New BRICS members Sentiment Analysis

[abhisheks008]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : New BRICS members Sentiment Analysis :red_circle: Aim : The aim of this project is to analyze the sentiments of the new members of BRICS. :red_circle: Dataset : https://www.kaggle.com/datasets/syedali110/6-new-brics-members-sentiment-analysis :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. 😎

[Yuvika-14]

Name :Yuvika Singh git hub profile link:https://github.com/Yuvika-14/Yuvikademo.git approch: ensemble methods, gradient boosting, neural networks , xgboost. Take care of the missing data if there are any categorical values then will use one hot encoder or label encoder depending upon the issue.

[abhisheks008]

Issue assigned to you @Yuvika-14

[abhisheks008]

Hi @Yuvika-14 as this a machine learning project with proper implementation, I know it requires time to put forward the highest quality in the project. You can take as much time as you, but make sure you are creating something which should be of highest quality.

[tanuj437]

Full name : Tanuj Saxena GitHub Profile Link : https://github.com/tanuj437 Participant ID (If not, then put NA) : NA Approach for this Project : Best approach can be done with Bagging and Boosting approach along with that can try for neural network as if activation function provide better result after doing EDA approaches properly Participant of SSOC

[abhisheks008]

Implement 5-6 models for this dataset.

Assigned @tanuj437

[github-actions[bot]]

Hello @tanuj437! Your issue #518 has been closed. Thank you for your contribution!