Python - Data Structures/Machine Learning (Spring 2020)
This is my final project for my Data Structures class (altough we had moved onto Machien Learning matrial). The goal was to use the pandas and sklearn modules in python to accuate fit country data into a linear regression model. The full assignement can be read in the Life satisfaction.docx file.
.idea - I didn't touch these, I think they are for the modules and pycharm
venv - I didn't touch these, I think they are for the modules and pycharm
Life metrics.csv - One of the given data files. Has a bunch of counrties and data values for a range of different categories
LifeSatisfaction.py - Main coding file. Here is where everthing that I wrote happens
WEO_Data.csv - The other given data file. This one is a little simplier as it has counrties' name and their GDP per captia
combinedData.csv - A file I created after combing the two given files
filteredData - A File I created after combing the two given files and removing all of the country's with outliers
The only place to run the program is in LifeSatisfaction.py. After compliling and running the code, the program will prompt you for input. The first input line is for you to either give the program more values to make prediction for or to tell the program to start the prediction. The second input line ask whether you want the prediction to be based on one varible (only the GDP) or multiple (GDP and everything else). I would suggest starting with one variable prediction, since it will give a more accurate prediction.