LR_Scikit_new_data_with_train and test.py

#follow same process which was discussed before converting lists into dictionary then dataframe
new_dictionary = {'X': new_x, 'y':new_y}

new_df = pd.DataFrame(new_dictionary)
#split the data of 10 rows into 80% train set and 20% test set
new_X_train, new_X_test, new_y_train, new_y_test = train_test_split(new_df[['X']],new_df.y,test_size=0.2, random_state=5)
#fit to find the best fit
lr.fit(new_X_train, new_y_train)
#print slope
print(lr.coef_[0])
#output: 2.27600849
print(lr.intercept_)
#output: 7.6284501061571159
new_y_pred = lr.predict(new_X_test)

metrics.r2_score(new_y_test, new_y_pred)*100
#output: 90.480057427405569 - accuracy and our model confidence score
print(new_y_pred)
# [ 32.66454352  23.56050955]- predicted
#actual: [34, 25]
print(new_X_test)
X : 11, 7