Update README.md

README.md

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+# Traceix AI Security Telemetry
+
+These files are uploaded monthly automatically by Traceix and provided as is under the CC BY 4.0 license. You can test the datasets simply by doing the following:
+
+```python
+# do_dataset_publishing()
+from datasets import load_dataset
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+
+
+# Load the Traceix telemetry dataset
+ds = load_dataset(
+    "PerkinsFund/traceix-ai-security-telemetry",
+    data_files="traceix-telemetry-corpus-2025-12.jsonl",  # Or whatever month you want
+    split="train",
+)
+
+# We will need to flatten nested JSON into columns
+df = ds.to_pandas()
+df_flat = pd.json_normalize(df.to_dict(orient="records"))
+
+# Define the features and label based on schema
+feature_cols = [
+    "decrypted_training_data.SizeOfCode",
+    "decrypted_training_data.SectionsMeanEntropy",
+    "decrypted_training_data.ImportsNb",
+]
+
+label_col = "model_classification_info.identified_class"
+
+# We don't have to but we will drop the rows with missing data
+df_flat = df_flat.dropna(subset=feature_cols + [label_col])
+
+X = df_flat[feature_cols].values
+y = (df_flat[label_col] == "malicious").astype(int)
+
+# Start the training and test split
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# Test the basic file classifier
+clf = LogisticRegression(max_iter=1000)
+clf.fit(X_train, y_train)
+
+print("Test accuracy:", clf.score(X_test, y_test))
+```
+
 ---
 license: cc-by-4.0
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