Viral Shorts Videos Analysis

[abhisheks008]

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Viral Shorts Videos Analysis
🔴 Aim : The aim of this project is to analyze the viral videos based on the given dataset.
🔴 Dataset : https://www.kaggle.com/datasets/kanchana1990/viral-shorts-youtubes-most-viewed
🔴 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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🔴🟡 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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✅ 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. 😎

[Vidip-Ghosh]

Can I work on this issue under JWOC 2.0?

[abhisheks008]

Implement the following models for this project,

1. Random forest
2. Decision tree
3. Logistic regression
4. Lasso
5. Ridge
6. Gradient boosting
7. XgBoost
8. MLP

Check the accuracy scores of the deployed models and find out the best one based on the best accuracy score.

Are you able to do this? @Vidip-Ghosh

[Vidip-Ghosh]

Implement the following models for this project,

1. Random forest
2. Decision tree
3. Logistic regression
4. Lasso
5. Ridge
6. Gradient boosting
7. XgBoost
8. MLP

Check the accuracy scores of the deployed models and find out the best one based on the best accuracy score.

Are you able to do this? @Vidip-Ghosh

So do I need to implement using all the 8 models? I am only familiar with Random forest, Decision tree, Logistic regression.

[abhisheks008]

Yeah you need to implement all the mentioned machine learning models. You can take your time, learn along with solving the issue. That would be a good hands-on experience I feel. Also you need to do some visualization/EDA before diving deep into the model.

[Vidip-Ghosh]

You can take your time, learn along with solving the issue.

Okay then. You can assign.

[abhisheks008]

Issue assigned to you @Vidip-Ghosh

[Vidip-Ghosh]

Sorry for the delay as I have to study about other algorithms, will raise a PR as soon as it gets completed.

[abhisheks008]

Cool. Thanks for the update.

[abhisheks008]

Please do not remove the assignment without letting us know @Vidip-Ghosh. It creates confusion as the label marked as Assigned but no one is assigned in this issue.

[HarshRaj29004]

Full name: Harsh Raj
GitHub Profile Link: https://github.com/HarshRaj29004
Participant ID (If not, then put NA): NA
Approach for this Project: I will preprocess Data to handle missing values and include required features. Then after finds relation between columns by visually encoding them basically a correlation analysis. Finally, will try to predict views based on likes and comments count.
What is your participant role? SSOC

@abhisheks008 please assign this issue to me

[abhisheks008]

Implement 5-6 models for this dataset.

Assigned @HarshRaj29004

[HarshRaj29004]

Ok i will do it

[github-actions]

Hello @HarshRaj29004! Your issue #509 has been closed. Thank you for your contribution!