| # 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) | |