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Introduction:

AutoML for Automated Data Preprocessing is an innovative approach that leverages machine learning algorithms to automate the data preprocessing tasks, which are often labor-intensive and time-consuming. By using automated tools and techniques, researchers and data scientists can save time and resources while ensuring the quality and consistency of their data preprocessing workflows. This thesis explores the potential benefits and challenges of using AutoML for Automated Data Preprocessing in various research and industry applications.

Masters Thesis Table of Contents:

Chapter 1: Introduction
1.1 Background and Context
1.2 Objectives of the Study
1.3 Limitations of the Study
1.4 Scope of the Study

Chapter 2: Literature Review
2.1 Overview of Data Preprocessing
2.2 Automated Data Preprocessing Techniques
2.3 AutoML for Data Preprocessing
2.4 Applications of AutoML in Data Preprocessing

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection and Analysis
3.3 Implementation of AutoML for Data Preprocessing
3.4 Evaluation Metrics

Chapter 4: Discussion of Findings
4.1 Comparison of Automated Data Preprocessing Techniques
4.2 Performance Evaluation of AutoML Tools
4.3 Challenges and Limitations of AutoML for Data Preprocessing

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Recommendations for Future Research

Thesis Overview:

AutoML for Automated Data Preprocessing is a cutting-edge approach that utilizes machine learning algorithms to automate the data preprocessing tasks. This thesis aims to explore the potential benefits and challenges of using AutoML in various research and industry applications. The literature review will provide an overview of data preprocessing, automated data preprocessing techniques, and the usage of AutoML in data preprocessing. The research methodology will outline the research design, data collection, implementation of AutoML tools, and evaluation metrics. The discussion of findings will include a comparison of automated data preprocessing techniques, performance evaluation of AutoML tools, and challenges and limitations associated with using AutoML for data preprocessing. The conclusion and summary section will summarize the findings, discuss the contributions to the field, and provide recommendations for future research in this area.

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