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We have said it earlier and we say it again - 'With Great Power Comes Great Responsibility' And yes we do have the power to do good for the world. Let us be responsible and put that power to use.
This time, we pick up our weapons against
Given information of different
risk factors in a woman,
predict as best as possible, the
cervical cancer in the woman.
Understand with code! Here is
getting started code for you.
This dataset contains indicators and risk factors for predicting whether a woman will get
cervical cancer. There are total of
15 attributes out of which first
14 features include demographic data such as
medical history. The last attribute called
Biopsy is target variable and it's value is
Cancer. The first
14 attributes are as:
- Age [ in years ]
- Number of sexual partners
- First sexual intercourse [ age in years ]
- Number of pregnancies
- Smoking [ yes or no ]
- Smoking [ in years ]
- Hormonal contraceptives [ yes or no ]
- Hormonal contraceptives [ in years ]
- Intrauterine device [ yes or no (IUD) ]
- Number of years with an intrauterine device (IUD)
- Has patient ever had a sexually transmitted disease (STD) [ yes or no ]
- Number of STD diagnoses
- Time since first STD diagnosis
- Time since last STD diagnosis
- The biopsy results - Target outcome.[
Following files are available in the
686samples) This csv file contains the attributes describing the risk factors along with its biopsy results.
172samples) File that will be used for actual evaluation for the leaderboard score but does not have its biopsy result.
- Prepare a CSV containing header as
Biopsyand predicted value as digit
1with name as
- Sample submission format available at
Make your first submission here 🚀 !!
🖊 Evaluation Criteria
- 💪 Challenge Page: https://www.aicrowd.com/challenges/cervc
- 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/cervc/discussion
- 🏆 Leaderboard: https://www.aicrowd.com/challenges/cervc/leaderboards
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Source: Kelwin Fernandes (kafc at inesctec dot pt) - INESC TEC & FEUP, Porto, Portugal. Jaime S. Cardoso - INESC TEC & FEUP, Porto, Portugal. Jessica Fernandes - Universidad Central de Venezuela, Caracas, Venezuela.