WH_Linear.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project 3 Idea: What Factors Are Most Important To Happiness Based on 2017 World Happiness Report Data 🤞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Country</th>\n",
       "      <th>HappinessRank</th>\n",
       "      <th>HappinessScore</th>\n",
       "      <th>Whiskerhigh</th>\n",
       "      <th>Whiskerlow</th>\n",
       "      <th>EconomyGDPperCapita</th>\n",
       "      <th>Family</th>\n",
       "      <th>HealthLifeExpectancy</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>TrustGovernmentCorruption</th>\n",
       "      <th>DystopiaResidual</th>\n",
       "      <th>country_id</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Predicted Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>7.594445</td>\n",
       "      <td>7.479556</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>0.635423</td>\n",
       "      <td>0.362012</td>\n",
       "      <td>0.315964</td>\n",
       "      <td>2.277027</td>\n",
       "      <td>109</td>\n",
       "      <td>60.472024</td>\n",
       "      <td>8.468946</td>\n",
       "      <td>Happier Than Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>7.581728</td>\n",
       "      <td>7.462272</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>0.626007</td>\n",
       "      <td>0.355280</td>\n",
       "      <td>0.400770</td>\n",
       "      <td>2.313707</td>\n",
       "      <td>39</td>\n",
       "      <td>56.263920</td>\n",
       "      <td>9.501785</td>\n",
       "      <td>Happier Than Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>7.622030</td>\n",
       "      <td>7.385970</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>0.627163</td>\n",
       "      <td>0.475540</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>2.322715</td>\n",
       "      <td>61</td>\n",
       "      <td>64.963051</td>\n",
       "      <td>-19.020835</td>\n",
       "      <td>Happier Than Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>7.561772</td>\n",
       "      <td>7.426227</td>\n",
       "      <td>1.564980</td>\n",
       "      <td>1.516912</td>\n",
       "      <td>0.858131</td>\n",
       "      <td>0.620071</td>\n",
       "      <td>0.290549</td>\n",
       "      <td>0.367007</td>\n",
       "      <td>2.276716</td>\n",
       "      <td>143</td>\n",
       "      <td>46.818188</td>\n",
       "      <td>8.227512</td>\n",
       "      <td>Happier Than Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Finland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.469</td>\n",
       "      <td>7.527542</td>\n",
       "      <td>7.410458</td>\n",
       "      <td>1.443572</td>\n",
       "      <td>1.540247</td>\n",
       "      <td>0.809158</td>\n",
       "      <td>0.617951</td>\n",
       "      <td>0.245483</td>\n",
       "      <td>0.382612</td>\n",
       "      <td>2.430182</td>\n",
       "      <td>47</td>\n",
