save a load from model

.gitignore

@@ -4,4 +4,5 @@ env/
 *.xlsx
 *.csv
 *.json
-cloud-functions/python/functions/__pycache__/
+cloud-functions/python/functions/__pycache__/
+*.pkl

scripts/logistic.py

@@ -3,6 +3,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import LabelEncoder
 from sklearn.metrics import accuracy_score, classification_report
+import pickle
 
 data = pd.read_excel('/Users/apple/Downloads/21092023-July_Sept.xlsx', sheet_name="op")
 data = data.dropna()
@@ -29,3 +30,14 @@ classification_report_result = classification_report(y_test, y_pred)
 print(f"Accuracy: {accuracy}")
 print("Classification Report:")
 print(classification_report_result)
+
+# with open('logistic_regression_model.pkl', 'wb') as model_file:
+#     pickle.dump(model, model_file)
+
+
+# with open('logistic_regression_model.pkl', 'rb') as model_file:
+#     loaded_model = pickle.load(model_file)
+
+# new_data = pd.DataFrame({'Age': [30], 'Gender': ['MALE'], 'Caste': ['SC'], 'Category': [''], 'Marital Status': ['Single']})
+# predicted_result = loaded_model.predict(new_data)
+# print(predicted_result)