Assignment notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Module%206/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Analyzing Where Do People Drink?\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Be confident about your data analysis skills\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is from the story: <a href=https://fivethirtyeight.com/features/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01>  Dear Mona Followup: Where Do People Drink The Most Beer, Wine And Spirits? </a>  The dataset contains average serving sizes per person such as average wine, spirit, beer servings, as well as several other metrics. You will be asked to analyze the data and predict the total liters served given the servings. At the end of the lab, you will be instructed on how you can share your notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will need the following libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/oscarbello/Documents/python/IBM Python Data Science'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Importing the Data</b>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the csv:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('drinks.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the method  <code>head()</code>  to display the first 5 columns of the dataframe:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 1</b>:  Display the data types of each column using the attribute `dtype`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                     int64\n",
       "spirit_servings                   int64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dfRev = df.drop([\"beer_servings\", \"spirit_servings\", \"total_litres_of_pure_alcohol\"], axis = 1, inplace = True)\n",
    "#dfRev.head()\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 2</b> Use the method <code>groupby</code> to get the number of wine servings per continent:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              country  wine_servings      continent\n",
      "0         Afghanistan              0           Asia\n",
      "1             Albania             54         Europe\n",
      "2             Algeria             14         Africa\n",
      "3             Andorra            312         Europe\n",
      "4              Angola             45         Africa\n",
      "5   Antigua & Barbuda             45  North America\n",
      "6           Argentina            221  South America\n",
      "7             Armenia             11         Europe\n",
      "8           Australia            212        Oceania\n",
      "9             Austria            191         Europe\n",
      "10         Azerbaijan              5         Europe\n",
      "11            Bahamas             51  North America\n",
      "12            Bahrain              7           Asia\n",
      "13         Bangladesh              0           Asia\n",
      "14           Barbados             36  North America\n",
      "15            Belarus             42         Europe\n",
      "16            Belgium            212         Europe\n",
      "17             Belize              8  North America\n",
      "18              Benin             13         Africa\n",
      "19             Bhutan              0           Asia\n"
     ]
    }
   ],
   "source": [
    "df_wineContinent = df.drop([\"beer_servings\", \"spirit_servings\", \"total_litres_of_pure_alcohol\"], axis = 1)\n",
    "df_wineContinent.groupby([\"wine_servings\", \"continent\"], as_index = False)\n",
    "\n",
    "#df_wineContinent.drop([\"beer_servings\", \"spirit_servings\", \"total_litres_of_pure_alcohol\"], axis = 1, inplace = True)\n",
    "\n",
