This analysis uncovers the hidden ingredients behind FoodWheel's success, equipping them with data-driven insights to satisfy their appetite for growth. Explore key findings, visualize culinary trends, and discover untapped opportunities to shape FoodWheel's next course!
- Culinary Landscape: Uncover FoodWheel's most popular cuisines and where expansion potential lies.
- Order Evolution: Track average order size over time to reveal growth patterns and potential challenges.
- Customer Segments: Peek into customer spending habits to distinguish loyal foodies from occasional users.
Data Manipulation:
- Pandas (groupby, reset_index, lambda, split, mean, std, range, len, sum, unique, value_counts)
Data Visualization:
- Matplotlib.pyplot (pie charts, subplots, bar charts, labels, autopct, axis, tight_layout, legend, yerr, ax, capsize, set_xticks, set_xticklabels, bins, color, fig)
- Clone this repository
- Install libraries:
pip install pandas matplotlib - Run the script:
jupyter notebook foodwheel_project.ipynb
- Explore visualizations: Dive into the generated charts and graphs to uncover insights and trends.
- Experiment with analysis: Modify the code to create your own visualizations and explore different perspectives.
- Found a bug? Have a suggestion? Create an issue or submit a pull request to contribute to this project!
Reza Sadeghi: https://github.com/xre22zax/