Multiple linear regression for GW depth estimaition

# importing the libraries
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
from sklearn import preprocessing

# Importing the data set and extracting the dependent and Independent variables
data=pd.read_excel("Amina.xls")
print(data)
x=data.drop(columns=['GWDepth'])
y=data['GWDepth']

#Standardisation
X=preprocessing.scale(x)
Y=preprocessing.scale(y)


# Data Visualisation
# Building the Correlation Matrix

print(sns.heatmap(data.corr()))
#plt.show()



# Splitting the dataset into training set and test set
from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test= train_test_split(X,Y, test_size=0.2, random_state=0)

# Fitting Multiple Linear Regression to the Training set

from sklearn.linear_model import LinearRegression
regressor=LinearRegression()
regressor.fit(X_train,Y_train)

# Predicting the Test set results
Y_pred=regressor.predict(X_test)
#print(Y_pred)

#Calculating the Coefficients
print(regressor.coef_)

#Calculating the Intercept
print(regressor.intercept_)

# Calculating R-squared value
from sklearn.metrics import r2_score
print(r2_score(Y_test, Y_pred))

regressor.fit(data[['Rainfall','Temp.','PET']],data['GWDepth'])
print(regressor.predict([[564.071642, 27 , 1640.5535560 ]]))