In this tutorial, we are going to implement the most commonly used Classification algorithm called the Logistic Regression from Scratch using Python NumPy and Matplotlib. We will understand the Sigmoid function, Hypothesis function, Decision Boundary, the Log Loss function and code them alongside. We will apply the Gradient Descent Algorithm to find the parameters, weights and bias . We will measure accuracy and plot the decision boundary for a linearly separable dataset and a non-linearly separable dataset.

In this article, we are going to implement the most commonly used Classification algorithm called the Logistic Regression. First, we will understand the **Sigmoid** function, **Hypothesis** function, **Decision Boundary**, the **Log Loss function** and code them alongside.

After that, we will apply the **Gradient Descent** Algorithm to find the parameters, `weights`

and `bias`

. Finally, we will measure **accuracy** and **plot the decision boundary** for a linearly separable dataset and a non-linearly separable dataset.

We will implement it all using Python NumPy and Matplotlib.

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