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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
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)
[x] 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: ☑️
[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my own code.
[x] I have commented my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[x] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[x] Any dependent changes have been merged and published in downstream modules.
Pull Request for ML-Crate 💡
Issue Title: Improved Accuracy Comparison for IELTS Success Analysis and Prediction
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:
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: ☑️