📄 README.md

### Neuron Class for Binary Classification

This project defines a `Neuron` class in Python that simulates a single neuron used for binary classification tasks in machine learning.

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### Features

- Validates input for number of features (`nx`)
- Initializes:
  - `W`: Weights vector using a random normal distribution
  - `b`: Bias, initialized to `0`
  - `A`: Activated output, initialized to `0`

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### Class Definition

```python
class Neuron:
    def __init__(self, nx):
        ...

Parameters

• nx (int): Number of input features to the neuron

Raises

• TypeError: If nx is not an integer
• ValueError: If nx is less than 1

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Public Attributes

• W: Weights vector (NumPy array)
• b: Bias (float)
• A: Activated output (float)

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Example Usage

from neuron import Neuron  # The class is saved in Live coding - Neuron .ipynb

n = Neuron(3)

print("Weights:", n.W)
print("Bias:", n.b)
print("Activated Output:", n.A)

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Error Handling

n = Neuron("3")   # Raises TypeError: nx must be an integer
n = Neuron(0)     # Raises ValueError: nx must be a positive integer

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Project Structure

project-folder/
│
├── Live coding - Neuron .ipynb        # Contains the Neuron class
└── README.md         # Project documentation

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Requirements

• Python 3.x
• NumPy

Install NumPy using pip:

pip install numpy

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Testing

You can test this class in a Python script or interactive shell. For unit testing, consider using unittest or pytest.

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✍️ Author

Kanisa

Jolly