Most Subscribed YouTube Channels Analysis

[mariam7084]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Most Subscribed YouTube Channels Analysis :red_circle: Aim : Perform EDA :red_circle: Dataset : https://www.kaggle.com/datasets/surajjha101/top-youtube-channels-data :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: 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.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[ghousiya47]

@abhisheks008 Can you please me assign this issue? Full name : Ghousiya Begum GitHub Profile Link : github.com/ghousiya47 Participant ID (If not, then put NA) : NA Approach for this Project : I will use different supervised machine leaening algorithms such as randomforest, svm to predict the data and matplotlib and seaborn for analysis, and we can check accuracy using skleanr.metrics package. in order to increase accuracy i will do hyperparameter tuning. It will be a regression model. What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) : JWoC

[ghousiya47]

@abhisheks008 i have requeted to assign me this issue.

[abhisheks008]

Assigned to you @ghousiya47

[ghousiya47]

@abhisheks008 sir please assign me this issue under JWOC'24