 21 Nov 2021   Prob (F-statistic):           1.36e-17\n",
      "Time:                        09:33:19   Log-Likelihood:                 12.382\n",
      "No. Observations:                  24   AIC:                            -8.763\n",
      "Df Residuals:                      16   BIC:                            0.6611\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                                 -10.7507      0.346    -31.094      0.000     -11.484     -10.018\n",
      "Monitor                                -0.9479      0.061    -15.515      0.000      -1.077      -0.818\n",
      "Protect from tobacco smoke             -1.4482      0.037    -39.229      0.000      -1.526      -1.370\n",
      "Offer help to quit tobacco use          3.1292      0.079     39.438      0.000       2.961       3.297\n",
      "Warn about the dangers of tobacco       1.4256      0.070     20.466      0.000       1.278       1.573\n",
      "Enforce bans on tobacco advertising    -0.5170      0.068     -7.622      0.000      -0.661      -0.373\n",
      "Anti-tobacco mass media campaigns      -0.2975      0.049     -6.068      0.000      -0.401      -0.194\n",
      "Price                                   0.0352      0.005      7.571      0.000       0.025       0.045\n",
      "==============================================================================\n",
      "Omnibus:                        0.479   Durbin-Watson:                   2.197\n",
      "Prob(Omnibus):                  0.787   Jarque-Bera (JB):                0.597\n",
      "Skew:                           0.183   Prob(JB):                        0.742\n",
      "Kurtosis:                       2.319   Cond. No.                         105.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Usage   R-squared:                       0.986\n",
      "Model:                            OLS   Adj. R-squared:                  0.975\n",
      "Method:                 Least Squares   F-statistic:                     88.89\n",
      "Date:                Sun, 21 Nov 2021   Prob (F-statistic):           1.39e-07\n",
      "Time:                        09:33:19   Log-Likelihood:                -3.7155\n",
      "No. Observations:                  17   AIC:                             23.43\n",
      "Df Residuals:                       9   BIC:                             30.10\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                                  -1.0678      0.678     -1.575      0.150      -2.602       0.466\n",
      "Monitor                                -1.4275      0.136    -10.466      0.000      -1.736      -1.119\n",
      "Protect from tobacco smoke              0.2575      0.127      2.021      0.074      -0.031       0.546\n",
      "Offer help to quit tobacco use          2.3139      0.246      9.413      0.000       1.758       2.870\n",
      "Warn about the dangers of tobacco      -2.2356      0.167    -13.423      0.000      -2.612      -1.859\n",
      "Enforce bans on tobacco advertising     0.2749      0.121      2.272      0.049       0.001       0.549\n",
      "Anti-tobacco mass media campaigns       0.1805      0.139      1.294      0.228      -0.135       0.496\n",
      "Price                                   0.0723      0.013      5.383      0.000       0.042       0.103\n",
      "==============================================================================\n",
      "Omnibus:                        1.785   Durbin-Watson:                   1.957\n",
      "Prob(Omnibus):                  0.410   Jarque-Bera (JB):                1.262\n",
      "Skew:                           0.443   Prob(JB):                        0.532\n",
      "Kurtosis:                       2.001   Cond. No.                         62.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Usage   R-squared:                       0.990\n",
      "Model:                            OLS   Adj. R-squared:                  0.981\n",
      "Method:                 Least Squares   F-statistic:                     114.3\n",
      "Date:                Sun, 21 Nov 2021   Prob (F-statistic):           2.21e-07\n",
      "Time:                        09:33:19   Log-Likelihood:               -0.78917\n",
      "No. Observations:                  16   AIC:                             17.58\n",
      "Df Residuals:                       8   BIC:                             23.76\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                                   3.9781      0.684      5.819      0.000       2.402       5.555\n",
      "Monitor                                -1.3173      0.217     -6.074      0.000      -1.817      -0.817\n",
      "Protect from tobacco smoke             -3.0016      0.121    -24.757      0.000      -3.281      -2.722\n",
      "Offer help to quit tobacco use         -0.1223      0.227     -0.539      0.605      -0.646       0.401\n",
      "Warn about the dangers of tobacco       0.4721      0.168      2.818      0.023       0.086       0.858\n",
      "Enforce bans on tobacco advertising     1.4197      0.100     14.222      0.000       1.190       1.650\n",
      "Anti-tobacco mass media campaigns      -0.0936      0.123     -0.760      0.469      -0.378       0.190\n",
      "Price                                  -0.0992      0.009    -10.537      0.000      -0.121      -0.078\n",
      "==============================================================================\n",
      "Omnibus:                        0.972   Durbin-Watson:                   1.826\n",
      "Prob(Omnibus):                  0.615   Jarque-Bera (JB):                0.079\n",
      "Skew:                           0.103   Prob(JB):                        0.961\n",
      "Kurtosis:                       3.274   Cond. No.                         114.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Usage   R-squared:                       0.986\n",
      "Model:                            OLS   Adj. R-squared:                  0.977\n",
      "Method:                 Least Squares   F-statistic:                     103.9\n",
      "Date:                Sun, 21 Nov 2021   Prob (F-statistic):           1.57e-08\n",
      "Time:                        09:33:19   Log-Likelihood:                0.10065\n",
      "No. Observations:                  18   AIC:                             15.80\n",
      "Df Residuals:                      10   BIC:                             22.92\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                                  -3.1299      0.525     -5.964      0.000      -4.299      -1.961\n",
      "Monitor                                -1.8364      0.169    -10.863      0.000      -2.213      -1.460\n",
      "Protect from tobacco smoke              0.8208      0.122      6.732      0.000       0.549       1.092\n",
      "Offer help to quit tobacco use         -0.6330      0.146     -4.347      0.001      -0.958      -0.309\n",
      "Warn about the dangers of tobacco       0.8021      0.165      4.856      0.001       0.434       1.170\n",
      "Enforce bans on tobacco advertising    -0.5591      0.104     -5.402      0.000      -0.790      -0.329\n",
      "Anti-tobacco mass media campaigns       0.9319      0.137      6.781      0.000       0.626       1.238\n",
      "Price                                   0.0266      0.009      3.012      0.013       0.007       0.046\n",
      "==============================================================================\n",
      "Omnibus:                       10.646   Durbin-Watson:                   2.623\n",
      "Prob(Omnibus):                  0.005   Jarque-Bera (JB):                8.122\n",
      "Skew:                          -1.163   Prob(JB):                       0.0172\n",
      "Kurtosis:                       5.328   Cond. No.                         81.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Usage   R-squared:                       0.920\n",
      "Model:                            OLS   Adj. R-squared:                  0.881\n",
      "Method:                 Least Squares   F-statistic:                     23.11\n",
      "Date:                Sun, 21 Nov 2021   Prob (F-statistic):           1.15e-06\n",
      "Time:                        09:33:19   Log-Likelihood:                -16.400\n",
      "No. Observations:                  22   AIC:                             48.80\n",
      "Df Residuals:                      14   BIC:                             57.53\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                                 -11.4435      1.055    -10.842      0.000     -13.707      -9.180\n",
      "Monitor                                 0.8383      0.201      4.169      0.001       0.407       1.270\n",
      "Protect from tobacco smoke              0.4128      0.133      3.101      0.008       0.127       0.698\n",
      "Offer help to quit tobacco use          1.3936      0.326      4.279      0.001       0.695       2.092\n",
      "Warn about the dangers of tobacco      -0.5250      0.197     -2.671      0.018      -0.947      -0.103\n",
      "Enforce bans on tobacco advertising     0.1608      0.184      0.873      0.397      -0.234       0.556\n",
      "Anti-tobacco mass media campaigns       0.7465      0.229      3.259      0.006       0.255       1.238\n",
      "Price                                  -0.1020      0.012     -8.539      0.000      -0.128      -0.076\n",
      "==============================================================================\n",
      "Omnibus:                        1.949   Durbin-Watson:                   2.338\n",
      "Prob(Omnibus):                  0.377   Jarque-Bera (JB):                0.679\n",
      "Skew:                           0.329   Prob(JB):                        0.712\n",
      "Kurtosis:                       3.555   Cond. No.                         114.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Usage   R-squared:                       0.998\n",
      "Model:                            OLS   Adj. R-squared:                  0.995\n",
      "Method:                 Least Squares   F-statistic:                     378.4\n",
      "Date:                Sun, 21 Nov 2021   Prob (F-statistic):           1.67e-06\n",
      "Time:                        09:33:19   Log-Likelihood:                 5.3205\n",
      "No. Observations:                  13   AIC:                             5.359\n",
      "Df Residuals:                       5   BIC:                             9.879\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                                 -12.0547      0.807    -14.936      0.000     -14.129      -9.980\n",
      "Monitor                                 4.6428      0.133     34.799      0.000       4.300       4.986\n",
      "Protect from tobacco smoke             -3.2704      0.087    -37.512      0.000      -3.494      -3.046\n",
      "Offer help to quit tobacco use          3.6599      0.163     22.457      0.000       3.241       4.079\n",
      "Warn about the dangers of tobacco      -4.6538      0.199    -23.357      0.000      -5.166      -4.142\n",
      "Enforce bans on tobacco advertising     1.4322      0.113     12.626      0.000       1.141       1.724\n",
      "Anti-tobacco mass media campaigns       0.2056      0.087      2.368      0.064      -0.018       0.429\n",
      "Price                                   0.0844      0.007     12.976      0.000       0.068       0.101\n",
      "==============================================================================\n",
      "Omnibus:                        1.239   Durbin-Watson:                   2.750\n",
      "Prob(Omnibus):                  0.538   Jarque-Bera (JB):                0.924\n",
      "Skew:                          -0.586   Prob(JB):                        0.630\n",
      "Kurtosis:                       2.424   Cond. No.                         166.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Usage   R-squared:                       0.987\n",
      "Model:                            OLS   Adj. R-squared:                  0.981\n",
      "Method:                 Least Squares   F-statistic:                     153.6\n",
      "Date:                Sun, 21 Nov 2021   Prob (F-statistic):           3.81e-12\n",
      "Time:                        09:33:19   Log-Likelihood:                 5.6626\n",
