Data Preprocessing Techniques for Enhancing Machine Learning Model Performance in Small Datasets

1. Introduction 1.1 Background and Motivation 1.2 Objective of the Study 1.3 Structure of the Paper 2. Understanding Small Datasets 2.1 Definition and Characteristics 2.2 Challenges in Machine Learning 2.3 Importance of Data Preprocessing 3. Data Preprocessing Overview 3.1 Definition and Importance 3.2 Common Techniques 3.3 Role in Machine Learning 4. Handling Missing Data 4.1 Types of Missing Data 4.2 Imputation Methods 4.3 Impact on Model Performance 5. Data Normalization and Scaling 5.1 Importance of Scaling 5.2 Normalization Techniques 5.3 Effects on Small Datasets 6. Feature Selection and Engineering 6.1 Definition and Goals 6.2 Selection Techniques 6.3 Feature Engineering Methods 7. Data Augmentation Strategies 7.1 Importance in Small Datasets 7.2 Synthetic Data Generation 7.3 Case Studies and Examples 8. Evaluation of Preprocessing Techniques 8.1 Metrics for Evaluation 8.2 Impact on Model Accuracy 8.3 Comparative Analysis of Techniques