IITM CS5691 course project on Pattern Recognition and Machine Learning, co created with Arnhav Datar The project contains the following items:
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Programming Assignments
- Assignment 1 contains from-scratch implementations of Naive Bayes classifiers, and linear regression analysis
- Assignment 2 contains from-scratch implementations of Logistic Regression (gradient descent for the sigmoid based model) and decision tree classifiers along with a comparitive analysis to SVM implemented in scikit-learn
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Data Contest
- Semi-real world problem on predicting if an employee leaves or not. Several attempts are added here to track the problem solving approach, and serve as levels of performance to beat. Finally an ensemble classifier gave us the best result.
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Crowdsourced Classification
- This real world problem deals with handling the problem of classifying data already classified by independent classifiers.
- With different probabilistic models assumed, different algorithms may fit a given dataset better/ worse
- The algorithm implemented here is Expectation-Maximization, and it is given as an API for easy use in applications.
- More algorithms will be added soon.