Machine Learning-Based Video Game Sales Prediction System: A Complete Practice from Data Preprocessing to Regression Modeling

This article introduces the video game sales prediction system project published by Jahnavi Gellanki on GitHub. Built using the Python tech stack, the project covers the complete machine learning workflow including data preprocessing, exploratory data analysis, feature engineering, and regression model training, aiming to provide references for data-driven decision-making in the gaming industry.

The video game industry is a multi-billion-dollar global market. Accurate sales prediction is of great strategic significance for developers and publishers, helping with decisions on investment, marketing budget allocation, platform selection, etc. Traditional prediction relies on expert experience, which is highly subjective and difficult to scale; machine learning technology can build automated prediction systems by analyzing historical data patterns, providing quantitative evaluations.

The project uses the Python ecosystem toolchain. Core libraries include: Pandas (data loading, cleaning, and transformation), NumPy (numerical computation), Scikit-learn (regression algorithms, model evaluation). The selection principle is pragmatism—choosing mature tools with comprehensive community support and rich documentation to reduce development and maintenance costs, ensuring algorithm reliability and reproducibility.

Raw data needs preprocessing before modeling. Key steps include: missing value handling (removing samples with excessive missing values, statistical imputation, or predictive imputation), data type conversion (date parsing, categorical variable encoding, string standardization), outlier detection and handling (correcting/deleting/retaining based on business needs), feature scaling (standardization or normalization to ensure fair algorithm training).

Exploratory Data Analysis (EDA)：Discover data patterns through visualization and statistics, including long-tail distribution of sales, relationship between platforms and sales, impact of genres/themes, and time trend analysis. Feature Engineering: Encoding processing (one-hot encoding for unordered categories, label encoding for ordered ones), feature combination (e.g., platform + genre), derived features (extracting year and month from dates), feature selection (filtering effective features via correlation analysis and importance evaluation).

Sales prediction is a regression problem. The algorithms tried include: Linear Regression (baseline model with strong interpretability), Decision Tree Regression (captures nonlinear interactions but prone to overfitting), Random Forest Regression (integrates multiple trees to reduce variance, good robustness), Gradient Boosting Regression (e.g., XGBoost/LightGBM, corrects previous errors, commonly used in competitions). Model evaluation uses cross-validation, with metrics including RMSE (sensitive to large errors), MAE (intuitively explainable), and R² score (goodness of fit).

Application Scenarios: Investment decision-making (evaluating project returns), resource allocation (marketing budget distribution), platform selection (reference for distribution platforms), pricing strategy (optimizing pricing). Limitations: Data quality affects performance; rapid market changes make historical patterns invalid; external factors (marketing, competition, social events) are difficult to quantify; long-tail distribution of sales increases prediction difficulty.

This project demonstrates the complete machine learning project workflow, covering common challenges in real projects. The mature tech stack is easy to learn and extend, making it a good practice case for data science learners. It also reflects the application potential of data-driven decision-making in the gaming industry. Although machine learning cannot completely replace human judgment, it can serve as a decision support tool to provide valuable insights and references.