Presenting HackLive – A Guided Community Hackathon by Analytics Vidhya’s Data Science Experts!
“There is no alternative to learning through experience.”
This quote rings true for every aspect of our life. And it takes on a whole new meaning in data science.
Data Science hackathons area great way to:
- Test your data science knowledge
- Compete against top data science experts from around the world and gauge where you stand
- Get hands-on practice of a data science problem working in a deadline environment
- Improve your existing data science skillset
- Enhance your existing data science resume
And much more! But here’s the thing about these data science competitions – they aren’t easy to navigate. In fact, we have seen thousands of newcomers and beginners struggle to get a decent rank on the hackathon leaderboard. A common question we get is – “Where do I start? How do I even compete when I can’t improve my solution?”
This can be a frustrating experience, especially if you don’t have any help.
Well, we are delighted to announce HackLive, a one of a kind guided community hackathon being held this weekend!
HackLive is a 6-hour live community guided hackathon that will help you overcome the fear of hackathons by answering fundamental questions along with live coding sessions! This will all be taken up by experts at Analytics Vidhya, who have poured through thousands of solutions to bring out the best practices you should employ.
But Wait – Is Participating in Hackathons Even Worth your Time?
This is the million-dollar question every data scientist asks himself/herself at the beginning of their journey. Hackathons have some differences from “typical” data science projects, but they still provide valuable experience and help you learn new skills by tackling a variety of problems:
- No need for data collection: There is no need of playing around with databases and combining them to define the problem statement as that is something which the organizers do for you
- Practice: The basic premise of a hackathon is that you learn by doing stuff – by building a model, by exploring that dataset, etc.
- Community Support: Each hackathon has its own discussion forum that gives you an excellent opportunity to peek into the thought-processes of other data scientists
Analytics Vidhya brings you our newest offering, HackLive, to guide you and to enable you to take that leap and participate in a live hackathon by going through a step by step process on not only how to approach a hackathon problem statement and make your first submission, but also how to improve your performance to crack those top positions on the hackathon leaderboard!
So What Does HackLive Have to Offer?
6 Hours of Live Hackathon Learning Experience!
During the weekend starting 26th September, we will do 2 live streams of total 6 hours led by top hackers from Analytics Vidhya with the following plan:
First Live Stream: Build your first model & make that first Submission! (3 Hours)
- 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 (EDA)
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 Rule-Based Benchmark Models & making your first submission
Benchmark prediction algorithm provides a set of predictions that you can evaluate as you would any predictions for your problems, such as classification accuracy or RMSE. The scores from these algorithms provide the required point of comparison when evaluating all other machine learning algorithms on your problem
Second Live Stream: Get Serious and do feature engineering to improve your model’s performance and set the final submission (3 Hours)
- Basic Preprocessing and building the first machine learning Model
Data preprocessing is an integral step in Machine Learning as the quality of data and the useful information that can be derived from it directly affects the ability of our model to learn. Therefore, in this step, we preprocess our data before feeding it into our model
- Identify feature engineering ideas and do feature selection to check the performance
The features in your data will directly influence the predictive models you use and the results you can achieve. You can say that the better the features that you prepare and choose, the better the results you will achieve. Here, the mentor will discuss various ways of thinking about engineering features that might give you better performance and then test them out to do feature selection
- Build Multiple Models and Do Grid Search to find the best set of hyperparameters
In this step, the mentor will discuss various ways of selecting the right model for the problem and also cover how you can use grid search or other such methods to build improved models and jump up on the leaderboard
- Ensemble Model to improve performance
Rarely do we see a winning solution without using ensemble modeling which is nothing but combining multiple diverse base models to predict an outcome
- Make and Set Final Submission with Code file
Learn how to choose your final submission and submit the code file to complete your participation in the hackathon
- What’s Next & QnA
There is always a scope for improvement when it comes to a machine learning model. Here, the mentor will share some tips on how to go forward and ways to improve the model even further
Awesome! Are there any Prerequisites for HackLive?
The prerequisites you really need to have is a basic understanding of the Python Data Science Stack such as Pandas, sklearn & a basic understanding of machine learning algorithms. For a super beginner-friendly course on Python and sklearn, you may enroll here:
The live stream links will be updated on this page itself when the hackathon goes live. Stay tuned!
Time for some FAQs on HackLive
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.
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 discussion tab to post your technical issues or any other issue with the problem statement.
How to Participate?
Now that you know about Hacklive, what are you waiting for?
Start your journey towards building top class models and gaining top ranks in Data Science Hackathons with us. You can register for our Live Hackathon here for further details.