🚗 Car Auction Data Analysis

📊 Final Project - MIS 6382 - The University of Texas at Dallas

📌 Overview

The dataset contains information about cars listed on an auction website, covering both electric and non-electric vehicles from various manufacturers. It includes attributes such as make, model, year, fuel type, transmission, mileage, price, and other relevant details. This dataset can be useful for data analysis, price prediction models, and trend analysis in the automotive market 🚗⚡📊 using Python and Object-Oriented Programming (OOP) principles.

🔗 Project Deliverables:

• 📝 Python Code (Jupyter Notebook)
• 📈 Data Visualizations & Analysis
• 📑 Documentation
• 🎤 Final Presentation Slides

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📂 Project Structure

📁 Car-Auction-Data-Analysis
│── 📄 FinalProjectCode.ipynb  # Jupyter Notebook with code & analysis
│── 📊 Group04_MIS6382.501_FinalPresentation.pptx  # Presentation slides
│── 📜 README.md  # This file

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📊 Data Visualization Requirements

Use Python to generate the following seven types of visualizations to explore and present data trends:

📊 Bar Charts - Comparing car prices across manufacturers.
📦 Boxplots - Displaying price distributions.
📈 Line Plots - Tracking car price trends over time.
🔵 Scatter Plots - Examining price vs. mileage relationships.
📊 Histograms - Understanding price distributions.
🥧 Pie Charts - Showing manufacturer market share.
🌡️ Heat Maps - Identifying customer rating trends (Extra).

Each visualization must have a clear explanation describing data trends.

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📝 Project Components

🏛 Object-Oriented Programming (OOP) Implementation

The project involves building Python classes to structure the dataset effectively.

📌 Class Structure:
1️⃣ Car Class 🏎️

• Attributes: date, model, company, type, rating, price, mileage
• Includes a Unique ID (unique_id) assigned using id(self).
• Implements __str__ method to return a formatted string representation.

2️⃣ Subclasses:

• 🔌 ElectricCar → Represents electric cars.
• 🚗 NonElectricCar → Represents non-electric cars.

3️⃣ Brand-Specific Subclasses:

• 🚘 Tesla (inherits ElectricCar)
• 🚙 Ford (inherits NonElectricCar)
• 🚗 BMW (inherits NonElectricCar)

📌 Example Usage:

FordCar = Ford("2022-11-15", "Model A", 2, 641, 86.06)
print(str(FordCar))
# Output: unique_id,2022-11-15,Model A,Ford,NonElectric,2,641,86.06

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📂 Dataset Processing & CSV Generation

📌 Steps to process and convert the dataset into CSV:
1️⃣ Load the provided dataset (pickle file).
2️⃣ Convert the objects into a structured CSV file.
3️⃣ Write the column headers:

unique_id,date,model,company,type,rating,price,mileage

4️⃣ Loop through objects and save them in CSV format.

📌 Snippet for CSV Generation:

with open('data.csv', 'w') as f:
    f.write("unique_id,date,model,company,type,rating,price,mileage
")
    for obj in objects:
        f.write(str(obj) + '\n')

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📜 Documentation Requirements

📌 The Jupyter Notebook should include:
✅ Introduction - Overview of the dataset & attributes.
✅ Data Cleaning - Handling missing values:
✅ Categorical → Replace with mode.
✅ Numerical → Replace with mean/median.
✅ Data Trends - Explanation of each visualization.
✅ Conclusion - Summary of key insights.
✅ Recommendations - Suggestions for improving dataset quality.

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📌 Key Insights from Analysis

✔ Price Comparison: No major differences in average car price across Tesla, Ford, and BMW.
✔ Trend Over Time: Price drops sharply in September 2022 and rises in December 2022.
✔ Price vs. Mileage: Higher mileage generally leads to lower prices.
✔ Market Share: Tesla holds the largest share, accounting for nearly 50% of the dataset.
✔ Customer Ratings: Most cars are rated between 1-3, with Tesla having higher ratings overall.

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💡 Recommendations

🔹 Include more vehicle specifications (e.g., engine size, fuel efficiency).
🔹 Add historical maintenance data to analyze long-term value.
🔹 Expand dataset coverage to more car manufacturers.
🔹 Incorporate geographic data for regional pricing trends.
🔹 Analyze seasonal patterns to identify best buying/selling periods.

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🚀 Technologies Used

• 🐍 Python (Jupyter Notebook)
• 📊 Matplotlib & Seaborn (Data Visualization)
• 🏗 Object-Oriented Programming (OOP)
• 📝 CSV & Pickle for Data Handling

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📥 How to Run the Project

1️⃣ Clone the repository:

git clone https://github.com/prayag-verma/Car-Auction-Data-Analysis.git
cd Car-Auction-Data-Analysis

2️⃣ Run the Jupyter Notebook:

jupyter notebook FinalProjectCode.ipynb

3️⃣ Review the visualizations and analysis.

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🤝 Contributors

👤 Prayag Verma
👥 Data Engineer

🔗 LinkedIn: → linkedin.com/in/prayagv
🔗 Portfolio: → prayagverma.com

💬 Feel free to raise an issue or contribute via pull requests!

Contributions are welcome! If you have additional exercises, improvements, or suggestions, please fork the repository and submit a pull request.

📄 License

This project is licensed under the MIT License.