7. Group Assignment & Presentation¶

You should be able to start up on this exercise after Lecture 1.

This exercise must be a group effort. That means everyone must participate in the assignment.

In this assignment you will solve a data science problem end-to-end, pretending to be recently hired data scientists in a company. To help you get started, we've prepared a checklist to guide you through the project. Here are the main steps that you will go through:

  1. Frame the problem and look at the big picture
  2. Get the data
  3. Explore and visualise the data to gain insights
  4. Prepare the data to better expose the underlying data patterns to machine learning algorithms
  5. Explore many different models and short-list the best ones
  6. Fine-tune your models
  7. Present your solution

In each step we list a set of questions that one should have in mind when undertaking a data science project. The list is not meant to be exhaustive, but does contain a selection of the most important questions to ask. We will be available to provide assistance with each of the steps, and will allocate some part of each lesson towards working on the projects.

Your group must submit a single Jupyter notebook, structured in terms of the first 6 sections listed above (the seventh will be a video uploaded to some streaming platform, e.g. YouTube, Vimeo, etc.).

1. Analysis: Frame the problem and look at the big picture¶

  1. Find a problem/task that everyone in the group finds interesting
  2. Define the objective in business terms
  3. How should you frame the problem (supervised/unsupervised etc.)?
  4. How should performance be measured?
  1. The problem we will discuss is the reason some countries are considered happier than others.
  2. Find if there is association between happiness and the features (Economy, Family, Health, Freedom, Trust, Generosity, Beer, Spirit and Wine consumption)
  3. Supervised learning because the data has a right answer, how happy each country is. It is continuous cause happiness has inifinite possible values.
  4. By looking at the accuracy of various machine learning algorithms.

2. Get the data¶

  1. Find and document where you can get the data from
  2. Get the data
  3. Check the size and type of data (time series, geographical etc)

There are two datasets we will use. One has the hapiness score, and data that usually are used to explain why a country's happiness is higher, like economy and health. The other database has the alcohol per capita consumption by country. Both of the datasets results were created in 2016. The datasets url: https://www.kaggle.com/datasets/cssouza91/hapiness?select=2016.csv

https://www.kaggle.com/datasets/marcospessotto/happiness-and-alcohol-consumption

In [1]:
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn
from scipy import stats
from sklearn.preprocessing import scale
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV
from sklearn.linear_model import ElasticNet, ElasticNetCV
from sklearn.metrics import mean_squared_error
import plotly.graph_objs as go
from plotly.offline import iplot

from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import AdaBoostRegressor
In [2]:
happiness = pd.read_csv("2016.csv")
happiness.head()
Out[2]:
Country Region Happiness Rank Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual
0 Denmark Western Europe 1 7.526 7.460 7.592 1.44178 1.16374 0.79504 0.57941 0.44453 0.36171 2.73939
1 Switzerland Western Europe 2 7.509 7.428 7.590 1.52733 1.14524 0.86303 0.58557 0.41203 0.28083 2.69463
2 Iceland Western Europe 3 7.501 7.333 7.669 1.42666 1.18326 0.86733 0.56624 0.14975 0.47678 2.83137
3 Norway Western Europe 4 7.498 7.421 7.575 1.57744 1.12690 0.79579 0.59609 0.35776 0.37895 2.66465
4 Finland Western Europe 5 7.413 7.351 7.475 1.40598 1.13464 0.81091 0.57104 0.41004 0.25492 2.82596
In [3]:
print("It has {} rows and {} columns.".format(happiness.shape[0], happiness.shape[1]))
happiness.dtypes

It has 157 rows and 13 columns.
Out[3]:
Country                           object
Region                            object
Happiness Rank                     int64
Happiness Score                  float64
Lower Confidence Interval        float64
Upper Confidence Interval        float64
Economy (GDP per Capita)         float64
Family                           float64
Health (Life Expectancy)         float64
Freedom                          float64
Trust (Government Corruption)    float64
Generosity                       float64
Dystopia Residual                float64
dtype: object

The dataset has data from 2016 like the happiness dataset. There are a few duplicate columns, which we are not interested in keeping, so we drop them before merging.

