8 Steps to Building a Python Machine Learning Model

Learn how to build a machine learning model for stock price prediction, including data collection, preprocessing, and model evaluation using simple steps.

This article provides a systematic introduction to building machine learning models, demonstrated through a practical case — stock price prediction.

By following these steps, readers will better understand and master the complete process of building machine-learning models.

Define the Problem

First, clearly define the problem. For example, we want to predict tomorrow’s stock price.

Why is this important?

• Clear goals help us choose the right data and algorithms.

• Defining the problem aids in evaluating the model’s effectiveness.

Example code:

# Our goal is to predict tomorrow's stock price
problem_statement = "Predict tomorrow's stock price."
print(f"Our problem statement is: {problem_statement}")

Output:

Our problem statement is: Predict tomorrow's stock price.