predicting values at point in time

[mk1216]

Missed the regression library call in previous post, here is the full program:

import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm
 
df=pd.read_csv("C:/Users/ABCDE/Downloads/PyTestdata.csv")
 
display(df)
 
X = df[['no_of_tables','total_rows_in_mill','total_bytes_gb']] # here we have 3 variables for multiple regression. 
Y = df['average_load_time']

# with sklearn
regr_avg_load_time = linear_model.LinearRegression()
regr_avg_load_time.fit(X, Y)
 
print('Intercept: \n', regr_avg_load_time.intercept_)
print('Coefficients: \n', regr_avg_load_time.coef_)
 
new_no_of_tables=20
new_total_rows_in_mill=80
new_total_bytes_gb=20
 
print ('Predicted average_load_time: \n', regr_avg_load_time.predict([[new_no_of_tables ,new_total_rows_in_mill,new_total_bytes_gb]]))