StyleGAN2-ADA trained on a dataset of 2000+ sneaker images

This project is a Flask-based platform for patients, donors, and doctors, integrating hospital management in one solution. It uses Machine Learning models for predictive health analysis and Power BI for interactive dashboards. Through this project, I enhanced my skills in full-stack development, applied ML, and healthcare data solutions.

This project applies 🤖Machine Learning techniques to analyze these features and build a predictive model that estimates the selling price of diamonds

Home Loan Approval Prediction System uses machine learning to predict loan approval outcomes, helping financial institutions streamline decisions. It preprocesses data, handles missing values, encodes features, and uses a Random Forest Classifier for feature selection, optimizing the model for accuracy and efficiency.

Body Area Network (BAN) This repository implements a machine learning model for anomaly detection in body sensor data collected through a Body Area Network (BAN). The model analyzes heart rate and body temperature readings to identify potential health concerns.

Works by building many individual decision trees and combining their outputs. For a final answer, it takes the average (for numbers) or majority vote (for categories) of all trees, making it stable and reliable.

Price comparison application using AI-ML model.

A machine learning system that predicts Netflix content popularity using Random Forest algorithms. Analyzes 8,800+ titles to forecast success based on content type, ratings, duration, and release patterns. Features interactive dashboard with 90.6% accuracy for data-driven content strategy decisions.

Fertilizer prediction Api (https://predict-fertilizer-api.onrender.com/predict)

Quantifying Integrity in the Digital Age Misinformation spreads rapidly, accountability often falters, and the lines between transparency and manipulation blur

Also builds decision trees, but does so sequentially. Each new tree is built to specifically correct the errors made by the ones before it, allowing the model to become progressively more accurate.

An LSTM is a type of AI model designed to understand sequences and time-based patterns. Its key feature is a "memory" that lets it remember important information from the past (like previous traffic jams) to make better predictions about the future.

Deploy a machine-learning web app that makes predictions based on data using Python and Flask.

A high-performance gradient boosting algorithm that specializes in handling categorical data (like location names or event types) directly. It excels at understanding "context" without needing complex data preprocessing.

This project is a Content-Based Movie Recommendation System that recommends movies to users based on similarity of overview, genres, keywords, cast, and crew. The system uses TF-IDF, Count Vectorization, Cosine Similarity, and NLP techniques to compute how similar two movies are.

Training model to predict passenger survival.