ML-Crate stands as the ultimate hub for a multitude of exciting ML projects, serving as the go-to resource haven for passionate and dedicated ML enthusiasts!🌟💫 Devfolio URL, https://devfolio.co/projects/mlcrate-98f9
:red_circle: Project Title : Improving Accuracy of Already Existing IELTS Success Analysis and Prediction
:red_circle: 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.
:red_circle: 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.
📍 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.
: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.
:white_check_mark: To be Mentioned while taking the issue :
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
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎
ML-Crate Repository (Proposing new issue)
:red_circle: Project Title : Improving Accuracy of Already Existing IELTS Success Analysis and Prediction
:red_circle: 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.
:red_circle: Dataset : IELTS Success Stories Dataset
:red_circle: Approach : The approach will involve thorough data cleaning, exploratory data analysis (EDA), and the implementation of the following machine learning models:
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.
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