In [4]:
alcoholCSV = pd.read_csv("HappinessAlcoholConsumption.csv")
alcoholCSV = alcoholCSV.drop(["Region", "Hemisphere", "HappinessScore", "HDI", "GDP_PerCapita"], axis=1)
alcoholCSV.head()
Out[4]:
Country Beer_PerCapita Spirit_PerCapita Wine_PerCapita
0 Denmark 224 81 278
1 Switzerland 185 100 280
2 Iceland 233 61 78
3 Norway 169 71 129
4 Finland 263 133 97
In [5]:
print("It has {} rows and {} columns.".format(alcoholCSV.shape[0], alcoholCSV.shape[1]))
alcoholCSV.dtypes

It has 122 rows and 4 columns.
Out[5]:
Country             object
Beer_PerCapita       int64
Spirit_PerCapita     int64
Wine_PerCapita       int64
dtype: object
In [6]:
combinedDatasets = happiness.merge(alcoholCSV, left_on="Country", right_on="Country")
combinedDatasets.head()
Out[6]:
Country Region Happiness Rank Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Beer_PerCapita Spirit_PerCapita Wine_PerCapita
0 Denmark Western Europe 1 7.526 7.460 7.592 1.44178 1.16374 0.79504 0.57941 0.44453 0.36171 2.73939 224 81 278
1 Switzerland Western Europe 2 7.509 7.428 7.590 1.52733 1.14524 0.86303 0.58557 0.41203 0.28083 2.69463 185 100 280
2 Iceland Western Europe 3 7.501 7.333 7.669 1.42666 1.18326 0.86733 0.56624 0.14975 0.47678 2.83137 233 61 78
3 Norway Western Europe 4 7.498 7.421 7.575 1.57744 1.12690 0.79579 0.59609 0.35776 0.37895 2.66465 169 71 129
4 Finland Western Europe 5 7.413 7.351 7.475 1.40598 1.13464 0.81091 0.57104 0.41004 0.25492 2.82596 263 133 97

3. Explore the data¶

  1. Create a copy of the data for explorations (sampling it down to a manageable size if necessary)
  2. Create a Jupyter notebook to keep a record of your data exploration
  3. Study each feature and its characteristics:
    • Name
    • Type (categorical, int/float, bounded/unbounded, text, structured, etc)
    • Percentage of missing values
    • Check for outliers, rounding errors etc
  4. For supervised learning tasks, identify the target(s)
  5. Visualise the data
  6. Study the correlations between features
  7. Identify the promising transformations you may want to apply (e.g. convert skewed targets to normal via a log transformation)
  8. Document what you have learned

1 Creating copy¶

In [9]:
dataset = combinedDatasets.copy()
In [10]:
print("It has {} rows and {} columns.".format(dataset.shape[0], dataset.shape[1]))
dataset.dtypes

It has 118 rows and 16 columns.
Out[10]:
Country                           object
Region                            object
Happiness Rank                     int64
Happiness Score                  float64
Lower Confidence Interval        float64
Upper Confidence Interval        float64
Economy (GDP per Capita)         float64
Family                           float64
Health (Life Expectancy)         float64
Freedom                          float64
Trust (Government Corruption)    float64
Generosity                       float64
Dystopia Residual                float64
Beer_PerCapita                     int64
Spirit_PerCapita                   int64
Wine_PerCapita                     int64
dtype: object

2 Jupyter Notebook¶

3 Features description¶

Feature description

  1. Country: Name of the country
  2. Region: The region the country belongs to
  3. Happiness Rank: Rank of the country based on the Happiness Score
  4. Happiness Score: A metric measured in 2016 by asking the sampled people the question: "How would you rate your happiness on a scale of 0 to 10 where 10 is the happiest"
  5. Lower Confidence Interval: indication of how much uncertainty there is in our estimate on the lower boundary
  6. Upper Confidence Interval: indication of how much uncertainty there is in our estimate on the upper boundary
  7. Economy (GDP per Capita): Country's GDP per capita
  8. Family: Strong relationship with a partner, family, friends
  9. Health (Life Expectancy): Longer and healthier life
  10. Freedom: Freedom to make life choices
  11. Trust (Government Corruption): The level of social trust in a society
  12. Generosity: How generous people are
  13. Dystopia Residual: Dystopia is a hypothetical country ranked lower than the lowest ranking country. Dystopia residual is the sum of the dystopia country's happiness score plus each country’s own prediction error, which measures the extent to which life evaluations are higher or lower than those predicted
  14. Beer_PerCapita the beer consumptions per capita in liters
  15. Spirit_PerCapita the spirit consumptions per capita in liters
  16. Wine_PerCapita the wine consumptions per capita in liters

