Update README.md

README.md

@@ -1,3 +1,68 @@
 ---
 license: mit
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+library_name: sklearn
+pipeline_tag: tabular-classification
+tags:
+- biology
 ---
+
+# Palmer Penguins Species Classifier
+
+## 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](https://github.com/allisonhorst/palmerpenguins).
+
+## 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).
+
+## Model in action
+
+```python
+# 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}")
+```
+
+
+### 🐧 Disclaimer
+
+No penguins were harmed while training this model 🐧.
+
+We were noot involved in collecting the 🐧 data.