Learn how to use Python to scrape hero equipment data from the King of Glory website, download images via multithreaded requests, and automatically generate comprehensive Markdown and Excel reports, complete with code snippets and troubleshooting tips for a seamless data extraction workflow.
Facing massive daily game logs from multiple million‑DAU titles, the Le Elements team overhauled its data warehouse by swapping a Hive + Presto stack for Alibaba Cloud’s Hologres, achieving real‑time, flexible, and scalable analytics that dramatically cut query latency, reduce operational costs, and boost game‑operation efficiency.
This article explains how feature engineering in game data involves classifying structured and unstructured, quantitative and qualitative features, and details practical methods for handling missing values and outliers to improve machine‑learning model performance.
Learn how to use Python to crawl the official King of Glory website, retrieve hero equipment data in JSON, download item images via a thread pool, and automatically generate organized Markdown and Excel documentation, complete with code snippets and troubleshooting tips.
This tutorial introduces a lightweight Python web crawler that, without relying on third‑party scraping frameworks, fetches high‑resolution skin images and all hero background stories from a popular game, explaining required libraries, implementation steps, and showing the resulting outputs.
This tutorial walks programmers through building a simple TensorFlow linear regression model that learns the relationship between attack power and damage in a game, covering data preparation, model definition, loss calculation, optimization, training loops, and evaluation with complete Python code examples.
This article explains how Riot's Game Data Server (GDS) replaces chaotic .ini editing with a JSON‑based RPC system, introduces property macros for type safety, and uses layered versioning to eliminate merge conflicts and simplify multi‑tool workflows for League of Legends development.
This article explains how game developers and marketers use internal, platform, and external data to forecast product performance, optimize user acquisition, predict market trends, model retention curves, and make informed budgeting decisions throughout a game's lifecycle.
