Applying big data techniques to implement a solution

Reference no: EM133697070

Assessment Details

Introduction
In this assessment you will work in groups on a major practical based case to leverage data by applying big data techniques to implement a solution, provide insights on analytics performed and make recommendations.

Objective(s)

You will work with your group and leverage the Google Play Store Apps data set.

Context
While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that 'Tunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modem-day techniques (like dynamic page load) using 1Query making scraping more challenging.

Content
Each app (row) has values for catergory, rating, size, and more.

Acknowledgements
This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration
The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market I
You will prepare a final report outlining the following tasks:

Task 1: Using Tableau or any other visualization tool, explore the dataset by creating at least six different charts to visualize the attributes and the relationship between the attributes in the dataset. It Is required to interpret the figures in the report.

Task 2: Propose a data analytics question and build a data analytic model based on this question (e.g. prediction or clustering model). Implement the model using Python or any other programming language. It Is required to cover the following subtasks:
• Propose the data analytics question.
• Describe the method used to create the model.
• Discuss the model construction.
• Create the experiments and discuss the results.
• Discuss the challenges working with the large dataset and how did you overcome these challenges?

For Python code, you can use Python Anaconda or Google Colab.

Submission requirements:

1) Your report should have 1500.2000 words addressing the tasks. The report structure Includes the following: a cover page, introduction about the case study, dataset description, addressing the above tasks, and conclusion.

2) The presentation should be a maximum of 7 minutes for the whole team. Each member should talk for at least 2 minutes related to the project and findings. The entire presentation should cover the dataset, results, and conclusion.

General Instructions

1. Your writing should be clear and concise and be in your own words.

2. The report must be in the range of 1,500-2,000 words in length excluding references.

3. Your report should be a single word or pdf document containing your report and need to be submitted through Moodie. 4.
One submission per group and make sure all group members are there with contribution table at the end of the report

5. One submission per group and make sure all group members are active in the video with at least 2 minutes' talk from the project.

6. Use headings to guide the reader and include tables or diagrams that make the case clearer.

7. The program code needs to be attached at the end of the report as an Appendix.

8. The referencing style must follow the IEEE referencing style.

Reference no: EM133697070

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