    "print(df_wineContinent.head(20))\n",
    "#print(df_wineContinent.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 3:</b> Perform a statistical summary and analysis of beer servings for each continent:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>beer_servings</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>193</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>21</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>261</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>279</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             country  beer_servings      continent\n",
       "0        Afghanistan              0           Asia\n",
       "1            Albania             89         Europe\n",
       "2            Algeria             25         Africa\n",
       "3            Andorra            245         Europe\n",
       "4             Angola            217         Africa\n",
       "5  Antigua & Barbuda            102  North America\n",
       "6          Argentina            193  South America\n",
       "7            Armenia             21         Europe\n",
       "8          Australia            261        Oceania\n",
       "9            Austria            279         Europe"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfBeerCont = df.drop([\"spirit_servings\", \"wine_servings\", \"total_litres_of_pure_alcohol\"], axis = 1)\n",
    "dfBeerCont.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Africa           53\n",
      "Europe           45\n",
      "Asia             44\n",
      "North America    23\n",
      "Oceania          16\n",
      "South America    12\n",
      "Name: continent, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dfBeerCont['continent'].value_counts())\n",
    "#print(dfBeerCont['country'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dfBeerCont['continent'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistical summary for ASIA:         beer_servings\n",
      "count      44.000000\n",
      "mean       37.045455\n",
      "std        49.469725\n",
      "min         0.000000\n",
      "25%         4.250000\n",
      "50%        17.500000\n",
      "75%        60.500000\n",
      "max       247.000000 \n",
      "\n",
      "Statistical summary for EUROPE:         beer_servings\n",
      "count      45.000000\n",
      "mean      193.777778\n",
      "std        99.631569\n",
      "min         0.000000\n",
      "25%       127.000000\n",
      "50%       219.000000\n",
      "75%       270.000000\n",
      "max       361.000000 \n",
      "\n",
      "Statistical summary for AFRICA:         beer_servings\n",
      "count      53.000000\n",
      "mean       61.471698\n",
      "std        80.557816\n",
      "min         0.000000\n",
      "25%        15.000000\n",
      "50%        32.000000\n",
      "75%        76.000000\n",
      "max       376.000000 \n",
      "\n",
      "Statistical summary for OCEANIA:         beer_servings\n",
      "count      16.000000\n",
      "mean       89.687500\n",
      "std        96.641412\n",
      "min         0.000000\n",
      "25%        21.000000\n",
      "50%        52.500000\n",
      "75%       125.750000\n",
      "max       306.000000 \n",
      "\n",
      "Statistical summary for N. AMERICA:         beer_servings\n",
      "count      23.000000\n",
      "mean      145.434783\n",
      "std        79.621163\n",
      "min         1.000000\n",
      "25%        80.000000\n",
      "50%       143.000000\n",
      "75%       198.000000\n",
      "max       285.000000 \n",
      "\n",
      "Statistical summary for S. AMERICA:         beer_servings\n",
      "count      12.000000\n",
