The main steps involved in the classification process are:1. Data Preprocessing: This involves cleaning and transforming the data to ensure its quality and compatibility with the classification algorithm.2. Feature Selection or Extraction: This step involves selecting the most relevant features from the dataset or extracting new features that can improve the classification accuracy.3. Training the Classifier: The classifier is trained using the labeled training data, where the algorithm learns the patterns and relationships between the features and their corresponding classes.4. Model Evaluation: The trained classifier is evaluated using a separate set of data called the testing data to assess its performance and accuracy.5. Prediction: Once the classifier is trained and evaluated, it can be used to predict the class or category of new, unseen data based on the learned patterns and relationships.