Accurate pricing models are crucial for Rusty Bargain to empower users with quick and precise market value estimations. This project demonstrates my ability to design and evaluate predictive models that balance accuracy, speed, and efficiency.

Project Overview

Rusty Bargain, a service specializing in used car sales, aimed to create an app for estimating car market values. Using historical data, technical specifications, trim versions, and prices, the goal was to develop a model capable of predicting car values reliably and efficiently. The key metrics for the project included prediction quality, speed, and training time.

Methodology and Results

The methodology was designed to ensure robust data analysis and model evaluation:

• Data Exploration
• Model Training
• Analysis
• Conclusion

Through systematic exploration, training, and evaluation:

• CatBoost: Stood out as a balanced model with robust accuracy and fast prediction capabilities.
• RandomForestRegressor: Delivered the lowest RMSE and demonstrated excellent predictive power for this task.
• Linear Regression: Validated the complexity of relationships between features and target price, underscoring the need for nonlinear approaches.

Recommendations

• Deploy CatBoost or Random Forest: Both models exhibit high accuracy and efficiency, making them suitable for real-world implementation.
• Regular Monitoring and Updates: Periodic updates to the model with new data can enhance accuracy and minimize performance degradation.
• Explore Additional Features: Incorporating variables like region-specific trends or market demands could further improve predictions.
• Refine Hyperparameters: Conduct focused hyperparameter optimization for LightGBM to enhance its potential performance.

This project highlights my ability to transform raw data into actionable insights, leveraging advanced predictive techniques to meet business goals effectively. It exemplifies my commitment to designing solutions that are both practical and scalable in dynamic industries. 😊