      "mean      175.083333\n",
      "std        65.242845\n",
      "min        93.000000\n",
      "25%       129.500000\n",
      "50%       162.500000\n",
      "75%       198.000000\n",
      "max       333.000000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "beerAsia = dfBeerCont['continent'] == 'Asia'\n",
    "beerEurope = dfBeerCont['continent'] == 'Europe'\n",
    "beerAfrica = dfBeerCont['continent'] == 'Africa'\n",
    "beerOceania = dfBeerCont['continent'] == 'Oceania'\n",
    "beerNorthAmer = dfBeerCont['continent'] == 'North America'\n",
    "beerSouthAmer = dfBeerCont['continent'] == 'South America'\n",
    "\n",
    "df_asia = dfBeerCont[beerAsia]\n",
    "df_europe = dfBeerCont[beerEurope]\n",
    "df_africa = dfBeerCont[beerAfrica]\n",
    "df_oceania = dfBeerCont[beerOceania]\n",
    "df_namerica = dfBeerCont[beerNorthAmer]\n",
    "df_samerica = dfBeerCont[beerSouthAmer]\n",
    "\n",
    "print(\"Statistical summary for ASIA: \", df_asia.describe(), '\\n')\n",
    "print(\"Statistical summary for EUROPE: \", df_europe.describe(), '\\n')\n",
    "print(\"Statistical summary for AFRICA: \", df_africa.describe(), '\\n')\n",
    "print(\"Statistical summary for OCEANIA: \", df_oceania.describe(), '\\n')\n",
    "print(\"Statistical summary for N. AMERICA: \", df_namerica.describe(), '\\n')\n",
    "print(\"Statistical summary for S. AMERICA: \", df_samerica.describe(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(df_asia.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Africa  Asia  Europe  North America  Oceania  South America\n",
       "0       0     1       0              0        0              0\n",
       "1       0     0       1              0        0              0\n",
       "2       1     0       0              0        0              0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oneHot = pd.get_dummies(df['continent'])\n",
    "df_oneHot.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfREV = df.drop([\"country\", \"spirit_servings\", \"wine_servings\", \"total_litres_of_pure_alcohol\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>beer_servings</th>\n",
       "      <th>continent</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beer_servings continent  Africa  Asia  Europe  North America  Oceania  \\\n",
       "0              0      Asia       0     1       0              0        0   \n",
       "1             89    Europe       0     0       1              0        0   \n",
       "\n",
       "   South America  \n",
       "0              0  \n",
       "1              0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dfREV.head(2)\n",
    "dfBeer = pd.concat([dfREV, df_oneHot], axis = 1)\n",
    "dfBeer.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson:  -0.27256217135297545  P-value:  0.00012565689184767227  for Africa vs Beer servings | STRONG Negative correlation + High certainty\n",
      "Pearson:  -0.37230454319014533  P-value:  9.738521337184028e-08  for Asia vs Beer servings | STRONG Negative correlation + Null certainty\n",
      "Pearson:  0.14319839177151114  P-value:  0.046955287994502985  for North America vs Beer servings | STRONG Positive correlation + Moderate certainty\n",
      "Pearson:  0.17591604786184548  P-value:  0.014399379091701347  for South America vs Beer servings | STRONG Positive correlation + Moderate certainty\n",
      "Pearson:  0.4789126627161972  P-value:  1.8526970706882204e-12  for Europe vs Beer servings | STRONG Positive correlation + VERY STRONG certainty\n",
      "Pearson:  -0.049095426307879234  P-value:  0.4977502539336095  for Oceania vs Beer servings | STRONG Negative correlation + Null certainty\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "#df_asia, df_europe, df_africa, df_oceania, df_namerica, df_samerica\n",
