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Time Series Forecasting with XGBoost - Use python and machine learning to predict energy consumption

Rob Mulla

627,862 views

In this video tutorial we walk through a time series forecasting example in python using a machine learning model XGBoost to predict energy consumption with python. We walk through this project in a kaggle notebook (linke below) that you can copy and explore while watching.

Notebook used in this video: https://www.kaggle.com/code/robikscub...

Timeline:
00:00 Intro
03:15 Data prep
08:24 Feature creation
12:05 Model
15:35 Feature Importance
17:33 Forecast

Follow me on twitch for live coding streams: Twitch: medallionstallion_

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Working with Audio data in Python: Audio Data Processing in Python
Efficient Pandas Dataframes: Speed Up Your Pandas Dataframes

Youtube: https://youtube.com/@robmulla?sub_con...
Discord: Discord: discord
Twitch: Twitch: medallionstallion_
Twitter: Twitter: Rob_Mulla
Kaggle: https://www.kaggle.com/robikscube

#xgboost #python #machinelearning

20 Comments

@casperj4784 4 years ago Pinned

A comprehensive yet succinct tutorial. And, having only just finished my Data Science degree, I found it very reassuring to see that you do get faster and more proficient with time.

@selenkokten1708 3 years ago

Don’t use features like year which will not have the same value in the future. It is a bad idea for prediction purposes. Instead use the difference from the minimum date to see if there is an increasing trend year by year.

@naderbazyari2 2 years ago

Second time watching this and doing every step on my notebook as Rob goes through the task. I am still blown away by the intricacy of his approach and how he investigates the case. fascinating how he makes it look effortless. Many thanks

@zhuoningli 3 years ago

Hi Rob! Your tutorials help me get a job offer! When I was searching for a job, I received a take-home technical exercise about time series forecasting. I watched this video and finished my exercise. Finally, I got my dream job! Thank you so much!!! I really appreciate your tutorials! 🥰

@karishmakapoor4285 3 years ago

Amazing flow, comprehensive yet smooth. Detailed yet generic. I love the way you think and your float across the entire process. I did this project myself and thoroughly enjoyed it. Cant wait to apply this to other datasets. A Big thumps up👍

@azizbekurmonov6278 3 years ago

more time series, please

@PatrickLane-f2n 3 years ago

You have helped me so much with this video, you don't even know!!! Thanks so much :)

@akshaymbhat9144 3 years ago

Thanks for the wonderful video. It's very insightful ❤️ from India .
Keep inspiring and aspiring always!!

@nirbhay_raghav 3 years ago

Hands down, the bestest (if that is a word) video on the entire internet about implementation. No fancy stuff. Not too beginner and toy examples. Hust the right thing what a budding data scientist needs to see. And it is definitely reassuring to see that one can really get better and faster at doing these after a while. It takes me a lot of time reach what you have done in under 30min. Debugging things take a lot of time.

@super-eth8478 4 years ago (edited)

Dude your channel is a gold mine ..

@Arieleyo 3 years ago

Love your videos Rob!! cheers from Argentina ♥

@Ricocase 2 years ago

How did you spot overfitting w/o a marker?

@flel2514 3 years ago

Hi Rob, I am a fresh data science graduate, and I find this tutorial very well done and very helpful for those that approach TS for the first time as well as for those that want to refresh the topic

@evandrogaio7003 3 years ago

Such an excellent video. Thanks for sharing!

@hussam_cheema 4 years ago

I love your content. Liked the video before watching it because I know this is gonna be a great tutorial.
Thanks for making these tutorials. 😊

@rohitraghunathan 3 years ago

Very good introduction to time series forecasting in xgboost. One thing to note is xgboost has a function for plotting the feature importance. xbg.plot_importance() would have done the trick for you