Profitable App Profiles

This project presents a data-driven analysis of the Google Play Store and Apple App Store to identify promising profiles for new, free-to-download mobile applications. It addresses the needs of a company whose revenue is primarily generated through in-app advertisements, making high user engagement critical for financial success.

By examining trends and characteristics of prevalent and engaging apps, this analysis aims to deliver actionable recommendations to development teams. The goal is to help align new app development with market demand, thereby enhancing user acquisition and revenue potential.

Tech Stack

     

Contents

• Project Structure
• Dataset
• Try it Online
• Steps to Run
• License

Project Structure

profitable-app-profiles/
│
├── data/              # Directory for raw and/or cleaned datasets used in the analysis
├── notebook/          # Contains the Jupyter Notebook that includes exploratory data analysis and results
├── .gitignore         # Specifies files and directories to be ignored by Git (e.g., virtual environments, cache files)
├── requirements.txt   # Lists Python packages required to run the project (generated from pyproject.toml)
├── pyproject.toml     # Project metadata and dependency configuration (used by modern Python packaging tools)
├── .python-version    # Defines the Python version for the virtual environment (used by tools like pyenv)
├── uv.lock            # Lock file for `uv` that ensures consistent dependency versions across environments
└── README.md          # Provides an overview of the project, setup instructions, and usage notes

Dataset

The datasets used in this project are publicly available on Kaggle:

• Google Play Store Apps: A dataset containing data about approximately ten thousand Android apps from Google Play. The dataset can be downloaded directly from this link.
• Mobile App Store: A dataset containing data about approximately seven thousand iOS apps from the App Store. The dataset can be downloaded directly from this link.

The CSV files used for analysis are stored in the data/ directory for reproducibility.

Try it Online

You can run the notebook directly in your browser via Binder:

Steps to Run

To run this project locally:

1. Clone the repository

   git clone https://github.com/xjwllmsx/profitable-app-profiles.git
   cd profitable-app-profiles

2. Create and activate a virtual environment

   uv venv
   uv pip install -r requirements.txt

3. Launch Jupyter Notebook

   jupyter notebook notebook/analysis.ipynb

   NOTE: If you're using uv, the dependencies are managed via pyproject.toml rather than requirements.txt.

License

This project is for educational purposes only. The dataset used is publicly available and credited to its original creator on Kaggle.