Steps to Build a Machine Learning Model

Building a machine learning model starts with collecting data because the computer needs examples to learn from. Then we clean the data so wrong or missing values don't confuse it. Next, we do feature extraction meaning selecting useful information and feature engineering meaning improving that information so learning becomes easier. After that, we train the model using the data. Then we test and evaluate the model to check how well it performs. Finally, the trained model is used to make predictions on new unseen data.