Supporting UN SDG 2: Zero Hunger

🌾 Crop Yield Predictor

Machine Learning-Powered Web Application for Sustainable Agriculture and Food Security

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Key Features

Real-time Predictions

Interactive web interface with live parameter updates using advanced machine learning algorithms.

Multi-factor Analysis

Considers weather, soil, nutrients, and farming practices for comprehensive yield prediction.

Visual Analytics

Comprehensive charts, gauges, and feature importance analysis for data-driven insights.

Dual Model Support

Random Forest and Linear Regression algorithms for robust and interpretable predictions.

Actionable Insights

Personalized recommendations based on predicted yields and environmental conditions.

Global Applicability

Designed for diverse agricultural contexts and regions worldwide.

Project Impact

10,000+
Data Samples
53+
Features Analyzed
2
ML Models
SDG 2
Zero Hunger

Application Demo

Main Interface
Interactive Main Interface

Real-time parameter adjustment and prediction display with user-friendly controls.

Prediction Results
Prediction Results

Comprehensive yield predictions with status indicators and confidence intervals.

Visual Analytics
Visual Analytics

Interactive charts, gauges, and feature importance analysis for data insights.

Model Comparison
Model Comparison

Side-by-side comparison of Random Forest and Linear Regression performance metrics.

Supporting SDG 2: Zero Hunger

Enhanced Productivity

Helping farmers optimize crop yields through data-driven insights and recommendations.

Reduced Food Waste

Better yield predictions enable improved harvest planning and storage management.

Smallholder Support

Accessible technology for resource-constrained agricultural communities worldwide.

Ready to Get Started?

Join the fight against global hunger with data-driven agriculture

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Python 3.8+ Streamlit Scikit-learn Machine Learning Open Source