Random Forest Regression

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Table of contents

Table of Contents
1. Introduction
1.1. Aim and Purpose
1.2. Research Questions
1.3. Limitations
1.4. Thesis Structure
2. Background
2.1. Multiple Linear Regression
2.2. Lasso Regression
2.3. Ridge Regression
2.4. Random Forest Regression
2.5. Artificial Neural Network
3. Method
3.1. Literature Study
3.2. Experiment
3.2.1. Evaluation Metrics
3.2.2. Computer Specifications
3.2.3. Algorithms’ Properties/Design
4. Literature Study
4.1. Related Work
4.2. Feature Engineering
4.2.1. Imputation
4.2.2. Outliers
4.2.3. Binning
4.2.4. Log Transformation
4.2.5. One-hot Encoding
4.2.6. Feature Selection
4.3. Evaluation Metrics
4.4. Research Question 1 Results
4.5. Factors
4.5.1. Crime Rate
4.5.2. Interest Rate
4.5.3. Unemployment Rate
4.5.4. Inflation Rate
4.6. Correlation
4.7. Research Question 2 Results
5. Experiment
5.1. Data Used
5.2. Public Data
5.3. Local Data
5.4. Correlation
5.5. Experiment Results
5.5.1. Prediction Accuracy
5.5.2. Correlation
5.5.3. Factors
6. Discussion
7. Conclusion
7.1. Ethics
8. Bibliography
9. Appendixes