K-Means Clustering for the coping strategies of Brief COPE Questionnaire

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.

This repository gives you access to the CLIMATEREADY survey dataset containing thermal comfort votes during the 2021 and 2022 heatwave periods in Pamplona, Spain, as well as other relevant parameters self-reported by surveyees (e.g. occupant characteristics and behaviour, key building/dwelling characteristics).

Implented ridge and lasso regression by understanding the use of parameters

A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.

Comparing the performance of three machine learning models for predicting car prices.

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Machine Learning | Fall 2023

Iterative shrinkage / thresholding algorithms (ISTAs) for linear inverse problems

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

The ability to predict prices and features affecting the appraisal of property can be a powerful tool in such a cash intensive market for a lessor. Additionally, a predictor that forecasts the number of reviews a specific listing will get may be helpful in examining elements that affect a property's popularity.

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.

A machine learning project by Sadhanha Anand, Cindy Jeon, and Mia Lai, aims to address the challenge of efficiently treating obese and hypertensive patients aged 40 to 75 by uncovering patterns and insights from healthcare data, focusing on medications like Ozempic.

📗 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.

Machine Learning Code Implementations in Python

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

Here you can find the code which was used in my dissertation named "Quantile Regression Methods for Modelling Indoor PAH Levels from Lifestyle and Indoor Environment Information".

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.