The points below are the things we should complete before the final project is due. This doesn't include anything that we should all contribute, like expanding on the report and creating the presentation.
Data visualization
Generate even more graphs (preferably varied)
Understand the data better
Feature engineering
(soft depends on data visualization as we need to understand the data better to extract more features)
Extract/derive even more features
Try to be creative and not be bound by existing data here (check the table in the README)
Feature ranking
(soft depends on feature engineering as we need to the extracted features for them to be ranked)
Perform more Shapley analysis, maybe with different explainers
Clean up the PCA and LDA code for feature ranking
Redo PCA and LDA for new engineered features
Maybe generate a graph for PCA that shows optimal number of features
Prediction model
(soft depends on feature engineering for new features and feature ranking for potential correlated features)
Continue iterating through multiple combinations of features
Redo on new engineered features
Clean up the Bayes and kNN models
Add Parzen windows and neural network models
Apply best practices from her slide
Model evaluation
Generate metrics Rahul suggests: accuracy, precision, recall, F1 score
Generate graphs ROC curve, confusion matrix, and others
Interpret the results
Strength, weaknesses of models
Recommendation to solve business problem (see Rahul's notes)
We need to predict the probability that these new buyers would purchase items from the same merchants again within 6 months
and write in words for conclusion section
The points below are the things we should complete before the final project is due. This doesn't include anything that we should all contribute, like expanding on the report and creating the presentation.