diff --git a/scripts/gaussian_nb.py b/scripts/gaussian_nb.py
new file mode 100644
index 0000000..00e2abd
--- /dev/null
+++ b/scripts/gaussian_nb.py
@@ -0,0 +1,99 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+import pickle
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+
+curdir = os.getcwd()
+path_delim = '/'
+data = pd.read_csv(curdir + path_delim + 'data/bquxjob_6ffc1cff_18c02a130c1.csv')
+data = data.dropna()
+print(data)
+
+data.plot()
+
+label_encoder = LabelEncoder()
+categorical_cols = [] #['deviceId', 'deviceSerialNumber', 'classificationResult']
+for col in categorical_cols:
+    data[col] = label_encoder.fit_transform(data[col])
+
+X = data[['calculatedRatio', 'deviceRatio', 'led1Buffer', 'led2Buffer', 'led1Sample', 'led2Sample']] #, 'deviceId', 'deviceSerialNumber']]
+y = data['classificationResult']
+
+print(X)
+print(y)
+
+# Fit the LabelEncoder on the entire column
+# label_encoder.fit(data['classificationResult'])
+
+with open('label_encoder.pkl', 'wb') as label_encoder_file:
+    pickle.dump(label_encoder, label_encoder_file)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = GaussianNB()
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+
+accuracy = accuracy_score(y_test, y_pred)
+classification_report_result = classification_report(y_test, y_pred)
+
+confusion = confusion_matrix(y_test, y_pred)
+
+plt.figure(figsize=(8, 6))
+sns.heatmap(confusion, annot=True, fmt='d', cmap='Blues', linewidths=0.5)
+plt.xlabel('Predicted')
+plt.ylabel('Actual')
+plt.title('Confusion Matrix')
+# plt.show()
+
+print(f"Accuracy: {accuracy}")
+print("Classification Report:")
+print(classification_report_result)
+
+with open('gaussian_naive_bayes_model.pkl', 'wb') as model_file:
+    pickle.dump(model, model_file)
+
+with open('label_encoder.pkl', 'rb') as label_encoder_file:
+    loaded_label_encoder = pickle.load(label_encoder_file)
+# print(loaded_label_encoder.classes_)
+# print(loaded_label_encoder.label)
+
+with open('gaussian_naive_bayes_model.pkl', 'rb') as model_file:
+    loaded_model = pickle.load(model_file)
+
+# loaded_label_encoder = label_encoder
+# loaded_model = model 
+
+# new_data = pd.DataFrame({'calculatedRatio': label_encoder.fit_transform([0.126976079]), 'led1Buffer': label_encoder.fit_transform([24313.67]), 'led1Sample': label_encoder.fit_transform([20531]), 'led2Buffer': label_encoder.fit_transform([26565]), 'led2Sample': label_encoder.fit_transform([9975.33])})
+# new_data = pd.DataFrame({'calculatedRatio': loaded_label_encoder.fit_transform([0.17500836]), 'led1Buffer': loaded_label_encoder.fit_transform([24843.33]), 'led1Sample': loaded_label_encoder.fit_transform([19678]), 'led2Buffer': loaded_label_encoder.fit_transform([26715.33]), 'led2Sample': loaded_label_encoder.fit_transform([13842.67])})
+# new_data = pd.DataFrame({'calculatedRatio': loaded_label_encoder.fit_transform([0.175881142]), 'led1Buffer': loaded_label_encoder.fit_transform([24256]), 'led1Sample': loaded_label_encoder.fit_transform([16303]), 'led2Buffer': loaded_label_encoder.fit_transform([27016.33]), 'led2Sample': loaded_label_encoder.fit_transform([4612.67])})
+# new_data = pd.DataFrame({'calculatedRatio': loaded_label_encoder.fit_transform([0.126976079]), 'led1Buffer': loaded_label_encoder.fit_transform([24313.67]), 'led1Sample': loaded_label_encoder.fit_transform([20531]), 'led2Buffer': loaded_label_encoder.fit_transform([26565]), 'led2Sample': loaded_label_encoder.fit_transform([9975.33])})
+new_data = pd.DataFrame({'calculatedRatio': 0.231057205, 'deviceRatio': 0.231057205,'led1Buffer': 23776.33, 'led2Buffer': 26401.67, 
+            'led1Sample': 16286, 'led2Sample': 6952.67}, index=[0])
+# new_data = pd.DataFrame({
+#     'calculatedRatio': [0.175881142],
+#     'led1Buffer': [24256],
+#     'led1Sample': [16303],
+#     'led2Buffer': [27016.33],
+#     'led2Sample': [4612.67]
+# })
+print(new_data)
+
+# new_data = new_data.apply(lambda col: loaded_label_encoder.transform(col))
+predicted_result = loaded_model.predict(new_data)
+print(predicted_result.item())
+
+X_test_encoded = X_test.copy()
+for col in categorical_cols:
+    X_test_encoded[col] = loaded_label_encoder.transform(X_test[col])
+
+print(X_test_encoded)
+
+y_pred = model.predict(X_test_encoded)
+print(pd.Series(y_pred).describe())
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