       "      <td>61.924110</td>\n",
       "      <td>25.748151</td>\n",
       "      <td>Happier Than Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1      Country  HappinessRank  HappinessScore  \\\n",
       "0           0             0       Norway              1           7.537   \n",
       "1           1             1      Denmark              2           7.522   \n",
       "2           2             2      Iceland              3           7.504   \n",
       "3           3             3  Switzerland              4           7.494   \n",
       "4           4             4      Finland              5           7.469   \n",
       "\n",
       "   Whiskerhigh  Whiskerlow  EconomyGDPperCapita    Family  \\\n",
       "0     7.594445    7.479556             1.616463  1.533524   \n",
       "1     7.581728    7.462272             1.482383  1.551122   \n",
       "2     7.622030    7.385970             1.480633  1.610574   \n",
       "3     7.561772    7.426227             1.564980  1.516912   \n",
       "4     7.527542    7.410458             1.443572  1.540247   \n",
       "\n",
       "   HealthLifeExpectancy   Freedom  Generosity  TrustGovernmentCorruption  \\\n",
       "0              0.796667  0.635423    0.362012                   0.315964   \n",
       "1              0.792566  0.626007    0.355280                   0.400770   \n",
       "2              0.833552  0.627163    0.475540                   0.153527   \n",
       "3              0.858131  0.620071    0.290549                   0.367007   \n",
       "4              0.809158  0.617951    0.245483                   0.382612   \n",
       "\n",
       "   DystopiaResidual  country_id   Latitude  Longitude     Predicted Outcome  \n",
       "0          2.277027         109  60.472024   8.468946  Happier Than Average  \n",
       "1          2.313707          39  56.263920   9.501785  Happier Than Average  \n",
       "2          2.322715          61  64.963051 -19.020835  Happier Than Average  \n",
       "3          2.276716         143  46.818188   8.227512  Happier Than Average  \n",
       "4          2.430182          47  61.924110  25.748151  Happier Than Average  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "WHR2017_df = pd.read_csv(os.path.join(\"data/csv/happy_2017.csv\"))\n",
    "WHR2017_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.savefig(\"TextFigurePlot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "# WHR: DOES FAMILY MATTER?\n",
    "# Yes. There is a clear visual linear pattern to the data. It says the happier you are with family, the happier you are.\n",
    "# Which is not a shock. What we want to build toward is Which Factor is THE MOST impactful in our data.\n",
    "# Seeing that a linear relationship appears to exist in the data should encourage us to fit a model to it and make\n",
    "# predictions upon it. \n",
    "Family_X = WHR2017_df.HappinessScore.values.reshape(-1,1)\n",
    "Family_y = WHR2017_df.Family.values.reshape(-1,1)\n",
