Exploring Google Play Store apps dataset to identify key factors for app engagement and success, revealing correlations between reviews, installs, categories, ratings, and user preferences.
- Updated
Jun 4, 2023 - Jupyter Notebook
Exploring Google Play Store apps dataset to identify key factors for app engagement and success, revealing correlations between reviews, installs, categories, ratings, and user preferences.
Exploring Google Play Store apps dataset to identify key factors for app engagement and success, revealing correlations between reviews, installs, categories, ratings, and user preferences.
A comprehensive guide to mastering Pandas for data analysis, featuring practical examples, real-world case studies, and step-by-step tutorials. For general information, see
Exploring Google Play Store apps dataset to identify key factors for app engagement and success, revealing correlations between reviews, installs, categories, ratings, and user preferences.
Exploring Google Play Store apps dataset to identify key factors for app engagement and success, revealing correlations between reviews, installs, categories, ratings, and user preferences.
A versioned, distributed key-value store designed with a focus on data integrity. Each value boasts a comprehensive history, ensuring eventual consistency across the system. It features seamless merging capabilities to harmonize divergent data states.
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An end-to-end data science project analyzing internal and external drivers of rental prices across Victoria, Australia. The project includes web scraping, data preprocessing, geospatial integration, exploratory data analysis, and predictive modeling. Built using Python, Pandas, GeoPandas, Scikit-learn, and web scraping frameworks.
Merge CSV/TXT/SQL files for harmonized ELN import; supports joins and preview with export
Data Analysis with the Pandas Library 📊
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