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mcs_kfold stands for "monte carlo stratified k fold". This library attempts to achieve equal distribution of discrete/categorical variables in all folds. The greatest advantage of this method is that it can be applied to multi-dimensional targets.
Performed univariate and bivariate analysis to understand the features and their relationships for loan approval prediction. Achieved highest accuracy of 98% for Extreme Gradient Boosting among all tested machine learning classification models.
Object detection exercise for the Neural Networks for Computer Vision course. Using stratified KFold for data with multiple labels and instances, and self-implementation of mAP with multiple configurations.
This is a machine learning project which implements three different types of regression techniques and formulates differences amongst them by predicting the price of a house based on Boston housing Data.
Human Activity Recognition (HAR) has a wide range of applications due to the widespread usage of acquisition devices such as smartphones and its ability to capture human activity data.
Machine Learning App in R for predicting whether a newly released movie will be a hit or a flop. Practically useful for streaming services and cinemas.