CottonProphet

CottonProphet is a predictive analytics project focused on forecasting cotton production using various machine learning techniques. This project aims to provide insights and predictions that can help farmers, agronomists, and stakeholders make informed decisions regarding cotton cultivation and management.

Table of Contents

• Overview
• Objectives
• Data Sources
• Methodology
• Key Features
• Installation
• Usage
• Contributing

Overview

CottonProphet leverages data science and machine learning to analyze historical data related to cotton production and predict future yields. By understanding trends and influencing factors, this project aims to assist in improving agricultural practices and optimizing production outcomes.

Objectives

• To predict cotton yield based on historical data and various influencing factors.
• To analyze the impact of different variables (such as weather conditions, soil quality, and crop management practices) on cotton production.
• To provide a user-friendly interface for stakeholders to visualize predictions and insights.

Data Sources

The project uses multiple datasets including:

• Weather Data: Historical weather conditions including temperature, rainfall, and humidity.
• Soil Data: Information on soil quality, nutrients, and pH levels.
• Crop Management Data: Data on planting, irrigation, and fertilization practices.
• Historical Yield Data: Past cotton production records to train predictive models.

Methodology

The project employs the following methodology:

1. Data Collection and Preprocessing: Gathering and cleaning data from various sources to ensure quality and consistency.
2. Feature Engineering: Identifying key features that influence cotton production and creating new features where necessary.
3. Model Development: Building and testing various machine learning models (e.g., linear regression, random forests, neural networks) to predict cotton yields.
4. Model Evaluation: Comparing models based on accuracy, precision, recall, and other relevant metrics.
5. Deployment: Implementing the best-performing model and integrating it into a user-friendly interface.

Key Features

• Predictive Analytics: Forecast future cotton yields based on historical data and machine learning models.
• Data Visualization: Interactive charts and graphs to help users understand trends and predictions.
• Model Comparison: Evaluate different models to determine the most accurate predictor.

Installation

To run CottonProphet on your local machine, follow these steps:

1. Clone the repository:

   git clone https://github.com/MohammedLike/CottonProphet.git

2. Navigate to the project directory:

   cd CottonProphet

3. Set up a virtual environment (optional but recommended):

   python3 -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

4. Install the required dependencies:

   pip install -r requirements.txt

Usage

Once the environment is set up, you can run the predictive models and explore the data by following these steps:

1. Run the prediction script:

   python predict.py

2. View the results: The script will output predictions and save them in a specified file or display them via the console.
3. Explore the visualizations: Use the provided notebooks or scripts to generate and view data visualizations.

Contributing

Contributions to CottonProphet are welcome! If you have ideas for improvements, new features, or additional analyses, feel free to fork the repository, create a feature branch, and submit a pull request. You can also open an issue to discuss your ideas.

Video Demo: https://vimeo.com/908592617?share=copy#t=0