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This project explores a comprehensive car dataset using Python libraries like NumPy, Pandas, Matplotlib, and scikit-learn. It includes data cleaning, statistical summaries, visualizations, and predictive modeling (linear regression) to derive insights into car features such as MSRP, horsepower, fuel efficiency, and more.

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🚗 Car Data Analysis with Python

This project explores a comprehensive car dataset using Python libraries like NumPy, Pandas, Matplotlib, and scikit-learn. It includes data cleaning, statistical summaries, visualizations, and predictive modeling (linear regression) to derive insights into car features such as MSRP, horsepower, fuel efficiency, and more.


📊 Key Features

  • Missing value analysis and summary stats
  • Grouped analysis by car origin and type
  • Price segmentation: Budget, Mid-Range, Luxury
  • Power-to-weight ratio computation
  • Predictive modeling using linear regression (MSRP vs Horsepower)
  • Data visualizations: bar chart, scatter plot, pie chart
  • NumPy-powered stats: normalization, std dev, mean filtering

⚙️ Technologies Used


▶️ How to Run the Notebook

  1. Clone this repository:
    git clone https://github.com/yourusername/car-data-analysis.git
    cd car-data-analysis

OR: 2. GO TO Github web adrees above your browser and add 'collab' to github: github.com-----> githubcollab.com

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This project explores a comprehensive car dataset using Python libraries like NumPy, Pandas, Matplotlib, and scikit-learn. It includes data cleaning, statistical summaries, visualizations, and predictive modeling (linear regression) to derive insights into car features such as MSRP, horsepower, fuel efficiency, and more.

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