Improved Accuracy for IELTS Success Analysis and Prediction from 0.918990 to 0.999671

[DevManpreet5]

Pull Request for ML-Crate 💡

Issue Title: Improved Accuracy Comparison for IELTS Success Analysis and Prediction

• Info about the related issue (Aim of the project): To predict the success of IELTS using machine learning models. The existing model used various different models and hyperparameter tuning, increasing performance from XGBoost Regression without tuning (Mean Squared Error: 1.422946e-07, R² Score: 1.000000, Mean Absolute Error: 0.016455, Explained Variance Score: 0.918990) to GradientBoosting Regression with tuning (Mean Squared Error: 1.459225e-06, R² Score: 0.999996, Mean Absolute Error: 0.000067, Explained Variance Score: 0.999671).
• Name: Manpreet Singh
• Email ID for further communication: singhman2005123@gmail.com
• GitHub ID: devmanpreet5
• Identify yourself: Contributing for VSOC 2024

Closes: #663

Describe the add-ons or changes you've made 📃

I have used various different models and hyperparameter tuning to improve the existing model. Specifically, I improved the performance from XGBoost Regression without tuning (1.422946e-07, 1.000000, 0.016455, 0.918990) to GradientBoosting Regression with tuning (1.459225e-06, 0.999996, 0.000067, 0.999671). The folder containing the updated models, datasets, and results has been added.

Type of change ☑️

What sort of change have you made:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested? ⚙️

I have tested the changes by dividing the dataset into train and test sets and have checked for over/underfitting. The models were evaluated to ensure performance improvements and robustness.

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

[github-actions]

Our team will soon review your PR. Thanks @DevManpreet5 :)

[abhisheks008]

You can not contribute in this repository without getting assigned to an issue.

Closing this as not planned.