Student Data Analysis and Performance Predictor using ML

[mariam7084]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Student Data Analysis and Performance Predictor using ML :red_circle: Aim : Perform EDA and create a prediction model for predicting the performance of the students based on the given dataset. :red_circle: Dataset : https://www.kaggle.com/datasets/erqizhou/students-data-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. 😎

[adi271001]

can you please assign it to me under SWOC 2024

[abhisheks008]

This project repository is not part of SWOC S4.

[adi271001]

@abhisheks008 sorry my bad jwoc I meant

[abhisheks008]

Please share your approach for this project in a detailed manner. @adi271001

[Sneha-Mahata]

Hello, I would like to work on this issue. The details hereby:

Full name : Sneha Mahata

GitHub Profile Link : https://github.com/Sneha-Mahata

Email ID : mahatasneha4@gmail.com

Participant ID (if applicable): NA

Approach for this Project : For this project, I will firstly do an Exploratory Data Analysis (EDA) to clean the data, handle missing values, and visualize patterns. Next, I will implement and compare models using advanced algorithms like AdaBoost, CatBoost, XGBoost and any other ensemble learning like bagging and boosting techniques. Each model will be trained on the dataset and evaluated using accuracy, precision, recall, and F1 score. The best model will be identified based on these metrics. I will provide Comprehensive documentation in README.md, along with necessary visualizations, conclusions, and a requirements.txt file listing all essential packages and libraries.

What is your participant role? SSOC 2024 Contributor

[abhisheks008]

Assigned @Sneha-Mahata

Implement 5-6 models for this project.

[Sneha-Mahata]

Assigned @Sneha-Mahata

Implement 5-6 models for this project.

Okay got it !

[Sneha-Mahata]

Assigned @Sneha-Mahata

Implement 5-6 models for this project.

Hi @abhisheks008 hope you're doing alright. My work is almost done but I just wanted to clear my doubt regarding the dataset which is given is a very small dataset containing only 104 samples and 17 features and by feature engineering I've reduced the features into only 5 but still facing some accuracy problems. Can you plz suggest me how to overcome this hurdle?

[abhisheks008]

Assigned @Sneha-Mahata Implement 5-6 models for this project.

Hi @abhisheks008 hope you're doing alright. My work is almost done but I just wanted to clear my doubt regarding the dataset which is given is a very small dataset containing only 104 samples and 17 features and by feature engineering I've reduced the features into only 5 but still facing some accuracy problems. Can you plz suggest me how to overcome this hurdle?

You need to get an accuracy of 90% for at least two models out the 5-6 models you have implemented.