For this guide, we will be using the Titanic dataset from Kaggle. This dataset contains a list of passengers who were on the Titanic and some information about them such as which cabin they were staying in, how old they were, if they had a sibling with them on board, etc. and whether or not they survived the titanic.
Our goal for this tutorial is creating a model that can predict whether or not a hypothetical passenger would have survived the titanic had they been on it based on the training data.
Open the terrene app, and on the left-hand menu, click on "New TFrame". After you TFrame is created, upload the downloaded data using the CSV parser.
Right-click on the "Survived" column and pick "Train a Model". Terrene will pre-populate the other variables in the dataset as input variables for the model but we will have to un-select some of them such as Name, PassengerId, etc.
The reason these variables have to be un-selected is that they are too unique and can cause the model to overfit.
Once on the model page, click on "Populate Demo Data" and then click on predict to make some predictions using the model.
You can also use the batch predictions feature to make predictions multiple at a time from a TFrame.