This repository contains the code and documentation for an automotive dealership data mining project aimed at enhancing customer experience and sales strategies. The project leverages various datasets, including vehicle catalogues, customer transactions, registrations, and marketing data, to extract valuable insights and provide personalized recommendations.

• Categorize vehicles into distinct segments based on customer preferences.
• Empower the sales team to recommend vehicles tailored to individual customer needs.
• Enhance strategic decision-making for the dealership through data-driven insights.

• DataMining_Project.ipynb: Jupyter notebooks containing data exploration, preprocessing, modeling, and analysis.
• app.py: Application Interface with Streamlit.
• README.md: Documentation providing an overview of the project, its objectives, and repository structure.

1. Clone this repository to your local machine.
2. Run the Jupyter notebooks to explore the data analysis, preprocess data and train models.
3. Use the provided source code in the app.py to make predictions and use the interface.
4. Refer to the documentation in the notebooks and source code for detailed explanations of each step.

• Python 3.x
• Jupyter Notebook
• NumPy, pandas, scikit-learn, matplotlib, seaborn (install using pip install -r requirements.txt)

This project is licensed under the MIT License.