      "No. Observations:                  22   AIC:                             4.675\n",
      "Df Residuals:                      14   BIC:                             13.40\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                                   5.6252      0.374     15.037      0.000       4.823       6.428\n",
      "Monitor                                 0.1718      0.106      1.618      0.128      -0.056       0.400\n",
      "Protect from tobacco smoke             -0.1300      0.070     -1.848      0.086      -0.281       0.021\n",
      "Offer help to quit tobacco use         -1.0513      0.093    -11.301      0.000      -1.251      -0.852\n",
      "Warn about the dangers of tobacco      -1.1071      0.133     -8.338      0.000      -1.392      -0.822\n",
      "Enforce bans on tobacco advertising    -0.8064      0.081     -9.998      0.000      -0.979      -0.633\n",
      "Anti-tobacco mass media campaigns       0.7066      0.091      7.748      0.000       0.511       0.902\n",
      "Price                                   0.0217      0.006      3.467      0.004       0.008       0.035\n",
      "==============================================================================\n",
      "Omnibus:                        1.516   Durbin-Watson:                   2.487\n",
      "Prob(Omnibus):                  0.469   Jarque-Bera (JB):                0.954\n",
      "Skew:                           0.507   Prob(JB):                        0.621\n",
      "Kurtosis:                       2.889   Cond. No.                         94.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Usage   R-squared:                       0.997\n",
      "Model:                            OLS   Adj. R-squared:                  0.994\n",
      "Method:                 Least Squares   F-statistic:                     328.7\n",
      "Date:                Sun, 21 Nov 2021   Prob (F-statistic):           3.36e-09\n",
      "Time:                        09:33:19   Log-Likelihood:                 9.8638\n",
      "No. Observations:                  16   AIC:                            -3.728\n",
      "Df Residuals:                       8   BIC:                             2.453\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                                  -6.0061      0.337    -17.831      0.000      -6.783      -5.229\n",
      "Monitor                                -1.8732      0.107    -17.582      0.000      -2.119      -1.627\n",
      "Protect from tobacco smoke             -0.4857      0.063     -7.659      0.000      -0.632      -0.339\n",
      "Offer help to quit tobacco use          2.1198      0.124     17.106      0.000       1.834       2.406\n",
      "Warn about the dangers of tobacco       0.1678      0.079      2.114      0.067      -0.015       0.351\n",
      "Enforce bans on tobacco advertising     1.5698      0.110     14.311      0.000       1.317       1.823\n",
      "Anti-tobacco mass media campaigns      -1.6661      0.106    -15.759      0.000      -1.910      -1.422\n",
      "Price                                   0.0865      0.004     22.468      0.000       0.078       0.095\n",
      "==============================================================================\n",
      "Omnibus:                        5.219   Durbin-Watson:                   1.566\n",
      "Prob(Omnibus):                  0.074   Jarque-Bera (JB):                2.669\n",
      "Skew:                          -0.511   Prob(JB):                        0.263\n",
      "Kurtosis:                       4.720   Cond. No.                         115.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/praneet/Library/Python/3.9/lib/python/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n",
      "/Users/praneet/Library/Python/3.9/lib/python/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n",
      "/Users/praneet/Library/Python/3.9/lib/python/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n",
      "/Users/praneet/Library/Python/3.9/lib/python/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=13\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n",
      "/Users/praneet/Library/Python/3.9/lib/python/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "x = newtobaccofinal.drop([\"Usage\", \"Location\", \"ParentLocation\"], axis=1)\n",
    "y = newtobaccofinal[\"Usage\"]\n",
    "x = sm.add_constant(x)\n",
    "count = 0\n",
    "for key in clustersrand[0].keys():\n",
    "    xtemp = x.iloc[clustersrand[0][key]]\n",
    "    ytemp = y.iloc[clustersrand[0][key]]\n",
    "    model = sm.OLS(ytemp.astype(float), xtemp.astype(float))\n",
    "    results = model.fit()\n",
    "    print(results.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "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>Location</th>\n",
       "      <th>Monitor</th>\n",
       "      <th>Protect from tobacco smoke</th>\n",
       "      <th>Offer help to quit tobacco use</th>\n",
       "      <th>Warn about the dangers of tobacco</th>\n",
       "      <th>Enforce bans on tobacco advertising</th>\n",
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       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2.25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.3</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-14.6</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>21.3</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>Europe</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.75</td>\n",
       "      <td>6.9</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>Americas</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>Europe</td>\n",
       "      <td>4</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.75</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>2.75</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>4.9</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-15.8</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>Africa</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location Monitor Protect from tobacco smoke  \\\n",
       "0       Albania    2.25                        5.0   \n",
       "1       Algeria     2.5                        3.0   \n",
       "2       Andorra     1.5                       2.75   \n",
       "3     Argentina    3.75                       4.25   \n",
       "4       Armenia     4.0                        3.0   \n",
       "..          ...     ...                        ...   \n",
       "143  Uzbekistan     2.0                        2.0   \n",
       "144     Vanuatu     2.5                        2.0   \n",
       "145       Yemen     2.0                        3.5   \n",
       "146      Zambia    1.75                        4.0   \n",
       "147    Zimbabwe     2.0                        3.0   \n",
       "\n",
       "    Offer help to quit tobacco use Warn about the dangers of tobacco  \\\n",
       "0                             3.75                               4.0   \n",
       "1                              3.0                               2.0   \n",
       "2                              4.0                               2.0   \n",
       "3                              4.0                              4.25   \n",
       "4                             3.75                              4.25   \n",
       "..                             ...                               ...   \n",
       "143                           3.75                               3.0   \n",
       "144                            3.0                               4.5   \n",
       "145                            3.0                              3.75   \n",
       "146                           3.25                               2.0   \n",
       "147                           3.25                               2.0   \n",
       "\n",
       "    Enforce bans on tobacco advertising Anti-tobacco mass media campaigns  \\\n",
       "0                                   5.0                               3.0   \n",
       "1                                   4.0                               1.5   \n",
       "2                                   2.0                              1.75   \n",
       "3                                   3.5                              3.75   \n",
       "4                                   2.0                               2.0   \n",
       "..                                  ...                               ...   \n",
       "143                                 3.5                              1.75   \n",
       "144                                 5.0                              1.75   \n",
       "145                                4.75                              2.75   \n",
       "146                                 2.0                              2.75   \n",
       "147                                 2.0                               2.0   \n",
       "\n",
       "    Price Usage         ParentLocation  Cluster  \n",
       "0    14.3  -1.9                 Europe        3  \n",
       "1   -14.6  -1.2                 Africa        1  \n",
       "2    21.3  -0.9                 Europe        6  \n",
       "3     6.9  -8.3               Americas        2  \n",
       "4    14.5  -3.3                 Europe        4  \n",
       "..    ...   ...                    ...      ...  \n",
       "143  15.0  -1.5                 Europe        0  \n",
       "144   3.1  -3.1        Western Pacific        0  \n",
       "145  -2.9  -3.6  Eastern Mediterranean        6  \n",
       "146   4.9  -2.0                 Africa        3  \n",
       "147 -15.8  -1.7                 Africa        4  \n",
       "\n",
       "[148 rows x 11 columns]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustercolumn = [None]*len(newtobaccofinal)\n",
    "for key in clustersrand[0].keys():\n",
    "    for country in clustersrand[0][key]:\n",
    "        clustercolumn[country] = key\n",
    "newtobaccofinal['Cluster'] = clustercolumn\n",
    "newtobaccofinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "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>Country Name</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Access to clean fuels and technologies for coo...</td>\n",
       "      <td>77.42</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Access to electricity (% of population)</td>\n",
       "      <td>99.89</td>\n",
       "      <td>99.89</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Access to electricity, urban (% of urban popul...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Account ownership at a financial institution o...</td>\n",
       "      <td>..</td>\n",
       "      <td>40.01517105</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212116</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Women who believe a husband is justified in be...</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212117</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Women who were first married by age 15 (% of w...</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>5.4</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212118</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Women who were first married by age 18 (% of w...</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>33.7</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212119</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Women's share of population ages 15+ living wi...</td>\n",
       "      <td>59.7</td>\n",
       "      <td>59.9</td>\n",
       "      <td>60</td>\n",
       "      <td>60.2</td>\n",
       "      <td>60.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212120</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
       "      <td>14000</td>\n",
       "      <td>12000</td>\n",
       "      <td>9700</td>\n",
       "      <td>9600</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212121 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country Name                                        Series Name   2016  \\\n",
       "0           Albania  Access to clean fuels and technologies for coo...  77.42   \n",
       "1           Albania            Access to electricity (% of population)  99.89   \n",
       "2           Albania  Access to electricity, rural (% of rural popul...    100   \n",
       "3           Albania  Access to electricity, urban (% of urban popul...    100   \n",