Data taken from https://worldhappiness.report/ed/2022/happiness-benevolence-and-trust-during-covid-19-and-beyond/

In [11]:
dataset.isnull().sum()
Out[11]:
Country                          0
Region                           0
Happiness Rank                   0
Happiness Score                  0
Lower Confidence Interval        0
Upper Confidence Interval        0
Economy (GDP per Capita)         0
Family                           0
Health (Life Expectancy)         0
Freedom                          0
Trust (Government Corruption)    0
Generosity                       0
Dystopia Residual                0
Beer_PerCapita                   0
Spirit_PerCapita                 0
Wine_PerCapita                   0
dtype: int64
In [12]:
dataset.dtypes
Out[12]:
Country                           object
Region                            object
Happiness Rank                     int64
Happiness Score                  float64
Lower Confidence Interval        float64
Upper Confidence Interval        float64
Economy (GDP per Capita)         float64
Family                           float64
Health (Life Expectancy)         float64
Freedom                          float64
Trust (Government Corruption)    float64
Generosity                       float64
Dystopia Residual                float64
Beer_PerCapita                     int64
Spirit_PerCapita                   int64
Wine_PerCapita                     int64
dtype: object
In [13]:
dataset.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 118 entries, 0 to 117
Data columns (total 16 columns):
 #   Column                         Non-Null Count  Dtype
---  ------                         --------------  -----
 0   Country                        118 non-null    object
 1   Region                         118 non-null    object
 2   Happiness Rank                 118 non-null    int64
 3   Happiness Score                118 non-null    float64
 4   Lower Confidence Interval      118 non-null    float64
 5   Upper Confidence Interval      118 non-null    float64
 6   Economy (GDP per Capita)       118 non-null    float64
 7   Family                         118 non-null    float64
 8   Health (Life Expectancy)       118 non-null    float64
 9   Freedom                        118 non-null    float64
 10  Trust (Government Corruption)  118 non-null    float64
 11  Generosity                     118 non-null    float64
 12  Dystopia Residual              118 non-null    float64
 13  Beer_PerCapita                 118 non-null    int64
 14  Spirit_PerCapita               118 non-null    int64
 15  Wine_PerCapita                 118 non-null    int64
dtypes: float64(10), int64(4), object(2)
memory usage: 15.7+ KB
In [14]:
dataset.iloc[:, 3:].describe()
Out[14]:
Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Beer_PerCapita Spirit_PerCapita Wine_PerCapita
count 118.000000 118.000000 118.000000 118.000000 118.000000 118.000000 118.000000 118.000000 118.000000 118.000000 118.000000 118.000000 118.000000
mean 5.557195 5.459975 5.654415 1.021216 0.840081 0.594344 0.383215 0.134977 0.229399 2.353952 138.906780 97.067797 68.093220
std 1.145853 1.153111 1.139521 0.381773 0.251482 0.222809 0.145206 0.114400 0.125321 0.519255 105.090599 78.595658 89.066189
min 3.069000 2.936000 3.202000 0.087090 0.000000 0.000000 0.005890 0.000000 0.000000 0.817890 1.000000 1.000000 1.000000
25% 4.640000 4.515250 4.767250 0.817762 0.708878 0.478178 0.275750 0.052440 0.140220 2.090750 42.000000 32.000000 5.000000
50% 5.553000 5.469500 5.677500 1.088420 0.877970 0.643315 0.404725 0.098535 0.211335 2.300940 127.500000 85.500000 16.000000
75% 6.480250 6.396750 6.570750 1.308817 1.041250 0.775190 0.502933 0.177445 0.303113 2.694330 224.750000 142.500000 115.250000
max 7.526000 7.460000 7.669000 1.824270 1.183260 0.947190 0.608480 0.480490 0.586960 3.559060 376.000000 373.000000 370.000000
In [15]:
dataset.iloc[:, :3].plot.box()
Out[15]:
<AxesSubplot:>
In [16]:
dataset.iloc[:, 3:13].plot.box(figsize=(20,12))
Out[16]:
<AxesSubplot:>

Family, Trust, Generosity and Dystopia Residual have outliers. We do not care about Dystopian Residual as it will not be used for our learning algorithms. However we will decide if to drop the outliers on the first three.

