Added Random Forest Regressor as an additional prediction model.

Author: priyanshu-8789

machine_learning/forecasting/run.py

@@ -10,11 +10,11 @@
          for the next 3 months sales or something,
          u can just adjust it for ur own purpose
 """
-
 from warnings import simplefilter
 
 import numpy as np
 import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import Normalizer
 from sklearn.svm import SVR
 from statsmodels.tsa.statespace.sarimax import SARIMAX
@@ -77,6 +77,28 @@ def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> f
     y_pred = regressor.predict(x_test)
     return float(y_pred[0])
 
+def random_forest_regressor(x_train: list, x_test: list, train_user: list) -> float:
+    """
+    Fourth method: Random Forest Regressor
+    Random Forest is an ensemble learning method for regression that operates
+    by constructing a multitude of decision trees at training time and outputting
+    the mean prediction of the individual trees.
+
+    It is more robust than a single decision tree and less prone to overfitting.
+    Good for capturing nonlinear relationships in data.
+
+    input : training data (date, total_event) in list of float
+            where x = list of set (date and total event)
+    output : list of total user prediction in float
+    
+    >>> random_forest_regressor([[5,2],[1,5],[6,2]], [[3,2]], [2,1,4])
+    2.3333333333333335
+    """
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(x_train, train_user)
+    prediction = model.predict(x_test)
+    return float(prediction[0])
+
 
 def interquartile_range_checker(train_user: list) -> float:
     """