HackLive 3: Guided Hackathon - NLP

Online 17-10-2020 12:00 PM to 25-10-2020 11:59 PM
  • 1282


  • 15

    Number of Teams

  • AV Points


The benefits of participating in Machine Learning hackathons are not just limited to the versatile set of problems available and the support from the community. It is also an important part of building a career in machine learning. Some competitions are explicitly meant for recruiting, where a high rank can get you an interview for a Machine Learning Software Engineer position. But even otherwise, Employers pay a lot of attention to your performance in hackathons. Strong hackathon performances are a really effective way to stand out from the crowd and demonstrate how well you can do on a problem.

Data Science competitions can be daunting for someone who has never participated in one. Some of them have hundreds of competitors with top-notch industry knowledge and splendid past record in such hackathons. To help data science career aspirants get over this threshold and start their journey in hackathons Analytics Vidhya has launched a unique initiative called HackLive!

What is HackLive?

HackLive is a unique Hackathon Solving Experience guided by experts to get started and later on utilise the guidance to improve your hackathon skills.

Just like we did last weekend, this time we will be back with a new problem statement. This time we will work on a text classification problem live on zoom & Youtube. The hackathon will launch on 17th October with the live stream along with the dataset and problem statement at 12 Noon.

The duration for the Livestream will be of 2 hours while the hackathon will continue till 25th October which means you will have the whole week to apply your learnings from the session and climb the leaderboard. Here's a peek into what will be covered in Livestream:

Problem Statement, Data Dictionary & Hypothesis Generation

The first step for a hackathon or any data science project is to understand the problem statement and the possible hypothesis related to the target variable.

Exploratory Data Analysis The ability to load, navigate, and plot your data (i.e. exploratory analysis) is the second step in data science because it informs the various decisions you'll make throughout model training.

Basic Benchmark Model using Bag of Words & TF-IDF We will set up a baseline or benchmark model using simple classical NLP techniques such as bag of words and TF-IDF and then fit simple ML models to check performance on the leaderboard. The scores from these algorithms provide the required point of comparison when evaluating all other machine learning algorithms on your problem.

Text Classification using Deep Learning

In this part, we will use deep learning models to see how much we can improve over the baseline models and jump positions on the leaderboard

Tips & Tricks for improving Hackathon performance

In this part, experts will share a few tips and tricks that work well for text classification and set up the way forward for all hackers to improve their score even further and set the final submission.


This is the part where the participants get a chance to ask questions related to the techniques discussed along with any doubts related to platform or problem statement.

And this is just the beginning, the hackathon will continue till 25th October and all participants will have an opportunity to exciting rewards!


AV Points

This competition makes you eligible for winning 250 AV Points and move up the prestigious Datahack Leaderboard. To know more about how AV points system work along, check out the Datahack Points System Page.


1. Where can I find the dataset and the problem statement for the hackathon?

The contest and the live session will start on the designated contest start date and time. There is a timer that is shown at the top of this page which shows the remaining time before the contest goes live. This is when you can access the problem statement and datasets from the problem statement tab and

2. Can I share my approach/code?

Absolutely. You are encouraged to share your approach and code file with the community. There is even a facility at the leaderboard to share the link to your code/solution description.

3. I am facing a technical issue with the platform/have a doubt regarding the problem statement. Where can I get support?

You may use the discuss tab to post your technical issues or any other issue with the problem statement

Sneak Peek into Previous HackLive Sessions

HackLive 1: Intelligent Targeting for Retail Banks

HackLive 2: Youtube Like Count Prediction


Business Analytics vs Data Science

Please register to participate in the contest

Please register to participate in the contest

Please register to participate in the contest


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