Update README.md

README.md

@@ -1,3 +1,65 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+---
+🌸 Iris Flower Species Predictor
+
+This is a simple yet effective machine learning model that predicts the species of an Iris flower (`setosa`, `versicolor`, or `virginica`) using four key features:
+
+- Sepal length (cm)
+- Sepal width (cm)
+- Petal length (cm)
+- Petal width (cm)
+
+The model is built using a **Decision Tree Classifier** from **scikit-learn**, trained on the classic Iris dataset.
+
+---
+
+ Model Overview
+
+- **Algorithm:** Decision Tree Classifier  
+- **Library:** Scikit-learn  
+- **Dataset:** `sklearn.datasets.load_iris()`  
+- **Accuracy:** ~95% (on test data using 70/30 split)
+
+---
+
+ How to Use
+
+ Input
+
+You need to provide these four numerical inputs:
+
+| Feature        | Type   | Example |
+|----------------|--------|---------|
+| Sepal length   | Float  | 5.1     |
+| Sepal width    | Float  | 3.5     |
+| Petal length   | Float  | 1.4     |
+| Petal width    | Float  | 0.2     |
+
+ Output
+
+The model returns the predicted **species** as one of the following:
+- `setosa`
+- `versicolor`
+- `virginica`
+
+---
+
+ Example Code
+
+```python
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+
+# Load and train model
+iris = load_iris()
+X, y = iris.data, iris.target
+model = DecisionTreeClassifier()
+model.fit(X, y)
+
+# Predict
+sample = [[5.1, 3.5, 1.4, 0.2]]
+predicted_class = model.predict(sample)[0]
+print("Predicted species:", iris.target_names[predicted_class])