Feb 5 – Researched project Ideas – 2 hours
Feb 7 – Wrote Project proposal – 3 hours
Feb 17 – Further research of learning algorithms – 1 hour
Feb 19 – Wrote code for: – 3 hours
- Reading the dataset
- Partitioning the data into training and testing sets
- Using K-fold cross validation
Feb 21 – Wrote biweekly update – 2 hours
Feb 28 – Researched Random Forests, and Implementation of Random Forests through Scikit Learn – 4 Hours
March 3 – Implementation of code for running Random Forests – 3 hours
March 7 – Wrote Biweekly update – 2 hours
March 9 – Reading/Researching SVMs in[1] – 3 hours
March 14 – Researched possible libraries to use for my base models – 2 hours
March 20 – Implemented Linear SVMs through Scikit learn – 2 hour
March 21 – Wrote Biweekly update and responded to feedback on my project – 2 hours
March 27 – Implemented Analysis in code – 4 hours
April 3 – Researched and implemented data scaling and pre-processing – 1 hour
April 4 – Implemented Gaussian SVM and analysis[1][2] – 2 hours
April 7 – Wrote and recorded project demo – 2 hours
April 7 – Wrote Biweekly update – 1 hour
April 13 – Researched and implemented and tested the neural network model – 4 hours
April 15 – Started writing my final report – 4 hours
April 18 – Finalized final report – 8 hours
April 18 – Responded to peer questions, updated the website, and wrote final logbook – 20 minutes