📱 Google Play Store Data Cleaning

This project focuses on cleaning and preparing a dataset of mobile applications from the Google Play Store. The goal is to make the dataset analysis-ready by handling missing values, correcting data types, and removing inconsistencies.

📂 Repository Structure

📁 google-playstore-data-cleaning/
│
├── Google_Playstore.ipynb     # Jupyter Notebook with cleaning steps
├── Google_playstore_sampled.csv       # Original dataset 
└── README.md                  # Project documentation

🧹 Cleaning Objectives

• Handle missing values
• Correct data types
• Clean string formatting issues (e.g., price, installs)
• Remove duplicates and outliers
• Prepare the dataset for further analysis or visualization

📊 Dataset Summary

The dataset includes app metadata such as:

• App Name
• Category
• Rating
• Number of Installs
• Price
• Size
• Last Updated
• Content Rating
• And more...

🛠️ Tools Used

• Python
• Pandas
• NumPy
• Regex
• Jupyter Notebook

🚀 How to Use

1. Clone the repository:

   git clone https://github.com/oluwakoya-ao/google-playstore-data-cleaning.git

2. Navigate into the project folder:

   cd google-playstore-data-cleaning

3. Open the notebook in Jupyter:

   jupyter notebook Google_Playstore.ipynb

4. Run each cell to see the data cleaning process.

📌 Key Takeaways

• The raw dataset contained inconsistencies such as:
  • Commas and symbols in numeric fields (e.g., "1,000+", "$4.99")
  • Missing values across several columns
  • Duplicated entries
• These issues were resolved through:
  • Type conversion
  • Regex-based cleaning
  • Null value handling
  • Deduplication
• The cleaned dataset is now ready for exploration, analysis, or modeling.

📸 Suggested Improvements

Optional additions to enhance this project:

• Add visual summaries of missing data using seaborn or missingno
• Include before/after samples of cleaned fields
• Export and include the final cleaned CSV dataset

🙌 Acknowledgments

Dataset sourced from Kaggle: Google Play Store Apps

👨‍💻 Author

Oluwakoya Oluwafemi
📧 ooluwakoyafavour@gmail.com
🔗 LinkedIn

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Feel free to fork, star, or contribute to this project!