1.0.ipynb

{
 "cells": [
  {
   "attachments": {
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Fz8VYiaWSMjKxvFZwU5U+sQorYTbdJMT6CN1VdTL6sqkXyIYQZapRci2mHDdFobY8hU+fnvuish1P7zk2iuUR8WwYSqOM5VEuBlgS35AovEP3MPSqJ/4kq+7P8LENVsGTJIC8TH/m5BI1RkT0HLVuIzfiLPFawn1aiz5iTNDk/DdOZb5eogS9AnQYtKtz6sXY5WCSmHALh0OhprXAB9VOixRlIWM5YvRpILgibksa+GzL4qAgNIBMtJhvzbHsvkUAOQjRmXiNJ8Gb5rPFQC4nOSQ6QdAn1fkd19mhhUyRKCdgc4kcNTxFA7lRgEOhWFpZFmjdZweBUUMDeSJH2mY6ZaBXHbpVI3Kych8nMk+hdk1y41hOJ21G9SAzw4FYL9No8BI6MQPWVswkVq65aykKx2sJHSLMbEwGwQvGDq+9oiugYYoMCjISaW0bvpoGnMS0P8jWAwZwUgLMj2xJDkyNYgxuKN/4+nMTrWt5jhGNjZRkOFe6+FIT1b4QjFpT+wN+3SBh+Xm81mfKSh+9xHmhLeLCC85JV6hP8clpLWVkYnmtYKtLyRHCiHPpiT1DF9HSZIy+bCouvaPRdCkfrsWU46Yo1Jan8OnTiz5RtuPpPcfmLMCIGGIYSju8yCDQjK90iKQh7PEjZ5QC8TeW6bkAveqJw0t8ZDv8XoesxwslTaFZkkvUaBmgnaPWbeRGnCX8UcLHtBZ9xJhgh8UDvTvBvIZqpFeADoN2dU499ja95kBDTPhIbHjubwCDzWua0RTTfRoIrkhLTYFdVHwGYbEuitwx8cew+xpBC1ev0ZlF0iEcKdyavLhzSHSCZHEIiez5K70dWPlnegr7i8oyHAp1x8qyQOs+OwiMGhoo/HKcbaZTBnrVoXc8keOj80DHKWP05QBb5T5OZJ7CPcu9UT1K90u9zE6mqLEEjOUehFlXrnlrKQrHoBI+xhjZmAyyd3jtlagsNENDmSIvIkrIa9vw1URwrUeks38ABsSVKLYkB6YGOsJFaLiqxAusH9ZP5jr5kAedqZGS7FdxC4FLRuwbEBymm5TLC/GHphGuTLU+U1YqCkgPVokeLgxBKlZCzB9BS+lPrFwrwsUtXbtDMkqTXI9jkcFoaTJGXzYVc4wOOEPihUgoxyXGFCrLi4UgpNOLvqhst9N7js1ZgBExd+BhykpoIhkEmvGVDmEsNppUEeh7G+nJAu5N6MWY+Ce/s3YN7/3mu8fFd1gukNYkl6gZi1aao6FbuSjNEv5Qj9m1Fn3EUNLn/Db8ZLw8YNC5q8OgXQ1PxFUiTikCjf2Bb3bPt8VHZyranRbn1u95WSkWyX0aCK5+d99agYF8k0pv3IRWnGgx35rj2E1vF+Y05wF4bUplh7y4c0h0gqBPLopxLSapYl+LGW/zGJ7CtiIPFhLInXJuTZYF1AWWGQRGDQ1gIkfeZjploFcdescjJyv30f6ZCpVrxvI290VtunqQmU29zE6mR2VcllfykGesck1cS1E4BllvsDNtNiaD7B1ee6WoCGhKK5BNkUFx1kyxtg1fTQReItKG9gMwIJoutiQHpgY60sLEpR7+L1+42P5JqRVBSoVFVzZCktE+W4DFppXLC7GAN+I0lL5RWZz0pBw5zKsgSMU5daQNKaMTK9bKzep1jUgSkFyusTOYLU9vGaMvm1oajcCxw7TjiB8VKsurhVCfnvuSst1O7zk2iaURqfhDPP4PRSSDQDO+0iGMJTbRfZhlemcCpYtejImjlkLXCrUVd/3IFywdHlL4NpgZ1XSipTzc6lYuSrOEP9o6amgxj7RKxln8QuELlUHnrg6DdrV2Shno57Dg4MF4+97Q7rT4O028lyNE06fpbOEaiP2Wqk2VTm5xZaLFfGuOYvfiI98kR2fitSmVHfLiziHRCYI+WUf5USXCbFyLqfwzPQWt0WUR4/KsyLJA6z47CEwnkHmS0VXJz6mhfTdajYNjUqNTBnrVoXc8kuNDqNrQxClj9OW5F16I5b6cyByFe5Z7XT2MEKXRfNHKnW/y5LxTuWaspSQcg3TyFhTZWBgEA6hZ7/DaK0VFQFNarSkySGQw5do2fDWR8M+9p0eaP1EYIM3kwNRYF/rF7sWrzGUTnSPcx0e9qZGSDOWkwlJe+gPuTq5v9Y3K4mfx9Igg3PzEPpzTFDEtpT+xNA1wR9lBvKNmQDJcntYYKStPNfqyqUWqUMj5PO24aQpby4uF0J6e+5Ky3U7vOTaLxYjQAGE1FHMOB9IhjB22+F+xwaVZQge9GBPHZoIrhzW2ANy8Ie9f5XPn6I16pClqzGi1czR0KxelWcIfqpxoLfqIpeQnMI7nh9cKPtKEQbsaGhunlIFGxaW7PhTX+ICiod1psaPJ3/0cMfo0nS1c00xbHgTKRIv51hzDbvrXi9eS5cmZRqm0yYs7h8RMEIkE5h2SjurEpGsx01NI4LyrM3BN9WAngJOLb2+07rODwHQCmScZm8nPqbHfNtMpA73q0DseOVW5T/bPVbhfuW8iSLUhmxmbcfTTeg2J72vwPWzohUboyjVrLSVTMEgnb03MxnROZ4fXXik8jmZoKFNkUOGAam0bvprMw8tzs95G0BFIDkyNdbFaYX5oLO7oh0VAelbJvMXLwNRISYa77t/yFgRjKS+O3JHw/CWVWt+oLLChqGFriROgPhTg9KQ3oKWAzsSKtYJ7qGH4eFvdXkgY34q7hGrBAaMvm4qQB6UAIee2dtxUhbXleSGo03NfUgZ2OL3n2CwWI6RFGwEV22QuN+MrHcLY7TV9dVbSt7z5kgVvTXwzfNBbDDCBP/qxlVu37OXYlPGWDDVHQzeoXZRmCX+ocqK16CO2kobr1XPpXcyUy6DOXR0GmZw06UU7pQg0F/0rev7few7f7rSYQDh3JEeMPk1nC1dg46wIlTjKnSz+EHYjYF+pOkVnmqXSIi/uHBKdILkvVofJ12K2p6CieXb6Em7cO1kWaNzXCQKz17XYPttMrwx0DnePC6cq98n++Qr3KffGzqdDFEfD1Aazcs1bS8kUDFLJ28nGHGx7hydqr4gyafKrNkVeCgdUa9u8SjgGOgLJgamBjmhY/SGIK/qZ79qR27hoUiMlWfq8XUIrz0cuNljVzQO+rG9UFsC+dEtODI4LfSgT5R0c0FIEY2J5rVwhM+Lvy4cwQhu9txNApFMYgdGXTYWFqa7iKFurHTdHYbY8hU+fnvuSMmHm6T3HZrEYIefG99pxVyKzJ3HxlQ5hLFYcpMm7lPBOmiX66MWaOBbOBS7cr3mbuoZtQXewrWyKGkOGnqOhW8guSrOETsmNjNaij/SVFOAOsFhyXNApZ3Tu6jBoV2un5GCCB9qq8b86KTLNTksf2+WSPJYjRp+mEdwFtuVxdE0Q/Cdyp4o/hN3ItW1+ehidide4Fmd8Xkxk6wTJfbJ1g4nXYransBHF778FsJuxKzpZFmjc1wkC0wlknkhsJj+nRtd3RjVOSy9Xmk4Z6BzuHhdOVe6T/bsp3LXcGzufDlEc/UTPw77oeRgKCr2srco1cy0lUzBIJW8nG3Ow2Sdqh49kr+gKaJgiL1lGvba7VwmHRkcgOTA10BHvkW7CV62u+DfpAH2oK55P4JzwZ2rkJIOD05XVeedSPB9B64Wkc/IqfaOywBXZvolBDn0wKd37rIdPOFtL6U6sWCuITTyLChFGoTilXRAeqtLI6MumQlT8WEb8Yo3huEkKleUpfPr01JeV7XR6z7FZLI0Iq4UNpXcfcUQ+YELiuFEvBryEdylR19MsYRW9WJ5GFO+f+aNo2FhWWKNhpYm0oilqDBl6jobu1kV5lmhJbmS0Fn3EUKKhD1dHFxKYBa1GnbsqDDTvxtXaKXRaCCbADeg7Cl/9dk1Bs9O+4AaTG2M5YvRpOlu44r3aM5P+uqhqDm83JH4XUYnOxGtTKjvkcpJDohMk982+FrM9RVUmfgSAgQb+in8nywKN+zpBYDqBzBOJzeTn1EDHHttMrwx0DnePCycq99n+mQr3LPe6eugQFb7FtTFH/DlOFmFuK9eIXqMvC0dLJa+djYVBcADJbnZ47RUcbyqgYaaUjeSAZm0bvjoOKgI0yVBDUgMd0Zg3DkrxrBV9pXWr6JzUyEmGQk+/oc/AI1jAWnk6ggFf5+SGb/yt9Y3KItbDBqGT5/Ghj5505sBQUikp/YkVa+VPvieg552QDE+lu014KGUpYfRlU3GDHg2AflZoOG6KQm15Cp8+PfUlZbud3nNsngONiO6AoSQIquRTyCSOG/VioF/T4XcpKTwyS3rDml71xFnn60fI0O2AWzfZC0Ra0RQ1hgw9R61buSjP0ionWos+YkzQAIrTZxdYF18qqdzVYTBcrZ2Sg0nAEbhxrHbrknqnJaFhUx7LEaNP09nCW8iFxXUi9qWwfUtk5UVzcLvpH+sprkeSM/Fal8oeuZzkkOgEyX1zr8V6nqKpFKc/4c/wiQA7ywK1+3qimU4g80RiM/k5NZTv1IpLpKVXVJpOGegc7h4XTlTus/3zFGrXJDeO5HTWpqtHYaY0C98iHbiyrGOEjMo1by1l4VbxtLOxMAgYO7z2Co40FdAws7kWa9e24avjoCJASRHyMjXQITOC3+n9Iyy44OOzxVZuKJbhB5E/4oV/ahRJhpsPcTB9YxQlQCuPR+hJIvkSJej93NI3JovAKfJDNkD6cM7fcM6zXLC3UvoTq6cRrvb5n83kOoQAh18hpqvHUJnieUZfYSoMCT/py/+8F83YcNwUhdryFD7j9NiXlO12es+xeQ40Qn5FH7nDP3mIweGnDy+wBsL1R7MYsEGEdylplvR6tqCPW3LL8DTKE2zmDeZtgInySYPCy9KMW7WW0fEwH4m6lYvyLM1yorXYR/hAnqCGit17LAb3sC+ohc31ClBhMFytnZKDScCwXL408EEae4FdIf6IpeG/sfzRlIJHwLzids3Aa+w/iWwMsOJQdsdFfYUMqD53FZ2J17pU9oAaSZkiJCpBct/ca7Gep0hH+rmmBWIdJ9nJMqaOTlc00QlkMUlpJj+nhvIdZHWqcVp6RaXplIHe4c7xE5f7bP88hdo1yY0jOV14C6Oa6qFCVIx+lM8EblPWk0XcSJVrRK/Rl4WbxdPMxjLY7IJ2h9degeKmAhpmStkQB6i1bfjqOKgI8PPB5+Vd0UDH8P24OFvEuyh6AvhGY5fwRliX6EFeL+JqSg0OiPianst+0mH6Z2LJRVq5vNC/gMC3G6RpZekbk0XgticmQpaKoe+4vl/AueGfTWyldCdWToPyhK6bL+9pQXGJpA+gvCOsFyiUkjDxPKOvMJU+FbdZ3T28kCQSZTluikJteQqfdXroy8p2Or3n2GIONGL4vpfcwRnhEyp/7s6unzZUDHlQvRho0YWFTp/jX2NFXtN6D0MNT9MSlDNzq/KyNONWrWVYc2x1ty4qZmmWE63FOKInaEDDh4/Xp+Uj/9pO+DfS9ArQ+a1drZ2Sg8lARHwfwgD+3fIHRtZrqm3Dh2zqYzli9GkKwYI84augCUZDGdym8u25RDYGWHEou/FKi5oK+zt2XwEhjc7Ea10qe+RyUoREJUjum3kt1vXU2YIuRTcvD8vl4ysFey2u6GQZA/fFUjommqiGZopJSjP5uSwme2wzrFq05Fb1R3W4cxytE5b7wv55Cvcs91b1aENUjv4TngZd5cjryjVrLRXCzeJpZWMVbMwAPml2eO0VXQENM6VsBBl6bRu+Og5tBAB7Vz5lzQ104L+Bq1i4YuRLyoGcFK/X0UIu3sfv1KQGJp19vcRcMBv+Hy11rVxesJnINyjDfYDWNyaLQfFKNSv28TnfNA/5DVklpTexahpUzbZrOvMdhvITz18UWJY8vNbnGX2lqQv8wTxDKRLDdNwUhdryFEfj9NBXKNvl9J5jC7EY8UXLirvk2TBFhvmNHj6CXhKT/JJ+8/iWZrnBXx8wgrsMT9MiFU/BZcMntyovS1PUWDKMOSrdjYuKWdrlRGvRR4wJGsCkzHucVZu7Rn4rV2unFIEmSFX/k6q8a2X+BLVgJEesPkUjGFCRbEEdTV9qZ2iPIoESWXnRHMputGlRI94liH10Jl7xX1kqO+TFXYREJUjum3kt1vUUeCbxkXAlwVDoEynLCLgvX2CNiW6GZopJxmbyc1FM8N+u20yvDPQOd46jecJyX9g/U+Ge5d6oHoWZ3CxHf4YnI7/k6SShK1dXr9FXCLeLp5GNlfnA2OG1V/Rmo82EW+nkIEOvbcNXx6GNALikkNO9cWxQB92rwLJ4WXxDMwJbdjlAG355jQsxNaokO1uIa7YrM/zxhfwRL4FxxtdC6xuTxUBGCl3qi+fwxTPTSOlNrJrGQqrZ5pGuu8P7xxf0uB98xexAOzbbvtrUx/V2+F7/kqOm4yYpVJanOBqnh75C2S6n9xxbisW1WLjHGb7C5QGgZ86IzhN18AFIo/OyX7BIwhs81zgGPkg+9xgT5xPluTMkxS0wS4tNUQOUDMvDSnftomKWnXKiLdVHjAkaXL+xy8BnXgAqd5swEK2rtVOKQPMfaMf3BjTFNc1m/TvV5dEcMX3bMu1aDP6vPpzDR8jzEtkc4IZD2Y2/KHK6Xkdn0mtTKm3y4i5CohIk9yGwc67Fup4iLl9oHwXb1xs5xNhZBqoLrFHR9dBMMcnYTH4ui8nu2wyAAF0Guoft40FBGR8zf9VCRjs227444UBV7gv75yrcs9xjWFM9CjNb23DZSo+ScBVP8gVVueaspUJ4p3jqbKzNt3d45RW92WgzYSSdHGTotU3nqEp7Mi6W8nWB0GAjz29Xq/IR4dVytfpV5BjzuJKCkRoti+Xj6nG5w4wMfbvIWiyfVnfV+x+tFHNiDZfkjGbLuoBk+9/rGO+L4GoeWjuOm6RQWZ7iaJxOfbWy2adLM1A4Vs1hcbu6r06/efzJG4nrJxWNKd78CSXD8rDSXbqom+IF2lJ9xJigxfJhtWq8aK0Ald9TXF0E87z/42LjjOWI5dv/FQ5rt1kqZ3GI5P6RxfIXkil+6azAzLJjkfwcGofcZg6LlQdjcZoSQy73PfsnKdyz3FvVo6QYfbFasZ5fq3glyKjKNX0tTQtcnY3TztEJoyqg5d2f2Pm65cDEi07n0Fzepm3zHLdLujY6zkm5l1tgZye8VO7Ov993Xu6dPfECcywW8iwXrNJ3QBznH+Pdt4h98FK5O/9+33m5d/bEC8zR2MRvptCPeFQPgB3n9GCLKD7N7czFS+Xu/Ad85+Xe2Q8vMEfjcRiG16flE/2TpvUnE53/IMuV4nQV+3KzRhZu5ZPtRzNlquAdDfhHXeilcg/+A77zcu/shxeY4yFf6gD5i/bOf5WV5ELBCUs264s/inU0U6YK3tGAf9aFXip357/gOy/3zl48r9fF18Cdg3L9Sj/C8v268xevnH8P9/KDpAUnfMtwPXy/pV9iOJopUwXvaMA/60Ivlbvzn/Cdl3vHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHORBnZ/8HYUgHFaNU+6QAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "b1fcb624",
   "metadata": {},
   "source": [
    "This research involved the use of Secondary Data gotten from https://fenixservices.fao.org/faostat/static/bulkdownloads/Food_Security_Data_E_All_Data.zip  from FAOSTAT https://www.fao.org/faostat/en/#data/FS/visualize \n",
    "![image.png](attachment:image.png) \n",
    "\n",
    "The research goal is to use Prevalence of Undernourishment as Predictor Model Based on 10 Features as a determinant for Food Inseucrity Analyses and Prediction System Model with the use of Regression Models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "186ab111",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fda76e29",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Year</th>\n",
       "      <th>Prevalence of undernourishment (percent) (3-year average) %</th>\n",
       "      <th>Percent of arable land equipped for irrigation (percent) (3-year average) %</th>\n",
       "      <th>Average dietary energy supply adequacy (percent) (3-year average) %</th>\n",
       "      <th>Value of food imports in total merchandise exports (percent) (3-year average) %</th>\n",
       "      <th>Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)</th>\n",
       "      <th>Percentage of population using safely managed drinking water services (Percent)</th>\n",
       "      <th>Percentage of population using at least basic drinking water services (percent)</th>\n",
       "      <th>Prevalence of obesity in the adult population (18 years and older)</th>\n",
       "      <th>Gross domestic product per capita, PPP, dissemination (constant 2011 international $)</th>\n",
       "      <th>Cereal import dependency ratio (percent) (3-year average)</th>\n",
       "      <th>Political stability and absence of violence/terrorism (index)</th>\n",
       "      <th>Per capita food supply variability (kcal/cap/day)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-2002</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>7.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>48.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-2003</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>49.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-2004</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>122</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>50.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3458.1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-2005</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>123</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>52.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3619.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-2006</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>53.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3854.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005-2007</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3997.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-2008</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>56.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4130.4</td>\n",
       "      <td>15.1</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007-2009</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>126</td>\n",
       "      <td>6.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>57.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4288.3</td>\n",
       "      <td>15.7</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008-2010</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4458.6</td>\n",
       "      <td>16.8</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-2011</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>124</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4690.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010-2012</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>124</td>\n",
       "      <td>6.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>61.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4932.3</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011-2013</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>63.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5056.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-2014</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>64.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5131.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-2015</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>65.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5329.1</td>\n",
       "      <td>20.3</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-2016</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>67.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5516.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-2017</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>14.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>68.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5514.8</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-2018</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>120</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5284.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-2019</td>\n",
       "      <td>10.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.1</td>\n",
       "      <td>71.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5190.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-2020</td>\n",
       "      <td>14.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5155.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Prevalence of undernourishment (percent) (3-year average) %  \\\n",
       "0   2000-2002                                                8.9             \n",
       "1   2001-2003                                                8.9             \n",
       "2   2002-2004                                                8.6             \n",
       "3   2003-2005                                                7.9             \n",
       "4   2004-2006                                                7.1             \n",
       "5   2005-2007                                                6.7             \n",
       "6   2006-2008                                                6.6             \n",
       "7   2007-2009                                                6.8             \n",
       "8   2008-2010                                                7.3             \n",
       "9   2009-2011                                                7.8             \n",
       "10  2010-2012                                                8.1             \n",
       "11  2011-2013                                                8.8             \n",
       "12  2012-2014                                                9.0             \n",
       "13  2013-2015                                                9.3             \n",
       "14  2014-2016                                                9.2             \n",
       "15  2015-2017                                                9.4             \n",
       "16  2016-2018                                               10.0             \n",
       "17  2017-2019                                               10.7             \n",
       "18  2018-2020                                               14.6             \n",
       "\n",
       "    Percent of arable land equipped for irrigation (percent) (3-year average) %  \\\n",
       "0                                                 0.9                             \n",
       "1                                                 0.9                             \n",
       "2                                                 0.8                             \n",
       "3                                                 0.8                             \n",
       "4                                                 0.8                             \n",
       "5                                                 0.8                             \n",
       "6                                                 0.8                             \n",
       "7                                                 0.8                             \n",
       "8                                                 0.9                             \n",
       "9                                                 0.9                             \n",
       "10                                                0.9                             \n",
       "11                                                0.9                             \n",
       "12                                                0.9                             \n",
       "13                                                0.9                             \n",
       "14                                                0.9                             \n",
       "15                                                0.9                             \n",
       "16                                                0.9                             \n",
       "17                                                NaN                             \n",
       "18                                                NaN                             \n",
       "\n",
       "    Average dietary energy supply adequacy (percent) (3-year average) %  \\\n",
       "0                                                 121                     \n",
       "1                                                 121                     \n",
       "2                                                 122                     \n",
       "3                                                 123                     \n",
       "4                                                 125                     \n",
       "5                                                 127                     \n",
       "6                                                 127                     \n",
       "7                                                 126                     \n",
       "8                                                 125                     \n",
       "9                                                 124                     \n",
       "10                                                124                     \n",
       "11                                                122                     \n",
       "12                                                122                     \n",
       "13                                                121                     \n",
       "14                                                122                     \n",
       "15                                                121                     \n",
       "16                                                120                     \n",
       "17                                                119                     \n",
       "18                                                118                     \n",
       "\n",
       "    Value of food imports in total merchandise exports (percent) (3-year average) %  \\\n",
       "0                                                 7.0                                 \n",
       "1                                                 8.0                                 \n",
       "2                                                 8.0                                 \n",
       "3                                                 6.0                                 \n",
       "4                                                 6.0                                 \n",
       "5                                                 6.0                                 \n",
       "6                                                 6.0                                 \n",
       "7                                                 6.0                                 \n",
       "8                                                 6.0                                 \n",
       "9                                                 7.0                                 \n",
       "10                                                6.0                                 \n",
       "11                                                7.0                                 \n",
       "12                                                7.0                                 \n",
       "13                                                9.0                                 \n",
       "14                                               10.0                                 \n",
       "15                                               14.0                                 \n",
       "16                                               13.0                                 \n",
       "17                                               11.0                                 \n",
       "18                                                NaN                                 \n",
       "\n",
       "    Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)  \\\n",
       "0                                                64.0                                                       \n",
       "1                                                63.0                                                       \n",
       "2                                                63.0                                                       \n",
       "3                                                63.0                                                       \n",
       "4                                                63.0                                                       \n",
       "5                                                63.0                                                       \n",
       "6                                                63.0                                                       \n",
       "7                                                64.0                                                       \n",
       "8                                                65.0                                                       \n",
       "9                                                66.0                                                       \n",
       "10                                               66.0                                                       \n",
       "11                                               66.0                                                       \n",
       "12                                               66.0                                                       \n",
       "13                                               67.0                                                       \n",
       "14                                               68.0                                                       \n",
       "15                                               68.0                                                       \n",
       "16                                               68.0                                                       \n",
       "17                                                NaN                                                       \n",
       "18                                                NaN                                                       \n",
       "\n",
       "    Percentage of population using safely managed drinking water services (Percent)  \\\n",
       "0                                                15.4                                 \n",
       "1                                                15.7                                 \n",
       "2                                                16.1                                 \n",
       "3                                                16.4                                 \n",
       "4                                                16.7                                 \n",
       "5                                                17.0                                 \n",
       "6                                                17.3                                 \n",
       "7                                                17.6                                 \n",
       "8                                                17.9                                 \n",
       "9                                                18.1                                 \n",
       "10                                               18.4                                 \n",
       "11                                               18.7                                 \n",
       "12                                               18.9                                 \n",
       "13                                               19.2                                 \n",
       "14                                               19.4                                 \n",
       "15                                               19.7                                 \n",
       "16                                               19.9                                 \n",
       "17                                               20.1                                 \n",
       "18                                                NaN                                 \n",
       "\n",
       "    Percentage of population using at least basic drinking water services (percent)  \\\n",
       "0                                                48.2                                 \n",
       "1                                                49.3                                 \n",
       "2                                                50.7                                 \n",
       "3                                                52.1                                 \n",
       "4                                                53.5                                 \n",
       "5                                                54.9                                 \n",
       "6                                                56.3                                 \n",
       "7                                                57.6                                 \n",
       "8                                                59.0                                 \n",
       "9                                                60.4                                 \n",
       "10                                               61.8                                 \n",
       "11                                               63.2                                 \n",
       "12                                               64.6                                 \n",
       "13                                               65.9                                 \n",
       "14                                               67.3                                 \n",
       "15                                               68.7                                 \n",
       "16                                               70.0                                 \n",
       "17                                               71.4                                 \n",
       "18                                                NaN                                 \n",
       "\n",
       "    Prevalence of obesity in the adult population (18 years and older)  \\\n",
