naive bayes theorem on iris data set

Objective: To build a simple yet effective model to classify iris flower species based on their features

Dataset Used: Classic Iris dataset (150 samples, 3 species, 4 features)

Key Steps:

Data Preprocessing: Checked for missing values and standardized feature names

Exploratory Data Analysis:

Visualized feature distributions using Seaborn and Matplotlib

Observed clear separability between species based on petal measurements

Model Building:

Applied Gaussian Naive Bayes from sklearn

Split data into training and test sets (80/20)

Evaluation:

Achieved high accuracy on the test set (typically ~95%+)

Visualized confusion matrix to assess classification performance

Outcome:

Demonstrated the effectiveness of Naive Bayes for small, clean datasets

Provided a clear and interpretable baseline model for multiclass classification