       "4           Albania  Account ownership at a financial institution o...     ..   \n",
       "...             ...                                                ...    ...   \n",
       "212116     Zimbabwe  Women who believe a husband is justified in be...     ..   \n",
       "212117     Zimbabwe  Women who were first married by age 15 (% of w...     ..   \n",
       "212118     Zimbabwe  Women who were first married by age 18 (% of w...     ..   \n",
       "212119     Zimbabwe  Women's share of population ages 15+ living wi...   59.7   \n",
       "212120     Zimbabwe  Young people (ages 15-24) newly infected with HIV  14000   \n",
       "\n",
       "               2017  2018  2019  2020  \n",
       "0                ..    ..    ..    ..  \n",
       "1             99.89   100   100    ..  \n",
       "2               100   100   100    ..  \n",
       "3               100   100   100    ..  \n",
       "4       40.01517105    ..    ..    ..  \n",
       "...             ...   ...   ...   ...  \n",
       "212116           ..    ..    ..    ..  \n",
       "212117           ..    ..   5.4    ..  \n",
       "212118           ..    ..  33.7    ..  \n",
       "212119         59.9    60  60.2  60.4  \n",
       "212120        12000  9700  9600  7500  \n",
       "\n",
       "[212121 rows x 7 columns]"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = pd.read_csv('international_country.csv')\n",
    "countries = countries.drop(['Country Code', 'Series Code'], axis=1)\n",
    "countries = countries[countries['Country Name'].isin(newtobaccofinal['Location'])]\n",
    "countries = countries.reset_index().drop('index', axis=1)\n",
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findNumericLoc(row, cols):\n",
    "    curr = None\n",
    "    for i in range(2, 7):\n",
    "        try:\n",
    "            val = int(row[cols[i]])\n",
    "            curr = cols[i]\n",
    "        except:\n",
    "            pass\n",
    "        try:\n",
    "            val = float(row[cols[i]])\n",
    "            curr = cols[i]\n",
    "        except:\n",
    "            pass\n",
    "    return curr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Country</th>\n",
       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
       "      <th>Access to electricity (% of population)</th>\n",
       "      <th>Access to electricity, rural (% of rural population)</th>\n",
       "      <th>Access to electricity, urban (% of urban population)</th>\n",
       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)</th>\n",
       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)</th>\n",
       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)</th>\n",
       "      <th>...</th>\n",
       "      <th>Women who believe a husband is justified in beating his wife (any of five reasons) (%)</th>\n",
       "      <th>Women who believe a husband is justified in beating his wife when she argues with him (%)</th>\n",
       "      <th>Women who believe a husband is justified in beating his wife when she burns the food (%)</th>\n",
       "      <th>Women who believe a husband is justified in beating his wife when she goes out without telling him (%)</th>\n",
       "      <th>Women who believe a husband is justified in beating his wife when she neglects the children (%)</th>\n",
       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>77.42</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.015171</td>\n",
       "      <td>38.102959</td>\n",
       "      <td>42.043503</td>\n",
       "      <td>42.513248</td>\n",
       "      <td>22.751028</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>27.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>92.62</td>\n",
       "      <td>99.5</td>\n",
       "      <td>98.681056</td>\n",
       "      <td>99.8</td>\n",
       "      <td>42.776627</td>\n",
       "      <td>29.26873</td>\n",
       "      <td>56.254936</td>\n",
       "      <td>48.747078</td>\n",
       "      <td>34.970802</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>45.6</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>98.4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>48.708488</td>\n",
       "      <td>50.757252</td>\n",
       "      <td>46.466377</td>\n",
       "      <td>53.370029</td>\n",
       "      <td>38.061279</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>30.8</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>96.9</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>47.759018</td>\n",
       "      <td>40.906437</td>\n",
       "      <td>55.751419</td>\n",
       "      <td>48.015057</td>\n",
       "      <td>34.419273</td>\n",
       "      <td>...</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>35.7</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>92.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>37.089737</td>\n",
       "      <td>35.969078</td>\n",
       "      <td>38.333168</td>\n",
       "      <td>43.62619</td>\n",
       "      <td>29.721619</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>36.9</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>12.57</td>\n",
       "      <td>64.666237</td>\n",
       "      <td>54.426443</td>\n",
       "      <td>94.750137</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>64.93</td>\n",
       "      <td>72.751701</td>\n",
       "      <td>60.632894</td>\n",
       "      <td>93.14653</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>37.2</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>16.43</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.939443</td>\n",
       "      <td>79.878265</td>\n",
       "      <td>45.863258</td>\n",
       "      <td>40.278877</td>\n",
       "      <td>51.680923</td>\n",
       "      <td>48.338139</td>\n",
       "      <td>31.244963</td>\n",
       "      <td>...</td>\n",
       "      <td>45.1</td>\n",
       "      <td>32.2</td>\n",
       "      <td>21.3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.3</td>\n",
       "      <td>29.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>61.8</td>\n",
       "      <td>26000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>29.05</td>\n",
       "      <td>41.089108</td>\n",
       "      <td>20.051849</td>\n",
       "      <td>85.36467</td>\n",
       "      <td>55.285137</td>\n",
       "      <td>51.664925</td>\n",
       "      <td>59.227501</td>\n",
       "      <td>58.877064</td>\n",
       "      <td>43.601181</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5.4</td>\n",
       "      <td>33.7</td>\n",
       "      <td>60.4</td>\n",
       "      <td>7500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 1444 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  \\\n",
       "0       Albania   \n",
       "1       Algeria   \n",
       "2       Andorra   \n",
       "3     Argentina   \n",
       "4       Armenia   \n",
       "..          ...   \n",
       "142  Uzbekistan   \n",
       "143     Vanuatu   \n",
       "144       Yemen   \n",
       "145      Zambia   \n",
       "146    Zimbabwe   \n",
       "\n",
       "    Access to clean fuels and technologies for cooking (% of population)  \\\n",
       "0                                                77.42                     \n",
       "1                                                92.62                     \n",
       "2                                                100.0                     \n",
       "3                                                 98.4                     \n",
       "4                                                 96.9                     \n",
       "..                                                 ...                     \n",
       "142                                               92.1                     \n",
       "143                                              12.57                     \n",
       "144                                              64.93                     \n",
       "145                                              16.43                     \n",
       "146                                              29.05                     \n",
       "\n",
       "    Access to electricity (% of population)  \\\n",
       "0                                     100.0   \n",
       "1                                      99.5   \n",
       "2                                     100.0   \n",
       "3                                     100.0   \n",
       "4                                     100.0   \n",
       "..                                      ...   \n",
       "142                                   100.0   \n",
       "143                               64.666237   \n",
       "144                               72.751701   \n",
       "145                                    43.0   \n",
       "146                               41.089108   \n",
       "\n",
       "    Access to electricity, rural (% of rural population)  \\\n",
       "0                                                100.0     \n",
       "1                                            98.681056     \n",
       "2                                                100.0     \n",
       "3                                                100.0     \n",
       "4                                                100.0     \n",
       "..                                                 ...     \n",
       "142                                              100.0     \n",
       "143                                          54.426443     \n",
       "144                                          60.632894     \n",
       "145                                          13.939443     \n",
       "146                                          20.051849     \n",
       "\n",
       "    Access to electricity, urban (% of urban population)  \\\n",
       "0                                                100.0     \n",
       "1                                                 99.8     \n",
       "2                                                100.0     \n",
       "3                                                100.0     \n",
       "4                                                100.0     \n",
       "..                                                 ...     \n",
       "142                                              100.0     \n",
       "143                                          94.750137     \n",
       "144                                           93.14653     \n",
       "145                                          79.878265     \n",
       "146                                           85.36467     \n",
       "\n",
       "    Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
       "0                                            40.015171                                                                \n",
       "1                                            42.776627                                                                \n",
       "2                                                 None                                                                \n",
       "3                                            48.708488                                                                \n",
       "4                                            47.759018                                                                \n",
       "..                                                 ...                                                                \n",
       "142                                          37.089737                                                                \n",
       "143                                               None                                                                \n",
       "144                                               None                                                                \n",
       "145                                          45.863258                                                                \n",
       "146                                          55.285137                                                                \n",
       "\n",
       "    Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
       "0                                            38.102959                                                                        \n",
