Project Overview
This project focuses on predicting the price of a car based on various features such as the make, model, year, engine size, and mileage. The goal is to create a predictive model that can estimate the market value of a car using machine learning techniques.
Key Features
The project involves the following key steps:
- Data collection and preprocessing
- Feature engineering to select relevant car attributes
- Model training using algorithms like Linear Regression and Decision Trees
- Model evaluation and optimization
- Deployment of the model for real-time car price prediction
Technologies Used
The project utilizes the following technologies:
- Python
- Pandas and NumPy for data manipulation
- Scikit-learn for machine learning
- Matplotlib and Seaborn for data visualization
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