This repository is structured as a complete ML roadmap combining theory (PDFs) with hands-on coding (Jupyter Notebooks) to help you build a solid foundation in data science and machine learning. Ideal for students, self-learners, and professionals looking to revise or upgrade.

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.

An R-based statistical inference project investigating the drivers of student academic performance. It moves beyond simple prediction to isolate statistically significant factors using multivariate regression, ANOVA, and t-tests.

Collections of supervised project completed using Python on DataCamp.

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. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

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

A simple implementation of Linear Regression using Python and scikit-learn to predict continuous target variables. This repository demonstrates basic model building, data preprocessing, and evaluation on real-world dataset.

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

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…

All the projects I have done so far are here

🚀 5 Days Machine Learning Course – NIELIT 📘 Complete beginner-to-intermediate ML roadmap in 5 days 🧠 Covers core concepts, algorithms & real-world use cases 🛠️ Hands-on notebooks, code examples & mini projects 📊 Creative visuals, icons & diagrams for easy understanding.

📊 Analyze student performance using R to uncover the factors that significantly impact grades with statistical inference and regression analysis.