In [17]:
def outliers(column):
    '''
    Return outliers for a given column name
    '''
    Q1 = dataset[column].quantile(0.25)
    Q3 = dataset[column].quantile(0.75)
    IQR = Q3 - Q1    #IQR is interquartile range. 
    return dataset[(dataset[column] < Q1 - 1.5 * IQR) | (dataset[column] > Q3 + 1.5 *IQR)]

outliers("Family")
Out[17]:
Country Region Happiness Rank Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Beer_PerCapita Spirit_PerCapita Wine_PerCapita
97 Georgia Central and Eastern Europe 126 4.252 4.164 4.340 0.83792 0.19249 0.64035 0.32461 0.31880 0.06786 1.87031 52 100 149
101 Malawi Sub-Saharan Africa 132 4.156 4.041 4.271 0.08709 0.14700 0.29364 0.41430 0.07564 0.30968 2.82859 8 11 1
115 Benin Sub-Saharan Africa 153 3.484 3.404 3.564 0.39499 0.10419 0.21028 0.39747 0.06681 0.20180 2.10812 34 4 13
116 Togo Sub-Saharan Africa 155 3.303 3.192 3.414 0.28123 0.00000 0.24811 0.34678 0.11587 0.17517 2.13540 36 2 19
117 Syria Middle East and Northern Africa 156 3.069 2.936 3.202 0.74719 0.14866 0.62994 0.06912 0.17233 0.48397 0.81789 5 35 16
In [18]:
outliers("Trust (Government Corruption)")
Out[18]:
Country Region Happiness Rank Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Beer_PerCapita Spirit_PerCapita Wine_PerCapita
0 Denmark Western Europe 1 7.526 7.460 7.592 1.44178 1.16374 0.79504 0.57941 0.44453 0.36171 2.73939 224 81 278
1 Switzerland Western Europe 2 7.509 7.428 7.590 1.52733 1.14524 0.86303 0.58557 0.41203 0.28083 2.69463 185 100 280
4 Finland Western Europe 5 7.413 7.351 7.475 1.40598 1.13464 0.81091 0.57104 0.41004 0.25492 2.82596 263 133 97
7 New Zealand Australia and New Zealand 8 7.334 7.264 7.404 1.36066 1.17278 0.83096 0.58147 0.41904 0.49401 2.47553 203 79 175
9 Sweden Western Europe 10 7.291 7.227 7.355 1.45181 1.08764 0.83121 0.58218 0.40867 0.38254 2.54734 152 60 186
20 Singapore Southeastern Asia 22 6.739 6.674 6.804 1.64555 0.86758 0.94719 0.48770 0.46987 0.32706 1.99375 60 12 11
32 Qatar Middle East and Northern Africa 36 6.375 6.178 6.572 1.82427 0.87964 0.71723 0.56679 0.48049 0.32388 1.58224 1 42 7
In [19]:
outliers("Generosity")
Out[19]:
Country Region Happiness Rank Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Beer_PerCapita Spirit_PerCapita Wine_PerCapita
28 Malta Western Europe 30 6.488 6.409 6.567 1.30782 1.09879 0.80315 0.54994 0.17554 0.56237 1.99032 149 100 120
31 Thailand Southeastern Asia 33 6.474 6.396 6.552 1.08930 1.04477 0.64915 0.49553 0.02833 0.58696 2.57960 99 258 1

We can plot the data too see how similar the countries' alcohol consumption is and look for outlisers. There are a few outliers, especially for wine consumtion, where 50% of the data is very low, indicating that many countries drink little to no wine.

In [20]:
dataset.iloc[:, 13:].plot.box(figsize=(20,12))
Out[20]:
<AxesSubplot:>

Below is the happiness score, because it does not fit on the above scale. There are no outliers.

In [21]:
outliers('Spirit_PerCapita')
Out[21]:
Country Region Happiness Rank Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Beer_PerCapita Spirit_PerCapita Wine_PerCapita
53 Belarus Central and Eastern Europe 61 5.802 5.723 5.881 1.13062 1.04993 0.63104 0.29091 0.17457 0.13942 2.38582 142 373 42
104 Haiti Latin America and Caribbean 136 4.028 3.893 4.163 0.34097 0.29561 0.27494 0.12072 0.14476 0.47958 2.37116 1 326 1

The outliers below are for Wine. We are not nessecarily interested in removing the outliers for wine because the data was so split.