       "1                                             29.26873                                                                        \n",
       "2                                                 None                                                                        \n",
       "3                                            50.757252                                                                        \n",
       "4                                            40.906437                                                                        \n",
       "..                                                 ...                                                                        \n",
       "142                                          35.969078                                                                        \n",
       "143                                               None                                                                        \n",
       "144                                               None                                                                        \n",
       "145                                          40.278877                                                                        \n",
       "146                                          51.664925                                                                        \n",
       "\n",
       "    Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)  \\\n",
       "0                                            42.043503                                                                      \n",
       "1                                            56.254936                                                                      \n",
       "2                                                 None                                                                      \n",
       "3                                            46.466377                                                                      \n",
       "4                                            55.751419                                                                      \n",
       "..                                                 ...                                                                      \n",
       "142                                          38.333168                                                                      \n",
       "143                                               None                                                                      \n",
       "144                                               None                                                                      \n",
       "145                                          51.680923                                                                      \n",
       "146                                          59.227501                                                                      \n",
       "\n",
       "    Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)  \\\n",
       "0                                            42.513248                                                                              \n",
       "1                                            48.747078                                                                              \n",
       "2                                                 None                                                                              \n",
       "3                                            53.370029                                                                              \n",
       "4                                            48.015057                                                                              \n",
       "..                                                 ...                                                                              \n",
       "142                                           43.62619                                                                              \n",
       "143                                               None                                                                              \n",
       "144                                               None                                                                              \n",
       "145                                          48.338139                                                                              \n",
       "146                                          58.877064                                                                              \n",
       "\n",
       "    Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)  \\\n",
       "0                                            22.751028                                                                             \n",
       "1                                            34.970802                                                                             \n",
       "2                                                 None                                                                             \n",
       "3                                            38.061279                                                                             \n",
       "4                                            34.419273                                                                             \n",
       "..                                                 ...                                                                             \n",
       "142                                          29.721619                                                                             \n",
       "143                                               None                                                                             \n",
       "144                                               None                                                                             \n",
       "145                                          31.244963                                                                             \n",
       "146                                          43.601181                                                                             \n",
       "\n",
       "     ...  \\\n",
       "0    ...   \n",
       "1    ...   \n",
       "2    ...   \n",
       "3    ...   \n",
       "4    ...   \n",
       "..   ...   \n",
       "142  ...   \n",
       "143  ...   \n",
       "144  ...   \n",
       "145  ...   \n",
       "146  ...   \n",
       "\n",
       "    Women who believe a husband is justified in beating his wife (any of five reasons) (%)  \\\n",
       "0                                                  6.8                                       \n",
       "1                                                 None                                       \n",
       "2                                                 None                                       \n",
       "3                                                 None                                       \n",
       "4                                                 10.1                                       \n",
       "..                                                 ...                                       \n",
       "142                                               None                                       \n",
       "143                                               None                                       \n",
       "144                                               None                                       \n",
       "145                                               45.1                                       \n",
       "146                                               None                                       \n",
       "\n",
       "    Women who believe a husband is justified in beating his wife when she argues with him (%)  \\\n",
       "0                                                  1.8                                          \n",
       "1                                                 None                                          \n",
       "2                                                 None                                          \n",
       "3                                                 None                                          \n",
       "4                                                  6.1                                          \n",
       "..                                                 ...                                          \n",
       "142                                               None                                          \n",
       "143                                               None                                          \n",
       "144                                               None                                          \n",
       "145                                               32.2                                          \n",
       "146                                               None                                          \n",
       "\n",
       "    Women who believe a husband is justified in beating his wife when she burns the food (%)  \\\n",
       "0                                                  0.8                                         \n",
       "1                                                 None                                         \n",
       "2                                                 None                                         \n",
       "3                                                 None                                         \n",
       "4                                                  0.4                                         \n",
       "..                                                 ...                                         \n",
       "142                                               None                                         \n",
       "143                                               None                                         \n",
       "144                                               None                                         \n",
       "145                                               21.3                                         \n",
       "146                                               None                                         \n",
       "\n",
       "    Women who believe a husband is justified in beating his wife when she goes out without telling him (%)  \\\n",
       "0                                                  3.7                                                       \n",
       "1                                                 None                                                       \n",
       "2                                                 None                                                       \n",
       "3                                                 None                                                       \n",
       "4                                                  5.2                                                       \n",
       "..                                                 ...                                                       \n",
       "142                                               None                                                       \n",
       "143                                               None                                                       \n",
       "144                                               None                                                       \n",
       "145                                               26.0                                                       \n",
       "146                                               None                                                       \n",
       "\n",
       "    Women who believe a husband is justified in beating his wife when she neglects the children (%)  \\\n",
       "0                                                  5.2                                                \n",
       "1                                                 None                                                \n",
       "2                                                 None                                                \n",
