Bird Species Classification Challenge
Develop an ML model to predict bird species using their attributes and geographical locations.
bitgrit
Updates
25 May 2022
A notebook for beginners has been added to the "Resources" section (More information can be found in the guidelines).
Brief
This is a learning competition. Aside from knowledge, there is no monetary prize at stake.
As we progress through the ages, we learn more and more about the world around us and the creatures that inhabit the earth with us. That being said, through zoological studies, taxonomists have taken up the challenge of identifying and describing everything that lives on earth.
Taxonomists describe thousands and thousands of new extant species every year. Sometimes these species are completely new ones that have never been studied before and sometimes they are subspecies that have enough unique characteristics to separate them from their original species designation.
Moreover, when a species is identified, its population needs to be determined and tracked. Thankfully, machine learning can make this monumental task more efficient.
Problem Statement:
Scientists have determined that a known species of bird should be divided into 3 distinct and separate species. These species are endemic to a particular region of the country and their populations must be tracked and estimated with as much precision as possible. As such, a non-profit conservation society has taken up the task. They need to be able to log which species they have encountered based on the characteristics that their field officers observe in the wild.
Using certain genetic traits and location data, can you predict the species of bird that has been observed?
This is a beginner-level practice competition and your goal is to predict the bird species (A, B, or C) based on attributes or location.
Timeline
- 18 May 2022 Competition Starts
Data Breakdown
The goal of this competition is to predict the bird species (species A, B and C) using attributes and geographical locations.
To build your machine learning model, we have provided the following datasets:
There are 2 datasets for this competition, train datasets and test datasets. Both datasets have bird data for locations 1 to 3. The training_set and the training_target can be joined with the 'id' column. Your goal is to build the algorithm(s) that predicts the "Species" in the training_target.csv.
- 'training_set.csv':
- 'species': animal species (A, B, C)
- 'bill_length': bill length (mm)
- 'bill_depth': bill depth (mm)
- 'wing_length': wing length (mm)
- 'mass': body mass (g)
- 'location': island type (Location 1, 2, 3)
- 'sex': animal sex (0: Male; 1: Female; NA: Unknown)
**The submission file should follow the same format as the example file (solution_format.csv).
Submissions are evaluated on accuracy (Number of correct predictions / Total number of predictions).
NOTE: You may submit a solution file up to 3 times a day.
To give you a helping hand, we've prepared a beginners guide notebook that walks you through step by step on how to make your first submission! It covers Exploratory Data Analysis, dealing with missing data, feature preprocessing and engineering, and building a simple Decision Tree Classifier. You can find the guide under the "Resources" section of this competition.
FAQs
Rules
- This competition is governed by the following Terms of Participation. Participants must agree to and comply with these Terms to participate.
- Users can make a maximum number of 3 submissions per day. If users want to submit new files after making three submissions in a day, they will have to wait until the following day to do so. Please keep this in mind when uploading a CSV file.
- The use of external datasets is not allowed.
- It is not allowed to upload the competition dataset to other websites.
- All submissions need to be made as an individual; no teams are allowed in this competition.
- This competition has a rolling leaderboard of 90 days. Once a submission is more than 90 days old, it will no longer count on the leaderboard.
- This competition is for learning and exploring. Aside from knowledge, there are no prizes for this competition.
- If you have any inquiries about this competition, please don’t hesitate to reach out to us at [email protected].
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Non-Disclosure Agreement (NDA)
An agreement to not reveal the information shared regarding this competition to others.
- This Non-Disclosure Agreement (“Agreement”) is hereby entered into on 21st December 2024 (“Effective Date”) between you (“Participant”), as a participant in the Bird Species Classification Challenge (the “Competition”) hosted at bitgrit.net (the “Competition Site”), and bitgrit Inc. (“Bitgrit”).
- Purpose: This Agreement aims to protect information disclosed by Bitgrit to Participant (the “Purpose”).
- Confidential Information: (1) Confidential Information shall mean any and all information disclosed by Bitgrit to the Participant with regard to the entry and participation in the Competition, including (i) metadata, source code, object code, firmware etc. and, in addition to these, (ii) analytes, compilations or any other deliverable produced by the Participant in which such disclosed information is utilized or reflected. (2) Confidential Information shall not include information which; (a) is now or hereafter becomes, through no act or omission on the Participant, generally known or available to the public, or, in the present or into the future, enters the public domain through no act or omission by the Participant; (b) is acquired by the Participant before receiving such information from Bitgrit and such acquisition was without restriction as to the use or disclosure of the same; (c) is hereafter rightfully furnished to the participant by a third party, without restriction as to use or disclosure of the same.
- Non-Disclosure Obligation: The Participant agrees: (a) to hold Confidential Information in strict confidence; (b) to exercise at least the same care in protecting Confidential Information from disclosure as the party uses with regard to its own confidential information; (c) not use any Confidential Information except for as it concerns the Purpose elaborated upon above; (d) not disclose such Confidential Information to third parties; (e) to inform Bitgrit if it becomes aware of an unauthorized disclosure of Confidential Information.
- No Warranty: All Confidential Information is provided “as is.” None of the Confidential Information shall contain any representation, warranty, assurance, or integrity by Bitgrit to the Participant of any kind.
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- Injunctive Relief: In the event of a breach or the possibility of breach of this Agreement by the Participant, in addition to any remedies otherwise available, Bitgrit shall be entitled to seek injunctive relief or equitable relief, as well as monetary damages.
- Return/Destruction of the Confidential Information: (1) On the request of Bitgrit, the Participant shall promptly, in a manner specified by Bitgrit, return or destroy the Confidential Information along with any copies of said information. (2) Bitgrit may request the Participant to submit documentation to confirm the destruction of said Confidential Information to Bitgrit in the event that Bitgrit requests the Participant to destroy this Confidential Information, pursuant to the provision of the preceding paragraph.
- Term: The obligations with respect to the Confidential Information under this Agreement shall survive for a period of three (3) years after the effective date. Provided however, if the Confidential Information could be considered to fall under the category of “Trade Secret” of Bitgrit or any related third parties, this Agreement is to remain effective relative to that information for as far as the said information is regarded as Trade Secret under applicable laws and regulations. If the Confidential Information contains personal information, the terms of this Agreement shall remain effective on that information permanently.
- Governing Law: This Agreement shall be governed by and construed and interpreted under the laws of Japan without reference to its principles governing conflicts of laws.
Terms & Conditions
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