This project predicts car prices using machine learning techniques. It involves data preprocessing, exploratory data analysis (EDA), feature engineering, model selection, and evaluation using regression models. The goal is to build an accurate model that estimates the price of a car based on various features.
- Handling missing values
- Encoding categorical variables
- Feature scaling and transformation
Models used: β Linear Regression
Evaluation Metrics:
- RΒ² Score
Model | RΒ² Score |
---|---|
Linear Regression | 0.85 |