       "0                                                 4.0                    \n",
       "1                                                 4.2                    \n",
       "2                                                 4.4                    \n",
       "3                                                 4.7                    \n",
       "4                                                 4.9                    \n",
       "5                                                 5.2                    \n",
       "6                                                 5.4                    \n",
       "7                                                 5.7                    \n",
       "8                                                 6.0                    \n",
       "9                                                 6.4                    \n",
       "10                                                6.7                    \n",
       "11                                                7.0                    \n",
       "12                                                7.4                    \n",
       "13                                                7.7                    \n",
       "14                                                8.1                    \n",
       "15                                                8.5                    \n",
       "16                                                8.9                    \n",
       "17                                                NaN                    \n",
       "18                                                NaN                    \n",
       "\n",
       "    Gross domestic product per capita, PPP, dissemination (constant 2011 international $)  \\\n",
       "0                                              2977.0                                       \n",
       "1                                              3075.0                                       \n",
       "2                                              3458.1                                       \n",
       "3                                              3619.1                                       \n",
       "4                                              3854.0                                       \n",
       "5                                              3997.4                                       \n",
       "6                                              4130.4                                       \n",
       "7                                              4288.3                                       \n",
       "8                                              4458.6                                       \n",
       "9                                              4690.4                                       \n",
       "10                                             4932.3                                       \n",
       "11                                             5056.9                                       \n",
       "12                                             5131.4                                       \n",
       "13                                             5329.1                                       \n",
       "14                                             5516.4                                       \n",
       "15                                             5514.8                                       \n",
       "16                                             5284.9                                       \n",
       "17                                             5190.4                                       \n",
       "18                                             5155.1                                       \n",
       "\n",
       "    Cereal import dependency ratio (percent) (3-year average)  \\\n",
       "0                                                15.2           \n",
       "1                                                16.0           \n",
       "2                                                15.5           \n",
       "3                                                16.4           \n",
       "4                                                16.1           \n",
       "5                                                16.4           \n",
       "6                                                15.1           \n",
       "7                                                15.7           \n",
       "8                                                16.8           \n",
       "9                                                19.2           \n",
       "10                                               19.2           \n",
       "11                                               19.2           \n",
       "12                                               19.8           \n",
       "13                                               20.3           \n",
       "14                                               19.2           \n",
       "15                                               18.8           \n",
       "16                                                NaN           \n",
       "17                                                NaN           \n",
       "18                                                NaN           \n",
       "\n",
       "    Political stability and absence of violence/terrorism (index)  \\\n",
       "0                                               -1.46               \n",
       "1                                               -1.63               \n",
       "2                                               -1.63               \n",
       "3                                               -1.75               \n",
       "4                                               -1.67               \n",
       "5                                               -2.03               \n",
       "6                                               -2.01               \n",
       "7                                               -1.86               \n",
       "8                                               -2.00               \n",
       "9                                               -2.21               \n",
       "10                                              -1.96               \n",
       "11                                              -2.04               \n",
       "12                                              -2.09               \n",
       "13                                              -2.13               \n",
       "14                                              -1.93               \n",
       "15                                              -1.88               \n",
       "16                                              -2.00               \n",
       "17                                              -2.09               \n",
       "18                                              -1.93               \n",
       "\n",
       "    Per capita food supply variability (kcal/cap/day)  \n",
       "0                                                  17  \n",
       "1                                                  24  \n",
       "2                                                  33  \n",
       "3                                                  37  \n",
       "4                                                  30  \n",
       "5                                                  19  \n",
       "6                                                  38  \n",
       "7                                                  51  \n",
       "8                                                  50  \n",
       "9                                                  33  \n",
       "10                                                 14  \n",
       "11                                                 24  \n",
       "12                                                 27  \n",
       "13                                                 24  \n",
       "14                                                 32  \n",
       "15                                                 29  \n",
       "16                                                 30  \n",
       "17                                                 31  \n",
       "18                                                 31  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('project data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5781faa6",
   "metadata": {},
   "source": [
    "# Number of Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "190071ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                                                                                                     0\n",
       "Prevalence of undernourishment (percent) (3-year average) %                                              0\n",
       "Percent of arable land equipped for irrigation (percent) (3-year average) %                              2\n",
       "Average dietary energy supply adequacy (percent) (3-year average) %                                      0\n",
       "Value of food imports in total merchandise exports (percent) (3-year average) %                          1\n",
       "Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)    2\n",
       "Percentage of population using safely managed drinking water services (Percent)                          1\n",
       "Percentage of population using at least basic drinking water services (percent)                          1\n",
       "Prevalence of obesity in the adult population (18 years and older)                                       2\n",
       "Gross domestic product per capita, PPP, dissemination (constant 2011 international $)                    0\n",
       "Cereal import dependency ratio (percent) (3-year average)                                                3\n",
       "Political stability and absence of violence/terrorism (index)                                            0\n",
       "Per capita food supply variability (kcal/cap/day)                                                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63d55d3",
   "metadata": {},
   "source": [
    "# Take care of missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67cf97d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Prevalence of undernourishment (percent) (3-year average) %</th>\n",
       "      <th>Percent of arable land equipped for irrigation (percent) (3-year average) %</th>\n",
       "      <th>Average dietary energy supply adequacy (percent) (3-year average) %</th>\n",
       "      <th>Value of food imports in total merchandise exports (percent) (3-year average) %</th>\n",
       "      <th>Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)</th>\n",
       "      <th>Percentage of population using safely managed drinking water services (Percent)</th>\n",
       "      <th>Percentage of population using at least basic drinking water services (percent)</th>\n",
       "      <th>Prevalence of obesity in the adult population (18 years and older)</th>\n",
       "      <th>Gross domestic product per capita, PPP, dissemination (constant 2011 international $)</th>\n",
       "      <th>Cereal import dependency ratio (percent) (3-year average)</th>\n",
       "      <th>Political stability and absence of violence/terrorism (index)</th>\n",
       "      <th>Per capita food supply variability (kcal/cap/day)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-2002</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>7.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>48.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-2003</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>49.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-2004</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>122</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>50.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3458.1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-2005</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>123</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>52.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3619.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-2006</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>53.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3854.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005-2007</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3997.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-2008</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>56.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4130.4</td>\n",
       "      <td>15.1</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007-2009</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>126</td>\n",
       "      <td>6.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>57.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4288.3</td>\n",
       "      <td>15.7</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008-2010</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4458.6</td>\n",
       "      <td>16.8</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-2011</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>124</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4690.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010-2012</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>124</td>\n",
       "      <td>6.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>61.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4932.3</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011-2013</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>63.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5056.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-2014</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>64.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5131.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-2015</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>65.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5329.1</td>\n",
       "      <td>20.3</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-2016</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>67.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5516.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-2017</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>14.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>68.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5514.8</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-2018</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>120</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5284.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-2019</td>\n",
       "      <td>10.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.1</td>\n",
       "      <td>71.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5190.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-2020</td>\n",
       "      <td>14.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5155.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Prevalence of undernourishment (percent) (3-year average) %  \\\n",
       "0   2000-2002                                                8.9             \n",
       "1   2001-2003                                                8.9             \n",
       "2   2002-2004                                                8.6             \n",
       "3   2003-2005                                                7.9             \n",
       "4   2004-2006                                                7.1             \n",
       "5   2005-2007                                                6.7             \n",
       "6   2006-2008                                                6.6             \n",
       "7   2007-2009                                                6.8             \n",
       "8   2008-2010                                                7.3             \n",
       "9   2009-2011                                                7.8             \n",
       "10  2010-2012                                                8.1             \n",
       "11  2011-2013                                                8.8             \n",
       "12  2012-2014                                                9.0             \n",
       "13  2013-2015                                                9.3             \n",
       "14  2014-2016                                                9.2             \n",
       "15  2015-2017                                                9.4             \n",
       "16  2016-2018                                               10.0             \n",
       "17  2017-2019                                               10.7             \n",
       "18  2018-2020                                               14.6             \n",
       "\n",
       "    Percent of arable land equipped for irrigation (percent) (3-year average) %  \\\n",
       "0                                                 0.9                             \n",
       "1                                                 0.9                             \n",
       "2                                                 0.8                             \n",
       "3                                                 0.8                             \n",
       "4                                                 0.8                             \n",
       "5                                                 0.8                             \n",
       "6                                                 0.8                             \n",
       "7                                                 0.8                             \n",
       "8                                                 0.9                             \n",
       "9                                                 0.9                             \n",
       "10                                                0.9                             \n",
       "11                                                0.9                             \n",
       "12                                                0.9                             \n",
       "13                                                0.9                             \n",
       "14                                                0.9                             \n",
       "15                                                0.9                             \n",
       "16                                                0.9                             \n",
       "17                                                NaN                             \n",
       "18                                                NaN                             \n",
       "\n",
       "    Average dietary energy supply adequacy (percent) (3-year average) %  \\\n",
       "0                                                 121                     \n",
       "1                                                 121                     \n",
       "2                                                 122                     \n",
       "3                                                 123                     \n",
       "4                                                 125                     \n",
       "5                                                 127                     \n",
       "6                                                 127                     \n",
       "7                                                 126                     \n",
       "8                                                 125                     \n",
       "9                                                 124                     \n",
       "10                                                124                     \n",
       "11                                                122                     \n",
       "12                                                122                     \n",
       "13                                                121                     \n",
       "14                                                122                     \n",
       "15                                                121                     \n",
       "16                                                120                     \n",
       "17                                                119                     \n",
       "18                                                118                     \n",
       "\n",
       "    Value of food imports in total merchandise exports (percent) (3-year average) %  \\\n",
       "0                                                 7.0                                 \n",
       "1                                                 8.0                                 \n",
       "2                                                 8.0                                 \n",
       "3                                                 6.0                                 \n",
       "4                                                 6.0                                 \n",
       "5                                                 6.0                                 \n",
       "6                                                 6.0                                 \n",
       "7                                                 6.0                                 \n",
       "8                                                 6.0                                 \n",
       "9                                                 7.0                                 \n",
       "10                                                6.0                                 \n",
       "11                                                7.0                                 \n",
       "12                                                7.0                                 \n",
       "13                                                9.0                                 \n",
       "14                                               10.0                                 \n",
       "15                                               14.0                                 \n",
       "16                                               13.0                                 \n",
       "17                                               11.0                                 \n",
       "18                                                NaN                                 \n",
       "\n",
       "    Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)  \\\n",
       "0                                                64.0                                                       \n",
       "1                                                63.0                                                       \n",
       "2                                                63.0                                                       \n",
       "3                                                63.0                                                       \n",
       "4                                                63.0                                                       \n",
       "5                                                63.0                                                       \n",
       "6                                                63.0                                                       \n",
       "7                                                64.0                                                       \n",
       "8                                                65.0                                                       \n",
       "9                                                66.0                                                       \n",
       "10                                               66.0                                                       \n",
       "11                                               66.0                                                       \n",
       "12                                               66.0                                                       \n",
       "13                                               67.0                                                       \n",
       "14                                               68.0                                                       \n",
       "15                                               68.0                                                       \n",
       "16                                               68.0                                                       \n",
       "17                                                NaN                                                       \n",
       "18                                                NaN                                                       \n",
       "\n",
       "    Percentage of population using safely managed drinking water services (Percent)  \\\n",
       "0                                                15.4                                 \n",
       "1                                                15.7                                 \n",
       "2                                                16.1                                 \n",
       "3                                                16.4                                 \n",
       "4                                                16.7                                 \n",
       "5                                                17.0                                 \n",
       "6                                                17.3                                 \n",
       "7                                                17.6                                 \n",
       "8                                                17.9                                 \n",
       "9                                                18.1                                 \n",
       "10                                               18.4                                 \n",
       "11                                               18.7                                 \n",
       "12                                               18.9                                 \n",
       "13                                               19.2                                 \n",
       "14                                               19.4                                 \n",
       "15                                               19.7                                 \n",
       "16                                               19.9                                 \n",
       "17                                               20.1                                 \n",
       "18                                                NaN                                 \n",
       "\n",
       "    Percentage of population using at least basic drinking water services (percent)  \\\n",
       "0                                                48.2                                 \n",
