Data Science with Python : House Prices Prediction

Description

Welcome to the 'DSWP' Team, good to see you here.

With this issue, the readers will know on how to use advanced regression techniques for predicting the house prices

Here is the dataset to follow : https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data

To get assigned to this issue, add your serial numbers mentioned in the spreadsheet of "Data Science with Python", the approach one would follow and the choice you prefer (Documentation, Audio, Video). You can go with all three or any number of options you're interested to work on.

If you had referred any resources, add them up in "DS Resources". Similarly, if you had used datasets, include them in "DS Datasets".

Domain: Kaggle Competitions

Mentors Assigned : Aarya Patel | Shivang Dubey

Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.

"Issue Title" and "PR Title" should be the same. Include issue number along with it.

Changes should be made inside the Datascience_With_Python/ directory & Datascience_With_Python branch.

Follow Contributing Guidelines & Code of Conduct before start Contributing.

This issue is only for 'GWOC' contributors of 'DSWP' domain.

All the best. Enjoy your journey ahead. 😎

Domain

Data Science with Python

Type of Contribution

Audio, Video, Documentation

Code of Conduct

[X] I follow Contributing Guidelines & Code of conduct of this project.

Hello @prathimacode-hub,
Thank you for opening an issue. :octocat:
Note - Self-assigns by the original author will be prioritised by mentors manually
To get assigned to this particular issue please use /assign
Check this guide before contributing.

Hello, I am Apoorv Yadav, I would like to work on this issue. Batch Number: 15 Serial Number: 537 GitHub Profile Link: https://github.com/Apoorv-17 contribution: Documentation, Video Approach: House price prediction training dataset, finding the best algorithm to find out the price and then the model evaluation next testing of the model and at last predicting the price of the house using our trained model.

This issue has been assigned to @yashh18! It will become unassigned if it isn't closed within 12 days. A maintainer can also add the pinned label to prevent it from being unassigned.

The assigned contributor has more than 3 open issues, kindly reassign manually (@Supervisors/Mentors)

@prathimacode-hub Please assign me this issue ! Type of contribution : Documentation

Approach please? Hope you read the issue? You have to use various regression techniques to work on this prediction. @yashh18

@prathimacode-hub can you tell me how many and which regression techniques do i have to use specifically here?

Thats your choice completely, but atleast use minimum of 3 regression techniques. @yashh18

Sr no: 161 Batch: 5 Name: Deepika Pavundoss Type : Video I would like to explain about the different algorithms, the libraries used here, process and the code in detail @prathimacode-hub

Issue assigned to @Deepika-Pavundoss for video contribution

@yashh18, according to FCFS this issue should go to @Apoorv-17. I had missed the message and you got it assigned using /assign. So kindly choose another issue to work on or audio if you would like to work on this issue. I have to be fair with everyone yes. Hope you understand.

@Apoorv-17, issue is assigned to you for documentation

@prathimacode-hub yes its totally okay. @Apoorv-17 you can go ahead and work on this issue!

There are many more issues awaiting for grabs. You can chose it up and let me know for you to get it assigned and yes, thanks for understanding. @yashh18

@Deepika-Pavundoss, issue is assigned to you for documentation

Hello, I am Deepthi M with serial number:172, Batch-5. I would like to do video on this issue. My approach: a detailed explanation

Issue assigned to @deepthi1107 for video contribution

girlscript / winter-of-contributing