Play Store is Google's official pre-installed app store on Android-certified devices. It provides access to content on the Google Play Store, including apps, books, magazines, music, movies, and television programs. This ppt is prepared under the predictive study of google play store. By applying data science and some concepts of machine learning here I built a prediction model. You can simply predict that how your next app will perform. will it succeed or not under certain circumstances?

1. Predictive Analysis
(Google Play Store)
USING
DATA SCIENCE & MACHINE LEARNING
Representing By:
Aakash Singh
C.S.E. (215237)

2. Outline
• Introduction to Python
• Basics of Python
• What is Machine Learning
• Types of Machine Learning
• What is Data Science
• Why Data Science
• Introduction to Anaconda & Jupyter
Notebook
• Google Play Store Analysis
• Conclusion

3. Introduction to Python
• Created by Guido van Rossum and first released in 1991
• An interpreted high-level programming language
• Multipurpose & Object Oriented
• Offers much faster application development
• Broad standard library
• Highly Portable
• Very few keywords

4. Machine Learning
Machine learning is a field of artificial intelligence that uses statistical
techniques to give computer systems the ability to "learn" (e.g.,
progressively improve performance on a specific task) from data,
without being explicitly programmed.

5. MACHINE
LEARNING

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. What is Data
Science
This aspect of data science is all about
uncovering findings from data. Diving in
at a granular level to mine and
understand complex behaviors, trends,
and inferences. It's about surfacing
hidden insight that can help enable
companies to make smarter business
decisions.

10. Why Data Science

11. Real World
Applications
of Data
Science
• Internet Search
• Recommender Systems (Netflix,
Amazon)
• Image Recognition (Facebook)
• Speech Recognition (Cortana, Alexa,
Siri)
• Self - Driving Cars (Tesla Motors )
• Delivery Logistics (FedEx)
• Airline Route Planning
• Fraud & Risk Detection

12. How to solve a problem in Data Science ?

13. Introduction to Anaconda & Jupyter
Notebook

14. Importing Libraries

15. Importing Dataset

16. Description of Dataset

17. Missing Data Study

18. No. Of Apps in each category

19. App Rating vs Category

20. Comparing Data Items

21. Rating of Apps

22. Data Correlation

25. Rating vs Category relationship

26. Categories vs Reviews

27. Apps with highest size

28. Free Apps matter ?

29. Thanks !