Project Name :Analysis of Video Games Sales
Project description This project is about statistically analyzing platform, genre, game rating, user score, and regional user-preferences against 11563 video games dating back from 1984 to 2016 for effective marketing strategies. We use descriptive statistic to understand user trends which is necessary to target our audiences and appeal to their preferences.
Technologies used:
Matplotlib, Numpy, Seaborn, Matplotlib.style, Plotly for graphing
Table of Contents:
Relationships to be analyzed Regions : Platform Sales Genre : Critic Ratings Genre : User Ratings User Scores: Sales Platforms : Global Sales Genre Rankings
Created a table with the total averaged user_score based on the rating and genre
What are the demands in each region for the different game platforms?
How do components such as Genre and Critic Rating change over time?
What is the highest user score by genre filtered by rating?-Elliot Einstein
What is the highest purchased games by genre filtered by rating?-Elliot Einstein
Which platform (game console) has the highest global sales per genre?
What is the ranking order by genre in NA, EU, and Japan?
Which platform (game console) has the highest global sales?
Which platform (game console) has the highest global sales?
What is the ranking order of Genre in NA, EU, and Japan?
Which platform (game console) has the highest global sales?
What is the ranking order of Genre in NA, EU, and Japan?
Responsibilities:
Elliott Einstein
Aggregrated the mean of the user_score based on the rating and genre.
Aggregrated the sum of the games purchased on the rating and genre.
Created a stacked bar char for the aggregration of mean of the user_score based on the rating and per genre.
Created a stacked bar char for the aggregration of sum of the user_score based on the rating and per genre.
Created a table with the total averaged user_score based on the rating and per genre.
Created a table with the total purchased games based on the rating and per genre.
Took leadership in the creation and maitainance of group repository.
Summarizing the video game rating system (ESRB), and how this study shows the indication of which games are more likely to be rated based on genre filtered by the rating, and the number of game purchased by genre filtered by the rating.
Samantha Meza
Analyzing the relationship between NA_Sales and the platforms selected by creating a scatterplot.
Analyzing the relationship between EU_Sales and the platforms selected by creating a scatterplot.
Analyzing the relationship between JP_Sales and the platforms selected by creating a scatterplot.
Summarizing and presenting how the relationships provided insight into the different markets, and how competition among the platforms differed between each region.
Presenting and creating an introduction segment for the presentation.
Stephanie Betanzo
Powerpoint format setup.
Import dependencies.
Read the csv file into the notebook.
Drop all rows that contain a NaN value.
Create a new dataframe to include the column needed.
Create a table to show popularity of video game genres across the overall Years_of_Release
Create a histogram to show if critics' “harshness” rating changed over time, using “Critic_Score” and “Years_of_Release”
Presented assigned powerpoint slides (9&10)
David Einstein
Presented slides 14-21
In Charge of Sharing Powerpoint, and keeping time under 10 minutes
Aggregrated the sum of the total Global Sales based on the Platform and genre.
Aggregated the Total Percentage based on NA, Europe, Japan Regions and showed results on stack chart
Displayed Genre across NA, Europe, Japan and Other Regions on a Table
Spoke briefly about the problems encountered with the original purpose
Gave future direction of the project
Summarized all the findings for my part and the entire group
Team Members:
Samantha Meza / https://github.com/samanthaameza
David Einstein / https://github.com/DavidEinstein
Elliott Einstein / https://github.com/Elliott-dev
Stephanie Betanzo / https://github.com/Stephbetanzo