Analyzing the Impact of Preprocessing Techniques on Small Dataset Machine Learning Models

1. Introduction 2. Background on Machine Learning 2.1 Definition of Machine Learning 2.2 Importance of Data Preprocessing 2.3 Challenges with Small Datasets 3. Preprocessing Techniques Overview 3.1 Data Cleaning Methods 3.2 Feature Scaling and Normalization 3.3 Data Augmentation Techniques 3.4 Dimensionality Reduction 3.5 Feature Selection 4. Impact on Classification Models 4.1 Decision Trees 4.2 Support Vector Machines 4.3 Neural Networks 5. Impact on Regression Models 5.1 Linear Regression 5.2 Decision Trees for Regression 5.3 Support Vector Regression 6. Comparative Analysis of Techniques 6.1 Evaluation Metrics 6.2 Case Study Methodologies 6.3 Results Interpretation 7. Challenges and Limitations 7.1 Scalability Issues 7.2 Overfitting Concerns 7.3 Dataset Variability 8. Future Research Directions 8.1 Automated Preprocessing Pipelines 8.2 Advances in Small Data Solutions 8.3 Integration with Deep Learning Approaches