Notebooks/C1_N1_Predicting Life Satisfaction using GDP per capita.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 1 – The Machine Learning landscape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code Example 1-1: Predicting Life Satisfaction using GDP per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.__version__ >= \"0.20\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function just merges the OECD's life satisfaction data and the IMF's GDP per capita data. It's a bit too long and boring and it's not specific to Machine Learning, which is why I left it out of the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "The code in the book expects the data files to be located in the current directory. I just tweaked it here to fetch the files in datasets/lifesat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "datapath = os.path.join('datasets', 'lifesat', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# To plot pretty figures directly within Jupyter\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading oecd_bli_2015.csv\n",
      "Downloading gdp_per_capita.csv\n"
     ]
    }
   ],
   "source": [
    "# Download the data\n",
    "import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
    "\n",
    "os.makedirs(datapath, exist_ok=True)\n",
    "\n",
    "for filename in ('oecd_bli_2015.csv', 'gdp_per_capita.csv'):\n",
    "    print('Downloading', filename)\n",
    "    url = DOWNLOAD_ROOT + 'datasets/lifesat/' + filename\n",
    "    urllib.request.urlretrieve(url, datapath + filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "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": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the data\n",
    "oecd_bli = pd.read_csv(datapath + 'oecd_bli_2015.csv', \n",
    "                       thousands=',')\n",
    "\n",
    "gdp_per_capita = pd.read_csv(datapath + 'gdp_per_capita.csv',\n",
    "                            thousands=',',\n",
    "                            delimiter='\\t',\n",
    "                            encoding='latin1',\n",
    "                            na_values='n/a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare the data\n",
    "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
    "\n",
    "X = np.c_[country_stats['GDP per capita']]\n",
    "y = np.c_[country_stats['Life satisfaction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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6wii9eyHg7UD29tPjJP1LvXMSx/sIcFZEXJ8u31+l3PHAhRFxM4Cks4Gv0ICE4SaqVou/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\nH1WhRLmX8YxSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e3ded68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data\n",
    "country_stats.plot(kind='scatter',\n",
    "                   x='GDP per capita',\n",
    "                   y='Life satisfaction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Select a linear model\n",
    "model = sklearn.linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.96242338]]\n"
     ]
    }
   ],
   "source": [
    "# Make a prediction for Cyprus\n",
    "X_new = [[22587]] # Cyprus' GDP per capita\n",
    "print(model.predict(X_new))"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}