       "3                                                 None                                                \n",
       "4                                                  8.6                                                \n",
       "..                                                 ...                                                \n",
       "142                                               None                                                \n",
       "143                                               None                                                \n",
       "144                                               None                                                \n",
       "145                                               31.3                                                \n",
       "146                                               None                                                \n",
       "\n",
       "    Women who believe a husband is justified in beating his wife when she refuses sex with him (%)  \\\n",
       "0                                                  0.9                                               \n",
       "1                                                 None                                               \n",
       "2                                                 None                                               \n",
       "3                                                 None                                               \n",
       "4                                                  0.8                                               \n",
       "..                                                 ...                                               \n",
       "142                                               None                                               \n",
       "143                                               None                                               \n",
       "144                                               None                                               \n",
       "145                                               29.5                                               \n",
       "146                                               None                                               \n",
       "\n",
       "    Women who were first married by age 15 (% of women ages 20-24)  \\\n",
       "0                                                  1.4               \n",
       "1                                                  0.0               \n",
       "2                                                 None               \n",
       "3                                                 None               \n",
       "4                                                  0.0               \n",
       "..                                                 ...               \n",
       "142                                               None               \n",
       "143                                               None               \n",
       "144                                               None               \n",
       "145                                                5.2               \n",
       "146                                                5.4               \n",
       "\n",
       "    Women who were first married by age 18 (% of women ages 20-24)  \\\n",
       "0                                                 11.8               \n",
       "1                                                  3.8               \n",
       "2                                                 None               \n",
       "3                                                 None               \n",
       "4                                                  5.3               \n",
       "..                                                 ...               \n",
       "142                                               None               \n",
       "143                                               None               \n",
       "144                                               None               \n",
       "145                                               29.0               \n",
       "146                                               33.7               \n",
       "\n",
       "    Women's share of population ages 15+ living with HIV (%)  \\\n",
       "0                                                 27.5         \n",
       "1                                                 45.6         \n",
       "2                                                 None         \n",
       "3                                                 30.8         \n",
       "4                                                 35.7         \n",
       "..                                                 ...         \n",
       "142                                               36.9         \n",
       "143                                               None         \n",
       "144                                               37.2         \n",
       "145                                               61.8         \n",
       "146                                               60.4         \n",
       "\n",
       "    Young people (ages 15-24) newly infected with HIV  \n",
       "0                                               100.0  \n",
       "1                                               200.0  \n",
       "2                                                None  \n",
       "3                                              1000.0  \n",
       "4                                               100.0  \n",
       "..                                                ...  \n",
       "142                                            1000.0  \n",
       "143                                              None  \n",
       "144                                             200.0  \n",
       "145                                           26000.0  \n",
       "146                                            7500.0  \n",
       "\n",
       "[147 rows x 1444 columns]"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "functioncols = countries.columns\n",
    "countrycolumns = list(countries['Series Name'].unique())\n",
    "countrycolumns.insert(0, 'Country')\n",
    "newcountries = pd.DataFrame(columns = countrycolumns, index = range(len(newtobaccofinal)))\n",
    "count = 0\n",
    "for country in countries['Country Name'].unique():\n",
    "    row = [country]\n",
    "    countrytemp = pd.DataFrame(countries[countries['Country Name'] == country])\n",
    "    finalcols = list(countrytemp.apply(lambda row: findNumericLoc(row=row, cols=functioncols), axis=1))\n",
    "    for c in range(len(finalcols)):\n",
    "        if finalcols[c]:\n",
    "            try:\n",
    "                row.append(float(countrytemp.iloc[c][finalcols[c]]))\n",
    "            except:\n",
    "                row.append(int(countrytemp.iloc[c][finalcols[c]]))\n",
    "        else:\n",
    "            row.append(None)\n",
    "    newcountries.iloc[count] = row\n",
    "    count += 1\n",
    "newcountries\n",
    "newcountries = newcountries.dropna(axis=1,how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tn/p4d84sld2pv0q6nj3m51pmqc0000gn/T/ipykernel_5035/3388288293.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  newcountries['Cluster'] = clusterjoin\n"
     ]
    }
   ],
   "source": [
    "clusterjoin = []\n",
    "for country in newcountries.Country.unique():\n",
    "    clusterjoin.append(int(newtobaccofinal[newtobaccofinal.Location == country].Cluster))\n",
    "newcountries['Cluster'] = clusterjoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Access to electricity (% of population)',\n",
       "       'Access to electricity, urban (% of urban population)',\n",
       "       'Adjusted savings: carbon dioxide damage (current US$)',\n",
       "       'Adjusted savings: mineral depletion (current US$)',\n",
       "       'Agricultural land (% of land area)', 'Agricultural land (sq. km)',\n",
       "       'Agricultural methane emissions (thousand metric tons of CO2 equivalent)',\n",
       "       'Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)',\n",
       "       'Bird species, threatened',\n",
       "       'Capture fisheries production (metric tons)', 'CO2 emissions (kt)',\n",
       "       'CO2 emissions (metric tons per capita)',\n",
       "       'Diabetes prevalence (% of population ages 20 to 79)',\n",
       "       'Fish species, threatened', 'Forest area (% of land area)',\n",
       "       'Forest area (sq. km)',\n",
       "       'Immunization, DPT (% of children ages 12-23 months)',\n",
       "       'Immunization, measles (% of children ages 12-23 months)',\n",
       "       'Incidence of tuberculosis (per 100,000 people)', 'Land area (sq. km)',\n",
       "       'Lower secondary school starting age (years)',\n",
       "       'Mammal species, threatened', 'Merchandise exports (current US$)',\n",
       "       'Merchandise imports (current US$)',\n",
       "       'Methane emissions (kt of CO2 equivalent)',\n",
       "       'Methane emissions in energy sector (thousand metric tons of CO2 equivalent)',\n",
       "       'Mortality rate, infant (per 1,000 live births)',\n",
       "       'Mortality rate, infant, female (per 1,000 live births)',\n",
       "       'Mortality rate, infant, male (per 1,000 live births)',\n",
       "       'Mortality rate, neonatal (per 1,000 live births)',\n",
       "       'Mortality rate, under-5 (per 1,000 live births)',\n",
       "       'Mortality rate, under-5, female (per 1,000 live births)',\n",
       "       'Mortality rate, under-5, male (per 1,000 live births)',\n",
       "       'Nitrous oxide emissions (thousand metric tons of CO2 equivalent)',\n",
       "       'Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)',\n",
       "       'Number of deaths ages 10-14 years',\n",
       "       'Number of deaths ages 15-19 years',\n",
       "       'Number of deaths ages 20-24 years', 'Number of deaths ages 5-9 years',\n",
       "       'Number of infant deaths', 'Number of neonatal deaths',\n",
       "       'Number of under-five deaths', 'Plant species (higher), threatened',\n",
       "       'Population density (people per sq. km of land area)',\n",
       "       'Primary education, duration (years)',\n",
       "       'Primary school starting age (years)',\n",
       "       'Probability of dying among adolescents ages 10-14 years (per 1,000)',\n",
       "       'Probability of dying among adolescents ages 15-19 years (per 1,000)',\n",
       "       'Probability of dying among children ages 5-9 years (per 1,000)',\n",
       "       'Probability of dying among youth ages 20-24 years (per 1,000)',\n",
       "       'Proportion of seats held by women in national parliaments (%)',\n",
       "       'Renewable energy consumption (% of total final energy consumption)',\n",
       "       'Scientific and technical journal articles',\n",
       "       'Secondary education, duration (years)', 'Secure Internet servers',\n",
       "       'Secure Internet servers (per 1 million people)',\n",
       "       'Statistical performance indicators (SPI): Pillar 1 data use score (scale 0-100)',\n",
       "       'Statistical performance indicators (SPI): Pillar 3 data products score  (scale 0-100)',\n",
       "       'Surface area (sq. km)', 'Total fisheries production (metric tons)',\n",
       "       'Total greenhouse gas emissions (kt of CO2 equivalent)',\n",
