“ penguings Artwork by @allison_horst”

Have you ever wondered how machine learning can help us uncover hidden patterns and relationships in the natural world? In a recent machine learning project I did as a part of my academic learning, I delved into the fascinating world of penguins and their unique characteristics. Using the Palmer Penguin dataset, I set out to classify different penguin species based on their morphological and isotopic features.

Exploring Hypotheses

Two hypotheses guided this project:

Hypothesis 1: Morphological Differences I hypothesized that the measurements of Culmen Length and Culmen Depth would exhibit significant variations among the three penguin species: Adelie, Chinstrap, and Gentoo. The expectation was that each species would possess distinct average values for these measurements, enabling us to differentiate between them based on these morphological features.

Hypothesis 2: Flipper Length and Body Mass Relationship I also aimed to explore the relationship between Flipper Length and Body Mass in penguins. The theory was that penguins with longer flippers might have higher body masses, potentially due to enhanced swimming efficiency provided by the additional wing surface area. This could lead to greater body mass to support the physiological adaptation.

The Journey: Data Wrangling and Exploratory Data Analysis (EDA)

Before diving into machine learning models, a crucial step was data preprocessing. This included handling missing values, ensuring data consistency, and encoding categorical variables. With clean data in hand, I employed popular Python libraries like Seaborn and Matplotlib to conduct Exploratory Data Analysis (EDA) and Multivariate Analysis. Visualizations brought the dataset to life, allowing me to identify trends, anomalies, and potential relationships among variables.

The Classification Models

The heart of the project lay in building classification models that could accurately categorize penguin species based on their features. A variety of classification algorithms were employed, each with its unique approach to pattern recognition. Here are some notable results from the classification pipeline:

• Logistic Regression: Accuracy Score of 96.43%

• Nearest Neighbors: Accuracy Score of 96.43%

• Linear SVC: Accuracy Score of 97.62%

• Decision Tree: Accuracy Score of 98.81%

• Naive Bayes: Accuracy Score of 96.43%

• Random Forest: Accuracy Score of 97.62%

• AdaBoost: Accuracy Score of 75.00%

• XGBoost: Accuracy Score of 98.81%

• CatBoost: Accuracy Score of 97.62%

These results showcase the power of various classification algorithms in accurately classifying penguin species based on their features.

A Glimpse into Neural Networks

Additionally, I explored the capabilities of Artificial Neural Networks (ANNs) for classification. However, the results from the ANN models were not as promising as the other algorithms, with an accuracy score of 47.62% for different hidden layer sizes.

Conclusions and Beyond

This project offered valuable insights into the world of penguins and how their physical characteristics can aid in species classification. The success of various machine learning algorithms in accurately categorizing species highlights the potential applications of AI and data science in ecological and biological studies.

So, whether you're a wildlife enthusiast intrigued by the diversity of species or a tech lover amazed by the potential of machine learning. Let's embark on this captivating expedition together and discover how data science can paint a clearer picture of the natural world. Enjoy the journey! 🐧📊🔍

This video will offer insights into the methodology employed throughout this project

As technology continues to advance, there's no doubt that machine learning will play an increasingly vital role in understanding and preserving the diversity of life on our planet. From penguins to other wildlife, the possibilities are as vast as the natural world itself.

Reference :
https://allisonhorst.github.io/palmerpenguins/index.html