The current work includes data cleaning, data visualization, and utilizing models including Linear Regression, ElasticNet, KNN, and LGBMRegressor to predict life expectancy values. Using four different types of regression models for prediction. A basic framework has been built, and subsequent research needs to be based on specific issues.
The code part contains most of the tools that need to be used, which can be further called and modified to solve subsequent problems.
│ preprocess.py # data cleaning.
│ plot.py # all drawing functions.
│ predict.py # training and prediction of models.
│ param.py # feature engineering
│ main.py # coding here to solve quesion, empty now.
│ readme.md
│ Life Expectancy Data.csv
Drawing optimization in plot.py, complete the shap part of the plotting. It may be possible to use the world_map() to complete the visualization of other parts of interest. Complete the analysis of question in main.py. Explore more ways of feature engineering.