student-performance-modelling

Data source: https://www.kaggle.com/aljarah/xAPI-Edu-Data

This project focuses on evaluating different factors affecting a student's academic performance:

Academic background factors : Educational stages, grade, levels, course topic

Behavioral factors : Raised hand, visited resources, discussion groups, etc.

Demographic factors : Nationality, gender, place of birth, etc.

• Use EDA to analyze how each of the factors in above mentioned categories contribute individually to student's academic performance as well as finding correlation between factors.

• View feature importance of variables and build a predictive model using Decision Tree to predict a student's performance based on these factors.

• Evaluating performance of the model developed.

Libraries used: plyr, ggplot2, moments, cluster, randomForest