Steps within supervised learning
In this section, we will explore in more detail all the steps involved in supervised learning. From data preparation to model deployment, we’ll walk through each stage, providing insights and examples along the way.
Data preparation – laying the foundation
The success of any supervised learning project hinges on the quality of the data. Data preparation is an important first step that involves the following:
- Data cleaning: Identifying and correcting erroneous, incomplete, or inconsistent data points to ensure the integrity of your dataset.
- Feature selection: Choosing the most informative and relevant attributes that contribute to the predictive power of your model, while discarding irrelevant or redundant features.
- Data transformation: Converting raw data into a format that can be effectively processed by machine learning algorithms. This may involve scaling numerical features, encoding categorical variables, or handling...
