PREDICTORS OF SUBJECTIVE WELL-BEING

Brief Description

The following code supports user to perform feature subset selection, calculate the prediction accuracy of the obtained subset and correspoding statistical significance of each factor, combine everything in the data table and calculate regression coefficients.

Datasets

The datasets can be found in the folder "input data".

• A008.W.pkl > Feeling of Happiness
• A170.W.pkl > Satisfaction with Life
• SWB.LS.pkl > Subjective Well-Being, Life Satisfaction
• ranking_iris.pkl > Ranking scores of countries made by me
• ranking_survey.pkl > Ranking scores of countries from the survey
• <target>_measure_units.csv > The subset of factors with corresponding measure units
• indicators_shorter.csv > Data table containing a shorter list of factors

Step 1. Feature Subset Selection

• Name of the file: "fss.py"
• Read the data: Change the filepath in line 358.
• Running in different modes: Modes are described in function model_acuracy. To select a mode, set the variable type in line 360.
• Define the strenght of regularization: Change range in line 363.
• Output: List of 10 factors for each ranking method and R-squared for LR and RF.

Step 2. Statistical Significance

• Name of the file: "p-values.py"
• Read the data: Change the filepath in line 129.
• Output: Dictionary of form [factor] --> (statistical significance, ranking method).

Step 3. Creating CSV Data Table

• Name of the file: "data_table.py"
• Pre-requirement: Before running this file you need to change some parameters in file ffs.py. The correct numbers are put in comments in functions relief_top_attributes, linear_top_attributes and rf_top_attributes.
• Read the data: Change the filepath in line 8.
• Import 2 additional data tables: Change the filepath in line 37 for indicators_shorter.csv and line 47 for <target>_measure_units.csv.
• Output: Data table saved as <target>_data_table.csv in new folder export data.

Step 4. Regression Coefficients

• Name of the file: "multiple_linear_regression.py"
• Read (all) the data: Change the filepath(s) in line 11, with manually chosen factors.
• Import additional data table: Change the file in line 94 for indicators_shorter.csv
• Output: Data table saved as <target>_regression_coeffs.csv in folder export data.

Step 5.* Results of the Pilot Survey

Materials for further steps can be found in survey folder.

a. Rankings Scores of Countries

• Name of the file: "country_preference_results.py"
• Output: Data table saved as out.csv

b. Data Table of Demographics and Macroeconomic Statements

• Name of the file: "demo_data_table.py"
• Output: Data table saved as demo_data.csv