Data science is all about presenting insights to the end-users in the most simplistic way possible. You work on a machine learning/deep learning model from data cleaning to hyperparameter tuning. However, you realize that the most important task of presenting it to the end-users has not even started yet. Here I discuss an easy and faster way to deploy ML models using Jupyter Notebook and Tableau.

We will use Scikit-Learn to process the data and build the model. Then we use TabPy to deploy the built model and access it in Tableau. If you are looking for a way to deploy models to use it in cloud platforms or distributed systems, you can discontinue reading now.

The Data

We will use the Titanic dataset available on Kaggle to build a Random Forest model. The goal of the project is to predict if a passenger will likely survive the Titanic disaster or not. We will use demographic variables like Age, Gender, sibling count, and also the ticket class of the passenger as independent variables.

The goal of the project is to predict if a passenger will likely survive the Titanic disaster or not.

#machine-learning #tabpy #tableau #deployment #python