In [22]:
outliers('Wine_PerCapita')
Out[22]:
Country Region Happiness Rank Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Beer_PerCapita Spirit_PerCapita Wine_PerCapita
30 France Western Europe 32 6.478 6.397 6.559 1.39488 1.00508 0.83795 0.46562 0.17808 0.12160 2.47440 127 151 370
78 Portugal Western Europe 94 5.123 5.030 5.216 1.27607 0.94367 0.79363 0.44727 0.01521 0.11691 1.53015 194 67 339

Below we can see the lowest wine consumption with less than 3l wine.

In [23]:
dataset[(dataset['Wine_PerCapita'] < 3)]
Out[23]:
Country Region Happiness Rank Happiness Score Lower Confidence Interval Upper Confidence Interval Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Beer_PerCapita Spirit_PerCapita Wine_PerCapita
31 Thailand Southeastern Asia 33 6.474 6.396 6.552 1.08930 1.04477 0.64915 0.49553 0.02833 0.58696 2.57960 99 258 1
34 Guatemala Latin America and Caribbean 39 6.324 6.213 6.435 0.83454 0.87119 0.54039 0.50379 0.08701 0.28808 3.19863 53 69 2
40 El Salvador Latin America and Caribbean 46 6.068 5.967 6.169 0.87370 0.80975 0.59600 0.37269 0.10613 0.08877 3.22134 52 69 2
41 Nicaragua Latin America and Caribbean 48 5.992 5.877 6.107 0.69384 0.89521 0.65213 0.46582 0.16292 0.29773 2.82428 78 118 1
66 Jordan Middle East and Northern Africa 80 5.303 5.187 5.419 0.99673 0.86216 0.60712 0.36023 0.13297 0.14262 2.20142 6 21 1
68 Philippines Southeastern Asia 82 5.279 5.160 5.398 0.81217 0.87877 0.47036 0.54854 0.11757 0.21674 2.23484 71 186 1
80 Vietnam Southeastern Asia 96 5.061 4.991 5.131 0.74037 0.79117 0.66157 0.55954 0.11556 0.25075 1.94180 111 2 1
84 Nigeria Sub-Saharan Africa 103 4.875 4.750 5.000 0.75216 0.64498 0.05108 0.27854 0.03050 0.23219 2.88586 42 5 2
85 Honduras Latin America and Caribbean 104 4.871 4.750 4.992 0.69429 0.75596 0.58383 0.26755 0.06906 0.20440 2.29551 69 98 2
88 Sierra Leone Sub-Saharan Africa 111 4.635 4.505 4.765 0.36485 0.62800 0.00000 0.30685 0.08196 0.23897 3.01402 25 3 2
89 Namibia Sub-Saharan Africa 113 4.574 4.374 4.774 0.93287 0.70362 0.34745 0.48614 0.10398 0.07795 1.92198 376 3 1
92 Egypt Middle East and Northern Africa 120 4.362 4.259 4.465 0.95395 0.49813 0.52116 0.18847 0.10393 0.12706 1.96895 6 4 1
94 Kenya Sub-Saharan Africa 122 4.356 4.259 4.453 0.52267 0.76240 0.30147 0.40576 0.06686 0.41328 1.88326 58 22 2
101 Malawi Sub-Saharan Africa 132 4.156 4.041 4.271 0.08709 0.14700 0.29364 0.41430 0.07564 0.30968 2.82859 8 11 1
103 Mali Sub-Saharan Africa 135 4.073 3.988 4.158 0.31292 0.86333 0.16347 0.27544 0.13647 0.21064 2.11087 5 1 1
104 Haiti Latin America and Caribbean 136 4.028 3.893 4.163 0.34097 0.29561 0.27494 0.12072 0.14476 0.47958 2.37116 1 326 1
106 Comoros Sub-Saharan Africa 138 3.956 3.860 4.052 0.27509 0.60323 0.29981 0.15412 0.18437 0.18270 2.25632 1 3 1
107 Cambodia Southeastern Asia 140 3.907 3.798 4.016 0.55604 0.53750 0.42494 0.58852 0.08092 0.40339 1.31573 57 65 1
109 Niger Sub-Saharan Africa 142 3.856 3.781 3.931 0.13270 0.60530 0.26162 0.38041 0.17176 0.20970 2.09469 3 2 1
110 Chad Sub-Saharan Africa 144 3.763 3.672 3.854 0.42214 0.63178 0.03824 0.12807 0.04952 0.18667 2.30637 15 1 1
113 Tanzania Sub-Saharan Africa 149 3.666 3.561 3.771 0.47155 0.77623 0.35700 0.31760 0.05099 0.31472 1.37769 36 6 1
114 Liberia Sub-Saharan Africa 150 3.622 3.463 3.781 0.10706 0.50353 0.23165 0.25748 0.04852 0.24063 2.23284 19 152 2

