Git Product home page Git Product logo

biranchi2018 / predict-the-happiness-hackerearth-challenge Goto Github PK

View Code? Open in Web Editor NEW

This project forked from abhijeet3922/predict-the-happiness-hackerearth-challenge

0.0 1.0 0.0 9.46 MB

It uses 2-layered fully connected/Dense Neural network model to predict whether the hotel reviews at TripAdvisor site are positive sentiment or negative sentiment.

Home Page: https://appliedmachinelearning.wordpress.com/2017/12/21/predict-the-happiness-on-tripadvisor-reviews-using-dense-neural-network-with-keras-hackerearth-challenge/

Python 100.00%

predict-the-happiness-hackerearth-challenge's Introduction

Predict-the-Happiness-HackerEarth-Challenge

It uses 2-layered fully connected/Dense Neural network model to predict whether the hotel reviews at TripAdvisor site are positive sentiment or negative sentiment.

Best thing would be to follow my blog-post for implementation. The description about the steps to build the application from scratch can be read from my blog:

https://appliedmachinelearning.wordpress.com/2017/12/21/predict-the-happiness-on-tripadvisor-reviews-using-dense-neural-network-with-keras-hackerearth-challenge/

It is a python implementation utilizing Keras library for DNN.

This problem statement came from a HackerEarth challenge: "Predict the Happiness" The accuracy score achieved was 88% when prediction file (sample_submisson.csv) is uploaded to their portal.

The link for corpus/dataset download is given in blog-post.

Having so much of discussion around BERT over internet, I also chose to apply BERT in the same competition in order to prove if tuning BERT model can take me to the top of leader board of the challenge. Here is the blog-post show casing the step by step process of using pre-trained BERT model which took me to RANK 4 in the leaderboard.

https://appliedmachinelearning.blog/2019/03/04/state-of-the-art-text-classification-using-bert-model-predict-the-happiness-hackerearth-challenge/

predict-the-happiness-hackerearth-challenge's People

Contributors

abhijeet3922 avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.