The ML Workflow
Machine learning is not just about training a model. A real ML project follows a series of steps that turn a real-world problem into a working prediction system.
Machine learning is a process, not just an algorithm.
You start with a problem, collect useful data, prepare that data, train a model, make predictions, evaluate the results, and improve the model when necessary.
Start With a Problem
Every machine learning project should begin with a clear problem.
For example, an online store may want to predict whether a customer is likely to buy a product.
Before writing Python code, we need to understand exactly what we are trying to predict.
This becomes the goal of our machine learning project.
The Complete ML Workflow
Once the problem is clear, the project moves through several important stages.
Decide what you want the model to predict.
Gather useful examples related to the problem.
Clean and organize the data for learning.
Let the model learn patterns from the data.
Use the trained model on new data.
Check how well the model performs.
Improve the model when its results are not good enough.
A Simple Example
Let's use house-price prediction to understand the entire workflow.
| Step | What Happens? |
|---|---|
| Define the Problem | Predict the price of a house. |
| Collect Data | Gather house sizes, bedrooms, locations, and prices. |
| Prepare Data | Clean missing or incorrect information. |
| Train Model | Let the model learn relationships between house information and price. |
| Make Prediction | Predict the price of a new house. |
| Evaluate | Compare predictions with actual prices. |
| Improve | Improve the model if its predictions are not accurate enough. |
Why Does the Order Matter?
These steps are connected.
A model cannot learn useful patterns from bad or irrelevant data.
Similarly, training a model does not mean the model is automatically good. We still need to test its predictions and measure its performance.
A sophisticated algorithm cannot magically fix poor data or a badly defined problem.
The Workflow Is Usually Iterative
The process does not always end after the first model is trained.
If the model performs poorly, we may go back and improve the data, features, model, or training process.
This is why machine learning development is often an iterative process rather than a one-time operation.
A Real-World View
Imagine an e-commerce company wants to predict whether a customer will purchase a product.
What You Will Learn in the Next Pages
The workflow is easy to understand at a high level. The next pages will examine each important step in more detail.
A Machine Learning Project Follows a Process.
Define the problem, collect data, prepare the data, train a model, make predictions, evaluate the results, and improve the model when necessary.
What Comes After Training?
Suppose you have trained a model to predict house prices. Is the project finished?
No.
We need to use the model to make predictions and then evaluate how well those predictions perform.
If the results are poor, we can improve the model and repeat the process.