Integrated Project 1

This school project focuses on reviewing historical consumer data from the game company ICE to extract key insights and predict outcomes for the following year. The dataset used is a dataframe containing descriptive information on games released and their global revenue throughout the years.

I will be using game sales data from 2016 to create a forecast for the following sales year by:

• Exploring game releases and sales across different years, examining platform popularity and their periods of relevancy.

• Identifying leading platforms, track sales trends, and pinpoint potentially profitable options for the company.

• Analyzing the impact of reviews on sales for a chosen platform and compare game sales across platforms.

• Investigating genre distribution to track trends in profitability.

The analysis consists of the following steps:

Data Review and Preprocessing: The historical consumer data is examined for any issues, and necessary preprocessing steps are taken to clean and enrich the dataset.

Exploratory Data Analysis (EDA): Relevant data, particularly the latest game purchases after 2015, is explored to identify trends across global revenue, gaming platforms, and genres. Data visualizations such as histograms, scatterplots, and box plots are utilized to understand the distribution of variables and relationships between them.

Statistical Data Analysis: Descriptive statistics are employed to summarize the characteristics of the dataset, while hypothesis testing is used to validate assumptions and identify significant relationships between variables.

Insights and Recommendations: Insights drawn from the analysis provide valuable information on which games, genres, and gaming platforms are likely to perform better globally and by region. These insights inform marketing strategies and other analyses for the following year.

Tools and Libraries Used:

Jupyter Notebook: For code execution and documentation. Python Libraries: Pandas, Matplotlib, Seaborn, Plotly Express for data manipulation, visualization, and statistical analysis.

Conclusion:

The analysis aims to provide actionable insights into consumer trends in the gaming industry, aiding decision-making processes for marketing strategies and future business endeavors. The project showcases the power of exploratory and statistical data analysis in deriving meaningful insights from complex datasets.