    "#use original dataframe instead to correlate continent vs beer servings\n",
    "\n",
    "#Pearson correlation and p-value for continent vs beer servings:\n",
    "pearsonAfrica, p_valAfrica = stats.pearsonr(dfBeer['Africa'], dfBeer['beer_servings'])\n",
    "pearsonAsia, p_valAsia = stats.pearsonr(dfBeer['Asia'], dfBeer['beer_servings'])\n",
    "pearsonNAmer, p_valNAmer = stats.pearsonr(dfBeer['North America'], dfBeer['beer_servings'])\n",
    "pearsonSAmer, p_valSAmer = stats.pearsonr(dfBeer['South America'], dfBeer['beer_servings'])\n",
    "pearsonEurope, p_valEurope = stats.pearsonr(dfBeer['Europe'], dfBeer['beer_servings'])\n",
    "pearsonOceania, p_valOceania = stats.pearsonr(dfBeer['Oceania'], dfBeer['beer_servings'])\n",
    "\n",
    "print(\"Pearson: \", pearsonAfrica, \" P-value: \", p_valAfrica, \" for Africa vs Beer servings | STRONG Negative correlation + High certainty\")\n",
    "print(\"Pearson: \", pearsonAsia, \" P-value: \", p_valAsia, \" for Asia vs Beer servings | STRONG Negative correlation + Null certainty\")\n",
    "print(\"Pearson: \", pearsonNAmer, \" P-value: \", p_valNAmer, \" for North America vs Beer servings | STRONG Positive correlation + Moderate certainty\")\n",
    "print(\"Pearson: \", pearsonSAmer, \" P-value: \", p_valSAmer, \" for South America vs Beer servings | STRONG Positive correlation + Moderate certainty\")\n",
    "print(\"Pearson: \", pearsonEurope, \" P-value: \", p_valEurope, \" for Europe vs Beer servings | STRONG Positive correlation + VERY STRONG certainty\")\n",
    "print(\"Pearson: \", pearsonOceania, \" P-value: \", p_valOceania, \" for Oceania vs Beer servings | STRONG Negative correlation + Null certainty\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 4:</b> Use the function boxplot in the seaborn library to produce a plot that can be used to show the number of beer servings on each continent.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='continent', ylabel='beer_servings'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t2sWKFStqHJFZ/cqTGD5FdnHb9RGxRtLrgNuKCcusuhYsWEBDQ3ZIrKGhgZNOOqnGEZnVrzxnJd0REadGxCVp/JGIeLEDc0k+Q8nqVmtrK73dvU+YMIHW1tYh5jAbv4Zza8+BvL2KdZlVVWNjIwsXLkQSCxcuZOrUqbUOyaxu5eld1WxUa21tZf369W4tFKi7u5tndmwbVfc42LpjE9G9s9Zh1BUnBhs3Ghsbuewy7/E0G0o1E8PouSWVmRWiubkZPb9l1N3BbUazdy2WqigxSJoIXBIRnxtksq9XJySzyuS98Xx3dzeQ/XlVwjeIt/GqosQQEXvSTXoGm2ZpVSIyK8jOnd6PbFaJPLuS7pO0DPgX0j0ZACLiRwPNIOkq4BRgU0S8IZWdD3wc2Jwm+2JE3JxeOw84B9gDLImIW3LEZ+NM3q353unb2tqKCMdszMiTGA4EtgAnlpQFMGBiIOtL6Z+Aq/uUX5p6a32RpLnA2WTdbLwa+DdJR0XEnhwxmpnZfspzz+eP5a08Iu6QNKvCyU8Dro2I54HfSeoCjgP+Pe/7mpnZ8OXpdvsoSbdKWp3Gj5H0pWG+7ycldUq6StJhqWwG8HjJNN2prL9YFknqkNSxefPm/iYxM7NhynPl87fI+kraBRARnWS7fvK6HDgSmAdsBL6at4KIuCIiWiKipampaRghmJnZQPIkhoMi4q4+ZbvzvmFEPBkReyJiL1myOS69tAE4omTS5lRmZmYjKM/B5x5JR5IdcEbSmWRb/LlImh4RvfOdAaxOw8uA70n6GtnB5zlA30RkNqp1d3ezDbgy+xmNChuB7ekaEBsf8iSGc4ErgKMlbQB+B/zpYDNI+j4wH2iU1A18GZgvaR5ZglkPfAIgdeV9HbCWrCVy7kickZTnIilfIGVm40Ges5IeAd6b7v08ISK2VTDPh/opvnKQ6S8CLqo0ppHmC6RsfzU3N7O1p4dzRlEPMlcSTKlwY8jGhooTg6RpwP8CXh0RC9N1BydExIB/9KNBni16XyBlZuNBnoPPS4FbyPb/A/wW+HSV4zEzsxrLkxgaI+I6YC9AROwm67rCzMzGkDyJ4feSprLvrKTjgWcKicrMzGomz1lJnyE7pfR1kn4JNAFnFhKVmY1aW3dsqvod3LY/9zQAkw88bIgp89u6YxMz8P0YSuVJDGuB64EdwDbgx2THGWyEPbZ9Ihd2TK56vU/uyBqQ0w7aW9V6H9s+kaOqWqPVq9mzZxdS77p1TwEw48jq/4HPYGphcY9WeRLD1cCzZGcmAfwJcA3wwWoHZQMrcgV+Yd06AA6cNaeq9R5FsXFb/Sjqup16OSMw782hKrUu/faK+PyGcz1VnsTwhoiYWzJ+m6S1ud7N9luRF8zVy4/PrF51dXWx+v77OeRl1bwrMuzenZ3H8+iDa6pa77YXcvdaBORLDPdKOj4i7gSQ9FagY1jvamY2Sh3ysgM4blr1j3UU4a4nnx7WfEMmBkkPkJ2J1AD8StJjafw1wEPDelczM6tblbQYTik8CjMzqxtDJoaIeHQkAjEzs/qQ5wI3MzMbB5wYzMysjBODmZmVcWIwM7MyTgxmZlam0MQg6SpJmyStLik7XNJKSevS82GpXJLaJHVJ6pT0piJjMzOz/hXdYlgKnNyn7AvArRExB7g1jQMsBOakxyLg8oJjMzOzfhSaGCLiDuCpPsWnAe1puB04vaT86sjcCUyRNL3I+MzM7KVqcYxhWkRsTMNPANPS8Azg8ZLpulPZS0haJKlDUsfmzZuLi9TMbByq6cHniAjSHeFyzndFRLREREtTU1MBkZmZjV+1SAxP9u4iSs+bUvkG4IiS6ZpTmZmZjaDqdipemWVAK3Bxer6hpPyTkq4F3go8U7LLyczGmDw3vcl7I5vh3JzG9ik0MUj6PjAfaJTUDXyZLCFcJ+kc4FHgrDT5zcD7gC6y24d+rMjYzGz0mDRpUq1DGFcKTQwR8aEBXnpPP9MGcG6R8ZhZ/fAWff3ylc9mZlbGicHMzMo4MZiZWRknBjMzK1OL01XNxrUngCvzX9c5qC3peWpVa808AUwpoF6rX04MZiNo9uzZhdS7OZ3nP2XOnKrXPYXi4rb65MRgdSXPRU955b1IKo9KL6gq6hTN3nrb2toKqd/GFycGqytdXV3ct+a+YvZd7M2e7ttwX3Xr3Vrd6sxqzYnB6s8U2Dt/b62jqNiE230Oh40tXqPNzKyME4OZmZVxYjAzszJODGZmVsaJwczMyozJs5KKOhe+Hs6DNzMr2phMDF1dXdz3wFr2HnR4VevVC1k3Bvc8/ERV652w46mq1mdmtj/GZGIA2HvQ4Tw395Rah1GRA9feVOsQzMxeVLPEIGk9sA3YA+yOiBZJhwP/DMwC1gNnRcTTtYrRzGw8qnWL4d0R0VMy/gXg1oi4WNIX0vhf1yY0M7Ny3d3dbHthN3c9OTq2V7e9sJvu7u7c89XbWUmnAe1puB04vXahmJmNT7VsMQSwQlIA34yIK4BpEbExvf4EMK1m0ZmZ9dHc3Myebc9w3LTDah1KRe568mmam5tzz1fLxPCOiNgg6ZXASkkPlb4YEZGSxktIWgQsApg5c2bxkZqZjSM125UUERvS8ybgeuA44ElJ0wHS86YB5r0iIloioqWpqWmkQjYzGxdqkhgkHSzpkN5h4CRgNbAMaE2TtQI31CI+M7PxrFa7kqYB10vqjeF7EfETSXcD10k6B3gUOKtG8ZmZjVs1SQwR8QhwbD/lW4D3jHxEZmbWq95OVzUzsxpzYjAzszJODGZmVqbWXWKYlenu7oZnYMLto2ibZSt0R/5uB8zq1Sj69ZmZ2Uhwi8HqSnNzM5u1mb3z99Y6lIpNuH0CzTPydztgVq/cYjAzszJODGZmVsaJwczMyvgYg5lZDkXcqGfH7j0AHHTAxKrWu+2F3cOaz4nBzKxCs2fPLqTedevWAfCaOXOqXvdwYh6TiaG7u5sJO57hwLU31TqUikzYsYXu7uFldjMbOUuWLCm03ra2tkLqz2tMJgYb5bYWdIHb9vQ8ucr1bgVmVLlOsxoak4mhubmZJ58/gOfmnlLrUCpy4NqbaG5+Va3DqAtFNdVhX3N9zowqN9dnFBu32Ugbk4nBRq+imuqldddLc92sXvl0VTMzK+PEYGZmZZwYzMysTN0dY5B0MvB1YCLw7Yi4eDj1TNjxVNVPV9VzzwIQBx5a1Xon7HgK8MFnM6sPdZUYJE0E/g+wAOgG7pa0LCLW5qmnuItQtgEw58hq/4m/yme12Eu0tbXR1dVV0bS9Z1zlOXg/e/bsQg/22+hVV4kBOA7oiohHACRdC5wG5EoMeVb2PD++vOrhh1fkn0utly/vdzfali+PSZMm1ToE62M0//bqLTHMAB4vGe8G3tp3IkmLgEUAM2fOHJnIGPs/Pi9ffRktScn2X72tm4qIWsfwIklnAidHxJ+n8Y8Ab42ITw40T0tLS3R0dIxUiGZmY4KkeyKipb/X6u2spA3AESXjzanMzMxGSL0lhruBOZJeK+llwNnAshrHZGY2rtTVMYaI2C3pk8AtZKerXhURa2oclpnZuFJXiQEgIm4Gbq51HGZm41W97UoyM7Mac2IwM7MyTgxmZlbGicHMzMrU1QVuwyFpM/DoCL5lI9Azgu830rx8o9dYXjbw8lXbayKiqb8XRn1iGGmSOga6WnAs8PKNXmN52cDLN5K8K8nMzMo4MZiZWRknhvyuqHUABfPyjV5jednAyzdifIzBzMzKuMVgZmZlnBjMzKyME0Mfkk6XFJKOHmK6myVNGaGwKiJpj6RVJY8v1DqmIvX9riQ1Sfq1pPskvbOf6b8taW6VYwhJXy0Z/5yk83PWMV/S20rGl6abVlUyb0Xr63BJ+htJD0vqSevUS+6oWGE9dbuM6T1aJLUNY75mSTdIWpc+p6+nWwYUStKrJf2gqPqdGF7qQ8Av0vOAIuJ9EbF1RCKq3M6ImFfyuLjSGSXVXU+7Fej7Xb0HeCAi3hgRPy+dUNLEiPjziMh1//AKPA98QFLjcGZOn/t84G1DTDqQitbX4ZB0AnAKMDciGoH3Un7r3TzmU4fLCNl3EBEdEZHrXqqSBPwI+HFEzAGOAiYDFxUQZpmI+I+IqCixDvcN/EgPsi91A9kX/JtUNh24A1gFrAbemcrXA41p+MfAPcAaYFEN498+QHlprC3A7Wn4fOAa4JfA94FZwE+BTuBWYGaabinwf4EO4LfAKal8IvAPZDdY6gQ+UavvCpgHPAZsTt/VJGA78FXgfuAdwO1AS5r/ZODe9Nqtqew44N+B+4BfAa+v5DMHzgMuSuOfA85Pw0N9nr8m+2N5Ii3LKuCd6fW2FMMjwJmVrq+pfD7wM+CGNP/FwIeBu4AHgCPTdE3AD9P3dzfw9j7rxYOp/vnATSXv+Z1U706yXgeuAr6Z1o8XgL8vXdfS51Cvy9i77ve3jA+k7+6PUvnlaRnXABeQbYjc0SfeQ4EtwMHAV8j+MzqBxen1N6e47yG778z0VP7xFN/9Kd6DStaVl3xO6TNdXTL8c7L1+V7gbfv9+6rVn1g9PtKKdWUa/lX6Ej8L/E0qmwgckobXs+/P9vD0PCmtCFNrFP+e9MPrffxxP7H2TQz3AJPS+I1Aaxr+b2RbQr0r50/IWphzgG7gQGAR8KU0zcvTj+a1Nfyu/gz4p5JpAjirZPz2tPxNZFu+r+3z/R0KHJCG3wv8sII4tqf51gOvoDwxDPZ53gRMLPkePldS51LgX9LnPRfoqvQzSMPzga1kGzUvJ/tjvSC99ingH9Pw94B3pOGZwIN91ovGtB49npbvvwCXAJelsqOAw4CrgS+WrGu/BI7hpetaPS7jpJL6ehPDJb3zp/HD+qwnE8nWpYuBS/uJ+b4Uww/Ytz4dDjSkZWhKZX9MdjMyKPnPAC5kXyLp93OiPDEcBByYhucAHfv7+xqNuw+K9CHg62n42jS+DLhKUgPZD3tVP/MtkXRGGj6C7MvZUnCs/dkZEfNyzrMsInam4ROAD6Tha4C/L5nuuojYC6yT9AhwNHAScEzJvuJXkC3774YTfE79fVer+0yzh2zrq6/jybb0fgcQEU+l8lcA7ZLmkCWVhkoCiYhnJV0NLCHbiu412Of5LxGxZ5Bqf5w+77WSpg0wTX+fwT1p/O6I2Agg6WFgRSp/AHh3Gn4vMDfbIwLAoZImp+FlEdEj6c3AYuAvgX8Gnkvjx0bEb1P97cBX0