{"id":25983910,"url":"https://github.com/kingsley-ezenwaka/app-profile-data-analysis","last_synced_at":"2026-04-29T12:39:34.118Z","repository":{"id":276993023,"uuid":"930952055","full_name":"kingsley-ezenwaka/app-profile-data-analysis","owner":"kingsley-ezenwaka","description":"A Python data analysis project that aims to propose an app profile based on analysis of Google Playstore dataset.","archived":false,"fork":false,"pushed_at":"2025-02-16T16:11:53.000Z","size":997,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-23T00:26:40.651Z","etag":null,"topics":["analysis","data","jupyter-notebook","matplotlib","pandas","python","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kingsley-ezenwaka.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-11T13:37:22.000Z","updated_at":"2025-02-22T23:29:24.000Z","dependencies_parsed_at":"2025-02-11T15:41:45.082Z","dependency_job_id":null,"html_url":"https://github.com/kingsley-ezenwaka/app-profile-data-analysis","commit_stats":null,"previous_names":["kingsley-3z3nw4k4/app-profile-data-analysis","kingsley-ezenwaka/app-profile-data-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kingsley-ezenwaka%2Fapp-profile-data-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kingsley-ezenwaka%2Fapp-profile-data-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kingsley-ezenwaka%2Fapp-profile-data-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kingsley-ezenwaka%2Fapp-profile-data-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kingsley-ezenwaka","download_url":"https://codeload.github.com/kingsley-ezenwaka/app-profile-data-analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242011759,"owners_count":20057529,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","data","jupyter-notebook","matplotlib","pandas","python","seaborn"],"created_at":"2025-03-05T10:35:21.810Z","updated_at":"2026-04-29T12:39:34.113Z","avatar_url":"https://github.com/kingsley-ezenwaka.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# App Profile Data Analysis\n\nThis project analyzes a Google Play Store dataset to propose an optimal app profile for a fictional client. The client aims to develop a free, English-language app that generates revenue solely through advertisements. The analysis focuses on identifying app characteristics that attract the most users.\n\n_Note: This README was generated with assistance from [ChatGPT](https://chatgpt.com), but all project code was written entirely by the author._\n\n---\n\n## Features\n\n- Data cleaning and preprocessing of the Google Play Store dataset\n- Exploratory data analysis (EDA) to uncover trends and patterns\n- Visualization of key metrics using Matplotlib and Seaborn\n- Insights to inform app development strategies\n\n---\n\n## Sample Visualizations\n\n\u003cimg src=\"images/most_popular_app_cat.png\" alt=\"Most Popular App Categories\" width=\"700\" height=\"320\"\u003e\n\n\u003cimg src=\"images/scatter_plot_1_popularity_indices.png\" alt=\"Scatter Plot - Popularity Indices\" width=\"420\" height=\"360\"\u003e\n\u003cimg src=\"images/scatter_plot_2_saturation_indices.png\" alt=\"Scatter Plot - Saturation Indices\" width=\"420\" height=\"360\"\u003e\n\n## Technologies Used\n\n- Python\n- Pandas\n- NumPy\n- Matplotlib\n- Seaborn\n- Jupyter Notebook\n\n---\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.x\n- Jupyter Notebook\n- Required Python libraries: Pandas, NumPy, Matplotlib, Seaborn\n\n### Installation\n1. Clone the repository (Linux - `bash`, Windows: `git bash`):\n   ```bash\n   git clone https://github.com/kingsley-ezenwaka/app-profile-data-analysis.git\n   ```\n   \n   Alternatively, you can simply download the repo by clicking on the green \"Code\" button and select \"Download zip\".\n\n3. Navigate to the project directory:\n   ```bash\n   cd app-profile-data-analysis\n   ```\n\n4. Install the required libraries (if not already installed):\n   ```bash\n   pip install pandas numpy matplotlib seaborn notebook\n   ```\n   Or:\n   ```cmd\n   py -m pip install pandas numpy matplotlib seaborn notebook\n   ```\n   \n6. Launch Jupyter Notebook:\n   ```bash\n   jupyter notebook\n   ```\n\n7. Open and run the `app-profiles-analysis.ipynb` notebook to explore the analysis.\n\n---\n\n## Key Insights\n\n- Free apps tend to have higher install counts compared to paid apps\n- Certain categories, such as Games and Communication, dominate in user engagement\n- App size and user ratings can influence the number of installs\n\n---\n\n## Future Enhancements\n\n- Integrate interactive visualizations using Plotly or Streamlit\n- Incorporate machine learning models to predict app success metrics\n- Expand the dataset to include more recent app data for a comprehensive analysis\n\n---\n\n## Project Structure\n\n```\napp-profile-data-analysis/\n├── app-profiles-analysis.ipynb\n├── app-profiles-analysis.py\n├── googleplaystore.csv\n├── googleplaystore_rev.csv\n└── README.md\n```\n---\n\n## Acknowledgements\n\nThe project idea is borrowed directly from [dataquest.io](https://www.dataquest.io/projects/guided-project-a-profitable-app-profiles-for-the-app-store-and-google-play-markets-2/). Modifications have been made to the original project instructions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkingsley-ezenwaka%2Fapp-profile-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkingsley-ezenwaka%2Fapp-profile-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkingsley-ezenwaka%2Fapp-profile-data-analysis/lists"}