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.

This python program demonstrates image classification with stratified k-fold cross validation technique.

Program akan melakukan klasifikasi terhadap tweet berbahasa Indonesia yang dianggap ujaran kebencian (Hate Speech) atau tidak (Non Hate Speech).

This repo contains examples of binary classification with ANN and hyper-parameter tuning with grid search.

使用比赛方提供的脱敏数据，进行客户信贷流失预测。

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.

Zindi hackathon

Melanoma is a deadly disease. Image analysis tools that automate the diagnosis of melanoma will improve dermatologists' diagnostic accuracy.

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.

Credit Card Fraud Detection using Python and Machine Learning.

This project aims to understand and implement all the cross validation techniques used in Machine Learning.

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.

Malicious URL detector built with deep exploration on feature engineering.

Enhancements to commonly used pyspark functions for modelling

Machine learning prediction project, 2019.

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.

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.

This research processed a fake news dataset, using TF-IDF and Count Vectorizer for feature extraction and evaluating multiple ML models through stratified cross-validation. Logistic Regression with TF-IDF was selected as one of the best models and further explained using LIME for interpretability.

Deep Learning vs Tranditional ML methods for TB Drug Resistance prediction from Genomic data