A large number of visitors come to your website every day, but you know that only a small percentage of them are likely to buy from you, most of them perhaps not going back. Right now you may be spending money to re-market to everyone, but perhaps we could use machine learning to identify the most valuable leads?
The situations that go wrong during a production can depend on many features. Enter any values and see if there is an anomaly in manufacturing.
A mobile phone's price range can be any range. With this example model, you can predict the price range probability.
With the data collected from thousands of people, you can predict whether there is a heart disease probability or not.
The quality of a wine can depend on dozens of values. With this model created with the collected data, you can predict the quality of a wine in the range of 1 to 10.
It is a model that examines financial news texts. Predicts whether the news is positive, neutral or negative. For example, if you type "RISING costs have forced packaging producer Huhtamaki to axe 90 jobs at its Hampshire manufacturing plant", it will guess that it is a negative text.
It is a model that predicts whether the message in the email box is spam. For example, if you type "Free entry in 2 a wkly comp to win FA Cup final", the model will say it's spam.