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