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Summary
This summary is machine-generated.

This study addresses challenges in Big Data analytics by proposing a hybrid model for imputing missing values. The hybrid approach, integrating machine learning and statistical techniques, significantly improved data quality and model accuracy.

Keywords:
Big Data AnalyticsData CleaningData ImputationFeature EngineeringIoT

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • The convergence of physical and digital worlds via the Internet of Things (IoT) generates vast, versatile Big Data.
  • Big Data analytics performance is significantly impacted by data quality issues, particularly missing or inaccurate values.
  • Discovering and repairing dirty data is a critical challenge in Big Data analytics to ensure reliable results.

Purpose of the Study:

  • To evaluate various missing value imputation techniques for Big Data.
  • To propose and validate a hybrid model integrating machine learning and statistical methods for enhanced data imputation.
  • To improve the accuracy and reliability of Big Data analytics through effective data cleaning.

Main Methods:

  • Comparison of different machine learning (ML) models for missing value imputation.
  • Development of a hybrid imputation model combining ML and sample-based statistical techniques.
  • Application of K-means clustering and principal component analysis for feature engineering.
  • Implementation of K-fold cross-validation to prevent overfitting.

Main Results:

  • The proposed hybrid imputation model demonstrated superior performance in handling missing values.
  • Feature engineering and hyperparameter tuning on the improved dataset led to significant accuracy gains.
  • The XGBoost model achieved high accuracy, with a root mean squared logarithmic error (RMSLE) of approximately 0.125.

Conclusions:

  • Effective missing value imputation is crucial for accurate Big Data analytics.
  • The hybrid ML and statistical imputation model offers a robust solution for data quality improvement.
  • The study validates the effectiveness of the proposed methods in enhancing predictive model performance.