Car Price predict

features such as manufacturing year, mileage, fuel type, transmission, and engine specifications. The goal was to transform raw car data into an accurate predictive model that can assist users in estimating the fair market value of a vehicle.

The workflow covered the full data science pipeline, starting with data cleaning and preprocessing, handling missing values, and encoding categorical features. I performed exploratory data analysis (EDA) to understand the relationships between variables and identify the most influential factors affecting car prices.

I then trained and evaluated regression models to achieve reliable predictions, using performance metrics such as R² Score and Mean Squared Error to ensure model quality. The project demonstrates my ability to build end-to-end predictive systems, apply machine learning techniques to real-world datasets, and deliver practical, data-driven solutions.

Tools & Technologies: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Machine Learning (Regression)