E-commerce Machine Learning Analysis

Project_E-commerce-ML is a data analysis project that applies machine learning techniques to analyze trends and patterns in an e-commerce dataset. By utilizing machine learning algorithms, this project aims to uncover valuable insights that can drive business decisions within the e-commerce domain.

Key Objectives

• Data Analysis: Explore and preprocess the provided e-commerce dataset.
• Machine Learning Models: Develop predictive models for customer behavior, sales forecasting, or other relevant factors.
• Insight Generation: Extract meaningful insights from the analysis and model results.
• Business Impact: Translate findings into actionable recommendations for the e-commerce industry.

Getting Started

1. Clone the repository: git clone https://github.com/sebagodoy1/Project_E-commerce-ML.git
2. Navigate to the project directory: cd Project_E-commerce-ML
3. Install dependencies: pip install -r requirements.txt
4. Run the analysis notebook: main.ipynb

Project Structure

• data/: Store the dataset files.
• notebooks/: Jupyter notebooks for data analysis and model development.
• results/: Store generated analysis reports and model outputs.

Screenshots

Explore and preprocess the e-commerce dataset.

Develop predictive models using machine learning techniques.

Contributing

Contributions are welcome! Feel free to open issues and pull requests for enhancements, fixes, or new features.

License

This project is licensed under the MIT License.