Melanoma Tumor Size Prediction: Weekend Hackathon #15

MachineHack welcomes all Data Science and Machine Learning enthusiasts to another exciting weekend hackathon. This weekend, machinehackers must use their data science skills to predict the melanoma tumor size based on a number of factors.

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The challenge will start on 7th  August Friday at 6 pm IST.

Problem Statement & Description

In this weekend hackathon, we are challenging all the MachineHackers to predict the melanoma tumor size based on various attributes. Melanomas can appear in many different shapes, sizes, and colors. That’s why it’s tricky to provide a comprehensive set of warning signs. Melanoma, also known as malignant melanoma, is a type of skin cancer that develops from the pigment-producing cells known as melanocytes. The primary cause of melanoma is ultraviolet light (UV) exposure in those with low levels of the skin pigment melanin. The UV light may be from the sun or other sources, such as tanning devices. 

Melanoma is the most dangerous type of skin cancer. Globally, in 2012, it newly occurred in 232,000 people. In 2015, there were 3.1 million people with active disease, which resulted in 59,800 deaths. Australia and New Zealand have the highest rates of melanoma in the world. There are also high rates in Northern Europe and North America, while it is less common in Asia, Africa, and Latin America. In the United States melanoma occurs about 1.6 times more often in men than women.

Data Description

Train.csv – 9146 rows x 9 columns – Glimpse of Training Data

Test.csv – 36584 rows x 8 columns – Glimpse of Test Data

Sample Submission – Acceptable submission format

Attributes Description:

  • mass_npeathe mass of the area understudy for melanoma tumor
  • size_npear: the size of the area understudy for melanoma tumor
  • malign_ratio: ration of normal to malign surface understudy
  • damage_size: unrecoverable area of skin damaged by the tumor
  • exposed_area: total area exposed to the tumor
  • std_dev_malign: standard deviation of malign skin measurements
  • err_malign: error in malign skin measurements
  • malign_penalty: penalty applied due to measurement error in the lab
  • damage_ratio: the ratio of damage to total spread on the skin
  • tumor_size: size of melanoma_tumor (target)

The datasets will be made available for download on August 07th, Friday at 6 pm IST.

This hackathon and the bounty will expire on August 10th, Monday at 7 am IST.

Bounties

The top 3 competitors will receive a free pass to the Computer Vision DevCon 2020

Know more about the Computer Vision DevCon 2020.

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. Use of any external dataset is prohibited and doing so will lead to disqualification.
  6. The provided sample submission will not be accepted as the final submission
  7. This hackathon will expire on 10th August, Monday at 7 am IST

Evaluation

  • The submission will be evaluated using the RMSE metric. One can use np.sqrt(sklearn.mean_squared_error(actual, predicted))
  • 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

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Anurag Upadhyaya
Experienced Data Scientist with a demonstrated history of working in Industrial IOT (IIOT), Industry 4.0, Power Systems and Manufacturing domain. I have experience in designing robust solutions for various clients using Machine Learning, Artificial Intelligence, and Deep Learning. I have been instrumental in developing end to end solutions from scratch and deploying them independently at scale.

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