About This Hackathon

<h3> <strong>Welcome to Week 43 of the Weekly MachineHack Hackathon Series!</strong> </h3><p>This week’s MachineHack challenge invites participants to tackle a problem in the domain of <strong>machine learning</strong>: Lean Body Mass Prediction Challenge based on Lean Body Mass Data.</p><h3>Challenge Details:</h3><p>In this data-driven hackathon, participants will develop machine learning models to predict the lean_body_mass based on Lean Body Mass Data. </p><h3>Participation and Benefits</h3><ul><li>Intermediate Level: This hackathon is ideal for participants with a basic understanding of machine learning and deep learning techniques.</li><li>Community Engagement: Join our dynamic community on <a target="_blank" rel="noopener noreferrer" href="https://t.me/joinchat/NJLxnlWiz9lFnEJU20Sccw">Telegram</a> to share ideas, ask questions, and collaborate with fellow participants.</li><li>Certificates: Every participant will receive a certificate from MachineHack, and winners will earn a spot on the leaderboard.</li></ul><h3>Submission and Evaluation</h3><ul><li>Submission Format: Participants must submit their predictions in the format specified in submission.csv.</li><li>Evaluation Metric: Submissions will be evaluated based on the R2_Score  , measuring how well the model predict the lean_body_mass.</li><li>Leaderboard: Track your progress and aim for the top spot on the leaderboard.</li></ul><h3>Data Description</h3><p>The dataset for this hackathon includes:</p><ul><li>train.csv: Contains Lean Body Mass Data .</li><li>test.csv: Contains data for testing.</li><li>submission.csv: The format in which your predictions should be submitted.</li></ul><h3>How to Crack This Challenge</h3><p>To tackle this challenge successfully, follow these steps:</p><p><strong>Data Pre-processing</strong></p><ul><li>Handle Missing Values: Identify and impute or remove missing values.</li><li>Remove the unwanted columns.</li></ul><p><strong>Model Development</strong></p><ul><li>Use machine learning models like Ridge/Lasso Regression, RandomForest, XGBoost, LightGBM and CatBoost for predicting .</li></ul><p><strong>Training and Optimization</strong></p><ul><li>Apply ridSearchCV, Bayesian Optimization,<strong> </strong>RandomizedSearchCV for hyperparameter tuning.</li><li>Use Cross-Validation to ensure model robustness.</li><li>Train model using Lean Body Mass Data. .</li></ul><p><strong>Validation and Testing</strong></p><ul><li>Ensure the model generalizes well to unseen data.</li><li>Generate predictions for the test dataset in the required format for submission.</li></ul><p>For our subscribers, a starter notebook will be available to guide you through data pre-processing and basic model building. You can customize and enhance this framework to develop your solution further.</p><h3><strong>Getting Started</strong></h3><ol><li><strong>Register Now:</strong> Make sure to register for the hackathon to stay updated.</li><li><strong>Download the Dataset:</strong> Access the dataset from the MachineHack platform and start working.</li><li><strong>Join the Community:</strong> Interact with fellow participants and mentors via our Telegram group for discussions and support.</li><li><strong>Submission guideline :</strong>You can submit up to 9 solutions per day. If you exceed this limit, please submit your solution on the next day.</li><li><strong>Notebook Formate </strong>: Only .ipynb file will get accepted.</li></ol><h3><strong>Support and Resources</strong></h3><p>For any questions or assistance, please reach out to our support team at support@machinehack.com. Stay informed about the latest announcements by subscribing to our newsletter.</p><h3><strong>Happy Hacking and Growing! 🚀</strong></h3>

Welcome to Week 43 of the Weekly MachineHack Hackathon Series!

This week’s MachineHack challenge invites participants to tackle a problem in the domain of machine learning: Lean Body Mass Prediction Challenge based on Lean Body Mass Data.

Challenge Details:

In this data-driven hackathon, participants will develop machine learning models to predict the lean_body_mass based on Lean Body Mass Data.

Participation and Benefits

Intermediate Level: This hackathon is ideal for participants with a basic understanding of machine learning and deep learning techniques.
Community Engagement: Join our dynamic community on Telegram to share ideas, ask questions, and collaborate with fellow participants.
Certificates: Every participant will receive a certificate from MachineHack, and winners will earn a spot on the leaderboard.

Submission and Evaluation

Submission Format: Participants must submit their predictions in the format specified in submission.csv.
Evaluation Metric: Submissions will be evaluated based on the R2_Score , measuring how well the model predict the lean_body_mass.
Leaderboard: Track your progress and aim for the top spot on the leaderboard.

Data Description

The dataset for this hackathon includes:

train.csv: Contains Lean Body Mass Data .
test.csv: Contains data for testing.
submission.csv: The format in which your predictions should be submitted.

How to Crack This Challenge

To tackle this challenge successfully, follow these steps:

Data Pre-processing

Handle Missing Values: Identify and impute or remove missing values.
Remove the unwanted columns.

Model Development

Use machine learning models like Ridge/Lasso Regression, RandomForest, XGBoost, LightGBM and CatBoost for predicting .

Training and Optimization

Apply ridSearchCV, Bayesian Optimization, RandomizedSearchCV for hyperparameter tuning.
Use Cross-Validation to ensure model robustness.
Train model using Lean Body Mass Data. .

Validation and Testing

Ensure the model generalizes well to unseen data.
Generate predictions for the test dataset in the required format for submission.

For our subscribers, a starter notebook will be available to guide you through data pre-processing and basic model building. You can customize and enhance this framework to develop your solution further.

Getting Started

Register Now: Make sure to register for the hackathon to stay updated.
Download the Dataset: Access the dataset from the MachineHack platform and start working.
Join the Community: Interact with fellow participants and mentors via our Telegram group for discussions and support.
Submission guideline :You can submit up to 9 solutions per day. If you exceed this limit, please submit your solution on the next day.
Notebook Formate : Only .ipynb file will get accepted.

Support and Resources

For any questions or assistance, please reach out to our support team at support@machinehack.com. Stay informed about the latest announcements by subscribing to our newsletter.

LeanBodyMass
PredictionChallenge