In this lesson, you will get an overview of the typical machine learning workflow. This involves an end-to-end process, beginning with the data sourcing and data preparation and ultimately culminating in the delivery of a machine learning model. However, there are many steps in between which will be covered in this lesson. Overall, the objective of a machine learning project is to build a mathematical model, to solve a given problem, by using input data and applying machine learning algorithms onto it.
Dataset Analysis using the California Housing Dataset
Let's consider the California housing dataset example, which includes various features like 'Median house age', 'Average number of rooms per household', 'Average number of bedrooms per household', and 'Median house value', among others.
The main focus lies on the 'Median house value' for California districts, which serves as the 'Target feature'. This indicates the outcome you aim to predict or understand. This scenario represents a supervised learning problem, where the model learns to predict the median house value based on historical data.
As Data Scientists and Developers, you must already be familiar with the four steps of the machine learning workflow. Let's revisit these generic steps, as shown in the diagram, before delving into them in detail using the California housing dataset example.
Step 1: Data Extraction - This stage involves carefully selecting relevant features and structuring the dataset for analysis.
Step 2: Dataset Partition - The dataset is divided into distinct training box and testing box, as subsets to facilitate effective model training and evaluation.
Step 3: Train an ML model - During this stage, the carefully prepared training dataset is fed into a machine learning algorithm to deliver a trained model.
Step 4: Model Evaluation - The model's performance is assessed; if desired outcomes aren't met, the model is iteratively fine-tuned, and the training process is repeated until satisfactory results are achieved.
Please watch the following video that outlines the machine learning workflow steps in detail.