Update README.md

README.md

@@ -93,49 +93,6 @@ for policy optimization.
 | Train | 4,000 | 80% |
 | Test | 1,000 | 20% |
 
-## Usage
-
-### Direct Loading
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("json", data_files={
-    "train": "data/train-00000-of-00001.jsonl",
-    "test": "data/test-00000-of-00001.jsonl"
-})
-```
-
-### Example: Training a Model
-
-```python
-import numpy as np
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
-
-# Load dataset
-dataset = load_dataset("json", data_files={
-    "train": "data/train-00000-of-00001.jsonl",
-    "test": "data/test-00000-of-00001.jsonl"
-})
-
-# Prepare features and labels
-X_train = np.array([sample["neural_channels"] for sample in dataset["train"]])
-y_train = np.array([sample["movement_intent"] for sample in dataset["train"]])
-
-X_test = np.array([sample["neural_channels"] for sample in dataset["test"]])
-y_test = np.array([sample["movement_intent"] for sample in dataset["test"]])
-
-# Train model
-clf = RandomForestClassifier(n_estimators=100)
-clf.fit(X_train, y_train)
-
-# Evaluate
-y_pred = clf.predict(X_test)
-accuracy = accuracy_score(y_test, y_pred)
-print(f"Accuracy: {accuracy:.4f}")
-```
-
 ## Citation
 
 ```bibtex