Steps in Machine Learning

1) Data collection – the quantity and quality of the data is one of the most important factors that determine how accurate the model is.

2) Data preparation – this includes data wrangling, cleaning (normalization, handling missing values, removing duplicates, etc.), visualization, and splitting the dataset into training, validation, and testing subsets.

3) Model selection – choose the right model for the task.

4) Model training – train the model with the defined training dataset.

5) Model evaluation – see how well the model performed with regard to a chosen metric by testing it against previously unseen data. For this step, we use the validation dataset.

6) Parameter tuning – also known as hyperparameter tuning. Changing some parameters that are important for the chosen model can improve its performance significantly.

7) Making predictions - use the testing dataset to test the model and see how it would perform in the real world.