0x01-LifeSatisfactionIndex.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b3ce6ea-5917-4666-9bc3-c1ae2dcc5bd1",
   "metadata": {},
   "source": [
    "# Life Satisfaction Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "877c9420-bcd2-42da-aad6-fc6a03932537",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc2cf9ec-9218-4036-8103-edb8fe80bfa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "oecd_bli = pd.read_csv(r\"C:\\Users\\arnol\\Desktop\\DataSets\\lifesat\\oecd_bli_2015.csv\", thousands=',')\n",
    "gdp_per_capita = pd.read_csv(r\"C:\\Users\\arnol\\Desktop\\DataSets\\lifesat\\gdp_per_capita.csv\", thousands=',', delimiter='\\t', encoding='latin1', na_values=\"n/a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a451133-83d3-4533-821d-65d834c4128a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>Country</th>\n",
       "      <th>INDICATOR</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>Measure</th>\n",
       "      <th>INEQUALITY</th>\n",
       "      <th>Inequality</th>\n",
       "      <th>Unit Code</th>\n",
       "      <th>Unit</th>\n",
       "      <th>PowerCode Code</th>\n",
       "      <th>PowerCode</th>\n",
       "      <th>Reference Period Code</th>\n",
       "      <th>Reference Period</th>\n",
       "      <th>Value</th>\n",
       "      <th>Flag Codes</th>\n",
       "      <th>Flags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1</td>\n",
       "      <td>E</td>\n",
       "      <td>Estimated value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZE</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  LOCATION         Country INDICATOR                           Indicator  \\\n",
       "0      AUS       Australia   HO_BASE  Dwellings without basic facilities   \n",
       "1      AUT         Austria   HO_BASE  Dwellings without basic facilities   \n",
       "2      BEL         Belgium   HO_BASE  Dwellings without basic facilities   \n",
       "3      CAN          Canada   HO_BASE  Dwellings without basic facilities   \n",
       "4      CZE  Czech Republic   HO_BASE  Dwellings without basic facilities   \n",
       "\n",
       "  MEASURE Measure INEQUALITY Inequality Unit Code        Unit  PowerCode Code  \\\n",
       "0       L   Value        TOT      Total        PC  Percentage               0   \n",
       "1       L   Value        TOT      Total        PC  Percentage               0   \n",
       "2       L   Value        TOT      Total        PC  Percentage               0   \n",
       "3       L   Value        TOT      Total        PC  Percentage               0   \n",
       "4       L   Value        TOT      Total        PC  Percentage               0   \n",
       "\n",
       "  PowerCode  Reference Period Code  Reference Period  Value Flag Codes  \\\n",
       "0     units                    NaN               NaN    1.1          E   \n",
       "1     units                    NaN               NaN    1.0        NaN   \n",
       "2     units                    NaN               NaN    2.0        NaN   \n",
       "3     units                    NaN               NaN    0.2        NaN   \n",
       "4     units                    NaN               NaN    0.9        NaN   \n",
       "\n",
       "             Flags  \n",
       "0  Estimated value  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "131089e9-5369-4812-b217-c43572f04b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Subject Descriptor</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Country/Series-specific Notes</th>\n",
       "      <th>2015</th>\n",
       "      <th>Estimates Start After</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>599.994</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>3995.383</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4318.135</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4100.315</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>14414.302</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Country                                 Subject Descriptor  \\\n",
       "0          Afghanistan  Gross domestic product per capita, current prices   \n",
       "1              Albania  Gross domestic product per capita, current prices   \n",
       "2              Algeria  Gross domestic product per capita, current prices   \n",
       "3               Angola  Gross domestic product per capita, current prices   \n",
       "4  Antigua and Barbuda  Gross domestic product per capita, current prices   \n",
       "\n",
       "          Units  Scale                      Country/Series-specific Notes  \\\n",
       "0  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "1  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "2  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "3  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "4  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "\n",
       "        2015  Estimates Start After  \n",
       "0    599.994                 2013.0  \n",
       "1   3995.383                 2010.0  \n",
       "2   4318.135                 2014.0  \n",
       "3   4100.315                 2014.0  \n",
       "4  14414.302                 2011.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_per_capita.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ed9bc67b-b007-4d27-b535-8a8a9bc1abc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
    "    oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
    "    oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
    "    gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
    "    gdp_per_capita.set_index(\"Country\", inplace=True)\n",
    "    full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
    "                                  left_index=True, right_index=True)\n",
    "    full_country_stats.sort_values(by=\"GDP per capita\", inplace=True)\n",
    "    remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
    "    keep_indices = list(set(range(36)) - set(remove_indices))\n",
    "    return full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e59dfc19-ada6-4197-bc0f-1545cc2ed8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Preparing the data\n",
    "\n",
    "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
    "X = np.c_[country_stats[\"GDP per capita\"]]\n",
    "y = np.c_[country_stats[\"Life satisfaction\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c1c11b1-da73-40dd-8d4a-ac5e740fcde4",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualizing the data\n",
    "\n",
    "country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5b5769b-59a2-4426-b6ba-5a86f41c4dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Select a linear model\n",
    "\n",
    "model = sklearn.linear_model.LinearRegression()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "50c9fef6-1d12-4ab7-8030-053ebd60be3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.96242338]]\n"
     ]
    }
   ],
   "source": [
    "# Make a prediction fot Cyprus\n",
    "X_new = [[22587]]  # Cyprus per capita\n",
    "print(model.predict(X_new))"
   ]
  }
 ],
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