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

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

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.

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.

A simple chatbot built using Python and regex

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.

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

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

In this project, IPL win predictor app is created that takes user input as batting team, bowling team, host city, target score, current score, overs completed, wickets taken and returns the winning probability percentage of each team.

Training model to predict passenger survival.

ML-Based Disease Prediction and Medical Recommendation Web App

Sentiment Analysis For Restaurant Reviews

This repository contains a machine learning model for classifying waste types (organic & inorganic), along with a Flask-based REST API for backend integration in the Trash to Cash platform.

The project was built with data handling performed through Pandas and NumPy for efficient manipulation of environmental datasets. Machine learning models, including Linear Regression and Random Forest Regressor, were implemented using Scikit-learn. Model performance was evaluated using metrics like R² Score, MAE, RMSE.Trained modules were in .pkl .

This is a web application that provides weather forecast predictions. It allows users to search for a city and view 6‑day forecasts with parameters such as temperature, humidity, pressure, wind speed, and general conditions. The app also includes a prediction history feature and customizable settings like unit or theme preferences.

Problem Statement: Financial institutions face significant challenges in detecting fraudulent activities due to the large volume of transactions and the sophistication of modern fraud techniques. The problem is to design AI models that can accurately detect fraudulent transactions in real-time while minimizing false positives.