Answer: A neural network is made up of interconnected nodes and links, and it is particularly effective at modeling non-linear relationships between the input data

## Explanation **Option C is correct** because: - Neural networks consist of interconnected nodes (neurons) organized in layers (input, hidden, output) - The connections between nodes have weights that are adjusted during training - They are particularly effective at modeling complex non-linear relationships due to their ability to learn hierarchical representations of data - The activation functions in neurons (like sigmoid, ReLU, tanh) introduce non-linearity, enabling the network to approximate complex functions **Why other options are incorrect:** - **Option A**: Neural networks are inspired by biological neural networks but are not precise replicas of the human nervous system - **Option B**: While neural networks can perform regression, they are not based on linear regression models - they can model non-linear relationships - **Option D**: This describes a decision tree structure, not a neural network. Neural networks have layered architectures, not tree-like structures Neural networks excel at pattern recognition, classification, and regression tasks where the relationships between variables are complex and non-linear.

Author: Tanishq Prabhu