Enhance IELTS Prediction: Refining Data and Models to Boost R² from 0.918990 to 0.999671

[DevManpreet5]

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Improving Accuracy of Already Existing IELTS Success Analysis and Prediction

🔴 Aim : To justify and improve the accuracy of the existing IELTS success prediction model in the ML-Crate repository through hyperparameter tuning and the application of various advanced algorithms. The goal is to enhance the R² score from the current value of 0.918990 to 0.999671.

🔴 Dataset : IELTS Success Stories Dataset

🔴 Approach : The approach will involve thorough data cleaning, exploratory data analysis (EDA), and the implementation of the following machine learning models:

• GradientBoosting Regression with tuning
• XGBoost Regression with tuning
• Random Forest Regression with tuning
• XGBoost Regression without tuning

I will compare the performance of these models to determine the best algorithm for predicting IELTS success, with the primary metric being the R² score.

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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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🔴🟡 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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✅ To be Mentioned while taking the issue :

• Full name : Manpreet Singh
• GitHub Profile Link : devmanpreet5
• Participant ID (If not, then put NA) : NA
• Approach for this Project : I will use data cleaning, exploratory data analysis (EDA), and implement the following models: GradientBoosting Regression with tuning, XGBoost Regression with tuning, Random Forest Regression with tuning, and XGBoost Regression without tuning. I will compare the performance of these models to determine the best algorithm for predicting IELTS success. I will also ensure thorough exploratory data analysis before creating any model.
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) : VSOC 2024

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[DevManpreet5]

i have all the files ready for same , please assign the same to me . @DevManpreet5