Iteration 2

In Iteration 1, you moved quickly through the complete machine-learning workflow and built your first classification, regression, and clustering models.

In Iteration 2, you will go through the workflow again, but this time in greater depth.

The work is still structured: notebooks and resources will guide you through the main steps. However, you will begin making more decisions yourself and applying concepts introduced in the self-study material.

Business Understanding

Before going deeper into the data, develop enough domain understanding to reason about what you are modelling.

Read:

• SMILES: A Simplified Chemical Language (Daylight Theory Manual)

Your goal is not to master chemistry. Focus on understanding what SMILES strings represent and how they describe molecular structure.

Data Understanding

Explore the HOPV data, identify the input and target variables, investigate data quality, and examine important distributions and relationships.

Complete in order:

1. Understanding HOPV Stability
2. Understanding HOMO/LUMO Data

Data Preparation

Turn the raw molecular data into data suitable for machine-learning models.

Complete:

• Data Preparation

Classification

Build a classification model to predict molecular stability.

Complete:

• Classification

Regression

Build regression models to predict HOMO and LUMO energy levels.

Complete:

• Regression

Clustering

Use clustering to investigate molecular structure and identify patterns that may also be useful for your supervised models.

Complete:

• Clustering

Keep iterating

Although the notebooks provide a structured path, the machine-learning workflow is not strictly linear.

What you discover during modelling may give you a reason to return to the data, change its preparation, investigate a new feature, reconsider your validation strategy, or revisit your understanding of the problem.

In Iteration 3+, this process becomes open-ended.