Introduction
Explore the systematic process of conducting an end-to-end machine learning project by breaking down the key steps. Learn how to understand real datasets, prepare data for algorithms, choose and evaluate models, fine-tune solutions, and present results to meet business objectives.
We'll cover the following...
A Dataset and a Machine Learning Problem, What Should You Do?
Say you have been recently hired as a Data Scientist to work on a project and you have been given some real estate data. How can you approach the problem in a systematic and structured way rather than ending up with a spaghetti code? What are the steps to follow?
In this section, we are going to deconstruct the main step needed to work on a ML project via a real end-to-end example with code. We are going to work with a challenge based on a Kaggle Competition.
Overview of the Main Steps:
There isn’t a golden approach that every data scientist must ...