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missing value imputation

2 articles · Page 1 of 1
Data Integration and Governance
Data Integration and Governance
May 27, 2026 · Big Data

10 Essential Data Cleaning Techniques Every AI Project Needs

The article outlines ten practical data‑cleaning methods—covering missing‑value imputation, duplicate handling, outlier detection, normalization, discretization, text cleaning, type conversion, multi‑source alignment, feature engineering, and sensitive‑data masking—explaining why each step matters for reliable AI model training.

Big Datadata cleaningdata masking
0 likes · 13 min read
10 Essential Data Cleaning Techniques Every AI Project Needs
Test Development Learning Exchange
Test Development Learning Exchange
Nov 21, 2024 · Artificial Intelligence

Data Preprocessing: Standardization, Normalization, and Missing Value Imputation with Python

This tutorial demonstrates how to perform essential data preprocessing techniques—including standardization, min‑max normalization, and various missing‑value imputation methods—using pandas and scikit‑learn in Python, providing code examples and explanations to help you prepare datasets for machine‑learning models.

PandasPythonStandardization
0 likes · 6 min read
Data Preprocessing: Standardization, Normalization, and Missing Value Imputation with Python