AI Data Analysis - Data Preprocessing (22) - Missing Value Handling: Missing Value Completion by Regression Model
Published:Dec 27, 2025 12:11
•1 min read
•Qiita AI
Analysis
This article discusses using AI, specifically regression models, to handle missing values in data preprocessing for AI data analysis. It mentions using Python for implementation and Gemini for AI utilization. The article likely provides a practical guide on how to implement this technique, potentially including code snippets and explanations of the underlying concepts. The focus is on a specific method (regression models) for addressing a common data issue (missing values), suggesting a hands-on approach. The mention of Gemini implies the integration of a specific AI tool to enhance the process. Further details would be needed to assess the depth and novelty of the approach.
Key Takeaways
- •Using regression models for missing value imputation.
- •Implementation in Python.
- •AI utilization with Gemini.
- •Focus on data preprocessing techniques.
Reference
“AIでデータ分析-データ前処理(22)-欠損処理：回帰モデルによる欠損補完”