Create README.md

README.md

@@ -0,0 +1,29 @@
+# Digit Recognition
+
+## Intended Use
+This model is designed to classify handwritten digits (0-9) based on pixel values from the MNIST-like dataset. It is intended for educational purposes and to demonstrate the use of Random Forest for multi-class classification.
+
+## Training Data
+- **Dataset**: The model was trained on a dataset with 42,000 samples, where each sample is a 28x28 grayscale image flattened into a vector of 784 pixel values.
+- **Labels**: The dataset contains 10 classes (digits 0-9).
+- **Train-Test Split**: 
+    - Training set: 33,600 samples (80%)
+    - Validation set: 8,400 samples (20%)
+
+## Evaluation Metrics
+- **Accuracy**: The model achieved an accuracy of approximately `accuracy_score(y_val, y_pred)` on the validation set.
+- **Classification Report**: Includes precision, recall, and F1-score for each class.
+- **Confusion Matrix**: Visualized to show the distribution of predictions across classes.
+
+## Limitations
+- The model may not generalize well to digits written in styles significantly different from the training data.
+- It is not optimized for real-time or large-scale applications.
+
+## Ethical Considerations
+- Ensure the dataset used does not contain any biases that could affect the fairness of the model.
+- The model should not be used in critical applications without further validation and testing.
+
+## How to Use
+1. Load the model using `joblib.load('digit_rf_model.joblib')`.
+2. Preprocess the input data to match the format of the training data (28x28 images flattened into 784-pixel vectors).
+  3. Use the `predict` method to classify new samples.