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project description.txt
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project description.txt
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Project description
You work for the online store Ice, which sells video games all over the world. User and expert reviews, genres, platforms (e.g. Xbox or PlayStation), and historical data on game sales are available from open sources. You need to identify patterns that determine whether a game succeeds or not. This will allow you to spot potential big winners and plan advertising campaigns.
In front of you is data going back to 2016. Let’s imagine that it’s December 2016 and you’re planning a campaign for 2017.
(The important thing is to get experience working with data. It doesn't really matter whether you're forecasting 2017 sales based on data from 2016 or 2017 sales based on data from 2016.)
The dataset contains the abbreviation ESRB. The Entertainment Software Rating Board evaluates a game's content and assigns an age rating such as Teen or Mature.
Instructions for completing the project
Keeping your conclusions in mind, compare the sales of the same games on other platforms.
Take a look at the general distribution of games by genre. What can we say about the most profitable genres? Can you generalize about genres with high and low sales?
Step 4. Create a user profile for each region
For each region (NA, EU, JP), determine:
The top five platforms. Describe variations in their market shares from region to region.
The top five genres. Explain the difference.
Do ESRB ratings affect sales in individual regions?
Step 5. Test the following hypotheses:
—Average user ratings of the Xbox One and PC platforms are the same.
—Average user ratings for the Action and Sports genres are different.
Set the alpha threshold value yourself.
Explain:
—How you formulated the null and alternative hypotheses
—What significance level you chose to test the hypotheses, and why0
Step 6. Write a general conclusion
Format: Complete the task in the Jupyter Notebook. Insert the programming code in the code cells and text explanations in the markdown cells. Apply formatting and add headings.
Data description
—Name
—Platform
—Year_of_Release
—Genre
—NA_sales (North American sales in USD million)
—EU_sales (sales in Europe in USD million)
—JP_sales (sales in Japan in USD million)
—Other_sales (sales in other countries in USD million)
—Critic_Score (maximum of 100)
—User_Score (maximum of 10)
—Rating (ESRB)
Data for 2016 may be incomplete.
How will my project be evaluated?
Read these project assessment criteria carefully before you get to work.
Here’s what project reviewers will be looking at when evaluating your project:
How do you describe the problems you identify in the data?
How do you prepare a dataset for analysis?
How do you build distribution graphs and how do you explain them?
How do you calculate standard deviation and variance?
Do you formulate alternative and null hypotheses?
What methods do you apply when testing them?
Do you explain the results of your hypothesis tests?
Do you follow the project structure and keep your code neat and comprehensible?
Which conclusions do you reach?
Did you leave clear, relevant comments at each step?
Everything you need to complete this project is in the takeaway sheets and summaries from previous chapters.
Good luck!