       "1                                                49.3                                 \n",
       "2                                                50.7                                 \n",
       "3                                                52.1                                 \n",
       "4                                                53.5                                 \n",
       "5                                                54.9                                 \n",
       "6                                                56.3                                 \n",
       "7                                                57.6                                 \n",
       "8                                                59.0                                 \n",
       "9                                                60.4                                 \n",
       "10                                               61.8                                 \n",
       "11                                               63.2                                 \n",
       "12                                               64.6                                 \n",
       "13                                               65.9                                 \n",
       "14                                               67.3                                 \n",
       "15                                               68.7                                 \n",
       "16                                               70.0                                 \n",
       "17                                               71.4                                 \n",
       "18                                                NaN                                 \n",
       "\n",
       "    Prevalence of obesity in the adult population (18 years and older)  \\\n",
       "0                                                 4.0                    \n",
       "1                                                 4.2                    \n",
       "2                                                 4.4                    \n",
       "3                                                 4.7                    \n",
       "4                                                 4.9                    \n",
       "5                                                 5.2                    \n",
       "6                                                 5.4                    \n",
       "7                                                 5.7                    \n",
       "8                                                 6.0                    \n",
       "9                                                 6.4                    \n",
       "10                                                6.7                    \n",
       "11                                                7.0                    \n",
       "12                                                7.4                    \n",
       "13                                                7.7                    \n",
       "14                                                8.1                    \n",
       "15                                                8.5                    \n",
       "16                                                8.9                    \n",
       "17                                                NaN                    \n",
       "18                                                NaN                    \n",
       "\n",
       "    Gross domestic product per capita, PPP, dissemination (constant 2011 international $)  \\\n",
       "0                                              2977.0                                       \n",
       "1                                              3075.0                                       \n",
       "2                                              3458.1                                       \n",
       "3                                              3619.1                                       \n",
       "4                                              3854.0                                       \n",
       "5                                              3997.4                                       \n",
       "6                                              4130.4                                       \n",
       "7                                              4288.3                                       \n",
       "8                                              4458.6                                       \n",
       "9                                              4690.4                                       \n",
       "10                                             4932.3                                       \n",
       "11                                             5056.9                                       \n",
       "12                                             5131.4                                       \n",
       "13                                             5329.1                                       \n",
       "14                                             5516.4                                       \n",
       "15                                             5514.8                                       \n",
       "16                                             5284.9                                       \n",
       "17                                             5190.4                                       \n",
       "18                                             5155.1                                       \n",
       "\n",
       "    Cereal import dependency ratio (percent) (3-year average)  \\\n",
       "0                                                15.2           \n",
       "1                                                16.0           \n",
       "2                                                15.5           \n",
       "3                                                16.4           \n",
       "4                                                16.1           \n",
       "5                                                16.4           \n",
       "6                                                15.1           \n",
       "7                                                15.7           \n",
       "8                                                16.8           \n",
       "9                                                19.2           \n",
       "10                                               19.2           \n",
       "11                                               19.2           \n",
       "12                                               19.8           \n",
       "13                                               20.3           \n",
       "14                                               19.2           \n",
       "15                                               18.8           \n",
       "16                                                NaN           \n",
       "17                                                NaN           \n",
       "18                                                NaN           \n",
       "\n",
       "    Political stability and absence of violence/terrorism (index)  \\\n",
       "0                                               -1.46               \n",
       "1                                               -1.63               \n",
       "2                                               -1.63               \n",
       "3                                               -1.75               \n",
       "4                                               -1.67               \n",
       "5                                               -2.03               \n",
       "6                                               -2.01               \n",
       "7                                               -1.86               \n",
       "8                                               -2.00               \n",
       "9                                               -2.21               \n",
       "10                                              -1.96               \n",
       "11                                              -2.04               \n",
       "12                                              -2.09               \n",
       "13                                              -2.13               \n",
       "14                                              -1.93               \n",
       "15                                              -1.88               \n",
       "16                                              -2.00               \n",
       "17                                              -2.09               \n",
       "18                                              -1.93               \n",
       "\n",
       "    Per capita food supply variability (kcal/cap/day)  \n",
       "0                                                  17  \n",
       "1                                                  24  \n",
       "2                                                  33  \n",
       "3                                                  37  \n",
       "4                                                  30  \n",
       "5                                                  19  \n",
       "6                                                  38  \n",
       "7                                                  51  \n",
       "8                                                  50  \n",
       "9                                                  33  \n",
       "10                                                 14  \n",
       "11                                                 24  \n",
       "12                                                 27  \n",
       "13                                                 24  \n",
       "14                                                 32  \n",
       "15                                                 29  \n",
       "16                                                 30  \n",
       "17                                                 31  \n",
       "18                                                 31  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new = data.copy()\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2079124c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a76903b",
   "metadata": {},
   "source": [
    "# The Descriptive Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1668afa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prevalence of undernourishment (percent) (3-year average) %</th>\n",
       "      <th>Percent of arable land equipped for irrigation (percent) (3-year average) %</th>\n",
       "      <th>Average dietary energy supply adequacy (percent) (3-year average) %</th>\n",
       "      <th>Value of food imports in total merchandise exports (percent) (3-year average) %</th>\n",
       "      <th>Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)</th>\n",
       "      <th>Percentage of population using safely managed drinking water services (Percent)</th>\n",
       "      <th>Percentage of population using at least basic drinking water services (percent)</th>\n",
       "      <th>Prevalence of obesity in the adult population (18 years and older)</th>\n",
       "      <th>Gross domestic product per capita, PPP, dissemination (constant 2011 international $)</th>\n",
       "      <th>Cereal import dependency ratio (percent) (3-year average)</th>\n",
       "      <th>Political stability and absence of violence/terrorism (index)</th>\n",
       "      <th>Per capita food supply variability (kcal/cap/day)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.721053</td>\n",
       "      <td>0.864706</td>\n",
       "      <td>122.631579</td>\n",
       "      <td>7.944444</td>\n",
       "      <td>65.058824</td>\n",
       "      <td>17.916667</td>\n",
       "      <td>59.716667</td>\n",
       "      <td>6.188235</td>\n",
       "      <td>4508.400000</td>\n",
       "      <td>17.431250</td>\n",
       "      <td>-1.910526</td>\n",
       "      <td>30.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.827743</td>\n",
       "      <td>0.049259</td>\n",
       "      <td>2.564946</td>\n",
       "      <td>2.508157</td>\n",
       "      <td>1.951621</td>\n",
       "      <td>1.479765</td>\n",
       "      <td>7.350250</td>\n",
       "      <td>1.556391</td>\n",
       "      <td>825.547682</td>\n",
       "      <td>1.856778</td>\n",
       "      <td>0.198592</td>\n",
       "      <td>9.548583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2977.000000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>-2.210000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.550000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>16.775000</td>\n",
       "      <td>53.850000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>3925.700000</td>\n",
       "      <td>15.925000</td>\n",
       "      <td>-2.035000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.800000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>59.700000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4690.400000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>-1.960000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.250000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>124.500000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>19.125000</td>\n",
       "      <td>65.575000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>5172.750000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>-1.805000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.600000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>71.400000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>5516.400000</td>\n",
       "      <td>20.300000</td>\n",
       "      <td>-1.460000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prevalence of undernourishment (percent) (3-year average) %  \\\n",
       "count                                          19.000000             \n",
       "mean                                            8.721053             \n",
       "std                                             1.827743             \n",
       "min                                             6.600000             \n",
       "25%                                             7.550000             \n",
       "50%                                             8.800000             \n",
       "75%                                             9.250000             \n",
       "max                                            14.600000             \n",
       "\n",
       "       Percent of arable land equipped for irrigation (percent) (3-year average) %  \\\n",
       "count                                          17.000000                             \n",
       "mean                                            0.864706                             \n",
       "std                                             0.049259                             \n",
       "min                                             0.800000                             \n",
       "25%                                             0.800000                             \n",
       "50%                                             0.900000                             \n",
       "75%                                             0.900000                             \n",
       "max                                             0.900000                             \n",
       "\n",
       "       Average dietary energy supply adequacy (percent) (3-year average) %  \\\n",
       "count                                          19.000000                     \n",
       "mean                                          122.631579                     \n",
       "std                                             2.564946                     \n",
       "min                                           118.000000                     \n",
       "25%                                           121.000000                     \n",
       "50%                                           122.000000                     \n",
       "75%                                           124.500000                     \n",
       "max                                           127.000000                     \n",
       "\n",
       "       Value of food imports in total merchandise exports (percent) (3-year average) %  \\\n",
       "count                                          18.000000                                 \n",
       "mean                                            7.944444                                 \n",
       "std                                             2.508157                                 \n",
       "min                                             6.000000                                 \n",
       "25%                                             6.000000                                 \n",
       "50%                                             7.000000                                 \n",
       "75%                                             8.750000                                 \n",
       "max                                            14.000000                                 \n",
       "\n",
       "       Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)  \\\n",
       "count                                          17.000000                                                       \n",
       "mean                                           65.058824                                                       \n",
       "std                                             1.951621                                                       \n",
       "min                                            63.000000                                                       \n",
       "25%                                            63.000000                                                       \n",
       "50%                                            65.000000                                                       \n",
       "75%                                            66.000000                                                       \n",
       "max                                            68.000000                                                       \n",
       "\n",
       "       Percentage of population using safely managed drinking water services (Percent)  \\\n",
       "count                                          18.000000                                 \n",
       "mean                                           17.916667                                 \n",
       "std                                             1.479765                                 \n",
       "min                                            15.400000                                 \n",
       "25%                                            16.775000                                 \n",
       "50%                                            18.000000                                 \n",
       "75%                                            19.125000                                 \n",
       "max                                            20.100000                                 \n",
       "\n",
       "       Percentage of population using at least basic drinking water services (percent)  \\\n",
       "count                                          18.000000                                 \n",
       "mean                                           59.716667                                 \n",
       "std                                             7.350250                                 \n",
       "min                                            48.200000                                 \n",
       "25%                                            53.850000                                 \n",
       "50%                                            59.700000                                 \n",
       "75%                                            65.575000                                 \n",
       "max                                            71.400000                                 \n",
       "\n",
       "       Prevalence of obesity in the adult population (18 years and older)  \\\n",
       "count                                          17.000000                    \n",
       "mean                                            6.188235                    \n",
       "std                                             1.556391                    \n",
       "min                                             4.000000                    \n",
       "25%                                             4.900000                    \n",
       "50%                                             6.000000                    \n",
       "75%                                             7.400000                    \n",
       "max                                             8.900000                    \n",
       "\n",
       "       Gross domestic product per capita, PPP, dissemination (constant 2011 international $)  \\\n",
       "count                                          19.000000                                       \n",
       "mean                                         4508.400000                                       \n",
       "std                                           825.547682                                       \n",
       "min                                          2977.000000                                       \n",
       "25%                                          3925.700000                                       \n",
       "50%                                          4690.400000                                       \n",
       "75%                                          5172.750000                                       \n",
       "max                                          5516.400000                                       \n",
       "\n",
       "       Cereal import dependency ratio (percent) (3-year average)  \\\n",
       "count                                          16.000000           \n",
       "mean                                           17.431250           \n",
       "std                                             1.856778           \n",
       "min                                            15.100000           \n",
       "25%                                            15.925000           \n",
       "50%                                            16.600000           \n",
       "75%                                            19.200000           \n",
       "max                                            20.300000           \n",
       "\n",
       "       Political stability and absence of violence/terrorism (index)  \\\n",
       "count                                          19.000000               \n",
       "mean                                           -1.910526               \n",
       "std                                             0.198592               \n",
       "min                                            -2.210000               \n",
       "25%                                            -2.035000               \n",
       "50%                                            -1.960000               \n",
       "75%                                            -1.805000               \n",
       "max                                            -1.460000               \n",
       "\n",
       "       Per capita food supply variability (kcal/cap/day)  \n",
       "count                                          19.000000  \n",
       "mean                                           30.210526  \n",
       "std                                             9.548583  \n",
       "min                                            14.000000  \n",
       "25%                                            24.000000  \n",
       "50%                                            30.000000  \n",
       "75%                                            33.000000  \n",
       "max                                            51.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename the columns.\n",
    "data_new.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594dbf38",
   "metadata": {},
   "source": [
    "# Fill in The  Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "55cbbe21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Prevalence of undernourishment (percent) (3-year average) %</th>\n",
       "      <th>Percent of arable land equipped for irrigation (percent) (3-year average) %</th>\n",
       "      <th>Average dietary energy supply adequacy (percent) (3-year average) %</th>\n",
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       "      <th>Percentage of population using safely managed drinking water services (Percent)</th>\n",
       "      <th>Percentage of population using at least basic drinking water services (percent)</th>\n",
       "      <th>Prevalence of obesity in the adult population (18 years and older)</th>\n",
       "      <th>Gross domestic product per capita, PPP, dissemination (constant 2011 international $)</th>\n",
       "      <th>Cereal import dependency ratio (percent) (3-year average)</th>\n",
       "      <th>Political stability and absence of violence/terrorism (index)</th>\n",
       "      <th>Per capita food supply variability (kcal/cap/day)</th>\n",
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       "      <th>0</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Prevalence of undernourishment (percent) (3-year average) %  \\\n",
       "0   False                                              False             \n",
       "1   False                                              False             \n",
       "2   False                                              False             \n",
       "3   False                                              False             \n",
       "4   False                                              False             \n",
       "5   False                                              False             \n",
       "6   False                                              False             \n",
       "7   False                                              False             \n",
       "8   False                                              False             \n",
       "9   False                                              False             \n",
       "10  False                                              False             \n",
       "11  False                                              False             \n",
       "12  False                                              False             \n",
       "13  False                                              False             \n",
       "14  False                                              False             \n",
       "15  False                                              False             \n",
       "16  False                                              False             \n",
       "17  False                                              False             \n",
