How to create minimum viable product for machine learning projects - Weather prediction

[@AIwithDrMo]

Timecodes 0:00 - Intro 0:19 - Problem Definition 2:14 - Importing Data 4:46 - Changing data types - to_datetime 5:48 - Changing data types - LabelEncoder 8:28 - Reindexing - set_index 9:47 - Converting time series to conventional ML problem by shifting dataframe 18:55 - Model training 23:28 - Model evaluation 28:00 - Creating python files for MVP 29:32 - train.py 36:51 - predict.py

[@SP-db6sh]

Step stone of DS projects ... Plz make video on it to work with this step with customisable pipelines for different usecases .

[@peterpham4410]

I have very important questions regarding the CDEGS. Please reply if you are existed.

[@kamakshishinde9984]

Thank you for the video. I found it very informative Can you please show how to run .py files for example where do we need to give filepath name and filter city name and can you also please show how the results looks like that are generated from .py file Thank you!