    "familyplot = plt.scatter(Family_X,Family_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "# WHR: DOES ECONOMY MATTER?\n",
    "\n",
    "Econ_X = WHR2017_df.HappinessScore.values.reshape(-1,1)\n",
    "Econ_y = WHR2017_df.EconomyGDPperCapita.values.reshape(-1,1)\n",
    "econplot = plt.scatter(Econ_X,Econ_y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "# WHR: DOES HealthLifeExpectancy MATTER?\n",
    "Health_X = WHR2017_df.HappinessScore.values.reshape(-1,1)\n",
    "Health_y = WHR2017_df.HealthLifeExpectancy.values.reshape(-1,1)\n",
    "HealthLifeExpectancyplot=plt.scatter(Health_X,Health_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "# WHR: DOES FREEDOM MATTER?\n",
    "Freedom_X = WHR2017_df.HappinessScore.values.reshape(-1,1)\n",
    "Freedom_y = WHR2017_df.Freedom.values.reshape(-1,1)\n",
    "Freedomplot = plt.scatter(Freedom_X,Freedom_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "# WHR: DOES GENEROSITY MATTER?\n",
    "Generosity_X = WHR2017_df.HappinessScore.values.reshape(-1,1)\n",
    "Generosity_y = WHR2017_df.Generosity.values.reshape(-1,1)\n",
    "Generosityplot = plt.scatter(Generosity_X,Generosity_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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luoJumyvU2HNvKdO0RN0zIW0PfqVpcg43S3VN1eq66vNtbCULBvcWCwfnbq8tGKTSDsIyUxtNCJxxVzJfffAp3LTzgJXcdZ6Toe2UWl1B17exlSwY3B2T92DN8zqTYFpnz6cJg19xPVsbuesiJ0PbPdo6g65vYyumGNwdkvdgzfs6k2Ba5UEYPiFF9SSBMwOq7bRNUju76johFTkZutCjbWvQrQuDu0PyHqx5X2c6qFXFQRh1QhIAUbdP7qYKbKdtRic6eO34SaNt68hdFx2UZHD1G4O7RXl6rlmeT3udzbxr1AlJgXkBPpgqsJm2CZ9YutJOSFWynTcvm+2rMt+wFNKSqFUJ5xenzUg7WPOWe9kqZRyd6MSeyBSIXUjLZs1y1IkFAPoX9VorB/WpFNWFVTp9w567JXl6rnHyDo5lybuW1avqHsRxBvv78Pjw2sjnbPZU404gU6+dwF3XX2ylx+lC3rwsTRhMbxoGd0vigkW355rlYC1ykJvkXcvMdcf1gIH0E5LNCo+kE4vN3LUvefM2zyStilFwF5F1AO4B0APgPlUdCT1/GYC7AXwQwCZVfbjshvomLlgMzgbmpNrzKFUe5GX2qpIO1rS1zG32VPOcWKKudmy133V1X5W1Ib+fGtxFpAfAvQCuADAJYJ+IjKnqM4HNXgLweQBfq6KRPooLFmsuGDDuJdf1BS2zV5V0UivrSqMKWU8sUVc7Wx9+ClDgxJt21nNxWZ1XZbarrupi0nNfBeCwqj4PACKyA8AGAHPBXVVfnH3uzQra6K239p419wXr7+vF7esvNO4lp31Bywz8ZfaqbE+eKSLLiSXqczxxan5dTdV55ab0UOu8KmtLft8kuA8COBL4eRLApXn+mIhsBrAZAJYuXZrnV3ghqqzujZMz50XTXnLabeLK7JmkBeQsASTuIAaA1SN7nQ9CprJc1aRNisqraT3Uuq7K2pLfNwnuURV6UaW9qVR1O4DtADA0NJTrd/ggKTCb9pKTvqBl9EzCAfu6Swbx2HPH5gXfPAEkfBDn+R2u90hNZrJ2CWb2p+z2x30Pbtx5YG5VT5fes7r4Nj8gjkmd+ySAJYGfFwM4Wk1z2iEpMJvWLifVthftmUTVHD+yv4OtV63ACyNX4/HhtWf0wIveaDrr7yi7Jtp0Cd9bRp/G+27ejWXDu/C+m3fjltH4ks6ozzGOApUstZv0ebe5jtyn+QFJTIL7PgDni8hyEVkIYBOAsWqb5bekwBxedzs8kacr6QtadA3rLMG2jEvcrL8jaWXGrMHK9ERxy+jTeOCJl+YWCDuligeeeCk2wJusnx5URUog7fOuc/12l5geY01ctz8oNS2jqidFZAuAPZgphbxfVQ+KyB0AxlV1TEQ+AuBRAOcAuEZEvq6qF1ba8gxcu4RPy2Gb5B7TBqCKDFpmCbZlXOJm/R1JKzNmzSmbprC+/eSR8EvnHv/zjRdFPmeyfnpXFSmBuLsvBbmWZ67rWE07xpo2XhHFqM5dVXcD2B167NbAv/dhJl3jHBc/pLIqA+K+oEV/f5Zgu+aCAXzriZcyz6oNylpBk5TPzjq2YHoi6/bYw+IeD0tqc1UpgeD3IO5vu5RndulY9aGixvsZqq5+SFVXBhT5/abBdnSig0f2d84I7ALgukvMZr2aDNiati8oS2/U9ETWIxIZyNPSLV1xbe6WwFb1Xeh+D6IqtKrOM2fthbt0rPpQUeN9cPfhQ6qbac8/bn2cx547Fvl7uwd7eHnf7oBt2gzVcPu++uBTkQE3S2/U9ER2w6VL8MATL817/Q2XLpn3WFKbbaUH6/77eXrhLh2rPlTUeB/cffiQymTamzLp+ZsejKMTHXz9uwfxi9dOzD0WDslZe2hxYwsA8OobJ+eVFsbtd1rQC77u7IU9eO34KShmeuw3XLokNt8e1+YqgmmZn2lZ8vTCXTpWmzzZrsv74O7Dh5TG9OAuO6dpcjDGrYMeJamHlrSP4RPH1PSJebN1k/Y7LuiFX/fq8VPo6+0xvsKog0t56m57knL8SZ+xS8eq7SutMngf3Jv8IZkE7SwHd57eVFIbTA7GpFUgw+J6aGn7uG3PoTOCe3i/6r7DVZ1caqPJifydfb2xz7l2rNpax6gs3gd3oP4PqYxyLtOgneXgzprTNOnxdtsQt69Z8qVR6RSTfUzbr7y53LjnO1PTWD68y3rwAYrnqcssPTQ5kaeNPzc9oLqkFcG9TmVdJpsG7Spr0k1voJ20X1mm4YfTKV1p+5i2X3lzuUltD054Cre3TkXy1GWndExOKFOhKyyqDm+zV7IypuMDyb3GoCyzUbNOu04LqiYz+OKm4ff39aI/4hI96r1K28e0/co73XzNBQOxtz5Mam+dlr07+r1Zc8FA6mvL+q52mZxQwts0fRaoyxjcS1ZWOVfcgdJdZKor7iCOejxt2nX4QIvLj57X32c8bT/qb959/cU4cNuV+OV0dC8u/F6lBee0/TKdbh4UVcMfpzM1bSUojU508K8/+Xnkc8Fy1LgAWnbp4darViSeDMMnVN43tVqNS8u4tpRAWNFyrrRqg+4iU919jqspj3vctDKkMzWN3h5B71kyd3MJ4PQBmiXXH/c3Td8rk9x+Wnooay43y0AwANy48wBuHztY6YSksG17DsWefIJXV3Gpl7JLDzeuHMSNOw/EPh8+obo0GOyjRvXcm3CmL7LiXHD/kgR7VllqzZMuf+NuLvG2ty6I7PGW0evL8l5tXDmIx4fXzluVsip5eq/dMYO6vo9JbewG6KQAWsXqiIMxJ4aoO225NGnJR43quTfhTF+knMu0txjsWeWpNY8aOIs7oKZeO4GJW6+M/P1Fe32m75WNq7W4/TtnUe+8ssugOr+PcW0UYC5AJwXQKkoPs9SquzRpyUeNCu5NOdPnLecy2Y/wgZK31jwchLIeaGVNOEl7r0YnOtj60FNn3Hd060NPzb22KnH7d9s1FyamzYDyv49xJ7eoNgqA3/7oUuPPtezSwywnDJcmLfmoUcHd9zN93P71iOBN1dhcM5Cv1jz4eNYDra4JJ7ePHTwj5w/M3GD69rGDlQb3tP1LmqxT5vcx6qrrpp0HcOPOAxg0WHCtjgAadfJ5fHht6utcm7TkG1HDJUvLNjQ0pOPj45leE7eynUvTwYuoav9Wj+yNPGkM9vedcRC6OFi9LGEd9BdHrq6tHeH3Zs0FA3j0Rx28evzMAF/08wr/nVffOImpmKoi079X5efq+zHpIhHZr6pDqds1KbgDbgagMlWxf008ANOqhoD6gnvU+9fbI4DijKuKbkpk6L3vyvUZ3jL69Ly18U2ET9J1Mu04UHlMg3uj0jKAf9OT817SZtG0y1+TNUrOWRS/RonJ7y+6zviJU/NDsALY9R//jUf2dzLP+hyd6OQK7IDdMaemjIO1UeOCu0/qXNGvSSfFtKqh3h7Bbdfku4tjnvfcdPkEAJGVNKaLlOW9hrY55lTGOJjvV+O2MLhb1ITSThuSen2DOQ7+YPA4K+KOSmnvedxdmLLIu0gZMHOVsmjhgnk3OQHMBkerDJ5FB2xdW7LYJwzuFvGSNlpcbzBPHjccPOKCdNJ7XjSwA/kXKRMAt11zetZr1kBddfCMSvmtuWAA2/Ycwk07D6S2kR2c6jC4W+R7aWdeZZbv5ZkYFjaYc0JTl0nbTWrWgXKWUSg7eAbblPVkwg5OdRq1/IBvqpj+XYaiK/UVfX2ehb7imASJ3h7Bq2+cjG1v3Od02zUXxk637xHJ1Paofb7r+osz3cYvStx4QZZxhCyyrjSZZVVTyoY9d4tcrGIJl+NlvYwvKw1Q1gBw2sSw/kW9eOX107XkUe0Nf079i3qhCty08wD6F/VGLq6W52RUxaB33HhBT+CuGWXm5LP2xDlLtTrsuVtW94JYSeLK8bKs8V32GuFFxfW6//LTH8ILI1dj0cIF82bARrW3+znddf3FeP3Em5iaPgHFbHWMzKxPX/Qqowpx4wXdx8tejC9rT7zMqzQ6E3vuNMdkCdk0ruVQ066OsrY3rub97LcswIHb5i+wZlvceEE3nVR2Tj5PT7xJZbpNwuBOc0yWkE3j4iBxUvBIa284ZRGXq3Z1ADAt2JZ9MnYx1dhWDO40x2QJ2TRNy6EmtTdq/CBO3F2rbEsLtlWcjNkTdwODO80xLcdLUlXPraqJOEntXT2y1/huTJJ2s9UKRS1qFl4pMm5+QNNOxmSucQuHUbVcnApua+Gz5cO7jJcEEAAv1LhKZZfJOjxp75XJycH2d4BO83bhMKqWi5fUtmYxJuXYo7a1wWSSVtp7VWQSErmLpZDkPFsVOFFllN2bhgfZTGOYnnxM3yvXSlkpP6PgLiLrROSQiBwWkeGI598iIjtnn39SRJaV3VBqL1uzGDeuHMR1lwzOTfjpEcH1H1mCbZ/6kBN12aMTHZim+k3fK9dKWSm/1LSMiPQAuBfAFQAmAewTkTFVfSaw2RcA/EJVf0NENgH4CwDXV9Fgah9bg36jEx08sr8zN+HnlCoe2d/B0Hvf5cSNKEyXCc7yXrlYykr5mPTcVwE4rKrPq+pxADsAbAhtswHAN2f//TCAj4vYrB8gn9iaxeh6iiJtaeQ875Wr6x1RdiYDqoMAjgR+ngRwadw2qnpSRH4J4N0AfhbcSEQ2A9gMAEuXLs3ZZGojGwO9rqcoylwauYuTkPxhEtyjeuDhq0GTbaCq2wFsB2ZKIQ3+NpE1rqcoqkpXuVgxRdmZpGUmASwJ/LwYwNG4bURkAYB3Avh5GQ0kssX1FAUX3aIkJj33fQDOF5HlADoANgH4TGibMQCfA/BvAD4JYK/amh1FVJImpCjYy6Y4qcF9Noe+BcAeAD0A7lfVgyJyB4BxVR0D8LcA/l5EDmOmx76pykYT1YXBk5rKaIaqqu4GsDv02K2Bf78O4FPlNo2IiPLiDFUiIg8xuBMReYjBnYjIQwzuREQeYnAnIvIQgzsRkYcY3ImIPMTgTkTkIQZ3IiIPMbgTEXlIbK3vJSLHAPzUyh+f71yE1p5vkbbue1v3G2jvvvuy3+9V1YG0jawFd5eIyLiqDtluhw1t3fe27jfQ3n1v234zLUNE5CEGdyIiDzG4z9huuwEWtXXf27rfQHv3vVX7zZw7EZGH2HMnIvJQq4O7iLxVRP5dRJ4SkYMi8nXbbaqTiPSIyISIfM92W+okIi+KyNMickBExm23py4i0i8iD4vIcyLyrIj8pu021UFEVsx+1t3/fiUiN9puV9WMbrPnsTcArFXVV0SkF8C/iMj3VfUJ2w2ryR8BeBbAO2w3xII1qupDzXMW9wD4gap+UkQWAlhku0F1UNVDAC4GZjo0ADoAHrXaqBq0uueuM16Z/bF39r9WDEKIyGIAVwO4z3ZbqHoi8g4Al2HmZvZQ1eOqOmW3VVZ8HMBPVNWVCZSVaXVwB+ZSEwcAvAzgn1T1SdttqsndAP4YwJu2G2KBAvhHEdkvIpttN6Ymvw7gGIC/m03F3SciZ9tulAWbAHzbdiPq0PrgrqqnVPViAIsBrBKRD9huU9VE5LcAvKyq+223xZLVqvphAJ8A8CURucx2g2qwAMCHAfyVqq4E8CqAYbtNqtdsKmo9gIdst6UOrQ/uXbOXqD8EsM5yU+qwGsB6EXkRwA4Aa0XkAbtNqo+qHp39/8uYyb2ustuiWkwCmAxcmT6MmWDfJp8A8CNV/V/bDalDq4O7iAyISP/sv/sAXA7gObutqp6q3qyqi1V1GWYuU/eq6mctN6sWInK2iLy9+28AVwL4T7utqp6q/g+AIyKyYvahjwN4xmKTbLgBLUnJAKyWeQ+Ab86OoJ8F4EFVbVVZYAv9GoBHRQSY+f7/g6r+wG6TavOHAL41m554HsDvWG5PbURkEYArAPye7bbUhTNUiYg81Oq0DBGRrxjciYg8xOBOROQhBnciIg8xuBMReYjBnYjIQwzuREQeYnAnIvLQ/wM4qLlHbhFH2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WHR: DOES TrustGovernmentCorruption MATTER?\n",
    "TrustGovt_X = WHR2017_df.HappinessScore.values.reshape(-1,1)\n",
    "TrustGovt_y = WHR2017_df.TrustGovernmentCorruption.values.reshape(-1,1)\n",
    "TrustGovtplot = plt.scatter(TrustGovt_X,TrustGovt_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WHR: DOES DystopiaResidual MATTER?\n",
    "Dystopia_X = WHR2017_df.HappinessScore.values.reshape(-1,1)\n",
    "Dystopia_y = WHR2017_df.DystopiaResidual.values.reshape(-1,1)\n",
    "DystopiaResidualplot = plt.scatter(Dystopia_X,Dystopia_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Linear Regression Models, Fit the Models, Assign variables to hold coefficients and Y intercepts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "FamilyX_train, FamilyX_test, Familyy_train, Familyy_test = train_test_split(Family_X, Family_y, random_state=1)\n",
    "EconX_train, EconX_test, Econy_train, Econy_test = train_test_split(Econ_X, Econ_y, random_state=1)\n",
    "HealthX_train, HealthX_test, Healthy_train, Healthy_test = train_test_split(Health_X, Health_y, random_state=1)\n",
    "FreedomX_train, FreedomX_test, Freedomy_train, Freedomy_test = train_test_split(Freedom_X, Freedom_y, random_state=1)\n",
    "GenerosityX_train, GenerosityX_test, Generosityy_train, Generosityy_test = train_test_split(Generosity_X, Generosity_y, random_state=1)\n",
    "TrustGovtX_train, TrustGovtX_test, TrustGovty_train, TrustGovty_test = train_test_split(TrustGovt_X, TrustGovt_y, random_state=1)\n",
