🌾 SmartHinga Crop Yield Prediction

🎯 Mission

Empowering farmers with data-driven insights to make informed agricultural decisions. This comprehensive solution combines machine learning predictions with an intuitive mobile application to optimize crop yields based on climatic and agricultural conditions, making small to medium-sized farming operations more productive and sustainable worldwide.

Data Source: Kaggle

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🎥 Demo

Watch the project in action:

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🚀 Live Deployment

The API is live and ready to use!

🔗 API Documentation: https://linear-regression-model-aef5.onrender.com/documentation/

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📱 Getting Started with the Mobile App

Prerequisites

• Flutter SDK (>=3.7.2)
• Android Studio / VS Code
• Git

Quick Setup

# 1. Clone the repository
git clone https://github.com/MikeManzi/linear_regression_model.git

# 2. Navigate to the Flutter app directory
cd linear_regression_model/summative/flutterapp

# 3. Install dependencies
flutter pub get

# 4. Run the app
flutter run

📂 Project Structure

linear_regression_model/
├── 📊 linear_regression/          # ML model training & analysis
│   ├── crop_yield_prediction.ipynb
│   ├── crop_yield.csv
│   └── optimal_crop_yield_model.pkl
├── 🔧 summative/
│   ├── 🚀 api/                   # Flask REST API
│   │   ├── app.py
│   │   ├── requirements.txt
│   │   └── optimal_crop_yield_model.pkl.gz
│   └── 📱 flutterapp/            # Mobile application
│       ├── lib/
│       ├── pubspec.yaml
│       └── README.md
└── 📖 README.md