Objective:
Conducted exploratory data analysis (EDA) on the auto-mpg dataset to gain insights into the relationship between various car attributes and fuel efficiency.
Methods and Techniques:
Employed Python programming language and utilized various data analysis libraries, such as Pandas, NumPy, Matplotlib, and seaborn. Performed data cleaning, visualization, statistical analysis.
Key Findings:
- Identified strong correlations between horsepower and MPG, indicating that higher horsepower tends to result in lower fuel efficiency.
- Discovered a non-linear relationship between acceleration and MPG, suggesting that different acceleration levels affect fuel efficiency differently.
- Observed variations in MPG across different model years, suggesting improvements in fuel efficiency over time.
- Examined the impact of car origin on MPG and found that certain origins tend to have higher or lower fuel efficiency on average. Results:
Provided actionable insights to inform decisions related to car design, fuel efficiency, and environmental impact.
Skills Demonstrated:
- Data cleaning and preprocessing.
- Exploratory data analysis (EDA) techniques.
- Data visualization using Matplotlib, Seaborn.
- Statistical analysis and inference.
- Python programming and libraries (Pandas, NumPy).
Conclusion:
This project demonstrates your ability to analyze and derive meaningful insights from a real-world dataset, as well as your skills in data preprocessing, visualization.