Machine Learning Models

Contributor:

Randy Kim randygkim@gmail.com

Structure of Repository

Supervised Learning

Regression

• Linear Regression
• Decision Tree
• Random Forests
• Neural Network

Classification

• Logistic Regression
• Support Vector Machine
• Naive Bayes

Unsupervised Learning

• Clustering (KNN, Hierarchical, Mean Shift, Density-Based)

Dimensionality Reduction

• Feature Selection
• PCA

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• Sklearn Pipelines
• Hyperparameter Tuning GridSearchCV

The main purpose of this repo is to practice and learn to create prediction models. Some of the examples will based off of tutiorals or projects I have done.