A Python implementation of the Naive Bayes algorithm for classifying Iris flowers. This project provides two implementations:
- A comprehensive version with cross-validation and visualization
- A simplified version focused on making predictions
- Gaussian Naive Bayes implementation from scratch
- K-fold cross-validation
- Performance visualization using box plots
- Data preprocessing utilities
- Simple interface for making predictions on new data
Dataset: The classic Iris dataset containing 150 samples with 4 features (sepal length, sepal width, petal length, petal width) and 3 classes of Iris flowers.
Average accuracy: ~95% using 5-fold cross-validation