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Introduction:

Imbalanced data refers to a situation where the distribution of classes within a dataset is skewed, with one class significantly outnumbering the other(s). This imbalance can pose a challenge for machine learning algorithms, as they tend to bias toward the majority class and overlook minority classes. As a result, accurate prediction and classification of the minority class may be compromised.

In order to address this issue, various techniques have been developed to handle imbalanced data and improve the performance of machine learning models. These techniques include oversampling, undersampling, hybrid methods, and ensemble strategies.

Objective of Study:

The objective of this study is to analyze and compare different techniques for handling imbalanced data in machine learning. The study will evaluate the effectiveness of each technique in improving the performance of machine learning models on imbalanced datasets.

Limitation of Study:

This study is limited to examining the performance of imbalanced data handling techniques on synthetic and real-world datasets. The study does not consider the computational complexity of the techniques or their scalability to large datasets.

Scope of Study:

The scope of this study includes a comprehensive review of the literature on imbalanced data handling techniques, an evaluation of the effectiveness of these techniques on various datasets, and a discussion of the implications and limitations of the study findings.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Research Questions
1.4 Objectives of Study
1.5 Limitations of Study
1.6 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Imbalanced Data
2.2 Imbalanced Data Handling Techniques
2.3 Evaluation Metrics
2.4 Previous Studies on Imbalanced Data Handling Techniques

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Preprocessing
3.3 Imbalanced Data Handling Techniques
3.4 Experimental Setup
3.5 Evaluation Criteria

Chapter 4: Discussion of Findings
4.1 Performance Comparison of Imbalanced Data Handling Techniques
4.2 Impact of Imbalanced Data Handling Techniques on Machine Learning Models
4.3 Strengths and Weaknesses of Imbalanced Data Handling Techniques

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusions
5.3 Recommendations for Future Research

Thesis Overview on Imbalanced Data Handling Techniques:

Imbalanced data poses a significant challenge for machine learning algorithms, as they tend to favor the majority class and overlook the minority class. In this thesis, we aim to address this issue by evaluating and comparing different techniques for handling imbalanced data. This study includes a comprehensive review of the literature on imbalanced data handling techniques, an evaluation of the effectiveness of these techniques on various datasets, and a discussion of the implications and limitations of the study findings. Through this research, we seek to provide insights into the best practices for handling imbalanced data and improving the performance of machine learning models.

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