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.

Collections of supervised project completed using Python on DataCamp.

this project develops a robust machine learning model to estimate house prices in the state.

This project aims to develop a machine learning model that predicts the prices of cars based on various factors such as make, model, year, mileage, engine size, and fuel type.

All the projects I have done so far are here

This repository contains code to build 21 Regression Machine Learning Models to predict the house price in Python using PyCaret. Models are compared against the statistics (RMSE), best model was picked, tuned, saved/loaded for model deployment and used to predict the observations on unseen data. The final file with predictions on unseen data was…

Exhibiting results of multiple linear regressions in a table with stargazer (R package).