Update run.py

Used matplotlib to plot actual vs predicted user count, forecast confidence intervals, outlier thresholds from IQR.
Added logging instead of print because in production, print() is not scalable.

Author: priyanshu-8789

machine_learning/forecasting/run.py

@@ -11,6 +11,7 @@
          u can just adjust it for ur own purpose
 """
 
+import logging
 from warnings import simplefilter
 
 import numpy as np
@@ -19,7 +20,10 @@
 from sklearn.preprocessing import Normalizer
 from sklearn.svm import SVR
 from statsmodels.tsa.statespace.sarimax import SARIMAX
+import matplotlib.pyplot as plt
 
+logging.basicConfig(level=logging.Info)
+logger = logging.getLogger(__name__)
 
 def linear_regression_prediction(
     train_dt: list, train_usr: list, train_mtch: list, test_dt: list, test_mtch: list
@@ -143,6 +147,21 @@ def data_safety_checker(list_vote: list, actual_result: float) -> bool:
             not_safe += 1
     return safe > not_safe
 
+def plot_forecast(actual, predictions):
+    plt.figure(figsize=(10, 5))
+    plt.plot(range(len(actual)), actual, label="Actual")
+    plt.plot(len(actual), predictions[0], 'ro', label="Linear Reg")
+    plt.plot(len(actual), predictions[1], 'go', label="SARIMAX")
+    plt.plot(len(actual), predictions[2], 'bo', label="SVR")
+    plt.plot(len(actual), predictions[3], 'yo', label="RF")
+    plt.legend()
+    plt.title("Data Safety Forecast")
+    plt.xlabel("Days")
+    plt.ylabel("Normalized User Count")
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
 
 if __name__ == "__main__":
     """
@@ -179,11 +198,11 @@ def data_safety_checker(list_vote: list, actual_result: float) -> bool:
         ),
         sarimax_predictor(train_user, train_match, test_match),
         support_vector_regressor(x_train, x_test, train_user),
-        random_forest_regressor(
-            x_train, x_test, train_user
-        ),  # Added Random Forest Regressor
+        random_forest_regressor(x_train, x_test, train_user),
     ]
 
     # check the safety of today's data
     not_str = "" if data_safety_checker(res_vote, test_user[0]) else "not "
-    print(f"Today's data is {not_str}safe.")
+    logger.info(f"Today's data is {not_str}safe.")
+        
+    plot_forecast(train_user, res_vote)