Topic 2:- Machine Learning Workflow
Workflow The Machine Learning (ML) workflow is a step-by-step process used to build, train, test, and evaluate an ML model. It ensures that data is properly prepared, the model is correctly trained, and its performance is accurately measured. Main Stages of the ML Workflow ML Overview(step-by-step) 1. Data Collection Gather data from various sources such as CSV files, databases, APIs, sensors, or online datasets. Example: Collecting house price data (area, location, price). 2. Data Preprocessing Clean and prepare data before training. Handle missing values, outliers, and categorical encoding. Apply feature scaling and normalization. 3. Train/Test Split Dataset Division Split the dataset into two parts: Training Set: Used to train the model (70–80% of data) Testing Set: Used to test the model (20–30% of data) Purpose: To check how well the model performs on unseen da...