Dockerized Palmer Penguins Classifier

A machine learning classification project demonstrating how to package and run a scikit-learn model inside a Docker container.

The project was originally developed as part of the DSC524 – Designing MLOps for Enterprises coursework in the M.Tech Artificial Intelligence and Data Science program at IIIT Kottayam.

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🎯 Project Objective

The objective of this project is to demonstrate a basic MLOps workflow by combining:

• Machine learning model development
• Data preprocessing
• Model evaluation
• Reproducible environments
• Docker containerization

The model predicts the species of a penguin using physical and categorical characteristics from the Palmer Penguins dataset.

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🐧 Dataset

The project uses the Palmer Penguins dataset, which contains measurements for three penguin species:

• Adelie
• Chinstrap
• Gentoo

Features include:

• Island
• Bill length
• Bill depth
• Flipper length
• Body mass
• Sex
• Year

Rows containing missing values are removed before training.

Dataset reference:

Palmer Penguins

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🛠 Tech Stack

• Python
• pandas
• scikit-learn
• Palmer Penguins
• Docker

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🔄 ML Workflow

Palmer Penguins Dataset
        ↓
Data Cleaning
        ↓
Feature / Target Split
        ↓
Train-Test Split
        ↓
Categorical Encoding
        ↓
Decision Tree Classifier
        ↓
Model Evaluation
        ↓
Docker Container