This folder contains a collection of Python projects I’ve completed to demonstrate data cleaning, analysis, and visualization skills.
Each project is based on real-world datasets and highlights specific Python techniques such as data wrangling with pandas, exploratory analysis, visualization, and storytelling in Jupyter notebooks.
This repo is a portfolio showcase of my Python data analysis skills, demonstrating:
- Cleaning messy, real-world datasets
- Wrangling and transforming data with pandas
- Exploring trends through visualizations
- Communicating insights for both technical and non-technical audiences
Dataset Sources:
Description:
Cleaned and compared datasets from Google Play and the Apple App Store to identify cross-platform app trends.
Explored ratings, engagement, and categories to recommend app genres with strong potential on both markets.
Skills Highlighted:
- Data cleaning and transformation in pandas
- Exploratory data analysis with Jupyter
- Visualization with matplotlib / seaborn
- Cross-dataset normalization and comparison
Key Findings:
- Genres like Social, Games, Travel, and Photography** maintain steady popularity across both stores.
- However, Travel is a less saturated genre, so it may offer developers a better chance to introduce an app that is popular in both markets.
(Additional projects will be added here. Each will have its own subfolder with notebooks, scripts, and results.)
- Python (pandas, numpy)
- Jupyter Notebook for step-by-step analysis
- GitHub for version control and documentation