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Data Science#Data Preprocessing📝 BlogAnalyzed: Dec 28, 2025 13:00

AI-Driven Data Analysis - Data Preprocessing (22) ② - Missing Value Handling: Missing Value Imputation with Classification Models

Published:Dec 28, 2025 12:44
1 min read
Qiita AI

Analysis

This article discusses using AI, specifically classification models, to handle missing data during the data preprocessing stage of AI-driven data analysis. It's the second part of a series focusing on data preprocessing. The article likely covers the methodology of using classification models to predict and impute missing values, potentially comparing it to other imputation techniques. The mention of Gemini suggests the use of Google's AI model for some aspect of the process, possibly for generating code or assisting in the analysis. The inclusion of Python implementation indicates a practical, hands-on approach to the topic. The article's structure includes an introduction to the data used, the Python implementation, the use of Gemini, and a summary.
Reference

“AIでデータ分析-データ前処理(22)②-欠損処理：分類モデルによる欠損補完”

PermalinkQiita AI
Research#traffic management🔬 ResearchAnalyzed: Jan 4, 2026 09:23

A Hybrid Inductive-Transductive Network for Traffic Flow Imputation on Unsampled Locations

Published:Dec 19, 2025 14:14
1 min read
ArXiv

Analysis

This article presents a research paper on a specific application of AI in traffic management. The focus is on using a hybrid network to predict traffic flow in areas where data is not directly collected. The approach combines inductive and transductive learning methods, which is a common strategy in machine learning to leverage both general patterns and specific instance information. The title clearly states the problem and the proposed solution.
Reference

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PermalinkArXiv