About This Hackathon

<h1>The Rising 2025 - India's Biggest Summit on Women in Tech &amp; AI</h1><p><strong>The Rising 2025</strong> goes beyond surface-level narratives, diving deep into the journey of women in tech. Rather than just highlighting positive initiatives, it focuses on the real steps taken to achieve significant milestones—the methods, challenges, and adaptive strategies that drive progress.</p><p>We bring an exciting new challenge in machine learning—<strong>Predicting User Travel Satisfaction</strong>—aimed at driving innovation in understanding and enhancing travel experiences, a crucial aspect of the tourism industry.</p><h3><strong>Challenge Details</strong></h3><p>Your task is to develop a machine learning model that predicts the user travel satisfaction scores based on factors like destination, travel type, transportation mode, hotel rating, and cost.</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 and the notebook.</li><li>Evaluation Metric: Submissions will be evaluated based on the RMSE , measuring how well the model predict user travel satisfaction scores.</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 user travel 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><li>Perform feature engineering.</li></ul><p><strong>Model Development</strong></p><ul><li>Use machine learning models like Random Forest and XGBoost for Predicting User Travel Satisfaction.</li></ul><p><strong>Training and Optimization</strong></p><ul><li>Apply Grid Search CV for hyperparameter tuning.</li><li>Train model using user travel 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></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>

Key Information

• Category: Hackathon
• Difficulty Level: Intermediate
• Status: Expired
• Start Date: 2025-03-19T17:04:00Z
• End Date: 2025-03-26T23:23:59Z
• Current Participants: 55

Prizes and Awards

Knowledge

Rules and Guidelines

<ul><li>The participants are required to provide the code for the work done.</li><li>The output of the code should match the submission file with the "Best Score" achieved by the participant.</li></ul>

Evaluation Criteria

<p>RMSE</p>

Quick Summary

Predicting User Travel Satisfaction is a intermediate level hackathon currently expired. It has 55 participants. Prizes include: Knowledge. The event runs from 2025-03-19T17:04:00Z to 2025-03-26T23:23:59Z.Registration is free and open to all skill levels.