CropYieldPredictor

A data science project for predicting crop yields using machine learning techniques. All code is provided in Jupyter Notebooks.

Overview

This repository contains notebooks and resources to build, train, and evaluate models that predict crop yields based on agricultural data. The goal is to enable better decision-making for farmers and stakeholders by forecasting expected yields using historical and environmental data.

Features

• Data Exploration: Analyze agricultural datasets with visualizations and statistics.
• Preprocessing: Clean and prepare data for modeling.
• Model Training: Implement and test a Random Forest Model.
• Evaluation: Assess model performance using appropriate metrics.
• Prediction: Make yield predictions on new/unseen data.

Repository Structure

• .ipynb files: Jupyter Notebooks with code and explanations.
• data/: Folder for input datasets (may be ignored in version control).
• models/: The model I trained first, if you would like to try beating it.
• requirements.txt or environment.yml: Dependencies (if present).

Getting Started

1. Clone the repository

   git clone https://github.com/MeghP89/CropYieldPredictor.git
   cd CropYieldPredictor

2. Install dependencies

   • Using pip:

     pip install -r requirements.txt

   • Or, with Anaconda:

     conda env create -f environment.yml
     conda activate cropyield

3. Launch Jupyter Notebook

   jupyter notebook

   Open the notebooks and follow the instructions within to run analyses or make predictions.

Usage

• Open the notebooks starting with data and follow the step-by-step cells.
• Modify code or parameters as needed to experiment with different models or datasets.
• Results and visualizations will be generated within the notebooks.

Contributing

Contributions are welcome! Please submit issues or pull requests for improvements, bug fixes, or new features.

License

This project is licensed under the MIT License.

Author

MeghP89

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For more details, refer to the documentation inside each notebook.