       "18  False                                              False             \n",
       "\n",
       "    Percent of arable land equipped for irrigation (percent) (3-year average) %  \\\n",
       "0                                               False                             \n",
       "1                                               False                             \n",
       "2                                               False                             \n",
       "3                                               False                             \n",
       "4                                               False                             \n",
       "5                                               False                             \n",
       "6                                               False                             \n",
       "7                                               False                             \n",
       "8                                               False                             \n",
       "9                                               False                             \n",
       "10                                              False                             \n",
       "11                                              False                             \n",
       "12                                              False                             \n",
       "13                                              False                             \n",
       "14                                              False                             \n",
       "15                                              False                             \n",
       "16                                              False                             \n",
       "17                                               True                             \n",
       "18                                               True                             \n",
       "\n",
       "    Average dietary energy supply adequacy (percent) (3-year average) %  \\\n",
       "0                                               False                     \n",
       "1                                               False                     \n",
       "2                                               False                     \n",
       "3                                               False                     \n",
       "4                                               False                     \n",
       "5                                               False                     \n",
       "6                                               False                     \n",
       "7                                               False                     \n",
       "8                                               False                     \n",
       "9                                               False                     \n",
       "10                                              False                     \n",
       "11                                              False                     \n",
       "12                                              False                     \n",
       "13                                              False                     \n",
       "14                                              False                     \n",
       "15                                              False                     \n",
       "16                                              False                     \n",
       "17                                              False                     \n",
       "18                                              False                     \n",
       "\n",
       "    Value of food imports in total merchandise exports (percent) (3-year average) %  \\\n",
       "0                                               False                                 \n",
       "1                                               False                                 \n",
       "2                                               False                                 \n",
       "3                                               False                                 \n",
       "4                                               False                                 \n",
       "5                                               False                                 \n",
       "6                                               False                                 \n",
       "7                                               False                                 \n",
       "8                                               False                                 \n",
       "9                                               False                                 \n",
       "10                                              False                                 \n",
       "11                                              False                                 \n",
       "12                                              False                                 \n",
       "13                                              False                                 \n",
       "14                                              False                                 \n",
       "15                                              False                                 \n",
       "16                                              False                                 \n",
       "17                                              False                                 \n",
       "18                                               True                                 \n",
       "\n",
       "    Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)  \\\n",
       "0                                               False                                                       \n",
       "1                                               False                                                       \n",
       "2                                               False                                                       \n",
       "3                                               False                                                       \n",
       "4                                               False                                                       \n",
       "5                                               False                                                       \n",
       "6                                               False                                                       \n",
       "7                                               False                                                       \n",
       "8                                               False                                                       \n",
       "9                                               False                                                       \n",
       "10                                              False                                                       \n",
       "11                                              False                                                       \n",
       "12                                              False                                                       \n",
       "13                                              False                                                       \n",
       "14                                              False                                                       \n",
       "15                                              False                                                       \n",
       "16                                              False                                                       \n",
       "17                                               True                                                       \n",
       "18                                               True                                                       \n",
       "\n",
       "    Percentage of population using safely managed drinking water services (Percent)  \\\n",
       "0                                               False                                 \n",
       "1                                               False                                 \n",
       "2                                               False                                 \n",
       "3                                               False                                 \n",
       "4                                               False                                 \n",
       "5                                               False                                 \n",
       "6                                               False                                 \n",
       "7                                               False                                 \n",
       "8                                               False                                 \n",
       "9                                               False                                 \n",
       "10                                              False                                 \n",
       "11                                              False                                 \n",
       "12                                              False                                 \n",
       "13                                              False                                 \n",
       "14                                              False                                 \n",
       "15                                              False                                 \n",
       "16                                              False                                 \n",
       "17                                              False                                 \n",
       "18                                               True                                 \n",
       "\n",
       "    Percentage of population using at least basic drinking water services (percent)  \\\n",
       "0                                               False                                 \n",
       "1                                               False                                 \n",
       "2                                               False                                 \n",
       "3                                               False                                 \n",
       "4                                               False                                 \n",
       "5                                               False                                 \n",
       "6                                               False                                 \n",
       "7                                               False                                 \n",
       "8                                               False                                 \n",
       "9                                               False                                 \n",
       "10                                              False                                 \n",
       "11                                              False                                 \n",
       "12                                              False                                 \n",
       "13                                              False                                 \n",
       "14                                              False                                 \n",
       "15                                              False                                 \n",
       "16                                              False                                 \n",
       "17                                              False                                 \n",
       "18                                               True                                 \n",
       "\n",
       "    Prevalence of obesity in the adult population (18 years and older)  \\\n",
       "0                                               False                    \n",
       "1                                               False                    \n",
       "2                                               False                    \n",
       "3                                               False                    \n",
       "4                                               False                    \n",
       "5                                               False                    \n",
       "6                                               False                    \n",
       "7                                               False                    \n",
       "8                                               False                    \n",
       "9                                               False                    \n",
       "10                                              False                    \n",
       "11                                              False                    \n",
       "12                                              False                    \n",
       "13                                              False                    \n",
       "14                                              False                    \n",
       "15                                              False                    \n",
       "16                                              False                    \n",
       "17                                               True                    \n",
       "18                                               True                    \n",
       "\n",
       "    Gross domestic product per capita, PPP, dissemination (constant 2011 international $)  \\\n",
       "0                                               False                                       \n",
       "1                                               False                                       \n",
       "2                                               False                                       \n",
       "3                                               False                                       \n",
       "4                                               False                                       \n",
       "5                                               False                                       \n",
       "6                                               False                                       \n",
       "7                                               False                                       \n",
       "8                                               False                                       \n",
       "9                                               False                                       \n",
       "10                                              False                                       \n",
       "11                                              False                                       \n",
       "12                                              False                                       \n",
       "13                                              False                                       \n",
       "14                                              False                                       \n",
       "15                                              False                                       \n",
       "16                                              False                                       \n",
       "17                                              False                                       \n",
       "18                                              False                                       \n",
       "\n",
       "    Cereal import dependency ratio (percent) (3-year average)  \\\n",
       "0                                               False           \n",
       "1                                               False           \n",
       "2                                               False           \n",
       "3                                               False           \n",
       "4                                               False           \n",
       "5                                               False           \n",
       "6                                               False           \n",
       "7                                               False           \n",
       "8                                               False           \n",
       "9                                               False           \n",
       "10                                              False           \n",
       "11                                              False           \n",
       "12                                              False           \n",
       "13                                              False           \n",
       "14                                              False           \n",
       "15                                              False           \n",
       "16                                               True           \n",
       "17                                               True           \n",
       "18                                               True           \n",
       "\n",
       "    Political stability and absence of violence/terrorism (index)  \\\n",
       "0                                               False               \n",
       "1                                               False               \n",
       "2                                               False               \n",
       "3                                               False               \n",
       "4                                               False               \n",
       "5                                               False               \n",
       "6                                               False               \n",
       "7                                               False               \n",
       "8                                               False               \n",
       "9                                               False               \n",
       "10                                              False               \n",
       "11                                              False               \n",
       "12                                              False               \n",
       "13                                              False               \n",
       "14                                              False               \n",
       "15                                              False               \n",
       "16                                              False               \n",
       "17                                              False               \n",
       "18                                              False               \n",
       "\n",
       "    Per capita food supply variability (kcal/cap/day)  \n",
       "0                                               False  \n",
       "1                                               False  \n",
       "2                                               False  \n",
       "3                                               False  \n",
       "4                                               False  \n",
       "5                                               False  \n",
       "6                                               False  \n",
       "7                                               False  \n",
       "8                                               False  \n",
       "9                                               False  \n",
       "10                                              False  \n",
       "11                                              False  \n",
       "12                                              False  \n",
       "13                                              False  \n",
       "14                                              False  \n",
       "15                                              False  \n",
       "16                                              False  \n",
       "17                                              False  \n",
       "18                                              False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "202fc9ea",
   "metadata": {
    "scrolled": true
   },
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Prevalence of undernourishment (percent) (3-year average) %</th>\n",
       "      <th>Percent of arable land equipped for irrigation (percent) (3-year average) %</th>\n",
       "      <th>Average dietary energy supply adequacy (percent) (3-year average) %</th>\n",
       "      <th>Value of food imports in total merchandise exports (percent) (3-year average) %</th>\n",
       "      <th>Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)</th>\n",
       "      <th>Percentage of population using safely managed drinking water services (Percent)</th>\n",
       "      <th>Percentage of population using at least basic drinking water services (percent)</th>\n",
       "      <th>Prevalence of obesity in the adult population (18 years and older)</th>\n",
       "      <th>Gross domestic product per capita, PPP, dissemination (constant 2011 international $)</th>\n",
       "      <th>Cereal import dependency ratio (percent) (3-year average)</th>\n",
       "      <th>Political stability and absence of violence/terrorism (index)</th>\n",
       "      <th>Per capita food supply variability (kcal/cap/day)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-2002</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>7.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>48.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-2003</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>49.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-2004</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>122</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>50.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3458.1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-2005</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>123</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>52.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3619.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-2006</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>53.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3854.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005-2007</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3997.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-2008</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>56.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4130.4</td>\n",
       "      <td>15.1</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007-2009</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>126</td>\n",
       "      <td>6.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>57.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4288.3</td>\n",
       "      <td>15.7</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008-2010</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4458.6</td>\n",
       "      <td>16.8</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-2011</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>124</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4690.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010-2012</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>124</td>\n",
       "      <td>6.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>61.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4932.3</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011-2013</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>63.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5056.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-2014</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>64.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5131.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-2015</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>65.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5329.1</td>\n",
       "      <td>20.3</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-2016</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>67.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5516.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-2017</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>14.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>68.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5514.8</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-2018</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>120</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5284.9</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-2019</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>119</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>71.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5190.4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-2020</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>118</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>71.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5155.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Prevalence of undernourishment (percent) (3-year average) %  \\\n",
       "0   2000-2002                                                8.9             \n",
       "1   2001-2003                                                8.9             \n",
       "2   2002-2004                                                8.6             \n",
       "3   2003-2005                                                7.9             \n",
       "4   2004-2006                                                7.1             \n",
       "5   2005-2007                                                6.7             \n",
       "6   2006-2008                                                6.6             \n",
       "7   2007-2009                                                6.8             \n",
       "8   2008-2010                                                7.3             \n",
       "9   2009-2011                                                7.8             \n",
       "10  2010-2012                                                8.1             \n",
       "11  2011-2013                                                8.8             \n",
       "12  2012-2014                                                9.0             \n",
       "13  2013-2015                                                9.3             \n",
       "14  2014-2016                                                9.2             \n",
       "15  2015-2017                                                9.4             \n",
       "16  2016-2018                                               10.0             \n",
