Weekend Hackathons are becoming more competitive, we are providing a platform to expose your skills to the machine learning community. In this weekend hackathon, many participants are racing for top 3 positions, so we are back with a tougher one this time. This week we are giving an opportunity to showcase your skills in machine learning classification models.
You need to classify various classes of flowers into 8 different classes. Using computer vision to do such recognition has reached state-of-the-art, now it’s time for the machine learning community to build a state-of-art classification model. Use your skills smartly to include your name in the top 3 positions.
The challenge will start on August 21st Friday at 6 pm IST.
Problem Statement & Description
To recognize the right flower you will be using 6 different attributes to classify them into the right set of classes. We are challenging the community to use classical machine learning classification techniques to come up with a machine learning model that can generalize well on the unseen data provided explanatory attributes about the flower species instead of a picture. In this competition, you will be exploring advanced classification techniques, handling higher cardinality categorical variables, and much more.
The unzipped folder will have the following files.
- Train.csv – 12666 rows x 7 columns (includes Class as target column)
- Test.csv – 29555 rows x 6 columns
- Sample Submission.csv – Please check the Evaluation section for more details on how to generate a valid submission.
- Area_Code – Generic Area code, species were collected from
- Locality_Code – Locality code, species were collected from
- Region_Code – Region code, species were collected from
- Height – Height collected from lab data
- Diameter – Diameter collected from lab data
- Species – Species of the flower
- Class – Target Column (0-7) classes
The datasets will be made available for download on August 21th, Friday at 6 pm IST.
This hackathon and the bounty will expire on August 24th, Monday at 7 am IST.
How to Generate a valid Submission File
Sklearn models support the predic_proba() method to generate the probabilities for every class.
You should submit a .csv/.xlsx file with exactly 29555 rows with 8 columns (one column per class). Your submission will return an Invalid Score if you have extra columns or rows.
The file should have exactly 8 (0-7) columns:
We have introduced a new set of prizes going forward.
- Continous 3 finishes In Weekend Hackathons Top-3 participants on the private leaderboard will be interviewed for #HackeroftheMonth.
- Stand a Chance to get an exclusive interview for your Data Science/Machine Learning journey by Analytics India Magazine
Who is the #hackerofthemonth ??
Any participant can become #hackerofthemonth by proving their mettle in the weekend hackathon leaderboards. We will award the #hackerofthemonth community recognition to participants who are in Top-3 for 3-consecutive weekend hackathons in a row. Yes, you got it right, it’s a hattrick!!
Stand a chance to get Interviewed by the biggest AL/ML media-house in the country for your Data Science and Machine Learning journey.
Please note this PRIZE is only for the Weekend Hackathon series of competitions.
- One account per participant/team. Submissions from multiple accounts will lead to disqualification.
- The submission limit for the hackathon is 10 per day after which the submissions will not be accepted.
- All registered participants are eligible to compete in the hackathon.
- We ask that you respect the spirit of the competition and do not cheat.
- The use of any external dataset is prohibited and doing so will lead to disqualification.
Hackathon Specific Rules
This hackathon will expire on 24th Aug, Monday at 7 am IST
- The submission will be evaluated using the Log Loss metric. One can use sklearn.metric.log_loss to calculate the same
- This hackathon supports private and public leaderboards
- The public leaderboard is evaluated on 30% of Test data
- The private leaderboard will be made available at the end of the hackathon which will be evaluated on 100% Test data.