Car Data Analysis

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Project Overview

This project analyzes a dataset of 11,914 car listings with 16 features.
The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.

Objectives

• Import and explore the dataset
• Clean and preprocess data (handling missing values, correcting errors)
• Engineer new features (e.g., Total MPG)
• Perform descriptive statistics and group analysis
• Visualize key relationships between car attributes
• Identify correlations and market trends

Tech Stack

• Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Jupyter Notebook

Key Insights

• Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
• MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
• Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
• Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
• Vehicle Size: larger cars are both more popular and more expensive.
• Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.

Repository Structure

• notebooks/ -> Jupyter notebooks with analysis
• data/ -> sample dataset + link to source
• images/ -> charts and visualizations
• requirements.txt -> list of dependencies

How to Run

1. Clone the repository

   git clone https://github.com/MapiAI/car-data-analysis.git
   

2. Install requirements

   pip install -r requirements.txt
   

3. Open the notebook

   jupyter notebook notebooks/car_data_analysis.ipynb
   
   

Visuals

This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.

Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.

The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.

Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.

For the complete set of visualizations, please refer to the Car Data Analysis Notebook.

Next Steps

• Extend analysis with machine learning models (e.g., car price prediction)

• Publish interactive dashboard in Tableau

License

This project is licensed under the MIT License – free to use, modify, and share with attribution.

Author & Contact

Created by Maria Petralia (MaPi)

• GitHub Profile
• LinkedIn
• Portfolio

⭐ If you find this project useful, please consider giving it a star on GitHub!

Feel free to connect or reach out for collaboration opportunities!