Update

main.py

@@ -6,7 +6,9 @@ import calendar
 #patch_sklearn()
 from sklearn.model_selection import GridSearchCV
 from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import StandardScaler
 
+# Improving dataset and modelling: https://medium.com/@maryamuzakariya/project-predict-stock-prices-using-random-forest-regression-model-in-python-fbe4edf01664
 rf_reg_grid = {
     'bootstrap': [True],
     'max_depth': [5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],
@@ -114,6 +116,11 @@ def split_data(df, split_value):
     X_train, y_train = df_train.drop("price", axis=1), df_train["price"]
     X_test, y_test = df_val.drop("price", axis = 1), df_val["price"]
 
+    # Standardize features by removing the mean and scaling to unit variance.
+    scale = StandardScaler()
+    x_train = scale.fit_transform(x_train)
+    x_test = scale.transform(x_test) 
+
     return X_train, X_test, y_train, y_test
 
 def find_best_hyperparameters_and_train(X_train, y_train):
@@ -150,7 +157,7 @@ def main():
     # Prepare data for moddeling
     df = process_data(df=df)
     # Split data into train and test sets
-    X_train, X_test, y_train, y_test = split_data(df=df, split_value=0.99999) 
+    X_train, X_test, y_train, y_test = split_data(df=df, split_value=0.99) 
 
     # Model the data
     model = find_best_hyperparameters_and_train(