Dry Bean Analysis Project

This repository contains a Jupyter Notebook for analyzing and modeling data related to the Dry Bean Dataset.

Overview

The notebook includes:

• Data preprocessing and exploration.
• Visualization of key features and patterns.
• Machine learning model training and evaluation.
• Insights and conclusions drawn from the data.

Requirements

The following Python libraries are required to run the notebook:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• Any additional libraries used in the notebook.

Dataset

The analysis uses the Dry Bean Dataset from the UCI Machine Learning Repository. This dataset contains information on various bean species and their features.

Usage

1. Clone the repository:

   git clone https://github.com/your-username/dry-bean-analysis.git
   cd dry-bean-analysis

2. Install the required libraries:

   pip install -r requirements.txt

3. Open and run the Jupyter Notebook:

   jupyter notebook DRYBEAN.ipynb

Results

Key results include:

• Descriptive statistics and feature distributions.
• Machine learning model performance metrics.

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you would like to contribute to this project.

License

This project is licensed under the MIT License.