sanaiqbalwani/moocdrop-mle-project

Predicting whether a student will dropout from a MOOC course on week basis

MOOC Dropout

This repo holds code analyzing MOOC dropout data from EdX using both RNN LSTM and an ensemble of other machine learning models.

Ensemble code

Before running feature creation, write a deadline file with deadlines.py. Feature creation is found in ensemble_features.py. Change the course names at the top of the file to analyze other courses.

Run clean_ensemble_input.py to write cleaned csv's from features. The course name at the top must be changed for each file.

Run run_ensemble.py. Set test and train courses with testdata and traindata.

With results of run_ensemble.py in memory, you can combine output results with combine_results.py.

RNN LSTM code

To collect data in the proper format for RNN anaysis, run collect_data_lstm.py and it will write to one file per week of analysis called week_1_data_courses.pickle. Change the filenames at the end to change the courses analyzed.

Run the LSTM with run_lstm.py. Change the courses analyzed by changing cross_sets in that file.