inference added

README.md

@@ -0,0 +1,25 @@
+# Flask Classification Model
+
+This is a classification model trained with scikit-learn. The model predicts binary classes based on four input features.
+
+## How to Use
+
+1. Clone the repository.
+2. Install necessary libraries.
+3. Run `inference.py` with your input features.
+
+Example:
+
+```python
+import joblib
+import numpy as np
+
+# Load model and scaler
+model = joblib.load("classification_model.joblib")
+scaler = joblib.load("scaler.pkl")
+
+# Make a prediction
+input_features = [0.5, 1.2, -0.3, 2.0]
+scaled_features = scaler.transform(np.array(input_features).reshape(1, -1))
+prediction = model.predict(scaled_features)
+print(prediction)

inference.py

@@ -0,0 +1,20 @@
+import joblib
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+# Load the model and scaler
+model = joblib.load("classification_model.joblib")
+scaler = joblib.load("scaler.pkl")
+
+def predict(features):
+    # Scale the features
+    scaled_features = scaler.transform(np.array(features).reshape(1, -1))
+    prediction = model.predict(scaled_features)
+    return prediction[0]
+
+# Sample usage
+if __name__ == "__main__":
+    # Sample feature data (replace with real data when calling)
+    sample_data = [0.5, 1.2, -0.3, 2.0]
+    result = predict(sample_data)
+    print(f"Prediction: {result}")