    "DystopiaX_train, DystopiaX_test, Dystopiay_train, Dystopiay_test = train_test_split(Dystopia_X, Dystopia_y, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5389112272665292\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19eaedbfb38>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "Familymodel = LinearRegression()\n",
    "Familymodel.fit(Familyy_train,FamilyX_train)\n",
    "print(Familymodel.score(Familyy_test, FamilyX_test))\n",
    "\n",
    "y_min = Family_y.min()\n",
    "y_max = Family_y.max()\n",
    "\n",
    "PredictedFamily_x_min = Familymodel.predict([[y_min]])\n",
    "PredictedFamily_x_max = Familymodel.predict([[y_max]])\n",
    "\n",
    "plt.scatter(Family_X,Family_y)\n",
    "plt.plot([PredictedFamily_x_min[0],PredictedFamily_x_max[0]],[y_min,y_max],c=\"red\")\n",
    "\n",
    "# plt.savefig(\"TextFigurePlot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6322149129373889\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19eaf619978>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "Econmodel = LinearRegression()\n",
    "Econmodel.fit(Econy_train,EconX_train)\n",
    "print(Econmodel.score(Econy_test, EconX_test))\n",
    "\n",
    "y_min = Econ_y.min()\n",
    "y_max = Econ_y.max()\n",
    "\n",
    "PredictedEcon_x_min = Econmodel.predict([[y_min]])\n",
    "PredictedEcon_x_max = Econmodel.predict([[y_max]])\n",
    "\n",
    "plt.scatter(Econ_X,Econ_y)\n",
    "plt.plot([PredictedEcon_x_min[0],PredictedEcon_x_max[0]],[y_min,y_max],c=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5562217186162541\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19eb0b52748>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "Healthmodel = LinearRegression()\n",
    "Healthmodel.fit(Healthy_train,HealthX_train)\n",
    "print(Healthmodel.score(Healthy_test, HealthX_test))\n",
    "\n",
    "y_min = Health_y.min()\n",
    "y_max = Health_y.max()\n",
    "\n",
    "PredictedHealth_x_min = Healthmodel.predict([[y_min]])\n",
    "PredictedHealth_x_max = Healthmodel.predict([[y_max]])\n",
    "\n",
    "plt.scatter(Health_X, Health_y)\n",
    "plt.plot([PredictedHealth_x_min[0],PredictedHealth_x_max[0]],[y_min,y_max],c=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002612206207640577\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19eb0afc780>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "Generositymodel = LinearRegression()\n",
    "Generositymodel.fit(Generosityy_train,GenerosityX_train)\n",
    "print(Generositymodel.score(Generosityy_test, GenerosityX_test))\n",
    "\n",
    "y_min = Generosity_y.min()\n",
    "y_max = Generosity_y.max()\n",
    "\n",
    "PredictedGenerosity_x_min = Generositymodel.predict([[y_min]])\n",
    "PredictedGenerosity_x_max = Generositymodel.predict([[y_max]])\n",
    "\n",
    "plt.scatter(Generosity_X, Generosity_y)\n",
    "plt.plot([PredictedGenerosity_x_min[0],PredictedGenerosity_x_max[0]],[y_min,y_max],c=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.34448850446108\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19eb0bb0e80>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "Freedommodel = LinearRegression()\n",
