Penguin Classifier Sklearn

🚀 Palmer Penguins Species Classifier

This model is designed to predict the species of penguins in the Palmer Penguins dataset, offering valuable insights for penguin - related classification and analysis.

🚀 Quick Start

The following code demonstrates how to load the model and make predictions:

# Load the model
from sklearn.externals import joblib

model = joblib.load('path/to/your/model.pkl')

# Input features
features = {
    'culmen_length_mm': 39.1,
    'culmen_depth_mm': 18.7,
    'flipper_length_mm': 181,
    'body_mass_g': 3750
}

# Predict species
predicted_species = model.predict([list(features.values())])[0]
print(f"Predicted species: {predicted_species}")

✨ Features

• Metrics: The model is evaluated using accuracy, precision, recall, and F1 - score.
• Library: It is built with the sklearn library.
• Pipeline Tag: It is designed for tabular - classification tasks.
• Tags: It is related to biology and penguins 🐧.
• Datasets: It is trained on the SIH/palmer - penguins dataset.

📦 Installation

The README does not provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

# Load the model
from sklearn.externals import joblib

model = joblib.load('path/to/your/model.pkl')

# Input features
features = {
    'culmen_length_mm': 39.1,
    'culmen_depth_mm': 18.7,
    'flipper_length_mm': 181,
    'body_mass_g': 3750
}

# Predict species
predicted_species = model.predict([list(features.values())])[0]
print(f"Predicted species: {predicted_species}")

📚 Documentation

Model description

This model is a scikit - learn classifier trained to predict the species of penguins in the Palmer Penguins dataset. The dataset contains measurements of penguin species including the species itself, making it suitable for classification tasks.

The model uses features such as culmen length, culmen depth, flipper length, and body mass to predict the species of penguins.

Intended uses & limitations

The model is intended for classifying the species of penguins based on their physical measurements. It can be used in applications related to penguin species classification and analysis.

Limitations:

• The model's performance may vary depending on the quality and representativeness of the input data.
• It is trained specifically on the Palmer Penguins dataset and may not generalize well to other penguin datasets or species outside of the dataset.

Training data

The model is trained on the Palmer Penguins dataset, which contains measurements of penguin species including Adelie, Chinstrap, and Gentoo. The dataset is publicly available and can be accessed here.

Training procedure

The model is trained using scikit - learn, a popular machine learning library in Python. It uses a classification algorithm (e.g., Random Forest, Support Vector Machine) to learn the relationship between the input features (culmen length, culmen depth, flipper length, body mass) and the target variable (species).

🔧 Technical Details

The model uses scikit - learn's classification algorithms to learn from the input features of the Palmer Penguins dataset. It analyzes the relationships between physical measurements like culmen length, culmen depth, flipper length, and body mass to predict the species of penguins.

📄 License

This project is licensed under the MIT license.

🐧 Disclaimer

No penguins were harmed while training this model 🐧. We were noot involved in collecting the 🐧 data.