Implementation of Saliency-based Agreement Metric for Ensemble Learning

Finding Donors with Machine Learning ( Support Vector Machine, Gradient Boosting Classifier, Random Forest Classifier )

This project focuses on leveraging AutoGluon's 'Tabular Prediction' to create accurate AutoML-based baseline models to predict the bike sharing demand using the Kaggle Bike Sharing demand dataset.

finding_donors machine learning model

Ensemble Learning with Pima_Indian dataset

Decision Tree, Random Forest, Bagging, Boosting

Developed an ensemble voting model that included Random Forests, Linear Regression, Orthogonal Matching Pursuit, and Gradient Boosting Regressor to predict future solar power generated by a solar plant in India at 98.7% accuracy. Placed 1st at the Virginia Tech Computational Modeling & Data Analytics Fall 2022 Data Competition.

CUE: CpG impUtation Ensemble for DNA Methylation Levels Across the HumanMethylation450 (HM450) BeadChip and HumanMethylation EPIC (HM850) BeadChip Platforms

Supervised Learning Modelings on White Wine Quality Dataset from UCI Machine Learning

First project Of machine learning Nanodegree (Supervised Learning)

Classification: Comparison between Logistic regression and ensemble methods

Finding Donor for CharityML - Machine Learning Nanodegree from Udacity

This project is an end to end data science project that is able to predict the chances of getting a disease I🤕🤒👩‍⚕️️ in organs like liver, kidey, heart, etc.

DCASE 2018 Challenge: Solution for task 5

This repository contains the code for a web-based diabetes prediction application using a machine learning model. The application is built using Flask and allows users to input various health parameters to predict the likelihood of diabetes using ensemble voting classifier.

Kaggle's: Machine Learning from Otto Group Product Classification Challenge. Demonstrates basic machine learning, analysis, and visualization techniques.

Udacity Machine Learning Engineer Nanodegree Supervised Learning Project: Finding Donors for CharityML