    "Freedommodel.fit(Freedomy_train,FreedomX_train)\n",
    "print(Freedommodel.score(Freedomy_test, FreedomX_test))\n",
    "\n",
    "y_min = Freedom_y.min()\n",
    "y_max = Freedom_y.max()\n",
    "\n",
    "PredictedFreedom_x_min = Freedommodel.predict([[y_min]])\n",
    "PredictedFreedom_x_max = Freedommodel.predict([[y_max]])\n",
    "\n",
    "plt.scatter(Freedom_X, Freedom_y)\n",
    "plt.plot([PredictedFreedom_x_min[0],PredictedFreedom_x_max[0]],[y_min,y_max],c=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Freedom_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08962827446772925\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19eb0bf9a90>]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "TrustGovtmodel = LinearRegression()\n",
    "TrustGovtmodel.fit(TrustGovty_train,TrustGovtX_train)\n",
    "print(TrustGovtmodel.score(TrustGovty_test, TrustGovtX_test))\n",
    "\n",
    "y_min = TrustGovt_y.min()\n",
    "y_max = TrustGovt_y.max()\n",
    "\n",
    "PredictedTrustGovt_x_min = TrustGovtmodel.predict([[y_min]])\n",
    "PredictedTrustGovt_x_max = TrustGovtmodel.predict([[y_max]])\n",
    "\n",
    "plt.scatter(TrustGovt_X, TrustGovt_y)\n",
    "plt.plot([PredictedTrustGovt_x_min[0],PredictedTrustGovt_x_max[0]],[y_min,y_max],c=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17722523852451122\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19eb0c816d8>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "Dystopiamodel = LinearRegression()\n",
    "Dystopiamodel.fit(Dystopiay_train,DystopiaX_train)\n",
    "print(Dystopiamodel.score(Dystopiay_test, DystopiaX_test))\n",
    "\n",
    "y_min = Dystopia_y.min()\n",
    "y_max = Dystopia_y.max()\n",
    "\n",
    "PredictedDystopia_x_min = Dystopiamodel.predict([[y_min]])\n",
    "PredictedDystopia_x_max = Dystopiamodel.predict([[y_max]])\n",
    "\n",
    "plt.scatter(Dystopia_X, Dystopia_y)\n",
    "plt.plot([PredictedDystopia_x_min[0],PredictedDystopia_x_max[0]],[y_min,y_max],c=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Family Model Weight Coefficients:  [[0.19114885]]\n",
      "Family Model y-axis intercept:  [0.16548312]\n",
      "Econ Model Weight Coefficients:  [[0.3022205]]\n",
      "Econ Model y-axis intercept:  [-0.63337623]\n",
      "HealthLifeExpectancy Model Weight Coefficients:  [[0.16387395]]\n",
      "HealthLifeExpectancy Model y-axis intercept:  [-0.32604354]\n",
      "Freedom Model Weight Coefficients:  [[0.07559827]]\n",
      "Freedom Model y-axis intercept:  [0.00403136]\n",
      "Generosity Model Weight Coefficients:  [[0.01849795]]\n",
      "Generosity Model y-axis intercept:  [0.14784509]\n",
      "TrustGovernmentCorruption Model Weight Coefficients:  [[0.03856024]]\n",
      "TrustGovernmentCorruption Model y-axis intercept:  [-0.08333207]\n",
