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Introduction:

Automated Feature Engineering for Machine Learning is a field of study that focuses on developing algorithms and techniques to automatically extract and create predictive features from raw data. By automating this process, researchers and practitioners can save time and resources, and potentially improve the performance of their machine learning models.

Table of Contents:

Chapter 1: Introduction
– Introduction
– Objective of Study
– Limitation of Study
– Scope of Study

Chapter 2: Literature Review
– Overview of Automated Feature Engineering
– Importance of Feature Engineering in Machine Learning
– Existing Approaches and Techniques in Automated Feature Engineering

Chapter 3: Research Methodology
– Data Collection and Preprocessing
– Feature Selection and Engineering Techniques
– Model Development and Evaluation

Chapter 4: Discussion of Findings
– Analysis of Results
– Comparison of Different Feature Engineering Techniques
– Challenges and Future Directions

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

Thesis Overview:

Automated Feature Engineering for Machine Learning is a rapidly evolving field that aims to streamline and optimize the feature engineering process in machine learning tasks. This thesis will investigate the current state of automated feature engineering techniques, their applications, limitations, and future implications.

Chapter one will provide an introduction to the topic, outlining the objective, limitations, and scope of the study. Chapter two will delve into the existing literature on automated feature engineering, highlighting its importance in machine learning and discussing various approaches and techniques.

Chapter three will detail the research methodology, including data collection and preprocessing, feature selection and engineering techniques, and model development and evaluation. Chapter four will present the findings of the study, analyzing results, comparing different feature engineering techniques, and discussing challenges and future directions.

Lastly, chapter five will offer a conclusion and summary of the thesis, summarizing key findings, highlighting contributions to the field, and providing recommendations for future research.Overall, this thesis aims to contribute to the advancement of automated feature engineering in machine learning and shed light on its potential impact on various industries and applications.

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