We aim to predict the world gross domesticproducts (GDP) based on GDPs of various countries. The GDP of countries is impacted by various social, economical, culturalparameters. We are analysing those parameters from 1960 to 2017 and will predict future GDP of the world. We areusing supervised learning methods to build our models. We have used evaluated following models.
- Multiple Linear Regression
- Polynomial Regression
- Decision Tree Regression
- Random Forest Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
Data Sources folder contains the csv files used as input parameter. Codes contain our step by step building of models. The codes file contain our step by step data prepration, data cleaning & visualization. For the summary and conclusin please read 'Project Presentation_Final.pptx' and for detailed description of the project please read 'Report.pdf'