      "DystopiaResidual Model Weight Coefficients:  [[0.21011733]]\n",
      "DystopiaResidual Model y-axis intercept:  [0.72526556]\n"
     ]
    }
   ],
   "source": [
    "FamilyCoefficients = Familymodel.coef_\n",
    "FamilyYintercept = Familymodel.intercept_\n",
    "print('Family Model Weight Coefficients: ', Familymodel.coef_)\n",
    "print('Family Model y-axis intercept: ', Familymodel.intercept_)\n",
    "\n",
    "\n",
    "EconCoefficients = Econmodel.coef_\n",
    "EconYintercept = Econmodel.intercept_\n",
    "print('Econ Model Weight Coefficients: ', Econmodel.coef_)\n",
    "print('Econ Model y-axis intercept: ', Econmodel.intercept_)\n",
    "\n",
    "HealthCoefficients = HealthLifeExpectancymodel.coef_\n",
    "HealthYintercept = HealthLifeExpectancymodel.intercept_\n",
    "print('HealthLifeExpectancy Model Weight Coefficients: ', HealthLifeExpectancymodel.coef_)\n",
    "print('HealthLifeExpectancy Model y-axis intercept: ', HealthLifeExpectancymodel.intercept_)\n",
    "\n",
    "\n",
    "FreedomCoefficients = Freedommodel.coef_\n",
    "FreedomYintercept = Freedommodel.intercept_\n",
    "print('Freedom Model Weight Coefficients: ', Freedommodel.coef_)\n",
    "print('Freedom Model y-axis intercept: ', Freedommodel.intercept_)\n",
    "\n",
    "\n",
    "GenerosityCoefficients = Generositymodel.coef_\n",
    "GenerosityYintercept = Generositymodel.intercept_\n",
    "print('Generosity Model Weight Coefficients: ', Generositymodel.coef_)\n",
    "print('Generosity Model y-axis intercept: ', Generositymodel.intercept_)\n",
    "\n",
    "\n",
    "TrustGovtCoefficients = TrustGovernmentCorruptionmodel.coef_\n",
    "TrustGovtYintercept = TrustGovernmentCorruptionmodel.intercept_\n",
    "print('TrustGovernmentCorruption Model Weight Coefficients: ', TrustGovernmentCorruptionmodel.coef_)\n",
    "print('TrustGovernmentCorruption Model y-axis intercept: ', TrustGovernmentCorruptionmodel.intercept_)\n",
    "\n",
    "\n",
    "DystopiaCoefficients = DystopiaResidualmodel.coef_\n",
    "DystopiaYintercept = DystopiaResidualmodel.intercept_\n",
    "print('DystopiaResidual Model Weight Coefficients: ', DystopiaResidualmodel.coef_)\n",
    "print('DystopiaResidual Model y-axis intercept: ', DystopiaResidualmodel.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "WHR2017_df[\"Family_X\"]=Family_X\n",
    "WHR2017_df[\"Family_y\"]=Family_y\n",
    "WHR2017_df[\"Health_X\"]=Health_X\n",
    "WHR2017_df[\"Health_y\"]=Health_y\n",
    "WHR2017_df[\"Econ_X\"]=Econ_X\n",
    "WHR2017_df[\"Econ_y\"]=Econ_y\n",
    "WHR2017_df[\"TrustGovt_X\"]=TrustGovt_X\n",
    "WHR2017_df[\"TrustGovt_y\"]=TrustGovt_y\n",
    "WHR2017_df[\"Freedom_X\"]=Freedom_X\n",
    "WHR2017_df[\"Freedom_y\"]=Freedom_y\n",
    "WHR2017_df[\"Dystopia_X\"]=Dystopia_X\n",
    "WHR2017_df[\"Dystopia_y\"]=Dystopia_y\n",
    "\n",
    "\n",
    "WHR2017_df.to_csv(\"2017_predicted.csv\", index=False)"
   ]
  }
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