Machine Learning Practice

Under this repository, I want to share my journey with Machine Learning Algorithms. The goal is to develop ready-to-use templates for Data Science Projects and present them to other developers. The topics will be shared here:

• Supervised Learning
  • Regression Models
    • Linear Regression
    • Polynomial Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
  • Classification Models
    • Logistic Regression
    • K-Nearest Neighbors
    • Support Vector Machine
    • Kernel Support Vector Machine
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification (Hyperparameter Tuning)
    • LightGBM Classification with Feature Selection and Hyperparameter Tuning
    • Credit Card Fraud studies
      • Classification Models results
      • LightGBM results with SMOTE
• Unsupervised Learning
  • Clustering
    • K-Means Clustering with Principal Component Analysis (PCA) visualization
    • Hierarchical Clustering with Dendrogram
  • Association Rule Learning
    • Apriori
    • Eclat
• Reinforcement Learning
  • Upper Confidence Bound
  • Thompson Sampling
• Natural Language Processing
  • Bag of words
    • Sentiment Analysis with Classification Models