Car Prices Prediction

This project focuses on predicting car prices using various regression techniques based on a set of technical and financial features. The analysis includes three different models: Simple Linear Regression using a single feature, Multiple Linear Regression using a selected set of features, and Polynomial Regression to capture non-linear relationships within the data. The workflow begins with loading the dataset (CarPrice_Assignment.csv), followed by exploratory data analysis (EDA) to understand data distributions and relationships. Data preprocessing steps include cleaning the dataset and transforming categorical variables into numerical format using one-hot encoding. Correlation analysis was conducted to identify the most influential features affecting car prices. The models were then built and evaluated using key performance metrics such as R-squared (R²) and Mean Squared Error (MSE). The results are further supported with visualizations to illustrate model performance and highlight the relationships between variables. This project demonstrates the practical application of regression techniques in building accurate and reliable predictive models.