Update README.md

README.md

@@ -19,4 +19,40 @@ Finally, it predicts the word for any given number (like 45) and decodes it back
 
 Example:
 
-45 → Model predicts label → Decoder converts to "forty-five"
+45 → Model predicts label → Decoder converts to "forty-five"
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+
+Usage:
+
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.preprocessing import LabelEncoder
+
+# Create input numbers from 1 to 100
+X = [[i] for i in range(1, 1000)]
+
+# Create corresponding output words
+def number_to_word(n):
+    import inflect
+    p = inflect.engine()
+    return p.number_to_words(n)
+
+y = [number_to_word(i) for i in range(1, 1000)]
+
+# Encode the output words to numbers
+le = LabelEncoder()
+y_encoded = le.fit_transform(y)
+
+# Train the ML model
+model = DecisionTreeClassifier()
+model.fit(X, y_encoded)
+
+# Predict: Try with any number from 1 to 100
+input_number = [[345]]  # Change this value as needed
+predicted_encoded = model.predict(input_number)
+predicted_word = le.inverse_transform(predicted_encoded)
+
+print(f"Input: {input_number[0][0]} → Output: {predicted_word[0]}")