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Data Science with Python : Linear Discriminant Analysis
Welcome to 'DSWP' Team, good to see you here
This issue will helps readers in giving all the guidance that one needs to learn about Linear Discriminant Analysis. Tutorial to Linear Discriminant Analysis and how it's applied using sample code.
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 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 : Machine Learning
Mentors Assigned : Kareem Negm / Aishani Singh
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 open source journey ahead. 😎
I would like to work on this issue documentation serial no 147
Also brief your approach. @AnnikaD0104
I would like to work on this issue. Name - Subhrojyoti Narayan Roy Batch - DSWP 5 Serial no. 171 Type- Documentation Approach - 1. What is Dimensionality Reduction? 2. Approach to an LDA model(Practical with Code Snippets) 3. Mechanism of LDA(Theory)
@AnnikaD0104, you were late by your approach. You can choose audio or video.
Hello, I am Deepthi M with serial number:172, Batch-5. I would like to do audio on this issue. My approach: a detailed explanation
