Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

Project Name: Crop Yield Prediction
Category: Regression Analysis
Model Algo: Decision Tree Regressor
Github Repo: Link

Technologies Used

This AI-based project leverages Decision Tree Regression to predict crop yields with high accuracy. The backend is built using Flask, and the model is stored using Pickle for efficient loading and saving. The frontend interface is developed using HTML, CSS, and Bootstrap to provide a clean and responsive user experience.

Details of Project

This project addresses a critical need in modern agriculture: accurate crop yield forecasting. Using a trained Decision Tree Regression model, the system processes historical and environmental data to predict expected crop yields. The integration of AI allows for smarter agricultural planning by offering insights on planting, fertilization, and irrigation strategies. The model is integrated into a Flask-based web app with a Bootstrap-powered interface and real-time response capabilities.

By helping farmers optimize resource usage and plan more effectively, this solution supports improved profitability and promotes sustainable farming practices. It also helps in managing risks related to overproduction or crop failure by offering early, data-driven forecasts.

Project Features

Predicts crop yield based on input parameters using Decision Tree Regression.
Enables farmers to make data-driven decisions for resource allocation.
Pickle integration for saving/loading trained models efficiently.
Responsive and clean UI built with HTML, CSS, and Bootstrap.
Flask-powered backend for real-time predictions and smooth interactions.
Supports sustainable farming by minimizing resource waste.