nrwarIt7rlA1yDLVy/LWPp9UTLP2b0jEfF0GjxL0iKyG5xNJ0uCT79k7sx84BsRsTvV8ZSkNwBvAFameCYCG9P0b5B0ITCF7PO7paSuoT6nBuCfJM0jW+ePGiCmijkxJJIOB04E/rOkIPvSAvg88C7gvwJLJX0tIq4umW8+2Yp0QkTskHQ72dZ0PdnNvuNJfWP7fYV19L3gJQCRbdnc0s/0hRnku+p7G9jnhvjz7evvgNsi4gxJs8i2Civ1j2TN+O9UOP1Qn/vzJcPq++JAn4Gkz/cz/96S8b3s+91PAI6PiOf61P1ifBGxR9Iqsl2IS4Fv9xPrq8g2CI4g2x3SQbaeDfU7qItlrISk15K1Bt8SEU9LWgpsItvIKJ3uULKWyfr+qgHWRMQJ/by2FDg9Iu6X9GdkiaXXoJ8T8FfAk8CxZMv7XD/T5OKDz/ucCVwTEa+JiFkRcQTZlu+7gCcj4ltkP4o39ZnvFcDTKSkcTZ8VpU6sJ9vVAvBHg0z3K/ZtKX2YbL9lrw9KmiDpSOB1ZPv1bwH+e2pNIekoSQdXM/ABDPRdHVHh/HcC70o/9t4/IMi+yw1p+M/yBJRaHdcB55QUD/Z5ltoGHJLn/Rj4M3jJ2ViDWEG29Q9A2uKkZPz1qfXUax7wMFlLcZak2ZIOA84CnkmP/wD+ME1fuq7V5TIOYCVwbsk8h5HtLvw98Ezaal8IrAUOkvTRNN1EsmNaS8l+G5/oPakjrWO/AZrSQX0kNUj6g/Q2hwAb02/pwzmWD7L1dmNqVXyELIHuFyeGfT4EXN+n7IdkX/L9ku4j2yf49T7T/AQ4QNKDZPsc7yw4zsFMUvnpqr1nJV0AfF1SB1lTcyCLgY9J6iRbwT5V8tpjZAf2lgN/kbbAvk3247hX0mqyA5Aj0Qod6Ls6r5KZI2Iz2fGRH0m6n2wXCWS7ev53+q6HsxxfJdsv32uwz7PUjcAZ6Tur9E9voM8gz5k7S4AWSZ2S1gJ/0ef1yUA72W/gXWS7hz5A9ie5h2yXzSqy3aa3Ag+R7e8+APgy5etavS5jfy4EDpO0Oq0f746I+8mOHTxEdtzil2naM8g2mtaRtaqeA75I9tt4DOhMdfxJRLxAluwuSWWr2Hem1t+SnYzwy/QeeXwDaE11Hk2OltBA3CWGDSk1m2+KiMLOmzaz+uEWg5mZlXGLwczMyrjFYGZmZZwYzMysjBODmZmVcWIw20+SZkn6k5LxYfXUWcH7nK4q9w5r1h8nBrP9Nwt4MTHEMHrqrNDpZNcSmBXKicHGPUkfTRdA3S/pmtQC+Gkqu1XSzDTdUkltkn4l6ZGSPqIuBt6ZLt76K2X3HrgpzXO+pKsk3Z7mWVLyvn8q6a403zfTlbNI2i7pohTPnZKmKbuXwanAP6TpjxzZT8nGEycGG9dSlwRfAk6MiGPJrk6+DGiPiGOA75J1e9xrOlkX3qeQJQSALwA/j+weGJf28zZHk3UTcRzw5dQVwn8iu5L+7anjwz3s6wrhYODOFM8dwMcj4ldkHTp+Pr3Pw9X5BMxeyp3o2Xh3IllPpz3wYi+Yg/WKWkmPoH39a0Q8DzwvaRMwjawv/zcDd6cO3SaRdcoG2T0NbkrD9wALhrVkZsPkxGCWz1A9XQ41zx6y353IWiX99e+0K/Zdedo7vdmI8a4kG+9+StYJ2lR4sRfMSntF7TWcnkNvBc6U9Mre95X0mgLexyw3JwYb1yJiDdk9en+Weqf8GpX3itqrE9iTDhb/VYXvu5bs2MaK9D4ryY5fDOZa4POS7vPBZyuS+0oyM7MybjGYmVkZJwYzMyvjxGBmZmWcGMzMrIwTg5mZlXFiMDOzMk4MZmZW5v8DdVG8JEB7rYYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.boxplot(x = 'continent', y = 'beer_servings', data = dfBeer)\n",
    "#Europeans drink the most, followed by people from north and south America and Australians.\n",
    "#Outliers from certain African countries suggest that heavy drinking happens in some regions of that continent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 5</b>: Use the function <code> regplot</code> in the seaborn library to determine if the number of wine servings is\n",
    "negatively or positively correlated with the number of beer servings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson  0.5271716935065209 P-Value  3.378401743961718e-15\n"
     ]
    },
    {
     "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": [
    "sns.regplot(x = \"beer_servings\", y = \"wine_servings\", data = df)\n",
    "plt.ylim(0,)\n",
    "\n",
    "pearsonWine, p_valWine = stats.pearsonr(df['beer_servings'], df['wine_servings'])\n",
    "print(\"Pearson \", pearsonWine, \"P-Value \", p_valWine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appears to be a strong positive correlation between the number of beer servings with the number of wine servings, as shown by the \n",
      "regression plot and the correlation coefficients (strong correlation + high certainty of correlation)\n"
     ]
    }
   ],
   "source": [
    "print(\"\"\"There appears to be a strong positive correlation between the number of beer servings with the number of wine servings, as shown by the \n",
    "regression plot and the correlation coefficients (strong correlation + high certainty of correlation)\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Question 6:</b> Fit a linear regression model to predict the <code>'total_litres_of_pure_alcohol'</code> using the number of <code>'wine_servings'</code> then calculate $R^{2}$:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.15407943]\n",
