A Machine Learning project for Cardiovascular disease prediction

This Project Predicts whether the Email/SMS is spam or ham by using the extensive knowledge of NLP and various ML Algorithms. Deployed on Streamlit & Herokuapp

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

A voting classifier based on decision tree arch.

A novel ML-based binary classifier to tell viral and non-viral long reads apart in metagenomic samples.

A tool for AMR gene family prediction, simple and ML-based

A simple binary email classifier that classifies emails into spam and ham and also performs topic modelling using LDA

Descriptive, predictive analysis of taxability

Advancing Cybersecurity with AI: This project fortifies phishing defense using cutting-edge models, trained on a diverse dataset of 737,000 URLs. It was the final project for the AI for Cybersecurity course in my Master's at uOttawa in 2023.

Stroke Prediction App

Kaggle Playground Series - Season 3, Episode 26 - Multi-Class Cirrhosis | EDA | MI-Score | Feature Engineering

A visual approach to understand the beauty of voting classifiers

Project for Machine Learning Data Mining course

Using classical machine learning techniques for classifying the data into 9 classes which can be further used for cancer detection.

Telecom churn prediction using a voting classifier

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.

Classification

This project focuses on predicting the likelihood of diabetes in individuals using ensemble machine learning models. It combines various ensemble techniques, including Random Forest, AdaBoost, Gradient Boosting, Bagging, Extra Trees, XGBoost, Voting Classifier and some others to get predictions.

This is a Machine learning project trained for Diabetes Prediction using Multiple Ensemble models like Random Forest, Ada boost, cat boost and a few more. It is trained on the Pima Indian Diabetes Dataset.

Welcome to our Classification Modeling Project! In this project, we've employed various machine learning algorithms and techniques to solve a classification problem.