HackLive 4: Guided Community Hackathon!

We are back with the latest HackLive, this time 3 times the fun as we go live on 8th of November and the hackathon will be live for complete 3 weeks!!

This time along with an insightful session at the beginning we will also discuss top solutions from the winners in a second session after the hackathon ends!

 

So What’s the plan?

We will be back with a brand new problem statement to apply your machine learning and data science skills and build a winning model also giving you an opportunity to win 250 AV Points.

Live Hackathon Learning Experience!

The weekend starting 8th November, we will do 2 live streams one at the launch and one at the hackathon close led by top hackers from Analytics Vidhya with the following plan:

First Live Stream: Build your first model & make that first Submission! (7th November 2020)

• 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 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 problem, 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.
• Basic Preprocessing and building first ML 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.
• QnA

Second Live Stream: Improving your model using Feature Engineering & Ensemble Models with a deep dive into Top Solutions from the winners (21st November)

• Recap from Stream 1
• Identify feature engineering ideas and do feature Selection to check 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 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.
• Top Winner Solutions
  Winners from the hackathons will be invited to share the top solutions and tips/tricks that helped them beat the leaderboard and finish at the podium. Learn from the top hackers at Analytics Vidhya or present as a top hacker and build your brand.
• What’s Next & QnA
  There is always a scope of improvement when it comes to a machine learning, here the winners and mentor from AV will share some tips on how to go forward and ways to improve the model even further

Sneak Peek at the HackLive1

What are the Prerequisites?

The prerequisites you really need to have is a basic understanding of Python Data Science Stack such as Pandas and sklearn & basic understanding of ML algorithms. For a super beginner friendly and short course on Python you may enrol here

• Python for Data Science

 

The live stream links will be updated on this page itself when the hackathon goes live. Stay tuned!

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.

 

FAQs

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