      "[[0.03160757]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "\n",
    "x = df[[\"wine_servings\"]] #predictor variable\n",
    "y = df[[\"total_litres_of_pure_alcohol\"]] #target variable\n",
    "\n",
    "lm.fit(x, y)\n",
    "Yhat = lm.predict(x)\n",
    "#print(Yhat)\n",
    "print(lm.intercept_)\n",
    "print(lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='wine_servings'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "sns.regplot(x = 'wine_servings', y = Yhat, data = df) #there appears to be a positive correlation between wine servings and total alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4456875459787605\n"
     ]
    }
   ],
   "source": [
    "#R-squared value:\n",
    "print(lm.score(x, y)) #R should be between 0 and 1, OK\n",
    "#approximately 44.6% of the total alcohol consumption is accounted for by wine servings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<b>Note:</b> Please use <code>test_size = 0.10</code> and <code>random_state = 0</code> in the following questions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 7: </b>Use list of features to predict the <code>'total_litres_of_pure_alcohol'</code>, split the data into training and testing and determine the $R^2$ on the test data by using the provided code:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 173\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#country\tbeer_servings\tspirit_servings\twine_servings\ttotal_litres_of_pure_alcohol\tcontinent\n",
    "x_data = df.drop(['country', 'continent', 'total_litres_of_pure_alcohol'], axis = 1)\n",
    "xTrain, xTest, yTrain, yTest = train_test_split(x_data, df['total_litres_of_pure_alcohol'], test_size = 0.1, random_state = 0)\n",
    "\n",
    "print(xTest.shape[0], xTrain.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   beer_servings  spirit_servings  wine_servings\n",
      "0              0                0              0\n"
     ]
    }
   ],
   "source": [
    "print(x_data.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "linReg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6990304512837943"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linReg.fit(xTrain, yTrain)\n",
    "linReg.score(xTest, yTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 8 :</b> Create a pipeline object that scales the data, performs a polynomial transform and fits a linear regression model. Fit the object using the training data in the question above, then calculate the R^2 using the test data. Take a screenshot of your code and the $R^{2}$. There are some hints in the notebook:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>'scale'</code>\n",
    "\n",
    "<code>'polynomial'</code>\n",
    "\n",
    "<code>'model'</code>\n",
    "\n",
    "The second element in the tuple contains the model constructor\n",
    "\n",
    "<code>StandardScaler()</code>\n",
    "\n",
    "<code>PolynomialFeatures(include_bias=False)</code>\n",
    "\n",
    "<code>LinearRegression()</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pr = PolynomialFeatures(include_bias = False)\n",
    "#pr.fit_transform(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SCALE = StandardScaler()\n",
    "#SCALE.fit(x_data)\n",
    "#x_scale=SCALE.transform(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Input = [('scale',StandardScaler()), ('polynomial',PolynomialFeatures(include_bias = False)), ('model', LinearRegression())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(Input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scale', StandardScaler()),\n",
