Participate In Weekend ML Hackathon #17: Flower Class Recognition

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. 

Data Description:-

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.

Attributes Description:

• 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:

Bounties

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.

Rules

Generic Rules

1. One account per participant/team. Submissions from multiple accounts will lead to disqualification.
2. The submission limit for the hackathon is 10 per day after which the submissions will not be accepted.
3. All registered participants are eligible to compete in the hackathon.
4. We ask that you respect the spirit of the competition and do not cheat.
5. 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

Evaluation

• 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.