Step 1: Start with data points classified into two classes (blue for class 1, red for class 0).
Step 2: Initialize model parameters (weights and bias) that define a decision boundary.
Step 3: Calculate the predicted probability using the sigmoid function: P(y=1) = 1/(1+e^(-z)), where z = w₀ + w₁x₁ + w₂x₂.
Step 4: Compute the cost function (binary cross-entropy loss), which measures the model's performance.
Step 5: Use gradient descent to update the parameters to reduce the cost.
Step 6: Repeat steps 3-5 until convergence or reaching maximum iterations.