This repository contains three Knime workflows that aim to analyze the Air Traffic Passenger Statistics dataset from the San Francisco International Airport. The workflows include tasks such as classification comparison, regression analysis, and outlier detection using various machine learning techniques.

The comprehensive study that investigates the implementation of machine learning algorithms for fraud detection in e-commerce using Python.

A collection of essential machine learning algorithms implemented from scratch and with libraries. Ideal for students and beginners to understand core ML concepts through hands-on examples.

outlier detection for supply chain and logistics

My list of EDA And ML exercises

A complete Python project that scrapes real-time material prices, cleans and validates data, applies ML for outlier detection & material matching, stores in MySQL, generates PDF reports, and delivers everything through Streamlit dashboard.