Data Cleaning and Exploration with Machine Learning

Table of Contents

1. Examining the Distribution of Features and Targets
2. Examining Bivariate and Multivariate Relationships between Features and Targets
3. Identifying and Fixing Missing Values
4. Encoding, Transforming, and Scaling Features
5. Feature Selection
6. Preparing for Model Evaluation
7. Linear Regression Models
8. Support Vector Regression
9. K-Nearest Neighbor, Decision Tree, Random Forest and Gradient Boosted Regression
10. Logistic Regression
11. Decision Trees and Random Forest Classification
12. K-Nearest Neighbors for Classification
13. Support Vector Machine Classification
14. Naive Bayes Classification
15. Principal Component Analysis
16. K-Means and DBSCAN Clustering