CropCareAI is a web application that uses artificial intelligence to identify and diagnose plant diseases. It's designed to help plant enthusiasts, farmers, and researchers by providing quick and accurate disease detection through image analysis.

A detailed spatiotemporal study, geospatial visualizations, and predictive modeling to offer insights into the agricultural sector's dynamics.

Tools & skills Used: Python, MATLAB, Seaborn, ML models

Simple and fast neural network framework for JavaSript

My IMBD Movie Rating Prediction Model

An online study guide for the SOA's predictive analytics exam.

Predictive Analysis for Musculoskeletal Injury Risk using Machine Learning and Flask

"Wind Power Predictor" is a machine learning project that forecasts turbine output using real-time data from Turkish wind farms. Its web app interface offers convenient access to predictions, enabling informed decisions for maximizing energy production and advancing renewable energy usage.

This project is part of the Statistical Learning course.

This repository contains my AVETTI Commerce internship's reports and predicting accuracy of the models.

This repository contains the prediction of accuracy of the models build with the help of Random forest and Support Vector machine.

Machine Learning Soccer Score Predictor Flask App

AutoValuate: A machine learning-driven tool for classifying used car prices as high or low, enabling smarter decisions in the car resale market.

The real estate app features three modules for predictive pricing, market insights, and personalized recommendations, utilizing machine learning and data analysis. It redefines real estate exploration by empowering users with valuable tools and insights.

Sliding Multi-Window Regression Model

Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.

Code and walkthrough for reproducibility of the Case Study (Crude Oil Price Prediction using Artificial Neural Network) presented in class.

Building a Logistic regression model for the prediction of Sales and implementing it in a web app based on Python 🐍

A Machine Learning exploration evaluating various models to predict Airbnb prices, culminating in an optimized Gradient Boosting Regressor.

Projects completed as coursework for my MS in Analytics from Georgia Tech, or as personal projects for fun.