Recognition of Persomnality Types from Facebook status using Machine Learning

A method to predict activating, deactivating and resistance mutations in kinases

Designed and developed Agriculture crop recommendation system, an AI-powered interactive system for farmers where we have used random forest classification model, using HTML, CSS, JavaScript, and Python.

This is a project to detect if a person is suffering from a mental health issue by using a questionaire along with facial analysis. NOTE-The predictions may not be completely accurate. This is only a project aimed to showcase technical skills.

🏥 A model which gives the rate of change of emotions by classifying the emotions. This can be used to diagnose brain related diseases such as Bipolar disorder.

An Employee Attrition detection web application, that predicts if an employee is going to leave an organization in near future.

A Google Earth Engine Land use (crops) classification workflow using Random Forest, one year of ground data, Sentinel-2, and Landsats; to produce multiyear annual 30-m crop maps

An end-to-end application for crime rate detection and crime type classification

Disease diagnosis using ML; 3rd place at Hackcoming 2 🏆

This is a machine learning project to detect whether a note is real or fake [Accuracy : 99% | No Overfitting].

Floodplain Area Classifier Using Optical and Radar Imagery

Anveshan Hackathon Project Submission Repo of Numeric Nomads

An all-in-one health analysis platform offering multi-disease prediction (11+ conditions), personalized nutrition guidance, and symptom checking with precautionary insights for over 60 diseases."

AquaScribe is a smart water management system that leverages IoT sensors, ML Algorithms and automation to optimize irrigation processes in real-time

The one-stop AI solution to increase crop yield and reduce wastage of crops for farmers.

A light infoSec recon extension for Chrome browser

Designed and developed Agriculture crop recommendation system, an AI-powered interactive system for farmers where we have used random forest classification model, using HTML, CSS, JavaScript, and Python.

NutriNavigator is not just a nutritional recommendation system; it's also an e-commerce platform offering organic food products. Now working to dockerize it then host on azure or aws .

A full-stack web app to predict the risk of heart attacks using a machine learning model (Random Forest, 98.1% accuracy). Built with React, Node.js, and Python