       "17  2017-2019                                               10.7             \n",
       "18  2018-2020                                               14.6             \n",
       "\n",
       "    Percent of arable land equipped for irrigation (percent) (3-year average) %  \\\n",
       "0                                                 0.9                             \n",
       "1                                                 0.9                             \n",
       "2                                                 0.8                             \n",
       "3                                                 0.8                             \n",
       "4                                                 0.8                             \n",
       "5                                                 0.8                             \n",
       "6                                                 0.8                             \n",
       "7                                                 0.8                             \n",
       "8                                                 0.9                             \n",
       "9                                                 0.9                             \n",
       "10                                                0.9                             \n",
       "11                                                0.9                             \n",
       "12                                                0.9                             \n",
       "13                                                0.9                             \n",
       "14                                                0.9                             \n",
       "15                                                0.9                             \n",
       "16                                                0.9                             \n",
       "17                                                0.9                             \n",
       "18                                                0.9                             \n",
       "\n",
       "    Average dietary energy supply adequacy (percent) (3-year average) %  \\\n",
       "0                                                 121                     \n",
       "1                                                 121                     \n",
       "2                                                 122                     \n",
       "3                                                 123                     \n",
       "4                                                 125                     \n",
       "5                                                 127                     \n",
       "6                                                 127                     \n",
       "7                                                 126                     \n",
       "8                                                 125                     \n",
       "9                                                 124                     \n",
       "10                                                124                     \n",
       "11                                                122                     \n",
       "12                                                122                     \n",
       "13                                                121                     \n",
       "14                                                122                     \n",
       "15                                                121                     \n",
       "16                                                120                     \n",
       "17                                                119                     \n",
       "18                                                118                     \n",
       "\n",
       "    Value of food imports in total merchandise exports (percent) (3-year average) %  \\\n",
       "0                                                 7.0                                 \n",
       "1                                                 8.0                                 \n",
       "2                                                 8.0                                 \n",
       "3                                                 6.0                                 \n",
       "4                                                 6.0                                 \n",
       "5                                                 6.0                                 \n",
       "6                                                 6.0                                 \n",
       "7                                                 6.0                                 \n",
       "8                                                 6.0                                 \n",
       "9                                                 7.0                                 \n",
       "10                                                6.0                                 \n",
       "11                                                7.0                                 \n",
       "12                                                7.0                                 \n",
       "13                                                9.0                                 \n",
       "14                                               10.0                                 \n",
       "15                                               14.0                                 \n",
       "16                                               13.0                                 \n",
       "17                                               11.0                                 \n",
       "18                                               11.0                                 \n",
       "\n",
       "    Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)  \\\n",
       "0                                                64.0                                                       \n",
       "1                                                63.0                                                       \n",
       "2                                                63.0                                                       \n",
       "3                                                63.0                                                       \n",
       "4                                                63.0                                                       \n",
       "5                                                63.0                                                       \n",
       "6                                                63.0                                                       \n",
       "7                                                64.0                                                       \n",
       "8                                                65.0                                                       \n",
       "9                                                66.0                                                       \n",
       "10                                               66.0                                                       \n",
       "11                                               66.0                                                       \n",
       "12                                               66.0                                                       \n",
       "13                                               67.0                                                       \n",
       "14                                               68.0                                                       \n",
       "15                                               68.0                                                       \n",
       "16                                               68.0                                                       \n",
       "17                                               68.0                                                       \n",
       "18                                               68.0                                                       \n",
       "\n",
       "    Percentage of population using safely managed drinking water services (Percent)  \\\n",
       "0                                                15.4                                 \n",
       "1                                                15.7                                 \n",
       "2                                                16.1                                 \n",
       "3                                                16.4                                 \n",
       "4                                                16.7                                 \n",
       "5                                                17.0                                 \n",
       "6                                                17.3                                 \n",
       "7                                                17.6                                 \n",
       "8                                                17.9                                 \n",
       "9                                                18.1                                 \n",
       "10                                               18.4                                 \n",
       "11                                               18.7                                 \n",
       "12                                               18.9                                 \n",
       "13                                               19.2                                 \n",
       "14                                               19.4                                 \n",
       "15                                               19.7                                 \n",
       "16                                               19.9                                 \n",
       "17                                               20.1                                 \n",
       "18                                               20.1                                 \n",
       "\n",
       "    Percentage of population using at least basic drinking water services (percent)  \\\n",
       "0                                                48.2                                 \n",
       "1                                                49.3                                 \n",
       "2                                                50.7                                 \n",
       "3                                                52.1                                 \n",
       "4                                                53.5                                 \n",
       "5                                                54.9                                 \n",
       "6                                                56.3                                 \n",
       "7                                                57.6                                 \n",
       "8                                                59.0                                 \n",
       "9                                                60.4                                 \n",
       "10                                               61.8                                 \n",
       "11                                               63.2                                 \n",
       "12                                               64.6                                 \n",
       "13                                               65.9                                 \n",
       "14                                               67.3                                 \n",
       "15                                               68.7                                 \n",
       "16                                               70.0                                 \n",
       "17                                               71.4                                 \n",
       "18                                               71.4                                 \n",
       "\n",
       "    Prevalence of obesity in the adult population (18 years and older)  \\\n",
       "0                                                 4.0                    \n",
       "1                                                 4.2                    \n",
       "2                                                 4.4                    \n",
       "3                                                 4.7                    \n",
       "4                                                 4.9                    \n",
       "5                                                 5.2                    \n",
       "6                                                 5.4                    \n",
       "7                                                 5.7                    \n",
       "8                                                 6.0                    \n",
       "9                                                 6.4                    \n",
       "10                                                6.7                    \n",
       "11                                                7.0                    \n",
       "12                                                7.4                    \n",
       "13                                                7.7                    \n",
       "14                                                8.1                    \n",
       "15                                                8.5                    \n",
       "16                                                8.9                    \n",
       "17                                                8.9                    \n",
       "18                                                8.9                    \n",
       "\n",
       "    Gross domestic product per capita, PPP, dissemination (constant 2011 international $)  \\\n",
       "0                                              2977.0                                       \n",
       "1                                              3075.0                                       \n",
       "2                                              3458.1                                       \n",
       "3                                              3619.1                                       \n",
       "4                                              3854.0                                       \n",
       "5                                              3997.4                                       \n",
       "6                                              4130.4                                       \n",
       "7                                              4288.3                                       \n",
       "8                                              4458.6                                       \n",
       "9                                              4690.4                                       \n",
       "10                                             4932.3                                       \n",
       "11                                             5056.9                                       \n",
       "12                                             5131.4                                       \n",
       "13                                             5329.1                                       \n",
       "14                                             5516.4                                       \n",
       "15                                             5514.8                                       \n",
       "16                                             5284.9                                       \n",
       "17                                             5190.4                                       \n",
       "18                                             5155.1                                       \n",
       "\n",
       "    Cereal import dependency ratio (percent) (3-year average)  \\\n",
       "0                                                15.2           \n",
       "1                                                16.0           \n",
       "2                                                15.5           \n",
       "3                                                16.4           \n",
       "4                                                16.1           \n",
       "5                                                16.4           \n",
       "6                                                15.1           \n",
       "7                                                15.7           \n",
       "8                                                16.8           \n",
       "9                                                19.2           \n",
       "10                                               19.2           \n",
       "11                                               19.2           \n",
       "12                                               19.8           \n",
       "13                                               20.3           \n",
       "14                                               19.2           \n",
       "15                                               18.8           \n",
       "16                                               18.8           \n",
       "17                                               18.8           \n",
       "18                                               18.8           \n",
       "\n",
       "    Political stability and absence of violence/terrorism (index)  \\\n",
       "0                                               -1.46               \n",
       "1                                               -1.63               \n",
       "2                                               -1.63               \n",
       "3                                               -1.75               \n",
       "4                                               -1.67               \n",
       "5                                               -2.03               \n",
       "6                                               -2.01               \n",
       "7                                               -1.86               \n",
       "8                                               -2.00               \n",
       "9                                               -2.21               \n",
       "10                                              -1.96               \n",
       "11                                              -2.04               \n",
       "12                                              -2.09               \n",
       "13                                              -2.13               \n",
       "14                                              -1.93               \n",
       "15                                              -1.88               \n",
       "16                                              -2.00               \n",
       "17                                              -2.09               \n",
       "18                                              -1.93               \n",
       "\n",
       "    Per capita food supply variability (kcal/cap/day)  \n",
       "0                                                  17  \n",
       "1                                                  24  \n",
       "2                                                  33  \n",
       "3                                                  37  \n",
       "4                                                  30  \n",
       "5                                                  19  \n",
       "6                                                  38  \n",
       "7                                                  51  \n",
       "8                                                  50  \n",
       "9                                                  33  \n",
       "10                                                 14  \n",
       "11                                                 24  \n",
       "12                                                 27  \n",
       "13                                                 24  \n",
       "14                                                 32  \n",
       "15                                                 29  \n",
       "16                                                 30  \n",
       "17                                                 31  \n",
       "18                                                 31  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find ways to fill data\n",
    "data_new = data_new.interpolate(method='linear', limit_direction='forward')\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "90597574",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Year',\n",
       "       'Prevalence of undernourishment (percent) (3-year average) %',\n",
       "       'Percent of arable land equipped for irrigation (percent) (3-year average) %',\n",
       "       'Average dietary energy supply adequacy (percent) (3-year average) %',\n",
       "       'Value of food imports in total merchandise exports (percent) (3-year average) %',\n",
       "       'Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)',\n",
       "       'Percentage of population using safely managed drinking water services (Percent)',\n",
       "       'Percentage of population using at least basic drinking water services (percent)',\n",
       "       'Prevalence of obesity in the adult population (18 years and older)',\n",
       "       'Gross domestic product per capita, PPP, dissemination (constant 2011 international $)',\n",
       "       'Cereal import dependency ratio (percent) (3-year average)',\n",
       "       'Political stability and absence of violence/terrorism (index)',\n",
       "       'Per capita food supply variability (kcal/cap/day)'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad872b9",
   "metadata": {},
   "source": [
    "# EXPLORE THE PDFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e03744f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Emmanuel Aminu\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Prevalence of undernourishment (percent) (3-year average) %', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_new['Prevalence of undernourishment (percent) (3-year average) %'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f736059",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Emmanuel Aminu\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Percent of arable land equipped for irrigation (percent) (3-year average) %', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_new['Percent of arable land equipped for irrigation (percent) (3-year average) %'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff330153",
   "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>Year</th>\n",
       "      <th>Prevalence of undernourishment (percent) (3-year average) %</th>\n",
       "      <th>Percent of arable land equipped for irrigation (percent) (3-year average) %</th>\n",
       "      <th>Average dietary energy supply adequacy (percent) (3-year average) %</th>\n",
       "      <th>Value of food imports in total merchandise exports (percent) (3-year average) %</th>\n",
       "      <th>Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)</th>\n",
       "      <th>Percentage of population using safely managed drinking water services (Percent)</th>\n",
       "      <th>Percentage of population using at least basic drinking water services (percent)</th>\n",
       "      <th>Prevalence of obesity in the adult population (18 years and older)</th>\n",
       "      <th>Gross domestic product per capita, PPP, dissemination (constant 2011 international $)</th>\n",
       "      <th>Cereal import dependency ratio (percent) (3-year average)</th>\n",
       "      <th>Political stability and absence of violence/terrorism (index)</th>\n",
       "      <th>Per capita food supply variability (kcal/cap/day)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-2002</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>7.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>48.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-2003</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>49.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-2004</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>122</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>50.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3458.1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-2005</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>123</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>52.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3619.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-2006</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>53.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3854.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005-2007</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3997.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-2008</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>56.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4130.4</td>\n",
       "      <td>15.1</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007-2009</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>126</td>\n",
       "      <td>6.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>57.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4288.3</td>\n",
       "      <td>15.7</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008-2010</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4458.6</td>\n",
       "      <td>16.8</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-2011</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>124</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4690.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010-2012</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>124</td>\n",
       "      <td>6.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>61.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4932.3</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011-2013</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>63.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5056.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-2014</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>64.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5131.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-2015</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>65.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5329.1</td>\n",
       "      <td>20.3</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-2016</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>122</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>67.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5516.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-2017</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>121</td>\n",
