1. Data Collection and Preprocessing: Gathering and cleaning data to ensure quality. 2. Feature Selection: Identifying the most relevant variables for the model. 3. Model Selection: Choosing the appropriate algorithm (e.g., logistic regression, neural networks). 4. Training: Feeding the data into the model and adjusting parameters to optimize performance. 5. Validation and Testing: Evaluating the model using separate datasets to ensure accuracy and generalizability.
