Car Price Prediction

Hands-on workshop on numerical data analysis and machine learning regression models to predict car prices.

This practical workshop focuses on Predictive Modeling and Numerical Data Analysis. Using real-world datasets, we explore how to build and evaluate robust machine learning regression models to accurately predict car prices.

Workshop Instructor: Zahra Amini

The repository contains the end-to-end pipeline, from exploratory data analysis (EDA) and feature engineering to model training and performance evaluation.

🚀 ACCESS WORKSHOP MATERIALS ON GITHUB

Workshop Highlights

Predicting numerical continuous values is a fundamental application of Machine Learning. This curriculum covers:

  • Exploratory Data Analysis (EDA): Visualizing data distributions and identifying correlations between car features (e.g., mileage, year, engine size) and the target price.
  • Data Preprocessing: Handling numerical outliers, encoding categorical variables (like car brand and fuel type), and feature scaling.
  • Regression Modeling: Implementing and comparing various algorithms such as Linear Regression, Decision Trees, and Random Forests.
  • Model Evaluation: Measuring the accuracy of predictions using industry-standard metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared ($R^2$).

Technologies used: Python, Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn.