Supervised machine learning pipeline for telecom customer churn prediction including EDA, feature engineering, model comparison, unit testing and performance evaluation.

This project is to build a model that *predicts the human activities* such as __Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing__ and __Laying__ as done in Smart-Watches.

Techniques For Feature Selection

Customer Segmentation using KMeans Clustering with PCA for dimensionality reduction and Variance Thresholding for feature selection

Predicting toxicity of molecules. Project on course "Data Mining 2"

Delved into advanced techniques to enhance ML performance during the uOttawa 2023 ML course. This repository offers Python implementations of Naïve Bayes (NB) and K-Nearest Neighbor (KNN) classifiers on the MCS dataset.

Praktikum Machine Learning 5 - Naive Bayes dengan Variance Thresholding, Mutual Information, dan K-Fold Cross ValidationAssignment