Implementation of algorithms such as normal equations, gradient descent, stochastic gradient descent, lasso regularization and ridge regularization from scratch and done linear as well as polynomial regression analysis. Implementation of several classification algorithms from scratch i.e. not used any standard libraries like sklearn or tensorflow.

Implented ridge and lasso regression by understanding the use of parameters

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

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Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn

Forecasting Ethereum return quantiles using a handful of different statistical learning models and selecting the best based on out of sample error. Hopsworks feature store and model registry is used to automate the process. Ethereum quantile returns are predicted daily and displayed on a Streamlit dashboard.

My Python learning experience 📚🖥📳📴💻🖱✏

This model utilizes regression models and accurately predicts employee salaries based on experience, previous CTC, and job roles, promoting fair salary structures and optimizing resource allocation for streamlined HR operations.

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

Ridge Estimation of Vector Auto-Regressive (VAR) Processes

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

University Admission Predictor is a sophisticated Flask-based web application designed to predict the likelihood of admission to graduate programs based on student profiles. It leverages a range of regression techniques to evaluate admission chances.This project showcases the practical application of machine learning in educational forecasting.

Regression Modelling on Appliances Energy Prediction data

"This project focuses on predicting car prices using machine learning models, specifically linear regression and ridge regression."

Machine learning model implementations from scratch in Python

ML models to provide estimated car auction prices in Myanmar

Trying to build a repository that can be used to analyze & forecast commodities prices

Practicum Workshop

Predicting economic mobility based on community characteristics using OLS, Ridge and Lasso regressions