This notebook focuses on implementing a perceptron model to predict diabetes outcomes using patient medical data. The code demonstrates the complete flow starting from data handling to model training and prediction using a single neuron based learning approach.
Dataset and Features
The dataset used in the file contains medical attributes related to diabetes diagnosis. Each row represents a patient record and each column represents a specific health related feature such as glucose level blood pressure BMI age and related indicators. The target variable represents whether diabetes is present or not.
Data Preparation
The data is loaded and converted into input features and target labels. Numerical values are structured into arrays suitable for mathematical operations. The dataset is prepared so that each training example can be passed into the perceptron during learning.
Perceptron Structure
The perceptron is implemented as a custom class. It includes
Initialization of weights based on the number of input features
Initialization of a bias value
A sigmoid activation function to convert weighted sums into probability values
The perceptron computes the weighted sum of inputs adds bias and applies the sigmoid function to generate predictions.
Prediction Logic
The prediction method calculates the dot product between inputs and weights adds the bias and applies the activation function. The output represents a probability score which is later converted into a class label based on a threshold.
Training Mechanism
The training process iterates over the dataset multiple times using epochs. For each data point
The model generates a prediction
The error between predicted and actual value is calculated
Weights and bias are updated using gradient based adjustments
This step by step update allows the perceptron to gradually reduce prediction error.
Learning Behavior
As training progresses the perceptron improves its ability to separate diabetic and non diabetic cases using a linear decision boundary. The learning rate controls how much the weights change during each update.
Model Evaluation
After training the model is tested on input data to observe prediction accuracy. The results show how a simple perceptron can learn patterns from medical data and make reasonable binary classifications.
Key Concepts Demonstrated
This file clearly demonstrates
How input features influence predictions through weights
How bias affects decision boundaries
How activation functions convert numerical values into meaningful outputs
How iterative learning improves model performance
Conclusion
This implementation provides a clear view of how a perceptron processes medical data to predict diabetes outcomes. The file highlights the full learning cycle of a basic neural model and serves as a practical example of binary classification using a single artificial neuron.