What are stages of building a model in Machine Learning?

The seven steps in building a machine learning model are,

Data Collection - In this step, we collect the data related to the problem.

Data Preparation - Here, we clean and organize the collected data based on the problem. We remove duplicate data, error data, fill missing data, etc in this process

Choosing an algorithm - As the name suggests, in this stage, you choose the appropriate algorithm for the problem.

Train the algorithm - We use the dataset to train the algorithm to create a model.

Evaluate the model - We use the test data from the dataset to find the accuracy of the model created.

Parameter Tuning - In this step, we tune the model parameters to improve its performance.

Make predictions - In this step, we apply the created model on a real dataset.