https://github.com/kingsley-ezenwaka/app-profile-data-analysis
A Python data analysis project that aims to propose an app profile based on analysis of Google Playstore dataset.
https://github.com/kingsley-ezenwaka/app-profile-data-analysis
analysis data jupyter-notebook matplotlib pandas python seaborn
Last synced: about 2 months ago
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A Python data analysis project that aims to propose an app profile based on analysis of Google Playstore dataset.
- Host: GitHub
- URL: https://github.com/kingsley-ezenwaka/app-profile-data-analysis
- Owner: kingsley-ezenwaka
- Created: 2025-02-11T13:37:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-16T16:11:53.000Z (over 1 year ago)
- Last Synced: 2025-02-23T00:26:40.651Z (over 1 year ago)
- Topics: analysis, data, jupyter-notebook, matplotlib, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 974 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# App Profile Data Analysis
This 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.
_Note: This README was generated with assistance from [ChatGPT](https://chatgpt.com), but all project code was written entirely by the author._
---
## Features
- Data cleaning and preprocessing of the Google Play Store dataset
- Exploratory data analysis (EDA) to uncover trends and patterns
- Visualization of key metrics using Matplotlib and Seaborn
- Insights to inform app development strategies
---
## Sample Visualizations
## Technologies Used
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
---
## Getting Started
### Prerequisites
- Python 3.x
- Jupyter Notebook
- Required Python libraries: Pandas, NumPy, Matplotlib, Seaborn
### Installation
1. Clone the repository (Linux - `bash`, Windows: `git bash`):
```bash
git clone https://github.com/kingsley-ezenwaka/app-profile-data-analysis.git
```
Alternatively, you can simply download the repo by clicking on the green "Code" button and select "Download zip".
3. Navigate to the project directory:
```bash
cd app-profile-data-analysis
```
4. Install the required libraries (if not already installed):
```bash
pip install pandas numpy matplotlib seaborn notebook
```
Or:
```cmd
py -m pip install pandas numpy matplotlib seaborn notebook
```
6. Launch Jupyter Notebook:
```bash
jupyter notebook
```
7. Open and run the `app-profiles-analysis.ipynb` notebook to explore the analysis.
---
## Key Insights
- Free apps tend to have higher install counts compared to paid apps
- Certain categories, such as Games and Communication, dominate in user engagement
- App size and user ratings can influence the number of installs
---
## Future Enhancements
- Integrate interactive visualizations using Plotly or Streamlit
- Incorporate machine learning models to predict app success metrics
- Expand the dataset to include more recent app data for a comprehensive analysis
---
## Project Structure
```
app-profile-data-analysis/
├── app-profiles-analysis.ipynb
├── app-profiles-analysis.py
├── googleplaystore.csv
├── googleplaystore_rev.csv
└── README.md
```
---
## Acknowledgements
The 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.