Agricultural Product Price Prediction

📌 Project Overview

This project aims to develop a machine learning model capable of predicting agricultural product prices using advanced data analysis and machine learning techniques.

🎯 Objectives

• Build an accurate predictive model for agricultural product prices
• Compare different machine learning techniques
• Provide a decision-support tool for farmers and traders

📦 Technical Requirements

Environment

• Python 3.8+
• Jupyter Notebook or compatible IDE

Dependencies

• numpy
• pandas
• matplotlib
• scikit-learn
• joblib
• seaborn
• openpyxl

🚀 Installation

1. Clone the repository

git clone https://github.com/Grand-Nord-Developpers-Community/live-coding-session-MachineLearning.git
cd agricultural-price-prediction

2. Install dependencies

pip install -r requirements.txt

📊 Project Contents

• ecocrops_dataset.xlsx: Agricultural prices dataset
• Prediction Prix Produits agricole.ipynb: Main Jupyter Notebook
• best_price_prediction_model.joblib: Exported model

🧠 Methodology

Data Processing Steps

1. Data exploration
2. Preprocessing and encoding
3. Training and test data separation
4. Multiple model training
5. Comparative evaluation

Evaluated Models

• Linear Regression
• Decision Tree
• Random Forest
• Gradient Boosting

📈 Model Performance

Model	MSE	R²	Performance
Linear Regression	481,529.90	0.96%	Low
Decision Tree	119,260.23	75.5%	Medium
Random Forest	94,404.43	80.6%	Very Good
Gradient Boosting	89,783.21	81.5%	Excellent

🔍 Model Usage

# Prediction example
predicted_price = predict_price(
    zone='MORA', 
    product='Rice', 
    month='April', 
    year=2024
)

🔮 Key Features

• Agricultural price prediction
• Price distribution analysis
• Trend visualization
• Best model export

📋 Limitations and Future Perspectives

• Current accuracy: ±3-8% of average prices
• Improvement areas:
  • Larger data collection
  • Integration of additional variables
  • Exploration of more advanced models

👥 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the project
2. Create a feature branch
3. Commit your changes
4. Submit a pull request

📄 License

MIT

🙏 Acknowledgments

• Development team
• Contributors
• Data sources