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https://github.com/jonathancaleb/adap
📊🌱 Agricultural Data Analysis Platform 🌍🚜 A personal initiative to analyze coffee growth trends in Uganda using Python, data science, and machine learning. This project supports sustainable farming with predictive models and interactive visualizations. 🍃📈
https://github.com/jonathancaleb/adap
data-analysis data-science python
Last synced: about 1 month ago
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📊🌱 Agricultural Data Analysis Platform 🌍🚜 A personal initiative to analyze coffee growth trends in Uganda using Python, data science, and machine learning. This project supports sustainable farming with predictive models and interactive visualizations. 🍃📈
- Host: GitHub
- URL: https://github.com/jonathancaleb/adap
- Owner: jonathancaleb
- Created: 2024-10-15T12:39:40.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-11-09T18:05:37.000Z (2 months ago)
- Last Synced: 2024-11-09T18:17:51.085Z (2 months ago)
- Topics: data-analysis, data-science, python
- Language: Python
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Agricultural Data Analysis Platform: Coffee Growth Trends in Uganda
## Read More
For more detailed information about this project, you can read the full documentation [here](https://docs.google.com/document/d/e/2PACX-1vT-IZQuFYBKlHodJV94y82YkorVJ02d8GLV8S-SW68Ui2UmOArPpmTSG0lBpAvcxw/pub).
## Project Status
>
> **This project is under active development**
> **Purpose:** This is a personal project aimed at expanding my knowledge in data science and machine learning while supporting sustainable agriculture in Uganda.
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Technology Stack](#technology-stack)
- [Project Structure](#project-structure)
- [Setup Instructions](#setup-instructions)
- [Usage](#usage)
- [Contributing](#contributing)
- [Code of Conduct](#code-of-conduct)
- [License](#license)
## Overview
The **Agricultural Data Analysis Platform** is a data-driven project focused on analyzing coffee growth trends in Uganda. By leveraging data science and machine learning, the platform aims to provide insights into coffee production to help support sustainable farming practices. This platform offers a range of tools for visualizing data, predicting crop yield, and understanding environmental impact.
## Features
- **Data Collection**: Pulls historical and current data on coffee growth, weather patterns, and soil conditions.
- **Data Processing**: Cleans and preprocesses data for more accurate analysis.
- **Predictive Modeling**: Uses machine learning models to predict coffee yields based on environmental factors.
- **Interactive Visualizations**: Provides a user-friendly interface with graphs, charts, and maps to illustrate key data insights.
- **Sustainable Farming Insights**: Offers tailored recommendations to support sustainable agricultural practices.
## Technology Stack
- **Python**: Primary language for data processing, machine learning, and data visualization.
- **R** (optional): For statistical analysis and data visualization.
- **JavaScript**: For interactive web-based visualizations, using libraries like D3.js or Plotly.
- **HTML/CSS**: Structure and styling for the web-based interface.
- **Flask** or **Django**: Backend framework for web application functionality.
- **Jupyter Notebooks**: Interactive notebooks for exploratory data analysis and model development.
## Project Structure
```plaintext
agricultural-data-analysis-platform/
│
├── data/ # Raw data and datasets
│ ├── raw/ # Raw data files
│ └── processed/ # Cleaned and processed data
│
├── notebooks/ # Jupyter notebooks for EDA and modeling
│ ├── data_cleaning.ipynb # Data cleaning notebook
│ ├── eda.ipynb # Exploratory data analysis notebook
│ └── modeling.ipynb # Model building and evaluation notebook
│
├── src/ # Source code
│ ├── data_processing.py # Data cleaning and preprocessing scripts
│ ├── model_training.py # Model training and evaluation scripts
│ ├── visualization.py # Data visualization scripts
│ └── main.py # Main script to run the platform
│
├── web_app/ # Web-based user interface
│ ├── static/ # Static assets (CSS, JavaScript)
│ ├── templates/ # HTML templates for the UI
│ └── app.py # Flask or Django server
│
├── requirements.txt # Python dependencies
└── README.md # Project overview and setup instructions
```
## Setup Instructions
Clone the repository:
```bash
git clone https://github.com/yourusername/agricultural-data-analysis-platform.git
cd agricultural-data-analysis-platform
```
Install dependencies:
```bash
pip install -r requirements.txt
```
### Data Preparation
Place raw datasets in the `data/raw/` directory.
Run the `data_processing.py` script to clean and preprocess the data:
```bash
python src/data_processing.py
```
### Model Training
Use the `model_training.py` script to train and evaluate the models:
```bash
python src/model_training.py
```
### Run the Web Application
Start the web application to access interactive visualizations:
```bash
python web_app/app.py
```
Open the application in your browser at [http://localhost:5000](http://localhost:5000).
## Usage
The platform provides the following functionalities:
- **Data Insights**: View graphs and charts illustrating trends in coffee growth and yield.
- **Yield Prediction**: Use predictive models to estimate future yields based on weather and soil data.
- **Sustainable Farming Recommendations**: Explore suggestions for sustainable farming practices that can be implemented to maximize yield while maintaining environmental integrity.
## Contributing
Contributions are welcome! If you have suggestions, improvements, or new features, please feel free to:
1. Fork the repository.
2. Create a new branch.
3. Make changes and test thoroughly.
4. Submit a pull request, and I will review it as soon as possible.
## Code of Conduct
Please ensure that all contributions adhere to the project's Code of Conduct.
## License
This project is licensed under the MIT License - see the LICENSE file for details.