Simple scikit learn model#

Does money make people happier? Simple version without data splitting.

Data#

Import data#

import pandas as pd

# Load the data from GitHub
LINK = "https://raw.githubusercontent.com/kirenz/datasets/master/oecd_gdp.csv"
df = pd.read_csv(LINK)

Data structure#

df
Country GDP per capita Life satisfaction
0 Russia 9054.914 6.0
1 Turkey 9437.372 5.6
2 Hungary 12239.894 4.9
3 Poland 12495.334 5.8
4 Slovak Republic 15991.736 6.1
5 Estonia 17288.083 5.6
6 Greece 18064.288 4.8
7 Portugal 19121.592 5.1
8 Slovenia 20732.482 5.7
9 Spain 25864.721 6.5
10 Korea 27195.197 5.8
11 Italy 29866.581 6.0
12 Japan 32485.545 5.9
13 Israel 35343.336 7.4
14 New Zealand 37044.891 7.3
15 France 37675.006 6.5
16 Belgium 40106.632 6.9
17 Germany 40996.511 7.0
18 Finland 41973.988 7.4
19 Canada 43331.961 7.3
20 Netherlands 43603.115 7.3
21 Austria 43724.031 6.9
22 United Kingdom 43770.688 6.8
23 Sweden 49866.266 7.2
24 Iceland 50854.583 7.5
25 Australia 50961.865 7.3
26 Ireland 51350.744 7.0
27 Denmark 52114.165 7.5
28 United States 55805.204 7.2 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 29 entries, 0 to 28
Data columns (total 3 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Country            29 non-null     object 
 1   GDP per capita     29 non-null     float64
 2   Life satisfaction  29 non-null     float64
dtypes: float64(2), object(1)
memory usage: 824.0+ bytes

Data corrections#

# Change column names (lower case and spaces to underscore)
df.columns = df.columns.str.lower().str.replace(' ', '_')

# show the first 5 rows
df.head()
country gdp_per_capita life_satisfaction
0 Russia 9054.914 6.0
1 Turkey 9437.372 5.6
2 Hungary 12239.894 4.9
3 Poland 12495.334 5.8
4 Slovak Republic 15991.736 6.1

Variable lists#

Prepare the data for later use

# define outcome variable as y_label
y_label = 'life_satisfaction'

# select features
X = df[["gdp_per_capita"]]

# create response
y = df[y_label]

Data exploration#

%matplotlib inline
import altair as alt

# Visualize the data
alt.Chart(df).mark_circle(size=100).encode(
    x='gdp_per_capita:Q',
    y='life_satisfaction:Q',
    color=alt.Color('country', legend=None),
    tooltip=['country', 'gdp_per_capita', 'life_satisfaction']

).interactive()

Linear regression model#

from sklearn.linear_model import LinearRegression

# Select a linear regression model
reg = LinearRegression()

Training#

# Fit the model
reg.fit(X, y)

LinearRegression()
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# Model intercept
reg.intercept_

4.853052800266436

# Model coefficient
reg.coef_

array([4.91154459e-05])

Prediction#

# Prediction for our data
y_pred = reg.predict(X)

# Make a prediction for a specific GDP value
X_new = pd.DataFrame({'gdp_per_capita': [50000]})

reg.predict(X_new)

array([7.30882509])

Evaluation#

from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error

# Mean squared error
mean_squared_error(y, y_pred)

0.18075033705835142

# Root mean squared error
mean_squared_error(y, y_pred, squared=False)

0.4251474297915388

mean_absolute_error(y, y_pred)

0.35530429427921734

K-Nearest Neighbor Model#

from sklearn.neighbors import KNeighborsRegressor

reg2 = KNeighborsRegressor(n_neighbors=2)

reg2.fit(X, y)

KNeighborsRegressor(n_neighbors=2)
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y_pred2 = reg2.predict(X)

reg2.predict(X_new) 

array([7.35])

mean_squared_error(y, y_pred2)

0.06181034482758619

mean_absolute_error(y, y_pred2)

0.20517241379310344