This project involves building a neural network to classify data generated from a synthetic, non-linear dataset called the "moons" dataset, commonly used to evaluate machine learning models due to its non-linear, interlocking crescent shapes. The objective is to develop a neural network capable of distinguishing between two classes represented by these crescents, which are not linearly separable. This classification requires a complex decision boundary that can only be learned by a model capable of capturing non-linear patterns, making a neural network an ideal choice.

The project workflow includes several key steps. First, synthetic data is generated using the make_moons function from scikit-learn, with added noise to simulate real-world imperfections. Next, a neural network is designed with a hidden layer configuration chosen to balance model complexity and prevent overfitting, ensuring the network captures the necessary patterns without learning noise. Key hyperparameters, such as the number of hidden layers, neurons per layer, and activation functions, are selected to optimize performance on this challenging classification task.

The neural network is then trained on the data through backpropagation, minimizing classification errors. A decision boundary plot is visualized to assess the model’s ability to capture the distribution of data points for each class. The decision boundary reveals the areas where each class is predicted, with overlapping regions indicating where misclassifications may occur.

To improve the model’s performance, fine-tuning steps are taken, such as adjusting the network architecture by adding layers or neurons, increasing training epochs, or applying regularization techniques to prevent overfitting.

This project showcases the neural network’s ability to learn complex, non-linear boundaries, which are essential for accurately classifying data that cannot be separated by a straight line. It serves as a foundational exercise in applying neural networks to non-linear classification tasks, highlighting the importance of model tuning and the challenges of working with non-linear data in machine learning.