So glad I can be a part of your journey! Sounds so similar to mine haha

Oh awesome! I'm glad you can follow along with classes! 😀

That's awesome - good luck on your journey and glad the channel can be a part of it!

Ahhhh this makes me so happy! Incorporating DS to solve business problems for the win!

Appreciate it!!!

Means a lot that you're following along, thank you!! Hope this helps 😀

Just finished the video - great work! I really enjoyed how you walked through all the aspects of the code and even re-ran some cells to really help explain what is going on. Excited to see the rest of this series, keep it up!

Thanks for watching! That’s awesome, I’ll have to check those libraries out! I’ve primarily used Prophet because it felt so easy to use and also explain to stakeholders haha. Appreciate you keeping up with the series!

I know but get hEr ( side note ) she told everyone to pleAse engage. Did everyone obliGe??? When the youth find her it’s a wrap. Ok I will show everyone how exciting she and this channel is. Y’all have no idea how you’re about to love learning again! Let’s gooooOoOo!

Hope you enjoy it!!

Great to hear, thanks for watching!

That’s awesome! Glad the video can help, thanks for following along! also so fun that you did physics too!

Of course! More to come with the series, thanks for following along! 😀

thank you!!!

Thanks so much Tata! 😄😄

I have a question related to this video series and perhaps a request. As mentioned, I'm trying to make a forecasting proposal for my workplace and would like to cover all the basis that would be applicable from a data science perspective I built an RFM and CLTV customer segmentation Kmeans model based off e-commerce data from Kaggle and wanted to use these clustering to make forecasting prediction based off leads received and classified into the identified clusters. I will be forecasting total sales for the month using regression and wanted to know if this is something you would be doing on a day to day as a data scientist in a sales environment or am I missing a step?

Awesome! To make the notebook, took about I'd say 20-30 minutes since I've worked with prophet before! The hardest part was honestly finding good open source data lol. And the whole notebook takes about 20ish minutes to run if you go through the whole hypertuning cross-validation for every category of products! I have that notebook pipeline for video 2 finished!

Thanks for watching! Absolutely! So you'd want to fully automate it as a pipeline, my second video (coming out Saturday this week) is a second full pipeline notebook and you'd want sometime like that pipeline either automated as a script OR you can use a service like Databricks/something similar to schedule regular notebook runs/jobs. :)

I would like to think so.. Did you end up utilizing Prophet for this use case?

Great question! Absolutely - I think what you define as transaction volume is what I'm referring to when I say total daily sales. i.e. the higher the transactions are daily/the higher the volume, the better signal we get. In the video, I use the "np.mean" function across the columns to see what the average daily sales (i.e. avg. transaction volume) is. In general, the lower the volume, (under $1000 usually) leads to higher errors since it's hard to get signal. So I use >=$1000 as a cut-off. Does this make sense? I think we mean the same thing haha

what data do you have exactly? Is it: agency | sales So just 2 columns?

Hmm that's weird. I download the "train.csv" from www.kaggle.com/c/favorita-grocery-sales-forecasting and I renamed it on my desktop to "store_data.csv" Maybe that's the issue if you can't read in the data?