4 Identify¶

The target is happiness score. We will take the columns, Economy, Family, Health, Freedom, Trust and Generosity to see if there is any association between those values and happiness. From the second dataset the target is happiness score and we will try to find association with the columns: Beer_PerCapita, Beer_PerCapita, Wine_PerCapita.

5 Visualize¶

In [24]:
dataset.plot(subplots=True, figsize=(8, 8));
In [25]:
#https://levelup.gitconnected.com/plotting-choropleth-maps-in-python-b74c53b8d0a6
def featureWorldMap(feature, title):
    '''
    Returns a world heatmap of a given feature
    '''
    return go.Figure(
        data = {
            'type':'choropleth',
            'locations':dataset['Country'],
            'locationmode':'country names',
            'colorscale':["darkred","red","lightcoral","white", "palegreen","green","darkgreen"],
            'z':dataset[feature],
            'colorbar':{'title': title},
            'marker': {
                'line': {
                    'color':'rgb(255,255,255)',
                    'width':2
                }
            }
        },
        layout = {
          'geo':{
              'scope':'world',
          }
        }
    )
In [26]:
featureWorldMap("Happiness Score", "Happiness Score")

world map of beer, wine, spirit https://www.kaggle.com/code/lily1917/happiness-alcohol-world-heat-map-eda¶

TODO Do we need this link? I just used the data from the alcohol dataset from the top

In [27]:
featureWorldMap("Beer_PerCapita", "Beer consumption")
In [28]:
featureWorldMap("Spirit_PerCapita", "Spirit  consumption")
In [29]:
featureWorldMap("Wine_PerCapita", "Wine  consumption")

6 Correlation between features¶

In [30]:
data_corr = dataset.iloc[:,[3,6,7,8,9,10,11,13,14,15]]
# pairplot
seaborn.pairplot(data_corr)
# to show
plt.show()
In [31]:
print("happiness and Economy\t correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,6])))
print("happiness and Family\t correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,7])))
print("happiness and Health\t correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,8])))
print("happiness and Freedom\t correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,9])))
print("happiness and Trust\t correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,10])))
print("happiness and Generosity correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,11])))
print("happiness and Beer\t correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,13])))
print("happiness and Spirit\t correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,14])))
print("happiness and Wine\t correlation is {}.".format(dataset.iloc[:,3].corr(dataset.iloc[:,15])))

happiness and Economy	 correlation is 0.7896671254192654.
happiness and Family	 correlation is 0.7509180595792792.
happiness and Health	 correlation is 0.7470862678123842.
happiness and Freedom	 correlation is 0.6183532344146925.
happiness and Trust	 correlation is 0.5161251180353214.
happiness and Generosity correlation is 0.3067203229436911.
happiness and Beer	 correlation is 0.4807494310940025.
happiness and Spirit	 correlation is 0.23063681111279222.
happiness and Wine	 correlation is 0.44042535523016324.

We can also see the correlation in a heatmap.

In [32]:
labels = data_corr.columns

seaborn.heatmap(data_corr.corr(),
             mask=np.triu(np.ones_like(data_corr.corr())),
             annot=True,
             xticklabels=labels,
             yticklabels=labels)\
    .set_title('Correlation between happiness and features')

plt.xticks(rotation=45)
Out[32]:
(array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5]),
 [Text(0.5, 0, 'Happiness Score'),
  Text(1.5, 0, 'Economy (GDP per Capita)'),
  Text(2.5, 0, 'Family'),
  Text(3.5, 0, 'Health (Life Expectancy)'),
  Text(4.5, 0, 'Freedom'),
  Text(5.5, 0, 'Trust (Government Corruption)'),
  Text(6.5, 0, 'Generosity'),
  Text(7.5, 0, 'Beer_PerCapita'),
  Text(8.5, 0, 'Spirit_PerCapita'),
  Text(9.5, 0, 'Wine_PerCapita')])