This project involves completing a series of tasks outlined below using the Jupyter Notebook named data_viz_task.ipynb. The tasks are based on analyzing the Cars93 dataset and answering questions provided within the notebook.
I have successfully completed the following tasks as instructed in the notebook:
- Generated box plots for the revs per mile for Audi, Hyundai, Suzuki, and Toyota manufacturers. Determined which manufacturer has the car with the highest revs per mile.
- Created histograms of MPG in the city and on the highway. Analyzed whether it's generally more fuel-efficient to drive in the city or on the highway.
- Plotted a line graph showing the relationship between 'Wheelbase' and 'Turning Circle'. Discussed the nature of this relationship and the impact of increasing wheelbase size.
- Constructed a bar plot illustrating the mean horsepower for each car type (Small, Midsize, etc.). Investigated whether larger cars tend to have more horsepower.
To replicate these tasks, simply open the Jupyter Notebook file data_viz_task.ipynb in a Jupyter environment and execute the provided code cells.
This project requires the following dependencies:
- Jupyter Notebook
- Python libraries: pandas, matplotlib, seaborn
Potential improvements for this project include:
- Adding more detailed analysis and insights to the task responses.
- Enhancing visualization aesthetics for better clarity and understanding.
- Exploring additional datasets for further analysis and comparison.
Contributions to this project are welcome. Please feel free to fork the repository, make changes, and submit pull requests.
This project is licensed under the [insert your license type here] license.
For any inquiries or suggestions regarding this project, please contact [your contact information].