OLS Statsmodels Summary Table Explanation in details | Linear Regression Machine Learning|Data Scien

[@alexpandian7402]

you have put great efforts in doing this video,what i didnt understood clearly in college ,now i understood thank u for this

[@My-Diary]

Very Helpful video. Thank you Atul.

[@techsavy5669]

Your AIC,BIC explanation doesn't seem right. Also @27.00 minutes, why did you select H1 , even though the value 0.068 is more than 0.05, so why did you reject null hypothesis!!

[@noihr4469]

You're amazing! Thanks for the material

[@tansutazegul8297]

Thats just an amazing work and great effort m8!!!

[@prajwalbhandary17]

Good explanation

[@AtulPatelds]

Thank you

[@HeyCurlyBoy]

Thank you so much for consolidating all the information together!!

[@m0tivati0n71]

Thank you for the great explanations.

[@NehaJoshi-x5n]

Excellent explanation!

[@billwindsor4224]

Sir, this is an excellent walk-through on the OLS Statsmodels Summary statistics, a high-quality work from your excellent preparation. *Thank you!*

[@AtulPatelds]

Thank you

[@prabhatverma4243]

Million times thanks!

[@tasin-sc]

Thanks

[@harishganesan2120]

Truly thankful

[@ravirajchaubey7660]

bahut achha explaination tha ... base line model kya hota h ? aur kaise banate ye explain kar dijiye

[@AtulPatelds]

Dear, baseline model we create using some deafult configuration and using some know feature selection and features engineering techniques. Once we create our first model using all above methods and let's say I get the 90% accuracy so we can say this accuracy will be my base line then I will try to improve our accuracy by applying in depth feature engineering and hyperparameter running and other different methods

[@myandrobox3427]

Very good explanation👌👌

[@AtulPatelds]

Thank you

[@sandeshlodhi7692]

Sir how to find a job for fresher

[@AtulPatelds]

This is not tuff buddy. Do hard practice and more focus on concepts rather than just copy paste. Try practice code by yourself.