This project focuses on predicting Sale Prices using the AmesHousing dataset. Initially, the dataset contained numerous features, requiring careful selection and processing. The project follows a structured approach by leveraging design patterns to enhance maintainability, scalability, and reusability.
To maintain a clean and modular code structure, multiple design patterns were implemented. These patterns helped in organizing various components systematically and improving flexibility.
- Implemented using Python's
abcmodule`. - Ensures a common interface for all method implementations.
- Enforces consistency across different strategies used in the project.
- Helps in maintaining a clear separation of concerns by defining methods that must be implemented in child classes.
💡 How It Helped?
- Allowed easy extension of functionalities without modifying existing code.
- Improved code readability and reusability.
- Encouraged a structured approach to defining multiple strategies.
The project is well-structured, separating concerns into dedicated folders. A key directory is:
This folder contains documentation and examples of different design patterns used, such as:
- Factory Design Pattern 🏭
- Strategy Design Pattern 🎯
- Template Design Pattern 📑
Since this project is built entirely using Abstract Method Patterns 🏗️, the implemented methods can be reused 🔄 in other projects as a reference 📖.
This structured approach enhances code maintainability 🛠️, making development more professional 👨💻🎯 and efficient ⚡.
Adopting this design pattern ensures that future projects are easier to implement 🚀 and follow a consistent & scalable architecture 🏢📈
By incorporating design patterns, this project effectively manages complexity and enhances modularity. The use of abstract method patterns ensures a solid foundation for adding new features with minimal code modification.