About This Hackathon

<h3><strong>Welcome to Week 25 of the Weekly MachineHack Hackathon Series!</strong></h3><p>This week brings an exciting new challenge in the domain of machine learning: Beauty Products Review Rating Prediction. In the e-commerce industry, customer reviews play a crucial role in influencing purchasing decisions. Platforms  receive millions of reviews on beauty products, and accurately predicting products ratings based on these reviews can help businesses improve recommendations, detect fake reviews, and enhance user experience.</p><h3><strong>Challenge Details</strong></h3><p>Your task is to develop a machine learning model that predicts the rating of beauty products. The goal of Beauty Products Review Rating Prediction is to build a model that predicts product ratings (1-5 stars) based on customer review.</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 product review rating.</li><li>Leaderboard: Track your progress and aim for the top spot on the leaderboard.Data Description.</li></ul><h3>Data Description</h3><p>The dataset for this hackathon includes:</p><ul><li>train.csv: Contains Customer Review 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>Text Processing: Convert title and text into numerical features using TF-IDF.</li><li>Feature Encoding: Convert asin, parent_asin, and verified_purchase into numerical representations.</li></ul><p><strong>Model Development</strong></p><ul><li>Use machine learning models like Random Forest and XGBoost for rating prediction.</li></ul><p><strong>Training and Optimization</strong></p><ul><li>Apply Grid Search CV for hyperparameter tuning.</li><li>Train model using Customer review 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>

Welcome to Week 25 of the Weekly MachineHack Hackathon Series!

This week brings an exciting new challenge in the domain of machine learning: Beauty Products Review Rating Prediction. In the e-commerce industry, customer reviews play a crucial role in influencing purchasing decisions. Platforms receive millions of reviews on beauty products, and accurately predicting products ratings based on these reviews can help businesses improve recommendations, detect fake reviews, and enhance user experience.

Challenge Details

Your task is to develop a machine learning model that predicts the rating of beauty products. The goal of Beauty Products Review Rating Prediction is to build a model that predicts product ratings (1-5 stars) based on customer review.

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 product review rating.
Leaderboard: Track your progress and aim for the top spot on the leaderboard.Data Description.

Data Description

The dataset for this hackathon includes:

train.csv: Contains Customer Review 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.
Text Processing: Convert title and text into numerical features using TF-IDF.
Feature Encoding: Convert asin, parent_asin, and verified_purchase into numerical representations.

Model Development

Use machine learning models like Random Forest and XGBoost for rating prediction.

Training and Optimization

Apply Grid Search CV for hyperparameter tuning.
Train model using Customer review 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.

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.

BeautyProductsReview
RatingPrediction