       "       'Tuberculosis case detection rate (%, all forms)', 'Cluster'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcountries.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "[CV] END bootstrap=True, max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=True, max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=True, max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1200; total time=   3.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1200; total time=   3.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1200; total time=   3.1s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000; total time=   6.3s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000; total time=   6.3s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000; total time=   6.4s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000; total time=   5.5s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000; total time=   5.6s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1600; total time=   5.2s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000; total time=   5.4s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time=   2.1s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time=   2.1s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   2.6s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1600; total time=   5.2s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   1.5s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   2.6s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1600; total time=   4.6s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.5s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=70, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=70, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.6s\n",
      "[CV] END bootstrap=True, max_depth=70, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.1s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1800; total time=   4.2s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1800; total time=   4.3s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1800; total time=   4.3s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000; total time=   4.9s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000; total time=   5.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1600; total time=   4.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1600; total time=   4.1s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1600; total time=   4.1s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000; total time=   5.4s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time=   1.9s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.7s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   5.1s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.6s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   5.3s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.7s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   2.1s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   2.0s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   1.9s\n",
      "[CV] END bootstrap=False, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1800; total time=   5.0s\n",
      "[CV] END bootstrap=False, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1800; total time=   5.2s\n",
      "[CV] END bootstrap=False, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1800; total time=   5.2s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1800; total time=   5.0s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1800; total time=   4.9s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   4.7s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1800; total time=   5.1s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   4.8s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=1400; total time=   4.6s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=1400; total time=   4.3s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=1400; total time=   4.1s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=1400; total time=   3.5s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=1400; total time=   3.6s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=1400; total time=   3.7s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1800; total time=   5.5s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1800; total time=   5.8s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1800; total time=   6.1s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   6.3s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   6.3s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1400; total time=   7.1s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   6.2s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1400; total time=   7.3s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1400; total time=   7.2s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000; total time=   8.1s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000; total time=   7.9s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=1200; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=1200; total time=   3.4s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1200; total time=   4.1s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1200; total time=   4.1s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1200; total time=   4.1s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000; total time=   5.5s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=800; total time=   2.1s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=800; total time=   2.1s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=1200; total time=   3.0s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=800; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=800; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=800; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=800; total time=   2.5s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.1s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=800; total time=   2.4s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   5.3s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1200; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   5.2s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   5.3s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1200; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=800; total time=   2.4s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1200; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=800; total time=   2.2s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1000; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1000; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1000; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time=   2.4s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time=   2.4s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.2s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time=   3.0s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time=   3.0s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.2s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.2s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.3s\n",
      "[CV] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.3s\n",
      "[CV] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=600; total time=   1.8s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1200; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=600; total time=   1.9s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1200; total time=   3.8s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1200; total time=   4.4s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000; total time=   7.8s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000; total time=   7.8s\n",
      "[CV] END bootstrap=False, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=600; total time=   2.9s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000; total time=   7.9s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=1000; total time=   3.2s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=1000; total time=   3.1s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1800; total time=   7.2s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1800; total time=   7.3s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1800; total time=   6.8s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   6.2s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=1000; total time=   2.8s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   6.2s\n",
      "[CV] END bootstrap=False, max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   6.3s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1000; total time=   2.7s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1000; total time=   3.0s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1000; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1400; total time=   4.5s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1400; total time=   4.6s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1400; total time=   4.5s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   6.4s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   6.1s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   6.0s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1800; total time=   6.6s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1800; total time=   6.6s\n",
      "[CV] END bootstrap=True, max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1400; total time=   5.5s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1400; total time=   5.6s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1800; total time=   6.6s\n",
      "[CV] END bootstrap=True, max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=   1.2s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1400; total time=   4.2s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1400; total time=   4.0s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1400; total time=   4.0s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=1000; total time=   2.4s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1400; total time=   4.1s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=1000; total time=   2.5s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=1000; total time=   2.5s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1200; total time=   3.2s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1200; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1200; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1600; total time=   5.7s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1600; total time=   5.7s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1600; total time=   5.7s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1600; total time=   5.6s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1600; total time=   5.6s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=1600; total time=   5.6s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   5.3s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   5.2s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1000; total time=   2.9s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=1800; total time=   5.1s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1000; total time=   3.1s\n",
      "[CV] END bootstrap=True, max_depth=70, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1400; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=70, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1400; total time=   4.9s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1000; total time=   3.1s\n",
      "[CV] END bootstrap=True, max_depth=70, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=1400; total time=   4.9s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time=   2.6s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time=   2.7s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   6.2s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1400; total time=   4.3s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1400; total time=   4.3s\n",
      "[CV] END bootstrap=False, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1400; total time=   4.2s\n",
      "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   6.1s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   6.0s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1800; total time=   5.3s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1800; total time=   5.3s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1800; total time=   5.3s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   4.7s\n",