       "      <td>14.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>68.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5514.8</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-2018</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>120</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5284.9</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-2019</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>119</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>71.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5190.4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-2020</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>118</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>71.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5155.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Prevalence of undernourishment (percent) (3-year average) %  \\\n",
       "0   2000-2002                                                8.9             \n",
       "1   2001-2003                                                8.9             \n",
       "2   2002-2004                                                8.6             \n",
       "3   2003-2005                                                7.9             \n",
       "4   2004-2006                                                7.1             \n",
       "5   2005-2007                                                6.7             \n",
       "6   2006-2008                                                6.6             \n",
       "7   2007-2009                                                6.8             \n",
       "8   2008-2010                                                7.3             \n",
       "9   2009-2011                                                7.8             \n",
       "10  2010-2012                                                8.1             \n",
       "11  2011-2013                                                8.8             \n",
       "12  2012-2014                                                9.0             \n",
       "13  2013-2015                                                9.3             \n",
       "14  2014-2016                                                9.2             \n",
       "15  2015-2017                                                9.4             \n",
       "16  2016-2018                                               10.0             \n",
       "17  2017-2019                                               10.7             \n",
       "18  2018-2020                                               14.6             \n",
       "\n",
       "    Percent of arable land equipped for irrigation (percent) (3-year average) %  \\\n",
       "0                                                 0.9                             \n",
       "1                                                 0.9                             \n",
       "2                                                 0.8                             \n",
       "3                                                 0.8                             \n",
       "4                                                 0.8                             \n",
       "5                                                 0.8                             \n",
       "6                                                 0.8                             \n",
       "7                                                 0.8                             \n",
       "8                                                 0.9                             \n",
       "9                                                 0.9                             \n",
       "10                                                0.9                             \n",
       "11                                                0.9                             \n",
       "12                                                0.9                             \n",
       "13                                                0.9                             \n",
       "14                                                0.9                             \n",
       "15                                                0.9                             \n",
       "16                                                0.9                             \n",
       "17                                                0.9                             \n",
       "18                                                0.9                             \n",
       "\n",
       "    Average dietary energy supply adequacy (percent) (3-year average) %  \\\n",
       "0                                                 121                     \n",
       "1                                                 121                     \n",
       "2                                                 122                     \n",
       "3                                                 123                     \n",
       "4                                                 125                     \n",
       "5                                                 127                     \n",
       "6                                                 127                     \n",
       "7                                                 126                     \n",
       "8                                                 125                     \n",
       "9                                                 124                     \n",
       "10                                                124                     \n",
       "11                                                122                     \n",
       "12                                                122                     \n",
       "13                                                121                     \n",
       "14                                                122                     \n",
       "15                                                121                     \n",
       "16                                                120                     \n",
       "17                                                119                     \n",
       "18                                                118                     \n",
       "\n",
       "    Value of food imports in total merchandise exports (percent) (3-year average) %  \\\n",
       "0                                                 7.0                                 \n",
       "1                                                 8.0                                 \n",
       "2                                                 8.0                                 \n",
       "3                                                 6.0                                 \n",
       "4                                                 6.0                                 \n",
       "5                                                 6.0                                 \n",
       "6                                                 6.0                                 \n",
       "7                                                 6.0                                 \n",
       "8                                                 6.0                                 \n",
       "9                                                 7.0                                 \n",
       "10                                                6.0                                 \n",
       "11                                                7.0                                 \n",
       "12                                                7.0                                 \n",
       "13                                                9.0                                 \n",
       "14                                               10.0                                 \n",
       "15                                               14.0                                 \n",
       "16                                               13.0                                 \n",
       "17                                               11.0                                 \n",
       "18                                               11.0                                 \n",
       "\n",
       "    Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)  \\\n",
       "0                                                64.0                                                       \n",
       "1                                                63.0                                                       \n",
       "2                                                63.0                                                       \n",
       "3                                                63.0                                                       \n",
       "4                                                63.0                                                       \n",
       "5                                                63.0                                                       \n",
       "6                                                63.0                                                       \n",
       "7                                                64.0                                                       \n",
       "8                                                65.0                                                       \n",
       "9                                                66.0                                                       \n",
       "10                                               66.0                                                       \n",
       "11                                               66.0                                                       \n",
       "12                                               66.0                                                       \n",
       "13                                               67.0                                                       \n",
       "14                                               68.0                                                       \n",
       "15                                               68.0                                                       \n",
       "16                                               68.0                                                       \n",
       "17                                               68.0                                                       \n",
       "18                                               68.0                                                       \n",
       "\n",
       "    Percentage of population using safely managed drinking water services (Percent)  \\\n",
       "0                                                15.4                                 \n",
       "1                                                15.7                                 \n",
       "2                                                16.1                                 \n",
       "3                                                16.4                                 \n",
       "4                                                16.7                                 \n",
       "5                                                17.0                                 \n",
       "6                                                17.3                                 \n",
       "7                                                17.6                                 \n",
       "8                                                17.9                                 \n",
       "9                                                18.1                                 \n",
       "10                                               18.4                                 \n",
       "11                                               18.7                                 \n",
       "12                                               18.9                                 \n",
       "13                                               19.2                                 \n",
       "14                                               19.4                                 \n",
       "15                                               19.7                                 \n",
       "16                                               19.9                                 \n",
       "17                                               20.1                                 \n",
       "18                                               20.1                                 \n",
       "\n",
       "    Percentage of population using at least basic drinking water services (percent)  \\\n",
       "0                                                48.2                                 \n",
       "1                                                49.3                                 \n",
       "2                                                50.7                                 \n",
       "3                                                52.1                                 \n",
       "4                                                53.5                                 \n",
       "5                                                54.9                                 \n",
       "6                                                56.3                                 \n",
       "7                                                57.6                                 \n",
       "8                                                59.0                                 \n",
       "9                                                60.4                                 \n",
       "10                                               61.8                                 \n",
       "11                                               63.2                                 \n",
       "12                                               64.6                                 \n",
       "13                                               65.9                                 \n",
       "14                                               67.3                                 \n",
       "15                                               68.7                                 \n",
       "16                                               70.0                                 \n",
       "17                                               71.4                                 \n",
       "18                                               71.4                                 \n",
       "\n",
       "    Prevalence of obesity in the adult population (18 years and older)  \\\n",
       "0                                                 4.0                    \n",
       "1                                                 4.2                    \n",
       "2                                                 4.4                    \n",
       "3                                                 4.7                    \n",
       "4                                                 4.9                    \n",
       "5                                                 5.2                    \n",
       "6                                                 5.4                    \n",
       "7                                                 5.7                    \n",
       "8                                                 6.0                    \n",
       "9                                                 6.4                    \n",
       "10                                                6.7                    \n",
       "11                                                7.0                    \n",
       "12                                                7.4                    \n",
       "13                                                7.7                    \n",
       "14                                                8.1                    \n",
       "15                                                8.5                    \n",
       "16                                                8.9                    \n",
       "17                                                8.9                    \n",
       "18                                                8.9                    \n",
       "\n",
       "    Gross domestic product per capita, PPP, dissemination (constant 2011 international $)  \\\n",
       "0                                              2977.0                                       \n",
       "1                                              3075.0                                       \n",
       "2                                              3458.1                                       \n",
       "3                                              3619.1                                       \n",
       "4                                              3854.0                                       \n",
       "5                                              3997.4                                       \n",
       "6                                              4130.4                                       \n",
       "7                                              4288.3                                       \n",
       "8                                              4458.6                                       \n",
       "9                                              4690.4                                       \n",
       "10                                             4932.3                                       \n",
       "11                                             5056.9                                       \n",
       "12                                             5131.4                                       \n",
       "13                                             5329.1                                       \n",
       "14                                             5516.4                                       \n",
       "15                                             5514.8                                       \n",
       "16                                             5284.9                                       \n",
       "17                                             5190.4                                       \n",
       "18                                             5155.1                                       \n",
       "\n",
       "    Cereal import dependency ratio (percent) (3-year average)  \\\n",
       "0                                                15.2           \n",
       "1                                                16.0           \n",
       "2                                                15.5           \n",
       "3                                                16.4           \n",
       "4                                                16.1           \n",
       "5                                                16.4           \n",
       "6                                                15.1           \n",
       "7                                                15.7           \n",
       "8                                                16.8           \n",
       "9                                                19.2           \n",
       "10                                               19.2           \n",
       "11                                               19.2           \n",
       "12                                               19.8           \n",
       "13                                               20.3           \n",
       "14                                               19.2           \n",
       "15                                               18.8           \n",
       "16                                               18.8           \n",
       "17                                               18.8           \n",
       "18                                               18.8           \n",
       "\n",
       "    Political stability and absence of violence/terrorism (index)  \\\n",
       "0                                               -1.46               \n",
       "1                                               -1.63               \n",
       "2                                               -1.63               \n",
       "3                                               -1.75               \n",
       "4                                               -1.67               \n",
       "5                                               -2.03               \n",
       "6                                               -2.01               \n",
       "7                                               -1.86               \n",
       "8                                               -2.00               \n",
       "9                                               -2.21               \n",
       "10                                              -1.96               \n",
       "11                                              -2.04               \n",
       "12                                              -2.09               \n",
       "13                                              -2.13               \n",
       "14                                              -1.93               \n",
       "15                                              -1.88               \n",
       "16                                              -2.00               \n",
       "17                                              -2.09               \n",
       "18                                              -1.93               \n",
       "\n",
       "    Per capita food supply variability (kcal/cap/day)  \n",
       "0                                                  17  \n",
       "1                                                  24  \n",
       "2                                                  33  \n",
       "3                                                  37  \n",
       "4                                                  30  \n",
       "5                                                  19  \n",
       "6                                                  38  \n",
       "7                                                  51  \n",
       "8                                                  50  \n",
       "9                                                  33  \n",
       "10                                                 14  \n",
       "11                                                 24  \n",
       "12                                                 27  \n",
       "13                                                 24  \n",
       "14                                                 32  \n",
       "15                                                 29  \n",
       "16                                                 30  \n",
       "17                                                 31  \n",
       "18                                                 31  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_place = np.log(data_new['Percent of arable land equipped for irrigation (percent) (3-year average) %'])\n",
    "\n",
    "\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa740c23",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Emmanuel Aminu\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Percent of arable land equipped for irrigation (percent) (3-year average) %', ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_new['Percent of arable land equipped for irrigation (percent) (3-year average) %'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ffa272",
   "metadata": {},
   "source": [
    "# compare y = POU and X = Value of imports for testing regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9aa0e587",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale this data.\n",
    "y = data_new['Prevalence of undernourishment (percent) (3-year average) %']\n",
    "x1 = data_new[['Average dietary energy supply adequacy (percent) (3-year average) %']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ed9476d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c5b18c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an instance of the StandardScaler class\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "82944dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaled = scaler.transform(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f0d4a5d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Emmanuel Aminu\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Prevalence of undernourishment (percent) (3-year average) %</td> <th>  R-squared:         </th> <td>   0.791</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                        <td>OLS</td>                             <th>  Adj. R-squared:    </th> <td>   0.779</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                                  <td>Least Squares</td>                        <th>  F-statistic:       </th> <td>   64.36</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                                  <td>Tue, 14 Dec 2021</td>                       <th>  Prob (F-statistic):</th> <td>3.51e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                                      <td>16:34:32</td>                           <th>  Log-Likelihood:    </th> <td> -23.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>                           <td>    19</td>                            <th>  AIC:               </th> <td>   50.06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>                               <td>    17</td>                            <th>  BIC:               </th> <td>   51.95</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                                   <td>     1</td>                            <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                           <td>nonrobust</td>                          <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    8.7211</td> <td>    0.197</td> <td>   44.217</td> <td> 0.000</td> <td>    8.305</td> <td>    9.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   -1.5822</td> <td>    0.197</td> <td>   -8.022</td> <td> 0.000</td> <td>   -1.998</td> <td>   -1.166</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>28.152</td> <th>  Durbin-Watson:     </th> <td>   1.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  49.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.384</td> <th>  Prob(JB):          </th> <td>2.28e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 9.259</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                                 OLS Regression Results                                                \n",
       "=======================================================================================================================\n",
       "Dep. Variable:     Prevalence of undernourishment (percent) (3-year average) %   R-squared:                       0.791\n",
       "Model:                                                                     OLS   Adj. R-squared:                  0.779\n",
