In this project, data was gathered from kaggle to help gain an insight into life expectancy. There are many factors that can effect the length of a person's life. This information will hopefully help guide policymakers to make changes to help increase the life expectancy of their people.
- What factors have the most influence on Life Expectancy?
- Can we predict how long a person will live on average?
- How does obesity relate to life expectancy?
- See data_cleaning_engineering.ipynb: This file contains the seperate data files obtained through Kaggle. The data was sorted through, analyzed, cleaned and concatted into one.
- See data_visualization.ipynb: To see how the data looked, a of hypothesis testing were ran and graphed.
- See regression_analysis.ipynb: Lastly, used linear regression techniques to create the most accurate model into predicting life expectancy.
- Data Cleaning and Visualization:
- Matplotlib
- Seaborn
- Plotly
- Pandas
- SkLearn
- Statsmodels
Through data analysis, factors such as Schooling, Obesity, Alcohol Consumption, Immunization, Percentage Expenditure, and country had the highest impact in predicting life expectancy with a Root Mean Squared Error of only 0.25 Standard deviation. This is important because policy makers can take these findings and create changes in the law to better serve their people.
- Schools should be made more accessible and more prevalent.
- Countries who have a lower life expectancy should increase their healthcare expenditure in order to improve its average lifespan.
- Governments should allocate more budget into physical and mental health to help increase life expectancy and quality of life.