This repository contains several machine learning algorithm implementations as well as templates for various ML pipeline works such as preprocessing and visualization.

Every model is implemented in both R and Python's scikit-learn.
For Python, both notebooks and .py files are included.

• 1. Regression:

  • a. Linear Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Regularization
      • Lasso
      • Ridge
      • ElasticNet
  • b. Decision Tree Regression
  • c. Random Forest Regression
  • d. Support Vector Regression

• 2. Classification

  • a. Logistic Regression
  • b. Decision Trees
  • c. Random Forest
  • d. Support Vector Machines
    • Kernel SVM
  • e. Näive Bayes Classifier
  • f. XGBoost

• 3. Clustering

  • a. K-Means
  • b. Hierarchical Clustering
    • Dendrograms

• 4. Reinforcement Learning

  • 1. Thompson Sampling
  • 2. Upper Confidence Bound

• 5. Association Rule

  • 1. Apriori

• 6. Dimensionality Reduction

  • 1. Principal Component Analysis
  • 2. Linear Discriminiant Analysis
  • 3. t-SNE

• 7. Model Selection and Hyperparameter Tuning

  • 1. Grid Search
  • 2. Random Search
  • 3. K-fold Cross Validation

• 8. Miscellaneous

  • 1. Artificial Neural Networks (with Keras)
  • 2. Preprocessing templates
    • Importing
    • Scaling
    • Encoding
    • Dummy variables
    • Non-linear transformation
    • Matrix manipulations
  • 3. NLP - Bag of Words

• Download or clone repository: git clone https://github.com/sukruc/machine-learning-algorithms.git
• Follow table of contents to find a specific application or template, or randomly explore the content.
• Enjoy!