       "Method:                                                          Least Squares   F-statistic:                     64.36\n",
       "Date:                                                         Tue, 14 Dec 2021   Prob (F-statistic):           3.51e-07\n",
       "Time:                                                                 16:34:32   Log-Likelihood:                -23.031\n",
       "No. Observations:                                                           19   AIC:                             50.06\n",
       "Df Residuals:                                                               17   BIC:                             51.95\n",
       "Df Model:                                                                    1                                         \n",
       "Covariance Type:                                                     nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          8.7211      0.197     44.217      0.000       8.305       9.137\n",
       "x1            -1.5822      0.197     -8.022      0.000      -1.998      -1.166\n",
       "==============================================================================\n",
       "Omnibus:                       28.152   Durbin-Watson:                   1.037\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               49.004\n",
       "Skew:                           2.384   Prob(JB):                     2.28e-11\n",
       "Kurtosis:                       9.259   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = sm.add_constant(x_scaled)\n",
    "results = sm.OLS(y,x).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "320fcf4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a regression works in the exact same way\n",
    "reg = LinearRegression()\n",
    "\n",
    "# We just need to specify that our inputs are the 'scaled inputs'\n",
    "reg.fit(x_scaled,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0b81ccd2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.5822484])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0fe6366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.721052631578946"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "625c0fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There are different ways to plot the data - here's the matplotlib code\n",
    "plt.scatter(x1,y)\n",
    "\n",
    "# Parametrized version of the regression line\n",
    "yhat = reg.coef_*x_scaled + reg.intercept_\n",
    "\n",
    "\n",
    "# Plotting the regression line\n",
    "fig = plt.plot(x1,yhat, lw=4, c='orange', label ='regression line')\n",
    "\n",
    "# Labelling our axes\n",
    "plt.xlabel('ADS', fontsize = 20)\n",
    "plt.ylabel('POU', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e20b9dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As usual we can try to arrange the information in a summary table\n",
    "# Let's create a new data frame with the names of the features\n",
    "# reg_summary = pd.DataFrame([['Bias'],['ADES'],['Import Value'], ['Political Staility'], ['GDP'], ['Cereal Dependency']], columns=['Features'])\n",
    "\n",
    "# Then we create and fill a second column, called 'Weights' with the coefficients of the regression\n",
    "# Since the standardized coefficients are called 'weights' in ML, this is a much better word choice for our case\n",
    "# Note that even non-standardized coeff. are called 'weights' \n",
    "# but more often than not, when doing ML we perform some sort of scaling\n",
    "# reg_summary['Weights'] = reg.intercept_, reg.coef_[0], reg.coef_[1], reg.coef_[2], reg.coef_[3], reg.coef_[4] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "378fa233",
   "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>Features</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bias</td>\n",
       "      <td>8.721053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADES</td>\n",
       "      <td>-2.115026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Import Value</td>\n",
       "      <td>-0.477760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Political Staility</td>\n",
       "      <td>-0.270644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GDP</td>\n",
       "      <td>1.020672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cereal Dependency</td>\n",
       "      <td>-1.015365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Features   Weights\n",
       "0                Bias  8.721053\n",
       "1                ADES -2.115026\n",
       "2        Import Value -0.477760\n",
       "3  Political Staility -0.270644\n",
       "4                 GDP  1.020672\n",
       "5   Cereal Dependency -1.015365"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "db5d888c",
   "metadata": {},
   "source": [
    "# CEREALS DIDN'T pass the linear regression assumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e46bc69",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data_new['Prevalence of undernourishment (percent) (3-year average) %']\n",
    "x1 = data_new['Cereal import dependency ratio (percent) (3-year average)']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de07cf8f",
   "metadata": {},
   "source": [
    "# try train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ee419a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_scaled,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "19f7e742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the regression model for the dataset\n",
    "reg=LinearRegression() #creat the object for the regression\n",
    "reg.fit(X_train, y_train)  #pass the data through the model, reg.fit(1st argument, 2nd argument);\n",
    "            #1st argument have to be two dimentional or 2D array\n",
    "            #2nd argument have to be y axis or the output, since y=mx+c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e4b59784",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.62589499, 9.93747017, 9.28168258, 9.93747017])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = reg.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c497a5c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     7.9\n",
       "15    9.4\n",
       "11    8.8\n",
       "1     8.9\n",
       "Name: Prevalence of undernourishment (percent) (3-year average) %, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3be8e72f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9461554594400747"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5c641e01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.1013440860215054\n",
      "MSE: 3.5726253035032984\n",
      "RMSE: 1.8901389640720332\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "684a69f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Emmanuel Aminu\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot((y_test-predictions));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86679217",
   "metadata": {},
   "source": [
    "# USING REGRESSION MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fbdf7438",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR()\n",
      "[7.95884117 6.9056222  7.40022228 9.19956323] \n",
      "\n",
      "The mean absolute error 0.19128060356654686\n",
      "The mean square error 0.041673720146296805\n",
      "The r2 score 0.958815347600942\n",
      "SGDRegressor()\n",
      "[7.63656845 6.29903668 6.96780257 9.64286612] \n",
      "\n",
      "The mean absolute error 0.2598646062361505\n",
      "The mean square error 0.08853355903342919\n",
      "The r2 score 0.9125054388798723\n",
      "BayesianRidge()\n",
      "[7.88478455 6.62876672 7.25677564 9.76881129] \n",
      "\n",
      "The mean absolute error 0.19540118847771026\n",
      "The mean square error 0.04927740582513039\n",
      "The r2 score 0.9513008960344604\n",
      "LassoLars()\n",
      "[8.97333333 8.97333333 8.97333333 8.97333333] \n",
      "\n",
      "The mean absolute error 1.4116666666666666\n",
      "The mean square error 2.447877777777777\n",
      "The r2 score -1.4191503671676595\n",
      "ARDRegression()\n",
      "[7.88478455 6.62876672 7.25677564 9.76881129] \n",
      "\n",
      "The mean absolute error 0.19540118847771026\n",
      "The mean square error 0.04927740582513039\n",
      "The r2 score 0.9513008960344604\n",
      "PassiveAggressiveRegressor()\n",
      "[7.39011522 6.52224214 6.95617868 8.69192485] \n",
      "\n",
      "The mean absolute error 0.38488477745595584\n",
      "The mean square error 0.19180248789156967\n",
      "The r2 score 0.8104484369200053\n",
      "TheilSenRegressor(max_subpopulation=10000)\n",
      "[7.77720217 6.73636136 7.25678177 9.33846339] \n",
      "\n",
      "The mean absolute error 0.07618871101178715\n",
      "The mean square error 0.008234223450765335\n",
      "The r2 score 0.9918624104254327\n",
      "LinearRegression()\n",
      "[7.85969101 6.5747191  7.21720506 9.78714888] \n",
      "\n",
      "The mean absolute error 0.19733146067415697\n",
      "The mean square error 0.05448394447907437\n",
      "The r2 score 0.9461554594400747\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "classifiers = [\n",
    "    svm.SVR(),\n",
    "    linear_model.SGDRegressor(),\n",
    "    linear_model.BayesianRidge(),\n",
    "    linear_model.LassoLars(),\n",
    "    linear_model.ARDRegression(),\n",
    "    linear_model.PassiveAggressiveRegressor(),\n",
    "    linear_model.TheilSenRegressor(),\n",
    "    linear_model.LinearRegression()]\n",
    "for item in classifiers:\n",
    "    print(item)\n",
    "    clf = item\n",
    "    clf.fit(X_train, y_train)\n",
    "    # print(clf.predict(X_test),'\\n')\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(y_pred, '\\n')\n",
    "    print(\"The mean absolute error {}\".format(mean_absolute_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The mean square error {}\".format(mean_squared_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The r2 score {}\".format(r2_score(y_test, y_pred),'\\n'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b49256",
   "metadata": {},
   "source": [
    "# VISUALISATION OF RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "53f9f426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Year to GDP')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=[8, 5])\n",
    "plt.plot(data_new['Year'], data_new['Gross domestic product per capita, PPP, dissemination (constant 2011 international $)'])\n",
    "plt.xlabel('Year', rotation = 'vertical')\n",
    "plt.ylabel('GDP')\n",
    "plt.title('Year to GDP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b59e3ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Prevalence of Undernourishment to GDP')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8, 5])\n",
    "plt.plot(data_new['Prevalence of undernourishment (percent) (3-year average) %'], data_new['Gross domestic product per capita, PPP, dissemination (constant 2011 international $)'], color = 'Red')\n",
    "plt.xlabel('POU')\n",
    "plt.ylabel('GDP')\n",
    "plt.title('Prevalence of Undernourishment to GDP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1c5b8c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypos = np.arange(len(data_new['Year']))\n",
    "ypos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2faf5b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "plt.xticks(ypos, data_new['Prevalence of undernourishment (percent) (3-year average) %'])\n",
    "plt.bar(ypos-0.2, data_new['Prevalence of undernourishment (percent) (3-year average) %'], width=0.4, label='POU')\n",
    "plt.bar(ypos+0.2, data_new['Average dietary energy supply adequacy (percent) (3-year average) %'], width=0.4, label='ADS')\n",
    "plt.legend()\n",
    "plt.title('The Prevalence of Undernourishment (POU) and Average dietary energy supply adequcy(ADS) chart')\n",
    "plt.savefig('POUADS barchart.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6ee683fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'POU and ADS')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15, 5])\n",
    "plt.plot(data_new['Year'], data_new['Prevalence of undernourishment (percent) (3-year average) %'], data_new['Average dietary energy supply adequacy (percent) (3-year average) %'])\n",
    "plt.xlabel('Year', labelpad= 8)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.ylabel('POU and ADS')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "10534129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.figure(figsize = [10,10])\n",
    "ax.plot(data_new['Year'], data_new['Prevalence of undernourishment (percent) (3-year average) %'], data_new['Average dietary energy supply adequacy (percent) (3-year average) %'])\n",
    "fig.subplots_adjust(bottom=0.1)\n",
    "ax.set_ylabel('POU AND ADS')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_title('Prevalence of Undernourishment trend and Average Dietary Energy Suppy Trend') \n",
    "ax.set_xticks(data_new['Year']) \n",
    "ax.set_xticklabels(data_new['Year'], rotation='vertical', fontsize=6)\n",
    "plt.show()\n",
    "plt.savefig('POUADS.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "48003bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.figure(figsize = [10,10])\n",
    "ax.plot(data_new['Year'], data_new['Cereal import dependency ratio (percent) (3-year average)'], data_new['Average dietary energy supply adequacy (percent) (3-year average) %'])\n",
    "fig.subplots_adjust(bottom=0.1)\n",
    "ax.set_ylabel('Cereal Import, ADS')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_title('Cereal import and Average dietary Energy Trend') \n",
    "ax.set_xticks(data_new['Year']) \n",
    "ax.set_xticklabels(data_new['Year'], rotation='vertical', fontsize=6)\n",
    "plt.show()\n",
    "plt.savefig('CAS.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5ef308ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.674000000000016\n",
      "MSE: 6.1565786666667215\n",
      "r2_score: -7.952384460726144\n",
      "RMSE: 2.48124538622578\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "results = [['Model', 'MAE']]\n",
    "\n",
    "\n",
    "rndm_frst = RandomForestRegressor(n_estimators=300, random_state=0)\n",
    "\n",
    "rndm_frst.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rndm_frst.predict(X_test)\n",
    "\n",
    "rms = sqrt(mean_squared_error(y_pred, y_test))\n",
    "\n",
    "print(\"MAE: \" + str(mean_absolute_error(y_pred, y_test)))\n",
    "print(\"MSE: \" + str(mean_squared_error(y_pred, y_test)))\n",
    "print(\"r2_score: \" + str(r2_score(y_pred, y_test)))\n",
    "print(\"RMSE: \" + str(rms))\n",
    "\n",
    "results.append(['Random Forest Regressor', str(mean_absolute_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(mean_squared_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(r2_score(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(rms)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397a3246",
   "metadata": {},
   "source": [
    "# Testing with other Features of  Nutrition Security"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "757de456",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale this data.\n",
    "y = data_new['Prevalence of undernourishment (percent) (3-year average) %']\n",
    "x1 = data_new[['Average dietary energy supply adequacy (percent) (3-year average) %', 'Prevalence of obesity in the adult population (18 years and older)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ce9d630",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37b1aa18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f92186b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaled = scaler.transform(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c678e250",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_scaled,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "341c74d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR()\n",
      "[8.7764372  8.781393   8.45051546 6.92539833 9.91539824] \n",
      "\n",
      "The mean absolute error 0.10033088403525489\n",
      "The mean square error 0.012168362145882693\n",
      "The r2 score 0.9885807412294645\n",
      "SGDRegressor()\n",
      "[ 8.27107149  9.14332737  7.91794319  6.74108079 10.93357768] \n",
      "\n",
      "The mean absolute error 0.5293619176500812\n",
      "The mean square error 0.3707330015156686\n",
      "The r2 score 0.6520899009800407\n",
      "BayesianRidge()\n",
      "[ 9.05886525  9.2824977   8.57364202  6.76906752 10.88000879] \n",
      "\n",
      "The mean absolute error 0.31573243949248153\n",
      "The mean square error 0.2068218457657518\n",
      "The r2 score 0.8059104300246324\n",
      "LassoLars()\n",
      "[8.75714286 8.75714286 8.75714286 8.75714286 8.75714286] \n",
      "\n",
      "The mean absolute error 0.7085714285714291\n",
      "The mean square error 1.0844081632653062\n",
      "The r2 score -0.01765030336458895\n",
      "ARDRegression()\n",
      "[ 9.53092007  9.28425177  8.94725035  6.67737944 10.74970786] \n",
      "\n",
      "The mean absolute error 0.46695012060300967\n",
      "The mean square error 0.2660480765783901\n",
      "The r2 score 0.7503302584662256\n",
      "PassiveAggressiveRegressor()\n",
      "[ 8.89941157 10.07235703  8.15755325  5.55835149 13.24993855] \n",
      "\n",
      "The mean absolute error 1.2413958549556112\n",
      "The mean square error 2.7836886996705177\n",
      "The r2 score -1.6123204764175276\n",
      "TheilSenRegressor(max_subpopulation=10000)\n",
      "[ 8.65906148  8.87204153  8.28171147  6.88583644 10.14895661] \n",
      "\n",
      "The mean absolute error 0.17321232457268518\n",
      "The mean square error 0.038820981292171874\n",
      "The r2 score 0.9635688989375264\n",
      "LinearRegression()\n",
      "[ 9.17463305  9.30114204  8.65059405  6.67954345 10.9247048 ] \n",
      "\n",
      "The mean absolute error 0.37430609839833195\n",
      "The mean square error 0.23974303195659769\n",
      "The r2 score 0.7750159234641538\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "classifiers = [\n",
    "    svm.SVR(),\n",
    "    linear_model.SGDRegressor(),\n",
    "    linear_model.BayesianRidge(),\n",
    "    linear_model.LassoLars(),\n",
    "    linear_model.ARDRegression(),\n",
    "    linear_model.PassiveAggressiveRegressor(),\n",
    "    linear_model.TheilSenRegressor(),\n",
    "    linear_model.LinearRegression()]\n",
    "for item in classifiers:\n",
    "    print(item)\n",
    "    clf = item\n",
    "    clf.fit(X_train, y_train)\n",
    "    # print(clf.predict(X_test),'\\n')\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(y_pred, '\\n')\n",
    "    print(\"The mean absolute error {}\".format(mean_absolute_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The mean square error {}\".format(mean_squared_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The r2 score {}\".format(r2_score(y_test, y_pred),'\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2453e6d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.674000000000016\n",
      "MSE: 6.1565786666667215\n",
      "r2_score: -7.952384460726144\n",
      "RMSE: 2.48124538622578\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "results = [['Model', 'MAE']]\n",
    "\n",
    "\n",
    "rndm_frst = RandomForestRegressor(n_estimators=300, random_state=0)\n",
    "\n",
    "rndm_frst.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rndm_frst.predict(X_test)\n",
    "\n",
    "rms = sqrt(mean_squared_error(y_pred, y_test))\n",
    "\n",
    "print(\"MAE: \" + str(mean_absolute_error(y_pred, y_test)))\n",
    "print(\"MSE: \" + str(mean_squared_error(y_pred, y_test)))\n",
    "print(\"r2_score: \" + str(r2_score(y_pred, y_test)))\n",
    "print(\"RMSE: \" + str(rms))\n",
    "\n",
    "results.append(['Random Forest Regressor', str(mean_absolute_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(mean_squared_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(r2_score(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(rms)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ded1cb2",
   "metadata": {},
   "source": [
    "# Test with Food Availabilty to the PoU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47c3ad02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale this data.\n",
    "y = data_new['Prevalence of undernourishment (percent) (3-year average) %']\n",
    "x1 = data_new[['Percent of arable land equipped for irrigation (percent) (3-year average) %', 'Cereal import dependency ratio (percent) (3-year average)', 'Value of food imports in total merchandise exports (percent) (3-year average) %',\n",
    "       'Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/day) (3-year average)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7ab86ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4bdc8b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaled = scaler.transform(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6bcc1729",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_scaled,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2f101c2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR()\n",
      "[8.5059954  8.56034876 9.41687815 9.47303658 9.41687815] \n",
      "\n",
      "The mean absolute error 1.4745872222748968\n",
      "The mean square error 5.773004190011062\n",
      "The r2 score -0.26224510014235247\n",
      "SGDRegressor()\n",
      "[7.78919119 8.4300465  9.1710691  9.5826141  9.1710691 ] \n",
      "\n",
      "The mean absolute error 1.7642476414852204\n",
      "The mean square error 6.680602300891425\n",
      "The r2 score -0.460687926554886\n",
      "BayesianRidge()\n",
      "[ 8.75910931  8.38727336  9.47852905 10.49704132  9.47852905] \n",
      "\n",
      "The mean absolute error 1.63872011031639\n",
      "The mean square error 5.8640479572249244\n",
      "The r2 score -0.28215146869532237\n",
      "LassoLars()\n",
      "[8.07857143 8.07857143 8.07857143 8.07857143 8.07857143] \n",
      "\n",
      "The mean absolute error 2.4414285714285726\n",
      "The mean square error 10.53417346938776\n",
      "The r2 score -1.3032563996387445\n",
      "ARDRegression()\n",
      "[ 8.58913371  8.24515859  9.62095179 10.65344024  9.62095179] \n",
      "\n",
      "The mean absolute error 1.6754488721230427\n",
      "The mean square error 5.638560389335727\n",
      "The r2 score -0.23284948166340036\n",
      "PassiveAggressiveRegressor()\n",
      "[10.10484085  8.73240191  9.39102475 10.4438337   9.39102475] \n",
      "\n",
      "The mean absolute error 1.8068446283891\n",
      "The mean square error 6.291935668065746\n",
      "The r2 score -0.37570746634286944\n",
      "TheilSenRegressor(max_subpopulation=10000)\n",
      "[ 8.9229704   8.52832504  9.27935534 10.64246467  9.27935534] \n",
      "\n",
      "The mean absolute error 1.6956798696702708\n",
      "The mean square error 6.418842838997629\n",
      "The r2 score -0.40345522979657833\n",
      "LinearRegression()\n",
      "[ 9.86338358  8.55470608  9.29595492 10.9327091   9.29595492] \n",
      "\n",
      "The mean absolute error 1.9298953508976122\n",
      "The mean square error 6.715965708208164\n",
      "The r2 score -0.4684199991709299\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "classifiers = [\n",
    "    svm.SVR(),\n",
    "    linear_model.SGDRegressor(),\n",
    "    linear_model.BayesianRidge(),\n",
    "    linear_model.LassoLars(),\n",
    "    linear_model.ARDRegression(),\n",
    "    linear_model.PassiveAggressiveRegressor(),\n",
    "    linear_model.TheilSenRegressor(),\n",
    "    linear_model.LinearRegression()]\n",
    "for item in classifiers:\n",
    "    print(item)\n",
    "    clf = item\n",
    "    clf.fit(X_train, y_train)\n",
    "    # print(clf.predict(X_test),'\\n')\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(y_pred, '\\n')\n",
    "    print(\"The mean absolute error {}\".format(mean_absolute_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The mean square error {}\".format(mean_squared_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The r2 score {}\".format(r2_score(y_test, y_pred),'\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5478751d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.674000000000016\n",
      "MSE: 6.1565786666667215\n",
      "r2_score: -7.952384460726144\n",
      "RMSE: 2.48124538622578\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "results = [['Model', 'MAE']]\n",
    "\n",
    "\n",
    "rndm_frst = RandomForestRegressor(n_estimators=300, random_state=0)\n",
    "\n",
    "rndm_frst.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rndm_frst.predict(X_test)\n",
    "\n",
    "rms = sqrt(mean_squared_error(y_pred, y_test))\n",
    "\n",
    "print(\"MAE: \" + str(mean_absolute_error(y_pred, y_test)))\n",
    "print(\"MSE: \" + str(mean_squared_error(y_pred, y_test)))\n",
    "print(\"r2_score: \" + str(r2_score(y_pred, y_test)))\n",
    "print(\"RMSE: \" + str(rms))\n",
    "\n",
    "results.append(['Random Forest Regressor', str(mean_absolute_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(mean_squared_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(r2_score(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(rms)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a592e0",
   "metadata": {},
   "source": [
    "# The Health Care Access and Other needs related to PoU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "054bb95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale this data.\n",
    "y = data_new['Prevalence of undernourishment (percent) (3-year average) %']\n",
    "x1 = data_new[['Percentage of population using safely managed drinking water services (Percent)',\n",
    "       'Percentage of population using at least basic drinking water services (percent)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2dc873e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "49e40871",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaled = scaler.transform(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bdcb0419",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_scaled,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a97e68bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR()\n",
      "[8.94750569 7.13049943 8.19391248 9.01314034 7.23882191] \n",
      "\n",
      "The mean absolute error 1.6107527938731003\n",
      "The mean square error 7.001819286515504\n",
      "The r2 score 0.0961195798673572\n",
      "SGDRegressor()\n",
      "[8.52170571 8.15297796 7.62331232 8.54039356 7.83290215] \n",
      "\n",
      "The mean absolute error 2.1600937035726537\n",
      "The mean square error 8.765639451037668\n",
      "The r2 score -0.1315758869975303\n",
      "BayesianRidge()\n",
      "[8.57894027 8.36056445 8.04687456 8.59000799 8.17100233] \n",
      "\n",
      "The mean absolute error 2.163148791163835\n",
      "The mean square error 8.656603451804843\n",
      "The r2 score -0.1175001874167152\n",
      "LassoLars()\n",
      "[8.38571429 8.38571429 8.38571429 8.38571429 8.38571429] \n",
      "\n",
      "The mean absolute error 2.2228571428571424\n",
