رگرسیون خطی با کتابخانه sklearn

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

df = pd.read_csv('bottle.csv')
df_binary = df[['Salnty', 'T_degC']]

# Taking only the selected two attributes from the dataset
df_binary.columns = ['Sal', 'Temp']
#display the first 5 rows
df_binary.head()

#plotting the Scatter plot to check relationship between Sal and Temp
sns.lmplot(x ="Sal", y ="Temp", data = df_binary, order = 2, ci = None)
plt.show()

# Eliminating NaN or missing input numbers
df_binary.fillna(method ='ffill', inplace = True)

X = np.array(df_binary['Sal']).reshape(-1, 1)
y = np.array(df_binary['Temp']).reshape(-1, 1)

# Separating the data into independent and dependent variables
# Converting each dataframe into a numpy array 
# since each dataframe contains only one column
df_binary.dropna(inplace = True)

# Dropping any rows with Nan values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

# Splitting the data into training and testing data
regr = LinearRegression()

regr.fit(X_train, y_train)
print(regr.score(X_test, y_test))

y_pred = regr.predict(X_test)
plt.scatter(X_test, y_test, color ='b')
plt.plot(X_test, y_pred, color ='k')

plt.show()
# Data scatter of predicted values

df_binary500 = df_binary[:][:500]

# Selecting the 1st 500 rows of the data
sns.lmplot(x ="Sal", y ="Temp", data = df_binary500,
							order = 2, ci = None)

df_binary500.fillna(method ='fill', inplace = True)

X = np.array(df_binary500['Sal']).reshape(-1, 1)
y = np.array(df_binary500['Temp']).reshape(-1, 1)

df_binary500.dropna(inplace = True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

regr = LinearRegression()
regr.fit(X_train, y_train)
print(regr.score(X_test, y_test))

y_pred = regr.predict(X_test)
plt.scatter(X_test, y_test, color ='b')
plt.plot(X_test, y_pred, color ='k')

plt.show()

from sklearn.metrics import mean_absolute_error,mean_squared_error

mae = mean_absolute_error(y_true=y_test,y_pred=y_pred)
#squared True returns MSE value, False returns RMSE value.
mse = mean_squared_error(y_true=y_test,y_pred=y_pred) #default=True
rmse = mean_squared_error(y_true=y_test,y_pred=y_pred,squared=False)

print("MAE:",mae)
print("MSE:",mse)
print("RMSE:",rmse)