Aleksey Korshuk

"My GitHub repository quickly filled up with impressive projects, showcasing my hard work and all that I've learned."

Aleksey Korshuk

Machine Learning Engineer & Researcher

Project overview

In this project, you’ll assume the role of a data analyst at a company that builds free, ad-supported apps for Android and iOS. Since the company’s revenue depends on in-app ads, your task is to analyze historical data from app markets to determine which kinds of apps attract the most users.

Using Python, you’ll clean the provided datasets and analyze them using frequency tables and averages to identify trends. By working through a complete data science workflow, you’ll develop hands-on experience in data-driven decision making to inform business strategy. Finally, you’ll make recommendations on the app profiles the company should pursue to maximize users and ad revenue.

Objective: Analyze mobile app market data to recommend app development strategies that maximize user engagement and advertising profits.

Key skill required

To complete this project, it's recommended to build these foundational skills in Python

  • Writing Python functions with arguments, return statements, and control flow
  • Debugging Python functions to ensure proper execution
  • Implementing conditional logic and loops within Python functions
  • Working with Jupyter Notebook to write and run Python code

Projects steps

Step 1: Analyzing Mobile App Data

Step 2: Opening and Exploring the Data

Step 3: Deleting Wrong Data

Step 4: Removing Duplicate Entries: Part One

Step 5: Removing Duplicate Entries: Part Two

Step 6: Removing Non-English Apps: Part One

Step 7: Removing Non-English Apps: Part Two

Step 8: Isolating the Free Apps

Step 9: Most Common Apps by Genre: Part One

Step 10: Most Common Apps by Genre: Part Two

Step 11: Most Common Apps by Genre: Part Three

Step 12: Most Popular Apps by Genre on the App Store

Step 13: Most Popular Apps by Genre on Google Play

Step 14: Next Steps

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