The goal of this project is to develop a machine learning model that accurately predicts the market value of used cars based on various features such as brand, mileage, fuel type, and transmission.
- Data Ingestion: The system must be able to process structured data (CSV or JSON) containing historical car sales records.
- Preprocessing: The system must handle missing values, encode categorical variables (e.g., One-Hot Encoding for Brand), and scale numerical features.
- Price Prediction: The core AI model must provide a continuous numerical output (Price) based on user input features.
- User Interface: A simple interface (Web or CLI) where users can input car details and receive an instant valuation.
- Accuracy: The model should aim for an R-squared value of 0.85 or higher on the test dataset.
- Performance: The model inference time should be less than 200ms per request.
- Scalability: The architecture should allow for adding more training data as it becomes available.
- Usability: The input form should validate data types (e.g., mileage cannot be negative).
- Language: Python 3.x
- Libraries: Pandas, Scikit-Learn, NumPy
- Framework: Flask or Streamlit (for the UI/API)
- Model: Random Forest Regressor or XGBoost