https://github.com/robinmillford/playstore-app-insights-uncovering-app-market-trends

In my Playstore App analysis, I uncovered valuable insights about app market trends. I discovered the top-rated apps, identified popular app categories, and explored user sentiments. My findings provide a comprehensive understanding of the app landscape, aiding in informed decision-making and strategy development for app developers and marketers.
https://github.com/robinmillford/playstore-app-insights-uncovering-app-market-trends

data-analysis data-cleaning data-visualization jupyter-notebook python3 sql

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In my Playstore App analysis, I uncovered valuable insights about app market trends. I discovered the top-rated apps, identified popular app categories, and explored user sentiments. My findings provide a comprehensive understanding of the app landscape, aiding in informed decision-making and strategy development for app developers and marketers.

• Host: GitHub
• URL: https://github.com/robinmillford/playstore-app-insights-uncovering-app-market-trends
• Owner: RobinMillford
• Created: 2023-10-03T17:48:19.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-21T05:53:05.000Z (about 1 year ago)
• Last Synced: 2023-10-21T06:29:35.134Z (about 1 year ago)
• Topics: data-analysis, data-cleaning, data-visualization, jupyter-notebook, python3, sql
• Language: Jupyter Notebook
• Homepage:
• Size: 5.06 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# Playstore App Insights: Uncovering App Market Trends

Dataset - https://www.kaggle.com/datasets/rakeshpanigrahy/playstore-dataset

### Data Preparation and Cleaning

I initiated this project by loading a CSV dataset into a pandas dataframe. I meticulously cleaned the data, addressing missing values, duplicates, and inconsistencies to ensure data accuracy.

### Data Exploration and Manipulation

With a focus on understanding the dataset's structure, I conducted exploratory data analysis. Additionally, I applied data manipulation techniques, such as calculating mean ratings and filling missing values, to enhance dataset quality.

### SQL Queries for Insight

In tandem with Python, I harnessed the power of SQL queries to extract crucial insights. These queries enabled data retrieval, filtering based on specific conditions, calculations, and the generation of meaningful patterns and trends.

### Data Visualization in Tableau

To provide a comprehensive view of the findings, I employed Tableau for data visualization. This step allowed for intuitive visual representations that aid in understanding app market trends.

Tableau Dashboard - https://public.tableau.com/app/profile/yamin3547/viz/PlaystoreAppInsightsUncoveringAppMarketTrends/Dashboard1?publish=yes

### Insightful Discoveries

Through this comprehensive process, I gained profound insights into app market dynamics, including top-rated apps, popular categories, and user sentiments. These findings equip app developers and marketers with valuable data to guide strategic decision-making.

This project serves as a robust foundation for further exploration, strategy development, and informed decision-making within the dynamic realm of PlayStore apps.