      "The mean square error 9.370204081632648\n",
      "The r2 score -0.20962047940109585\n",
      "ARDRegression()\n",
      "[8.57894027 8.36056445 8.04687456 8.59000799 8.17100233] \n",
      "\n",
      "The mean absolute error 2.163148791163835\n",
      "The mean square error 8.656603451804843\n",
      "The r2 score -0.1175001874167152\n",
      "PassiveAggressiveRegressor()\n",
      "[9.38093272 8.50288244 7.24159126 9.42543405 7.74068689] \n",
      "\n",
      "The mean absolute error 1.939122259705011\n",
      "The mean square error 6.513172354703343\n",
      "The r2 score 0.159200098793847\n",
      "TheilSenRegressor(max_subpopulation=10000)\n",
      "[8.64048758 7.64347694 6.21130302 8.69101805 6.77801732] \n",
      "\n",
      "The mean absolute error 2.204530195416121\n",
      "The mean square error 9.095863862992465\n",
      "The r2 score -0.1742052905856224\n",
      "LinearRegression()\n",
      "[8.70021989 8.34477898 7.83419948 8.71823434 8.03623688] \n",
      "\n",
      "The mean absolute error 2.1256724327262813\n",
      "The mean square error 8.257870712945405\n",
      "The r2 score -0.06602689158130293\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "classifiers = [\n",
    "    svm.SVR(),\n",
    "    linear_model.SGDRegressor(),\n",
    "    linear_model.BayesianRidge(),\n",
    "    linear_model.LassoLars(),\n",
    "    linear_model.ARDRegression(),\n",
    "    linear_model.PassiveAggressiveRegressor(),\n",
    "    linear_model.TheilSenRegressor(),\n",
    "    linear_model.LinearRegression()]\n",
    "for item in classifiers:\n",
    "    print(item)\n",
    "    clf = item\n",
    "    clf.fit(X_train, y_train)\n",
    "    # print(clf.predict(X_test),'\\n')\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(y_pred, '\\n')\n",
    "    print(\"The mean absolute error {}\".format(mean_absolute_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The mean square error {}\".format(mean_squared_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The r2 score {}\".format(r2_score(y_test, y_pred),'\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e1908498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.674000000000016\n",
      "MSE: 6.1565786666667215\n",
      "r2_score: -7.952384460726144\n",
      "RMSE: 2.48124538622578\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "results = [['Model', 'MAE']]\n",
    "\n",
    "\n",
    "rndm_frst = RandomForestRegressor(n_estimators=300, random_state=0)\n",
    "\n",
    "rndm_frst.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rndm_frst.predict(X_test)\n",
    "\n",
    "rms = sqrt(mean_squared_error(y_pred, y_test))\n",
    "\n",
    "print(\"MAE: \" + str(mean_absolute_error(y_pred, y_test)))\n",
    "print(\"MSE: \" + str(mean_squared_error(y_pred, y_test)))\n",
    "print(\"r2_score: \" + str(r2_score(y_pred, y_test)))\n",
    "print(\"RMSE: \" + str(rms))\n",
    "\n",
    "results.append(['Random Forest Regressor', str(mean_absolute_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(mean_squared_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(r2_score(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(rms)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88ae7f76",
   "metadata": {},
   "source": [
    "# The Houshold Income through GDP in relation to the PoU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "316ae268",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale this data.\n",
    "y = data_new['Prevalence of undernourishment (percent) (3-year average) %']\n",
    "x1 = data_new[['Gross domestic product per capita, PPP, dissemination (constant 2011 international $)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b6cf5555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d056d188",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaled = scaler.transform(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "af0ed7d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_scaled,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9764e70a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR()\n",
      "[8.17025871 7.04560934 9.50001326 7.10682715 9.40657366] \n",
      "\n",
      "The mean absolute error 0.31775293957602313\n",
      "The mean square error 0.11575081225955594\n",
      "The r2 score 0.9153868331435995\n",
      "SGDRegressor()\n",
      "[7.77852181 8.41040824 9.71309245 8.55881649 9.5370515 ] \n",
      "\n",
      "The mean absolute error 1.028169375252357\n",
      "The mean square error 1.4730594298964277\n",
      "The r2 score -0.07679782887165754\n",
      "BayesianRidge()\n",
      "[8.30555164 8.71855688 9.57000009 8.81555752 9.45493854] \n",
      "\n",
      "The mean absolute error 0.9907002792374469\n",
      "The mean square error 1.7596722468340054\n",
      "The r2 score -0.286310121954682\n",
      "LassoLars()\n",
      "[8.94285714 8.94285714 8.94285714 8.94285714 8.94285714] \n",
      "\n",
      "The mean absolute error 1.0885714285714285\n",
      "The mean square error 2.078408163265304\n",
      "The r2 score -0.5193042129132335\n",
      "ARDRegression()\n",
      "[8.30555164 8.71855688 9.57000009 8.81555752 9.45493854] \n",
      "\n",
      "The mean absolute error 0.9907002792374469\n",
      "The mean square error 1.7596722468340054\n",
      "The r2 score -0.286310121954682\n",
      "PassiveAggressiveRegressor()\n",
      "[6.88382032 7.06908204 7.45101384 7.11259361 7.39940069] \n",
      "\n",
      "The mean absolute error 1.2294881595982958\n",
      "The mean square error 1.9868511457019256\n",
      "The r2 score -0.4523765684955594\n",
      "TheilSenRegressor(max_subpopulation=10000)\n",
      "[6.48959492 7.42512784 9.35380349 7.64485215 9.09316788] \n",
      "\n",
      "The mean absolute error 0.828204135388753\n",
      "The mean square error 1.182971152197611\n",
      "The r2 score 0.1352550057035009\n",
      "LinearRegression()\n",
      "[7.93483675 8.58808344 9.93480351 8.74150847 9.75281168] \n",
      "\n",
      "The mean absolute error 1.1564740689229394\n",
      "The mean square error 1.7818695321589029\n",
      "The r2 score -0.3025362077184959\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "classifiers = [\n",
    "    svm.SVR(),\n",
    "    linear_model.SGDRegressor(),\n",
    "    linear_model.BayesianRidge(),\n",
    "    linear_model.LassoLars(),\n",
    "    linear_model.ARDRegression(),\n",
    "    linear_model.PassiveAggressiveRegressor(),\n",
    "    linear_model.TheilSenRegressor(),\n",
    "    linear_model.LinearRegression()]\n",
    "for item in classifiers:\n",
    "    print(item)\n",
    "    clf = item\n",
    "    clf.fit(X_train, y_train)\n",
    "    # print(clf.predict(X_test),'\\n')\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(y_pred, '\\n')\n",
    "    print(\"The mean absolute error {}\".format(mean_absolute_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The mean square error {}\".format(mean_squared_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The r2 score {}\".format(r2_score(y_test, y_pred),'\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2ce626d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.674000000000016\n",
      "MSE: 6.1565786666667215\n",
      "r2_score: -7.952384460726144\n",
      "RMSE: 2.48124538622578\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "results = [['Model', 'MAE']]\n",
    "\n",
    "\n",
    "rndm_frst = RandomForestRegressor(n_estimators=300, random_state=0)\n",
    "\n",
    "rndm_frst.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rndm_frst.predict(X_test)\n",
    "\n",
    "rms = sqrt(mean_squared_error(y_pred, y_test))\n",
    "\n",
    "print(\"MAE: \" + str(mean_absolute_error(y_pred, y_test)))\n",
    "print(\"MSE: \" + str(mean_squared_error(y_pred, y_test)))\n",
    "print(\"r2_score: \" + str(r2_score(y_pred, y_test)))\n",
    "print(\"RMSE: \" + str(rms))\n",
    "\n",
    "results.append(['Random Forest Regressor', str(mean_absolute_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(mean_squared_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(r2_score(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(rms)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1bab3b0",
   "metadata": {},
   "source": [
    "# food accessibility in relationship with the Food Utilization (PoU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2b9cc0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale this data.\n",
    "y = data_new['Prevalence of undernourishment (percent) (3-year average) %']\n",
    "x1 = data_new[['Political stability and absence of violence/terrorism (index)',\n",
    "       'Per capita food supply variability (kcal/cap/day)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e589a2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4e0cf48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaled = scaler.transform(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b416c599",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_scaled,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c0c72648",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR()\n",
      "[9.17798032 7.72880478 8.54288038 7.85911307 8.23700369] \n",
      "\n",
      "The mean absolute error 1.5508435547709771\n",
      "The mean square error 6.234348902790263\n",
      "The r2 score -0.07001731820511203\n",
      "SGDRegressor()\n",
      "[8.0864506  7.50902708 8.7132267  7.53515472 7.8931054 ] \n",
      "\n",
      "The mean absolute error 1.912607101252685\n",
      "The mean square error 9.021559887702917\n",
      "The r2 score -0.5483934998803586\n",
      "BayesianRidge()\n",
      "[8.31399283 8.31215641 8.31731595 8.31111313 8.31548988] \n",
      "\n",
      "The mean absolute error 1.710431613223032\n",
      "The mean square error 8.214614827428525\n",
      "The r2 score -0.40989544614659623\n",
      "LassoLars()\n",
      "[8.31428571 8.31428571 8.31428571 8.31428571 8.31428571] \n",
      "\n",
      "The mean absolute error 1.711428571428571\n",
      "The mean square error 8.215632653061224\n",
      "The r2 score -0.4100701381747265\n",
      "ARDRegression()\n",
      "[8.30827237 8.28890864 8.37384587 8.25204434 8.37255201] \n",
      "\n",
      "The mean absolute error 1.6816930904011027\n",
      "The mean square error 8.188502490767975\n",
      "The r2 score -0.40541371872304977\n",
      "PassiveAggressiveRegressor()\n",
      "[8.65636636 9.04505264 8.94714897 8.42377471 9.91843404] \n",
      "\n",
      "The mean absolute error 1.6567492149039595\n",
      "The mean square error 7.392180891384944\n",
      "The r2 score -0.26873899687370373\n",
      "TheilSenRegressor(max_subpopulation=10000)\n",
      "[8.78880175 8.3345271  9.28244117 8.35462691 8.63754595] \n",
      "\n",
      "The mean absolute error 1.3622621867845115\n",
      "The mean square error 6.823275399267109\n",
      "The r2 score -0.17109628574541946\n",
      "LinearRegression()\n",
      "[8.27455949 7.91377047 8.74146495 7.86632937 8.2733143 ] \n",
      "\n",
      "The mean absolute error 1.6461122810024058\n",
      "The mean square error 8.237587729327936\n",
      "The r2 score -0.4138383443168918\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "classifiers = [\n",
    "    svm.SVR(),\n",
    "    linear_model.SGDRegressor(),\n",
    "    linear_model.BayesianRidge(),\n",
    "    linear_model.LassoLars(),\n",
    "    linear_model.ARDRegression(),\n",
    "    linear_model.PassiveAggressiveRegressor(),\n",
    "    linear_model.TheilSenRegressor(),\n",
    "    linear_model.LinearRegression()]\n",
    "for item in classifiers:\n",
    "    print(item)\n",
    "    clf = item\n",
    "    clf.fit(X_train, y_train)\n",
    "    # print(clf.predict(X_test),'\\n')\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(y_pred, '\\n')\n",
    "    print(\"The mean absolute error {}\".format(mean_absolute_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The mean square error {}\".format(mean_squared_error(y_test, y_pred),'\\n'))\n",
    "    print(\"The r2 score {}\".format(r2_score(y_test, y_pred),'\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d56659f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.674000000000016\n",
      "MSE: 6.1565786666667215\n",
      "r2_score: -7.952384460726144\n",
      "RMSE: 2.48124538622578\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "results = [['Model', 'MAE']]\n",
    "\n",
    "\n",
    "rndm_frst = RandomForestRegressor(n_estimators=300, random_state=0)\n",
    "\n",
    "rndm_frst.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rndm_frst.predict(X_test)\n",
    "\n",
    "rms = sqrt(mean_squared_error(y_pred, y_test))\n",
    "\n",
    "print(\"MAE: \" + str(mean_absolute_error(y_pred, y_test)))\n",
    "print(\"MSE: \" + str(mean_squared_error(y_pred, y_test)))\n",
    "print(\"r2_score: \" + str(r2_score(y_pred, y_test)))\n",
    "print(\"RMSE: \" + str(rms))\n",
    "\n",
    "results.append(['Random Forest Regressor', str(mean_absolute_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(mean_squared_error(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(r2_score(y_pred, y_test))])\n",
    "results.append(['Random Forest Regressor', str(rms)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "572ade0f",
   "metadata": {},
   "source": [
    "# Run Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80950d0e",
   "metadata": {},
   "source": [
    "# ARDRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "85c09a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARDRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "# use the regression model for the dataset\n",
    "clf =linear_model.ARDRegression() #creat the object for the regression\n",
    "clf.fit(X_train, y_train)  #pass the data through the model, reg.fit(1st argument, 2nd argument);\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "01110a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.30827237, 8.28890864, 8.37384587, 8.25204434, 8.37255201])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = clf.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5e3a6218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18    14.6\n",
       "2      8.6\n",
       "13     9.3\n",
       "3      7.9\n",
       "1      8.9\n",
       "Name: Prevalence of undernourishment (percent) (3-year average) %, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "11c666d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.40541371872304977"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f21fc426",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha_1': 1e-06,\n",
       " 'alpha_2': 1e-06,\n",
       " 'compute_score': False,\n",
       " 'copy_X': True,\n",
       " 'fit_intercept': True,\n",
       " 'lambda_1': 1e-06,\n",
       " 'lambda_2': 1e-06,\n",
       " 'n_iter': 300,\n",
       " 'normalize': False,\n",
       " 'threshold_lambda': 10000.0,\n",
       " 'tol': 0.001,\n",
       " 'verbose': False}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d15e12",
   "metadata": {},
   "source": [
    "# Lasso Lars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f09302f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LassoLars(alpha=0.01, normalize=False)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "# use the regression model for the dataset\n",
    "clf =linear_model.LassoLars(alpha=0.01, normalize=False) #creat the object for the regression\n",
    "clf.fit(X_train, y_train)  #pass the data through the model, reg.fit(1st argument, 2nd argument);\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b220051a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.27631613, 7.93880346, 8.72151582, 7.88729359, 8.28851897])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = clf.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "56e1535f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18    14.6\n",
       "2      8.6\n",
       "13     9.3\n",
       "3      7.9\n",
       "1      8.9\n",
       "Name: Prevalence of undernourishment (percent) (3-year average) %, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1abc124f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.41201678610480674"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7149f954",
   "metadata": {},
   "source": [
    "# Bayesian Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "7adcc3f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BayesianRidge()"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = linear_model.BayesianRidge()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "8165f547",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 4 is different from 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-191-f649b58415e1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_bayes.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, return_std)\u001b[0m\n\u001b[0;32m    322\u001b[0m             \u001b[0mStandard\u001b[0m \u001b[0mdeviation\u001b[0m \u001b[0mof\u001b[0m \u001b[0mpredictive\u001b[0m \u001b[0mdistribution\u001b[0m \u001b[0mof\u001b[0m \u001b[0mquery\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m         \"\"\"\n\u001b[1;32m--> 324\u001b[1;33m         \u001b[0my_mean\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    325\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_std\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0my_mean\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'coo'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 221\u001b[1;33m         return safe_sparse_dot(X, self.coef_.T,\n\u001b[0m\u001b[0;32m    222\u001b[0m                                dense_output=True) + self.intercept_\n\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[1;34m(a, b, dense_output)\u001b[0m\n\u001b[0;32m    150\u001b[0m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 152\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m@\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m     if (sparse.issparse(a) and sparse.issparse(b)\n",
      "\u001b[1;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 4 is different from 2)"
     ]
    }
   ],
   "source": [
    "clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "7edef1cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18    14.6\n",
       "2      8.6\n",
       "13     9.3\n",
       "3      7.9\n",
       "1      8.9\n",
       "Name: Prevalence of undernourishment (percent) (3-year average) %, dtype: float64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1b17372e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.41004398660034"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "bbfd6ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6307726716234836"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.alpha_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "7ecae36b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00015703, -0.00054637])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9f9b33e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "395.1860073057007"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.lambda_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dccd9625",
   "metadata": {},
   "source": [
    "#  svm, SGD Regressor, Passive Agressive Regressor, TheilSien Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e0e845b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "csvr= svm.SVR()\n",
    "Sgd = linear_model.SGDRegressor()\n",
    "Par = linear_model.PassiveAggressiveRegressor()\n",
    "tls = linear_model.TheilSenRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "3797283f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csvr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "018e5965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.17798032, 7.72880478, 8.54288038, 7.85911307, 8.23700369])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1 = csvr.predict(X_test)\n",
    "pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ae0d33f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07001731820511203"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csvr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "8cebd044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor()"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the SGBRegressor\n",
    "Sgd.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "ab0094cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.08690586, 7.50754718, 8.71591137, 7.53365324, 7.89311732])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sgd.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "2aa022fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred2 = Sgd.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "310a280a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassiveAggressiveRegressor()"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Par.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "0a58bb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.53910708,  7.02148951, 10.20956207,  7.07057048,  8.06767589])"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred3 = Par.predict(X_test)\n",
    "pred3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "213661f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.42228951748495946"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Par.score(X_test, y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "457bb7e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.82803094, -1.08035113])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Par.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "05c47383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TheilSenRegressor(max_subpopulation=10000)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tls.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "30c3f126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.78880175, 8.3345271 , 9.28244117, 8.35462691, 8.63754595])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred4 = tls.predict(X_test)\n",
    "pred4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "991783bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18    14.6\n",
       "2      8.6\n",
       "13     9.3\n",
       "3      7.9\n",
       "1      8.9\n",
       "Name: Prevalence of undernourishment (percent) (3-year average) %, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "a587a514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.17109628574541946"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tls.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c509603c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=2, random_state=0)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "regr = RandomForestRegressor(max_depth=2, random_state=0)\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "002ce421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.0213763 , 7.94217792, 9.18339177, 7.22765675, 8.44222475])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "9f4df9bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.10630830008366954"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "8ec8f854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3, 3, 5, 3, 3, 2, 3, 6, 2, 5, 3, 5, 3, 3, 6, 5, 3, 3, 3, 3, 6,\n",
       "        3, 3, 3, 3, 5, 2, 2, 3, 3, 3, 2, 6, 6, 3, 3, 3, 3, 5, 3, 3, 3, 3,\n",
       "        3, 4, 6, 2, 3, 5, 3, 6, 3, 6, 3, 3, 3, 5, 3, 3, 3, 3, 3, 3, 3, 6,\n",
       "        3, 6, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 6, 3, 5, 3, 2, 3, 6, 5, 2, 2,\n",
       "        3, 3, 2, 5, 6, 2, 6, 2, 3, 2, 3, 3],\n",
       "       [6, 6, 5, 5, 3, 5, 3, 5, 6, 6, 5, 5, 6, 3, 3, 6, 6, 3, 6, 3, 6, 6,\n",
       "        3, 6, 6, 6, 5, 6, 6, 6, 3, 3, 5, 6, 6, 3, 3, 3, 5, 6, 3, 3, 6, 5,\n",
       "        3, 4, 6, 3, 3, 6, 3, 6, 6, 6, 3, 5, 6, 6, 6, 5, 3, 6, 6, 5, 3, 6,\n",
       "        5, 6, 4, 6, 6, 5, 5, 3, 3, 3, 3, 5, 6, 5, 5, 3, 3, 6, 6, 5, 3, 3,\n",
       "        3, 5, 5, 5, 6, 6, 6, 6, 3, 3, 6, 3],\n",
       "       [2, 2, 2, 2, 3, 3, 2, 3, 3, 2, 3, 2, 5, 3, 2, 3, 5, 2, 2, 3, 2, 3,\n",
       "        2, 3, 3, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 3, 2, 3, 3, 2, 2,\n",
       "        3, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 2, 5, 3, 2, 2, 2, 3, 3, 3, 5,\n",
       "        2, 3, 3, 3, 3, 2, 3, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2,\n",
       "        2, 3, 2, 3, 2, 2, 2, 2, 3, 2, 3, 3],\n",
       "       [6, 6, 6, 6, 4, 5, 6, 5, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6,\n",
       "        5, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 3, 5, 6, 6, 3, 5, 6, 6,\n",
       "        3, 4, 6, 6, 3, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 6, 6, 6, 6, 5, 3, 6,\n",
       "        6, 6, 4, 6, 6, 6, 5, 6, 3, 6, 6, 6, 6, 6, 5, 5, 4, 6, 6, 5, 3, 5,\n",
       "        6, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 5],\n",
       "       [3, 3, 2, 5, 3, 3, 3, 3, 6, 3, 3, 3, 5, 3, 2, 6, 6, 2, 6, 3, 2, 5,\n",
       "        3, 3, 3, 3, 5, 6, 3, 2, 3, 2, 3, 6, 6, 2, 3, 2, 3, 6, 3, 3, 2, 3,\n",
       "        3, 3, 6, 3, 3, 3, 3, 3, 3, 6, 2, 3, 2, 5, 3, 2, 2, 2, 6, 5, 3, 5,\n",
       "        2, 3, 4, 3, 3, 3, 3, 3, 3, 2, 3, 3, 6, 3, 5, 3, 3, 2, 6, 5, 3, 3,\n",
       "        2, 5, 3, 5, 6, 3, 5, 3, 3, 3, 3, 3]], dtype=int64)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.apply(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "14e2d8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<5x688 sparse matrix of type '<class 'numpy.int64'>'\n",
       " \twith 1498 stored elements in Compressed Sparse Row format>,\n",
       " array([  0,   7,  14,  21,  28,  33,  40,  47,  54,  61,  68,  75,  82,\n",
       "         89,  96, 103, 110, 117, 124, 131, 138, 145, 152, 159, 166, 173,\n",
       "        180, 187, 194, 201, 208, 215, 222, 229, 236, 243, 250, 257, 264,\n",
       "        271, 278, 285, 292, 299, 306, 311, 316, 323, 330, 337, 344, 351,\n",
       "        358, 365, 372, 379, 386, 393, 400, 407, 414, 421, 428, 435, 442,\n",
       "        449, 456, 463, 470, 475, 482, 489, 496, 503, 510, 515, 522, 529,\n",
       "        536, 543, 550, 557, 564, 569, 576, 583, 590, 597, 604, 611, 618,\n",
       "        625, 632, 639, 646, 653, 660, 667, 674, 681, 688], dtype=int32))"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.decision_path(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "996c5935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(pred1, label=\"Support Vector Regression\")\n",
    "plt.plot(predictions, label=\"Linear Regression\")\n",
    "plt.plot(pred4, label=\"TheilSienRegressor\")\n",
    "plt.plot(pred2, label=\"Stochastic Gradient Descent\")\n",
    "\n",
    "\n",
    "plt.tick_params(axis=\"x\", which=\"both\", bottom=False, top=False, labelbottom=False)\n",
    "plt.ylabel(\"predicted\")\n",
    "plt.xlabel(\"training samples\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.title(\"Regressor predictions and their average\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7104e75b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}