Student Attendance based on Weather Conditions

[abhisheks008]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Student Attendance based on Weather Conditions :red_circle: Aim : The aim of this project is to analyze the weather conditions and predict the attendance of the students. :red_circle: Dataset : https://www.kaggle.com/datasets/joshjohnson9596/student-attendance-and-weather-conditions :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. 😎

[rajneeshkabdwal4]

Name: Rajneesh Kabdwal Git: https://github.com/rajneeshkabdwal4 PID: NA Approach : I will use logistic regression. Program: JWoC

[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? @rajneeshkabdwal4

[rajneeshkabdwal4]

Yes, I will do that and get back to you asap

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From: Abhishek Sharma @.> Sent: Monday, January 15, 2024 8:24:59 PM To: abhisheks008/ML-Crate @.> Cc: Rajneesh Kabdwal @.>; Mention @.> Subject: Re: [abhisheks008/ML-Crate] Student Attendance based on Weather Conditions (Issue #514)

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? @rajneeshkabdwal4https://github.com/rajneeshkabdwal4

— Reply to this email directly, view it on GitHubhttps://github.com/abhisheks008/ML-Crate/issues/514#issuecomment-1892325337, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AWVRY3BZF32V2P2JNAL25GDYOU7MHAVCNFSM6AAAAABBZ3INS2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOJSGMZDKMZTG4. You are receiving this because you were mentioned.Message ID: @.***>

[abhisheks008]

Cool, this issue is assigned to you. Start working on it @rajneeshkabdwal4