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

Here the prediction and analysis of student scores using selected features is done entirely by linear regression machine learning algorithm. This project covers all methods of linear regression theory.

Machine Learning Regression Model to Predict Energy Efficiency of Buildings

📗 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.

Prediction of Insurance Charges using Regression

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

A taxi company called Sweet Lift has collected historical data on taxi orders at the airport and they need to predict the number of taxi orders for the next hour.

Machine learning models

Predicting Marijuana Use Disorder: A Machine Learning Approach

Analysis will help Jamboree in understanding what factors are important in graduate admissions and how these factors are interrelated among themselves. It will also help predict one's chances of admission given the rest of the variables.

This repository contains a Jupyter Notebook for the analysis of Non-Small Cell Lung Cancer (NSCLC) data using various machine learning techniques.

A MLR algorithm that analyzes diabetes data in African Americans to predict a diabetes diagnosis

This project analyses global salary data from the AI/ML industry, encompassing various job roles, company sizes, and geographic locations. Lasso regression achieved lowest RMSE.

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, sleep problems, heat-related symptoms)

An R package for modern methods for non-probability surveys

End-to-End Machine Learning project I made as a machine learning intern @ Mentorness

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 project develops a machine learning model to predict the salaries of baseball players based on their past performance.

Final project for ECON434