# Is it ok to normalize data using minmaxscalar on dependent variable?

I'm trying to make a sales prediction using the column X = item_amount and y = item_price_total, I'm confused whether it's okay to normalize data on the dependent variable using minmaxscalar?

With the data that I have more or less like this:

|    date     | item_amount | item_price_total |
|  :--------: |   :--------  |     :--------     |
|  2018-01-07 |       148    |      13750000     |
|  2018-01-14 |       749    |      93921000     |
|  2018-01-21 |      3175    |     439218700     |
|  2018-01-28 |        23    |       3029700     |
|  2018-02-04 |       203    |      41661000     |

item_amount = [148, 749, 3175, 23, 203]
item_price_total = [13750000, 93921000, 439218700, 3029700, 41661000]


And the code i will use is like this:

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error, r2_score

# Determine the X and y variables
X = df['item_amount'].values.reshape(-1, 1)
y = df['item_price_total'].values.reshape(-1, 1)

# Normalize data using MinMaxScaler
scalar = MinMaxScaler()
X_scaled = scalar.fit_transform(X)
y_scaled = scalar.fit_transform(y)

# Dividing the dataset into training data and test data
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.3, shuffle=False)

# Create a linear regression model
model_linreg = LinearRegression()
# Define parameters
param_lr = {
"positive": [True, False],
"fit_intercept": [True, False],
"copy_X": [True, False],
"n_jobs": [4, 3, 6, 5, 9, 12]
}

# Create a support vector regression model
model_svr = SVR()
# Define parameters
param_svr = {
"C": [0.5, 1, 10, 100, 1000],
"gamma": [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 5],
"epsilon": [0.001, 0.01, 0.1, 1, 2, 4],
"kernel": ["rbf", "poly", "sigmoid", "linear"]
}

# Train models
grid_search_linreg = GridSearchCV(model_linreg, param_lr)
grid_search_linreg.fit(X_train, y_train)
grid_search_svr = GridSearchCV(model_svr, param_svr, n_jobs=-1, verbose=2)
grid_search_svr.fit(X_train, y_train.ravel())

# Predict the y-value
y_pred = grid_search_linreg.predict(X_test)
y_prediction = grid_search_svr.predict(X_test)

# Calculate R2
r2_linreg = r2_score(y_test, y_pred)
print("R2 Score: ", r2_linreg)
r2_svr = r2_score(y_test, y_prediction)
print("R2 Score: ", r2_svr)

# Calculate RMSE
rmse_linreg = np.sqrt(mean_squared_error(y_test, y_pred))
print("RMSE: ", rmse_linreg)
rmse_svr = np.sqrt(mean_squared_error(y_test, y_prediction))
print("RMSE: ", rmse_svr)


In the way I did, I got pretty good R2 and RMSE values. But if I normalize only the independent variable (X), the R2 and RMSE values will be bad.

# Determine the X and y variables
X = df['item_amount '].values.reshape(-1, 1)
y = df['item_price_total '].values.reshape(-1, 1)

# Dividing the dataset into training data and test data
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.3, shuffle=False)

# Normalize data using MinMaxScaler
scalar = MinMaxScaler()
X_train = scalar.fit_transform(X_train)
X_test = scalar.transform(X_test)


Is the value of y (dependent variable) based on the data I have, normalization is required using minmaxscalar? Is there a journal/paper reference that I can read? Thank You!