Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)

The objective of this study is to explore the impact of the structure of a Bayesian Network on its overall run-time, potential unwanted bias, and accuracy in performing a classification task. A credit card default dataset was utilised to construct six networks with varying structures and to learn their conditional probability distributions.

Decision tree, Adaboost, KNN, Naive bayes, PCA, EM algorithm assignments of Machine Learning Sessional Course CSE 472 in Level-4, Term-2 of CSE, BUET

Credit card fraud detection application using Machine Learning and Streamlit

In this project, for supervised learning, I used regression and decision tree techniques to build predictive models and tested model accuracy by evaluating MSE and misclassification cost. For unsupervised learning, I performed cluster analysis on Iris dataset to identify subgroups and I used association rules to analyze transaction details in th…