📱 Google Play Store Data Analytics Dashboard

An analytical dashboard that visualizes key insights from Google Play Store apps, built using Python, Flask, Pandas, and Matplotlib.

🚀 Overview

This project was developed as a capstone project for the Data Analytics Specialization at Algoritma Data Science School.

It explores patterns and trends among mobile applications, focusing on category dominance, user engagement, app sizing strategies, and the relationship between price and rating.

Dashboard Highlights

• 🏆 Top 5 Categories in the Market – The most popular app categories by count and total market presence.
• ⭐ App Performance Distribution – Visualizes the relationship between app reviews and ratings, showing how user feedback aligns with performance.
• 🧮 Top 10 Apps Based on Reviews – Lists the top 10 apps with the highest number of reviews, including category, app name, rating, and total reviews.
• 📦 Size Strategy (in Megabytes) – Displays the distribution of app sizes, revealing optimization trends.
• 💵 Price vs. Rating Relationship – Explores whether higher app prices correlate with better ratings.

🧩 Project Objectives

• Build a clean and functional Flask dashboard for app analytics.
• Perform exploratory data analysis (EDA) on app ratings, sizes, prices, and installs.
• Demonstrate data cleaning, wrangling, and visualization skills using Python.

📈 Key Insights

• Top Categories: Family dominates the market, showing high competition.
• User Ratings: Most apps score between 4.0–4.5, reflecting positive user experiences.
• Price vs. Rating: Free apps show more diversity in ratings and higher installation counts.
• App Size Distribution: Most apps are under 30MB, highlighting developers’ focus on optimization.

🧱 Project Workflow

1. Environment Setup

a. Create a virtual environment

conda create -n capstone_DA python=3.10  
conda activate capstone_DA

b. Install dependencies All required packages are listed in requirements.txt.

pip install -r requirements.txt --user

2. Data Preparation

a. Data Cleansing

• Removed duplicate and irrelevant records
• Cleaned Installs and Price columns (removed symbols such as “+” and “,”)
• Converted columns to appropriate numeric data types

b. Data Wrangling

• Grouped data by category for total installs, average ratings, and price distribution
• Generated summary statistics for visualizations
• Prepared data for dashboard rendering

3. Data Visualization

Created four visualizations using Matplotlib:

• Bar Chart: Top 5 app categories
• Scatter Plot: Reviews vs. Ratings (bubble size = installs)
• Histogram: App size distribution
• Scatter Plot: Price vs. Rating

4. Flask Dashboard Development

• Rendered visualizations dynamically using Flask templates (index.html)
• Designed a clean, minimalist HTML layout for dashboard presentation

📂 Dataset Summary

Variable	Description
App	Application name
Category	App category
Rating	Average user rating
Reviews	Number of user reviews
Size	App size (MB)
Installs	Number of installs
Type	Free or Paid
Price	App price
Content Rating	Target age group
Genres	App genre
Last Updated	Last update date
Current Ver	Current version
Android Ver	Minimum Android version required

⚙️ Technical Stack

• Language: Python
• Framework: Flask
• Libraries: Pandas, NumPy, Matplotlib
• IDE: Visual Studio Code

💡 Why This Project Matters

This project demonstrates the ability to analyze and visualize real-world data using Python and Flask. By exploring app trends, user ratings, and category insights, it showcases end-to-end data analytics skills, from cleaning and wrangling raw data to deploying a structured, visual dashboard.

👩🏻‍💻 Author

Zahra Nur Anisah – Data Science Enthusiast
Passionate about transforming raw data into meaningful business insights through analytics and visualization.

📧 Email: [email protected]
💼 LinkedIn: linkedin.com/in/zahranuranisah