      "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=1600; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1600; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1600; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   3.1s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   3.1s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=1600; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=   3.4s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000; total time=   6.6s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000; total time=   6.8s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000; total time=   6.9s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time=   2.2s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000; total time=   6.0s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1000; total time=   3.3s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000; total time=   6.0s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1000; total time=   3.2s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000; total time=   6.0s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=1000; total time=   3.3s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=600; total time=   1.7s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.4s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=600; total time=   1.8s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=600; total time=   1.8s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=70, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=800; total time=   2.6s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=800; total time=   2.6s\n",
      "[CV] END bootstrap=True, max_depth=100, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=800; total time=   2.6s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1000; total time=   3.8s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1000; total time=   3.8s\n",
      "[CV] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=600; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=600; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1600; total time=   5.7s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1600; total time=   5.8s\n",
      "[CV] END bootstrap=True, max_depth=80, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=1600; total time=   5.8s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1000; total time=   3.8s\n",
      "[CV] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=600; total time=   2.1s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.7s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.8s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=800; total time=   2.8s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   2.0s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   1.6s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time=   1.9s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000; total time=   5.4s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000; total time=   5.5s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000; total time=   5.5s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.5s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.0s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.9s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000; total time=   5.8s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   5.6s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000; total time=   5.7s\n",
      "[CV] END bootstrap=True, max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000; total time=   5.8s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   5.7s\n",
      "[CV] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000; total time=   4.8s\n",
      "{'n_estimators': 600, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 110, 'bootstrap': False}\n"
     ]
    }
   ],
   "source": [
    "X = newcountries.drop(['Country', 'Cluster'], axis=1)\n",
    "y = newcountries['Cluster']\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "regr_random = RandomizedSearchCV(estimator = clf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "# Fit the random search model\n",
    "regr_random.fit(X, y)\n",
    "print(regr_random.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = newcountries.drop(['Country', 'Cluster'], axis=1)\n",
    "y = newcountries['Cluster']\n",
    "clf = RandomForestClassifier(n_estimators=600, min_samples_split=2, min_samples_leaf=2, max_features='sqrt', max_depth=110, bootstrap=False)\n",
    "clf.fit(x, y)\n",
    "ypred = clf.predict(x)\n",
    "sum([y[i] == ypred[i] for i in range(len(y))])/len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = clf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)\n",
    "forest_importances = pd.Series(importances, index=x.columns)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "forest_importances.plot.bar(yerr=std, ax=ax)\n",
    "ax.set_title(\"Feature importances using MDI\")\n",
    "ax.set_ylabel(\"Mean decrease in impurity\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Forest area (% of land area)                                                   0.033804\n",
       "Fish species, threatened                                                       0.033352\n",
       "Renewable energy consumption (% of total final energy consumption)             0.030442\n",
       "Mammal species, threatened                                                     0.029974\n",
       "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)    0.029262\n",
       "                                                                                 ...   \n",
       "Lower secondary school starting age (years)                                    0.007833\n",
       "Access to electricity (% of population)                                        0.006264\n",
       "Access to electricity, urban (% of urban population)                           0.005547\n",
       "Primary school starting age (years)                                            0.003745\n",
       "Primary education, duration (years)                                            0.002917\n",
       "Length: 62, dtype: float64"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = clf.feature_importances_\n",
    "colimportance = pd.Series(importances, index=x.columns)\n",
    "colimportance.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 0: 41.307800\n",
      "Feature 1: 11.535041\n",
      "Feature 2: 1151451592824.009521\n",
      "Feature 3: 124861630254.461563\n",
      "Feature 4: 52.170584\n",
      "Feature 5: 15942923.255828\n",
      "Feature 6: 1678834.730062\n",
      "Feature 7: 1088764.447305\n",
      "Feature 8: 538.028829\n",
      "Feature 9: 24513556.235367\n",
      "Feature 10: 27175729.593020\n",
      "Feature 11: 111.097445\n",
      "Feature 12: 10.326610\n",
      "Feature 13: 535.742906\n",
      "Feature 14: 148.404278\n",
      "Feature 15: 18067117.560861\n",
      "Feature 16: 17.830419\n",
      "Feature 17: 17.055168\n",
      "Feature 18: 561.381269\n",
      "Feature 19: 36374084.936178\n",
      "Feature 20: 0.160205\n",
      "Feature 21: 541.833368\n",
      "Feature 22: 8029496937262.353516\n",
      "Feature 23: 7369260252013.634766\n",
      "Feature 24: 3045704.540593\n",
      "Feature 25: 1518327.695456\n",
      "Feature 26: 89.271968\n",
      "Feature 27: 80.889141\n",
      "Feature 28: 97.557559\n",
      "Feature 29: 40.661957\n",
      "Feature 30: 135.780351\n",
      "Feature 31: 127.562491\n",
      "Feature 32: 144.535494\n",
      "Feature 33: 1470887.316637\n",
      "Feature 34: 129769.050422\n",
      "Feature 35: 96925.506222\n",
      "Feature 36: 132103.090896\n",
      "Feature 37: 268086.720278\n",
      "Feature 38: 71049.104498\n",
      "Feature 39: 982210.975372\n",
      "Feature 40: 984487.669495\n",
      "Feature 41: 1179424.279247\n",
      "Feature 42: 4084.511314\n",
      "Feature 43: 8462.703378\n",
      "Feature 44: 0.148384\n",
      "Feature 45: 0.157640\n",
      "Feature 46: 11.534403\n",
      "Feature 47: 15.193425\n",
      "Feature 48: 22.212245\n",
      "Feature 49: 17.196271\n",
      "Feature 50: 8.756919\n",
      "Feature 51: 90.637785\n",
      "Feature 52: 1568875.546988\n",
      "Feature 53: 0.444552\n",
      "Feature 54: 199638224.109147\n",
      "Feature 55: 358629.595673\n",
      "Feature 56: 37.389163\n",
      "Feature 57: 10.343133\n",
      "Feature 58: 37379195.315121\n",
      "Feature 59: 268107240.169091\n",
      "Feature 60: 31035882.589189\n",
      "Feature 61: 9.325391\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = newcountries.drop(['Country', 'Cluster'], axis=1)\n",
    "y = newcountries['Cluster']\n",
    "fs = SelectKBest(chi2, k='all')\n",
    "fs.fit_transform(x, y)\n",
    "for i in range(len(fs.scores_)):\n",
    "\tprint('Feature %d: %f' % (i, fs.scores_[i]))\n",
    "# plot the scores\n",
    "plt.bar([i for i in range(len(fs.scores_))], fs.scores_)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = [i for i in list(range(len(fs.scores_))) if fs.scores_[i] > 1000]\n",
    "len(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = [i for i in list(range(len(fs.scores_))) if fs.scores_[i] > 1000]\n",
    "selectcolumns = x.columns[val]\n",
    "x = newcountries[selectcolumns]\n",
    "y = newcountries['Cluster']\n",
    "clf = RandomForestClassifier(n_estimators=600, min_samples_split=2, min_samples_leaf=2, max_features='sqrt', max_depth=110, bootstrap=False)\n",
    "clf.fit(x, y)\n",
    "ypred = clf.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)          0.054913\n",
       "Forest area (sq. km)                                                                 0.045138\n",
       "Total fisheries production (metric tons)                                             0.044119\n",
       "Methane emissions (kt of CO2 equivalent)                                             0.042662\n",
       "Capture fisheries production (metric tons)                                           0.041724\n",
       "Secure Internet servers (per 1 million people)                                       0.041658\n",
       "Merchandise exports (current US$)                                                    0.041559\n",
       "Agricultural land (sq. km)                                                           0.037861\n",
       "Plant species (higher), threatened                                                   0.037718\n",
       "Population density (people per sq. km of land area)                                  0.036727\n",
       "Scientific and technical journal articles                                            0.034890\n",
       "Agricultural methane emissions (thousand metric tons of CO2 equivalent)              0.033927\n",
       "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)        0.033584\n",
       "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)    0.033533\n",
       "Merchandise imports (current US$)                                                    0.033299\n",
       "Adjusted savings: mineral depletion (current US$)                                    0.032309\n",
       "Total greenhouse gas emissions (kt of CO2 equivalent)                                0.031749\n",
       "Adjusted savings: carbon dioxide damage (current US$)                                0.031536\n",
       "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)                     0.031348\n",
       "CO2 emissions (kt)                                                                   0.031161\n",
       "Secure Internet servers                                                              0.029914\n",
       "Land area (sq. km)                                                                   0.029364\n",
       "Surface area (sq. km)                                                                0.028981\n",
       "Number of deaths ages 10-14 years                                                    0.025153\n",
       "Number of neonatal deaths                                                            0.024149\n",
       "Number of under-five deaths                                                          0.023705\n",
       "Number of infant deaths                                                              0.023379\n",
       "Number of deaths ages 20-24 years                                                    0.022772\n",
       "Number of deaths ages 5-9 years                                                      0.021471\n",
       "Number of deaths ages 15-19 years                                                    0.019697\n",
       "dtype: float64"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = clf.feature_importances_\n",
    "colimportance = pd.Series(importances, index=x.columns)\n",
