Roster prediction with conditions include .1.When their is public holiday two members to be in each shift with respect to location , when their no public Holiday their should be min 3 members in shift. 2.understand incident volume in shift if their are more high average count of incidents in a day then there should be more employees allocated in a shift. Atleast 2 employees of same location should be in a same shift. High level employees should be available in a shift if there is high frequency of p1 or p2 observed in shift. 6.If there is any planned changes seen on a particular day then there should be more employees on that day allocated so on that week. 7 .If there are multiple planned changes seen on a particular day then there should be more employees allocated on that day son on that week.
Sir we need to know how to perform the same on our local python files. There is too many errors and dependency issues when doing in local. It would be really helpful if you can show how to do the same locally without google colab!
Sir, I noticed two courses on Udemy: "Complete Python with DSA Bootcamp" and "Complete Data Science, Machine Learning, DL, NLP Bootcamp," which also includes Python topics. Could you please explain the difference between them? Thank you 🎉
Processing the data in a fast way is very useful. But one of the major problems that I've faced is loading the data into memory, because the data can not be loaded when it is too large. Someone knows what to do in this case, like chunking the data with a known library? Or must this necessarily be done with a personal code?
