Clustering Example with Iris Dataset

This example demonstrates how to perform clustering on the Iris dataset using the Fast-ML library. It includes data ingestion, preprocessing, model training, evaluation, and deployment.

Prerequisites

Ensure you have the following installed:

• Python 3.x
• Required libraries: pandas, numpy, scikit-learn, joblib, matplotlib, seaborn, mlflow
• Fast-ML library (ensure it's correctly installed and accessible in your environment)

Steps

Setup

Adjust the path to include the project root directory.

Data Ingestion

Use the CSV connector to load the Iris dataset.

Preliminary Exploratory Data Analysis (EDA)

Perform initial analysis and visualize the dataset.

Data Cleaning

Clean the dataset by filling missing values, removing duplicates, and correcting data types.

Data Normalization

Normalize the feature columns.

Load and Prepare Data

Load and prepare the data for clustering.

Model Selection and Training

Select and instantiate the clustering model using the factory design pattern, then train and evaluate it.

Model Management and Deployment

Set up MLflow for experiment tracking, log parameters and metrics, and log the trained model.

Conclusion

We have successfully clustered the Iris dataset and evaluated the model using the silhouette score. This example demonstrates the end-to-end process of building a clustering pipeline using the Fast-ML library.