    "colimportance.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Adjusted savings: carbon dioxide damage (current US$)  \\\n",
      "0                                   898138333.986671      \n",
      "1                                  2410415979.058824      \n",
      "2                                  25577627560.80455      \n",
      "3                                 19702376340.438065      \n",
      "4                                  6653973071.656861      \n",
      "5                                   4446092293.13225      \n",
      "6                                 12306722883.176922      \n",
      "7                                  3680559180.608834      \n",
      "\n",
      "  Adjusted savings: mineral depletion (current US$)  \\\n",
      "0                                   62785099.831675   \n",
      "1                                  170897928.888229   \n",
      "2                                  2049846850.06403   \n",
      "3                                     236532239.425   \n",
      "4                                  483414936.113814   \n",
      "5                                  167515451.007871   \n",
      "6                                 1013338609.560022   \n",
      "7                                   58079107.651515   \n",
      "\n",
      "  Agricultural land (sq. km)  \\\n",
      "0               59435.182941   \n",
      "1              180934.735824   \n",
      "2              632774.040909   \n",
      "3              413409.433625   \n",
      "4              265764.663091   \n",
      "5                 339040.115   \n",
      "6              319448.230769   \n",
      "7                 123204.933   \n",
      "\n",
      "  Agricultural methane emissions (thousand metric tons of CO2 equivalent)  \\\n",
      "0                                       10792.352941                        \n",
      "1                                        9787.058824                        \n",
      "2                                       49507.272727                        \n",
      "3                                          22066.875                        \n",
      "4                                       16319.090909                        \n",
      "5                                          12910.625                        \n",
      "6                                       49005.384615                        \n",
      "7                                            9241.25                        \n",
      "\n",
      "  Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)  \\\n",
      "0                                        5684.117647                              \n",
      "1                                        7153.529412                              \n",
      "2                                            35455.0                              \n",
      "3                                          17053.125                              \n",
      "4                                       10657.272727                              \n",
      "5                                            8348.75                              \n",
      "6                                       24816.153846                              \n",
      "7                                             6802.5                              \n",
      "\n",
      "  Capture fisheries production (metric tons) CO2 emissions (kt)  \\\n",
      "0                              331437.370462       21214.705882   \n",
      "1                              579732.106618       61059.411765   \n",
      "2                             1215458.049319      597137.272727   \n",
      "3                                 822620.125          476071.25   \n",
      "4                                   631988.0      151706.818182   \n",
      "5                              270871.081056           100582.5   \n",
      "6                              685932.641226      271686.923077   \n",
      "7                              268714.463333            88807.5   \n",
      "\n",
      "  Forest area (sq. km) Land area (sq. km) Merchandise exports (current US$)  \\\n",
      "0         90894.164706           180440.0                34027235294.117645   \n",
      "1        163552.217647      640057.910529                37582705882.352943   \n",
      "2        489801.313636     1432135.995455               258884681818.181824   \n",
      "3          249205.7375           963665.5                    196187125000.0   \n",
      "4        470724.468182     1131767.817727                75288909090.909088   \n",
      "5             56332.55      518930.178125                     46979562500.0   \n",
      "6        368495.253846     1157980.023077               102975461538.461533   \n",
      "7         64002.016667      258356.267917                    101168750000.0   \n",
      "\n",
      "   ... Number of neonatal deaths Number of under-five deaths  \\\n",
      "0  ...               7042.176471                13626.941176   \n",
      "1  ...              10849.352941                30004.176471   \n",
      "2  ...              10241.727273                21339.272727   \n",
      "3  ...                 6525.5625                  15267.4375   \n",
      "4  ...              12865.863636                27455.909091   \n",
      "5  ...                18770.0625                  32037.6875   \n",
      "6  ...              44267.076923                     71126.0   \n",
      "7  ...                 15318.375                   44196.875   \n",
      "\n",
      "  Plant species (higher), threatened  \\\n",
      "0                          39.647059   \n",
      "1                          65.176471   \n",
      "2                         172.681818   \n",
      "3                            86.4375   \n",
      "4                         115.636364   \n",
      "5                             17.875   \n",
      "6                         127.461538   \n",
      "7                               52.0   \n",
      "\n",
      "  Population density (people per sq. km of land area)  \\\n",
      "0                                         217.424529    \n",
      "1                                         201.310712    \n",
      "2                                         135.764968    \n",
      "3                                         199.252768    \n",
      "4                                         149.103415    \n",
      "5                                          109.49012    \n",
      "6                                         278.910799    \n",
      "7                                         459.964055    \n",
      "\n",
      "  Scientific and technical journal articles Secure Internet servers  \\\n",
      "0                               3622.252353           159159.764706   \n",
      "1                               6317.734118           126091.764706   \n",
      "2                              38977.327273           660842.909091   \n",
      "3                              38472.531875            3129677.5625   \n",
      "4                               8046.606364           175706.636364   \n",
      "5                               6449.271875             133539.9375   \n",
      "6                              19251.584615           296588.307692   \n",
      "7                               14096.85375           363515.208333   \n",
      "\n",
      "  Secure Internet servers (per 1 million people) Surface area (sq. km)  \\\n",
      "0                                   25202.057381         189837.117647   \n",
      "1                                   11138.526916         649278.134647   \n",
      "2                                    17416.36223        1455400.459091   \n",
      "3                                   11111.375624         1012277.11875   \n",
      "4                                    7265.707035           1170710.455   \n",
      "5                                   11583.279373         547619.036875   \n",
      "6                                   20142.796484             1257830.0   \n",
      "7                                   23165.877976         266637.932917   \n",
      "\n",
      "  Total fisheries production (metric tons)  \\\n",
      "0                            546552.014103   \n",
      "1                            627862.972832   \n",
      "2                           5017361.856852   \n",
      "3                           1061485.517181   \n",
      "4                            891922.180448   \n",
      "5                            383320.841665   \n",
      "6                           1309090.223562   \n",
      "7                             334870.19855   \n",
      "\n",
      "  Total greenhouse gas emissions (kt of CO2 equivalent)  \n",
      "0                                       43533.529412     \n",
      "1                                       90823.529412     \n",
      "2                                      772975.909091     \n",
      "3                                          581681.25     \n",
      "4                                           235210.0     \n",
      "5                                           145425.0     \n",
      "6                                      380093.076923     \n",
      "7                                          128601.25     \n",
      "\n",
      "[8 rows x 30 columns]\n",
      "[8961864635.5606, 690050915.8209834, 181042.55046819724, 17057.636517196126, 10661.008668122562, 322349.1685569961, 210910.16291035133, 179236.43584184413, 454299.2655721655, 80909363971.25159, 81123571878.31781, 33646.52976740783, 14660.247316798826, 14093.910541812857, 1336.8985822562092, 1515.642168554204, 2183.2403617512136, 3489.510585186126, 1431.0541351478298, 15177.56552556277, 12224.80528738701, 18652.18631577614, 51.37422201426849, 110.94674847833969, 14352.964727250499, 1025390.6930293132, 6529.119244568749, 472385.4056910948, 1550439.3506245655, 260879.10870389326]\n",
      "[22 23 14 18 15 16 17 26  4 20 13 24 12 19  3 21 11  7  2  6 29  5  8 27\n",
      " 25 28  1  0  9 10]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Plant species (higher), threatened',\n",
       "       'Population density (people per sq. km of land area)',\n",
       "       'Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)',\n",
       "       'Number of deaths ages 5-9 years', 'Number of deaths ages 10-14 years',\n",
       "       'Number of deaths ages 15-19 years',\n",
       "       'Number of deaths ages 20-24 years',\n",
       "       'Secure Internet servers (per 1 million people)',\n",
       "       'Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)',\n",
       "       'Number of neonatal deaths',\n",
       "       'Nitrous oxide emissions (thousand metric tons of CO2 equivalent)',\n",
       "       'Scientific and technical journal articles',\n",
       "       'Methane emissions in energy sector (thousand metric tons of CO2 equivalent)',\n",
       "       'Number of infant deaths',\n",
       "       'Agricultural methane emissions (thousand metric tons of CO2 equivalent)',\n",
       "       'Number of under-five deaths',\n",
       "       'Methane emissions (kt of CO2 equivalent)', 'Forest area (sq. km)',\n",
       "       'Agricultural land (sq. km)', 'CO2 emissions (kt)',\n",
       "       'Total greenhouse gas emissions (kt of CO2 equivalent)',\n",
       "       'Capture fisheries production (metric tons)', 'Land area (sq. km)',\n",
       "       'Surface area (sq. km)', 'Secure Internet servers',\n",
       "       'Total fisheries production (metric tons)',\n",
       "       'Adjusted savings: mineral depletion (current US$)',\n",
       "       'Adjusted savings: carbon dioxide damage (current US$)',\n",
       "       'Merchandise exports (current US$)',\n",
       "       'Merchandise imports (current US$)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = pd.DataFrame(columns = x.columns, index = range(8))\n",
    "count = 0\n",
    "for cluster in newcountries.Cluster.unique():\n",
    "    countriestemp = x[newcountries.Cluster == cluster]\n",
    "    diff.iloc[count] = list(countriestemp.mean(axis=0))\n",
    "    count += 1\n",
    "print(diff)\n",
    "stds = list(diff.std(axis=0))\n",
    "print(stds)\n",
    "print(np.argsort(stds))\n",
    "x.columns[np.argsort(stds)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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