Gaming Company Sales Prediction Project

In this project, I used real-world customer data from a gaming company and applied a rule-based classification approach to estimate potential customer revenue. Instead of purely machine learning, I opted for rule logic combined with structured analysis including data preprocessing, exploration, visualization, and interpretation.

During the analysis:

• ▹ I cleaned and formatted the raw data.
• ▹ Applied rule-based classification to predict customer revenue.
• ▹ Conducted exploratory data analysis with visualizations.
• ▹ Interpreted results to derive strategic business insights.

Project Story and Challenges

When I began this project, I was working with a fairly raw gaming company dataset, so data preprocessing and cleaning was initially the most demanding part. Making the data consistent and handling missing/outlier values required careful preparation. When setting up the rule-based classification, defining effective rules tied to revenue outcomes took additional experimentation.

The most challenging aspect was interpreting findings through visualizations and turning those insights into actionable recommendations for marketing strategies. This gave me a deep understanding of both technical and business aspects of real-world data analytics.