About This Hackathon

<h3><strong>Welcome to Week 32 of the Weekly MachineHack Hackathon Series!</strong></h3><p>This week’s MachineHack challenge invites participants to tackle a problem in the domain of machine learning: Fuel Efficiency Prediction Challenge based on various vehicle attributes such as engine capacity, fuel type, brand, and transmission .</p><h3>📣 Challenge Details:</h3><p>Your goal is to Build a machine learning model to <strong>predict fuel_efficiency_kmpl </strong>using used cars 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 RMSE , measuring how well the model Predict the fuel_efficiency_kmpl.</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 used cars 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 RandomForest, XGBoost, LightGBM and CatBoost for predicting .</li></ul><p><strong>Training and Optimization</strong></p><ul><li>Apply ridSearchCV, RandomizedSearchCV for hyperparameter tuning.</li><li>Use Cross-Validation to ensure model robustness.</li><li>Train model using worker productivity  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></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 32 of the Weekly MachineHack Hackathon Series!

This week’s MachineHack challenge invites participants to tackle a problem in the domain of machine learning: Fuel Efficiency Prediction Challenge based on various vehicle attributes such as engine capacity, fuel type, brand, and transmission .

📣 Challenge Details:

Your goal is to Build a machine learning model to predict fuel_efficiency_kmpl using used cars 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 RMSE , measuring how well the model Predict the fuel_efficiency_kmpl.
Leaderboard: Track your progress and aim for the top spot on the leaderboard.

Data Description

The dataset for this hackathon includes:

train.csv: Contains used cars 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 RandomForest, XGBoost, LightGBM and CatBoost for predicting .

Training and Optimization

Apply ridSearchCV, RandomizedSearchCV for hyperparameter tuning.
Use Cross-Validation to ensure model robustness.
Train model using worker productivity 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.

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.

FuelEfficiencyPredictionChallenge

Welcome to Week 32 of the Weekly MachineHack Hackathon Series!

📣 Challenge Details:

Participation and Benefits

Submission and Evaluation

Data Description

How to Crack This Challenge

Getting Started

Support and Resources

Happy Hacking and Growing! 🚀

Fuel Efficiency Prediction Challenge

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