       "                ('polynomial', PolynomialFeatures(include_bias=False)),\n",
       "                ('model', LinearRegression())])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(xTrain[[\"beer_servings\", \"spirit_servings\", \"wine_servings\"]], yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "yHatPipeline = pipe.predict(xTrain[[\"beer_servings\", \"spirit_servings\", \"wine_servings\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.21017029 0.9400267  0.67483964 1.46189861 1.73360873]\n"
     ]
    }
   ],
   "source": [
    "print(yHatPipeline[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xR = x_data[[\"beer_servings\", \"spirit_servings\", \"wine_servings\"]]\n",
    "yR = y\n",
    "linM = LinearRegression()\n",
    "linM.fit(xTest, yTest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7067463833426811"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linM.score(xTest, yTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6990304512837944, 0.707637613388604, 0.7152842453015493, 0.734607828473983]\n"
     ]
    }
   ],
   "source": [
    "rSquareTest = []\n",
    "order = [1, 2, 3, 4]\n",
    "for n in order:\n",
    "    polyReg = PolynomialFeatures(degree = n)\n",
    "    xTrainPR = polyReg.fit_transform(xTrain)\n",
    "    xTestPR = polyReg.fit_transform(xTest)\n",
    "    linM.fit(xTrainPR, yTrain)\n",
    "    rSquareTest.append(linM.score(xTestPR, yTest))\n",
    "\n",
    "print(rSquareTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 9</b>: Create and fit a Ridge regression object using the training data, setting the regularization parameter to 0.1 and calculating the $R^{2}$ using the test data. Take a screenshot of your code and the $R^{2}$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge(alpha=0.1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8741728224192626"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "RidgeModel = Ridge(alpha = 0.1)\n",
    "RidgeModel.fit(xR, y)\n",
    "print(RidgeModel)\n",
    "\n",
    "RidgeModel.score(xR, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 10</b>: Perform a 2nd order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data and setting the regularization parameter to 0.1. Calculate the $R^{2}$ utilizing the test data provided. Take a screenshot of your code and the $R^{2}$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polyRFinal = PolynomialFeatures(degree = 2)\n",
    "xTrainFinal = polyRFinal.fit_transform(xTrain)\n",
    "xTestFinal = polyRFinal.fit_transform(xTest)\n",
    "\n",
    "ridgeFinal = Ridge(alpha = 0.1)\n",
    "ridgeFinal.fit(xTrainFinal, yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7076376228095796"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgeFinal.score(xTestFinal, yTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/share-notebooks.html/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\"> CLICK HERE</a> to see how to share your notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Sources</b>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=https://fivethirtyeight.com/features/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01> Dear Mona Followup: Where Do People Drink The Most Beer, Wine And Spirits?</a> by By Mona Chalabi. You can download the dataset <a href=https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01>here</a>.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "## Change Log\n",
    "\n",
    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
    "|---|---|---|---|\n",
    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab  |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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