This project focuses on predicting car prices using machine learning techniques. It involves data preprocessing, feature engineering, and building regression models to estimate the price of a car based on various attributes such as mileage, year of manufacture, brand, and more. The goal is to create an accurate and efficient model that can assist in evaluating the market value of cars.
Key features:
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Model Training (using algorithms such as Linear Regression, Decision Trees, etc)
- Model Evaluation and Optimization