https://github.com/netcodez/playstore-apps-reviews-analysis
Google Play Store Apps and Reviews Analysis is a repository that provides tools and resources for analyzing and extracting insights from app data and user reviews from the Google Play Store. It aims to facilitate data-driven decision-making and market research in the Android app ecosystem.
https://github.com/netcodez/playstore-apps-reviews-analysis
Last synced: 6 months ago
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Google Play Store Apps and Reviews Analysis is a repository that provides tools and resources for analyzing and extracting insights from app data and user reviews from the Google Play Store. It aims to facilitate data-driven decision-making and market research in the Android app ecosystem.
- Host: GitHub
- URL: https://github.com/netcodez/playstore-apps-reviews-analysis
- Owner: Netcodez
- Created: 2023-06-28T23:43:19.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-29T19:43:45.000Z (over 2 years ago)
- Last Synced: 2025-03-05T06:33:19.644Z (12 months ago)
- Language: Jupyter Notebook
- Size: 241 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Google Play Store Apps and Reviews Analysis
Overview
This repository contains the code and data for the Google Play Store Apps and Reviews Analysis project. The project aims to provide a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. The analysis focuses on extracting insights from the data to devise strategies for driving growth and retention in the app market.
## Table of Contents
- Introduction
- Data Cleaning
- Correcting Data Types
- Exploring App Categories
- Distribution of App Ratings
- Size and Price of an App
- Relationship between App Category and Price
- Filtering Out "Junk" Apps
- Popularity of Paid Apps vs Free Apps
- Sentiment Analysis of User Reviews
- Conclusion
## Introduction
Mobile apps have become prevalent and lucrative in today's market. This project aims to analyze the Android app market by comparing various apps available on Google Play. The analysis provides valuable insights to drive growth and improve user retention strategies.
## Data Cleaning
The data cleaning phase involves removing special characters from certain columns to ensure their suitability for mathematical calculations. The dataset used includes information about the applications on Google Play, such as the number of installs and price.
## Correcting Data Types
To facilitate further analysis, the data types of the "Installs" and "Price" columns are corrected. These columns initially contain mixed input types, including digits and special characters. By converting them to float data types, compatibility for numerical calculations is ensured.
## Exploring App Categories
This project investigates the distribution of apps across different categories. By analyzing the data, the category with the highest share of active apps in the market is determined. This information helps identify dominant categories and categories with a smaller number of apps.
## Distribution of App Ratings
App ratings play a significant role in user engagement and the overall brand image. This section explores the distribution of app ratings on a scale of 1 to 5. Understanding the average rating and the distribution of ratings across different categories provides valuable insights into app performance.
## Size and Price of an App
The size and price of an app are important factors to consider when developing and pricing mobile applications. This section investigates the relationship between app size and rating, as well as the impact of app price on ratings. Analyzing these relationships helps in formulating effective app sizing and pricing strategies.
## Relationship between App Category and Price
Determining the appropriate pricing strategy for mobile apps is crucial for maximizing profit. This section explores the relationship between app category and price. By examining the pricing trends across different categories, insights are gained into which categories demand different price ranges.
## Filtering Out "Junk" Apps
Some apps in the dataset may be considered "junk" apps, lacking a clear purpose or having malicious intent. This section focuses on filtering out such apps and re-evaluating the pricing trends across categories. By excluding these apps, a more accurate representation of pricing patterns is obtained.
## Popularity of Paid Apps vs Free Apps
Understanding the popularity of paid apps compared to free apps is crucial for monetization strategies. This section compares the number of app installations for both paid and free apps. By analyzing the difference in installation numbers, insights are gained into user preferences regarding app pricing models.
## Sentiment Analysis of User Reviews
Sentiment analysis of user reviews provides valuable feedback on how users