 |
| Photo by Franck V. on Unsplash |
In terms of predictive analytics, many would prefer having the convenience that machine learning brings into the process. What better way to do technically sound predictions with the use of models developed via machine learning packages? With it, one can arrange the setup and run the data with the machine that will assist in looking for the model with best fit. Best of all, the data scientist can do other important stuff while the machine performs what it's been programmed to... efficiency indeed.
Here are some examples of machine learning process and outputs:
Machine Learning - Predicting High Risk Credit Card Customers
Machine Learning - Predicting House Prices Using Gradient Boost Machine Learning
Machine Learning - Classification concerning fraudulent Credit Card Transactions
Machine Learning - Class prediction with 160 variables and 19622 observations
Machine Learning - Sample Classification and Regression Trees Output
