Machine Learning Algorithms in a Nutshell

Requirements

• pandas
• sklearn
• matplotlib
• numpy
• pickle

Topics covered are as follows:

• Logistic regeression
• LDA Classification
• KNN Classification
• Gaussian Naive Bayes Classification
• Classification and Regression Test Classification
• Support Vector Machines Classification
• Linear Regression
• Lasso Regression
• ElasticNet Regression
• KNN Regression
• Decision Tree Regression
• Support Veector Machines Regression
• Comparing the Algorithms
• Create a pipeline that standardizes the data then creates a model
• Create a pipeline that extracts features from the data then creates a model
• Bagged Decision Trees for Classification
• Random Forest Classification
• Extra Trees Classification
• AdaBoost Classification
• Stochastic Gradient Boosting Classification
• Voting Ensemble for Classification
• Grid Search for Algorithm Tuning
• Randomized for Algorithm Tuning
• Save Model Using Pickle
• Save Model Using joblib

Dataset used: Pima Indians Diabetes Database and Boston Housing Dataset