Finding the maximum value from a Random Forest Regression model in python

I've been using random forest regression to calculate the Return On Ad Spend (ROAS) when the user gives the upper bound. My model takes in three input variables: the cost of TV, Radio and Newspaper ads. However, to find the most optimal value, I need to use a for loop to iterate through every dollar, which is time-consuming. Is there a faster method to find the highest y value in my program?

def ROASPrediction(Q,TV,Radio,Newspaper):rec="Recommended Investment for Best Sales"y_big=0x_b=0y_b=0z_b=0for x in range(TV//2,TV):  for y in range(Radio//2,Radio):    for z in range(Newspaper//2,Newspaper):      customer_features =np.array([x,y,z])      customer_features1=customer_features.reshape(1, -1)#customer_features1 =pd.DataFrame(customer_features)      model_fit1 = joblib.load('/content/drive/MyDrive/LUV BARNWAL/ROAS.joblib')      y_future_pred = model_fit1.predict(customer_features1)      print("y_future_pred", y_future_pred)      if(y_future_pred[0]>=y_big):        y_big=y_future_pred[0]        x_b=x        y_b=y        z_b=z#y_future_pred1= str(y_future_pred[0]) +"M$"#y_roas= y_future_pred[0]*1000000 / (TV+Radio+Newspaper)y_future_pred1= str(y_big) +"M$"y_roas=  y_big*1000000 / (TV+Radio+Newspaper)x_b1=str(x_b)y_b1=str(y_b)z_b1=str(z_b)y_roas1=str(y_roas) +"%"return rec, x_b1,y_b1,z_b1,y_future_pred1,  y_roas1

And the following code is my Random Forest Model.

df = pd.read_csv('/Advertising.csv')df.head()x = df[['TV', 'Radio','Newspaper']]y = df[['Sales']]x_train, x_test, y_train, y_test = train_test_split (x, y, test_size=0.20 , random_state=41)rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)rf_regressor.fit(x_train, y_train)y_pred = rf_regressor.predict(x_test)

And this is the csv file I'm using.

Is there any way to make the ROASPrediction function more efficient so that it does not take like 5 minutes to compute just $30 of TV, Radio and Newspaper?