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--- |
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license: mit |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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library_name: sklearn |
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pipeline_tag: tabular-classification |
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tags: |
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- biology |
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- 🐧 |
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datasets: |
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- SIH/palmer-penguins |
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--- |
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# Palmer Penguins Species Classifier |
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## Model description |
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This model is a scikit-learn classifier trained to predict the species of penguins in the Palmer Penguins dataset. |
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The dataset contains measurements of penguin species including the species itself, making it suitable for classification tasks. |
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The model uses features such as culmen length, culmen depth, flipper length, and body mass to predict the species of penguins. |
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## Intended uses & limitations |
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The model is intended for classifying the species of penguins based on their physical measurements. |
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It can be used in applications related to penguin species classification and analysis. |
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Limitations: |
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- The model's performance may vary depending on the quality and representativeness of the input data. |
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- It is trained specifically on the Palmer Penguins dataset and may not generalize well to other penguin datasets or species outside of the dataset. |
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## Training data |
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The model is trained on the Palmer Penguins dataset, which contains measurements of penguin species including Adelie, Chinstrap, and Gentoo. |
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The dataset is publicly available and can be accessed [here](https://github.com/allisonhorst/palmerpenguins). |
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## Training procedure |
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The model is trained using scikit-learn, a popular machine learning library in Python. |
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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). |
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## Model in action |
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```python |
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# Load the model |
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from sklearn.externals import joblib |
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model = joblib.load('path/to/your/model.pkl') |
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# Input features |
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features = { |
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'culmen_length_mm': 39.1, |
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'culmen_depth_mm': 18.7, |
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'flipper_length_mm': 181, |
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'body_mass_g': 3750 |
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} |
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# Predict species |
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predicted_species = model.predict([list(features.values())])[0] |
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print(f"Predicted species: {predicted_species}") |
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``` |
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### 🐧 Disclaimer |
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No penguins were harmed while training this model 🐧. |
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We were noot involved in collecting the 🐧 data. |
