ACCEPTED DATA CHALLENGE
This is to inform you that your project is selected to present at the IndabaX 2022 conference which will be held on December 23 and 24, 2022 at University of Gondar based on reviewers results.
- Abel Marie (BSc Student)
- Birku Abere (BSc Student)
- Habtamu Ayenew (MSc Student)
Deep learning IndabaXEthiopia is launching a Data Challenge to provide participants with the opportunity to learn and practice Machine Learning (ML) skills on a real-world problem. This event is intended to provide a venue for young talents (particularly undergraduate and first-year postgraduate students) to demonstrate their ML abilities.
- Title: Predicting the Pregnancy Outcome
- Sources: Dabat Research Center which is established by University of Gondar
- Relevant Information: The data is related to predicting the outcome of pregnancy. This dataset was collected from 2014 to 2021, and is organized into three tables: demographic information, location of people, and the pregnancy observation related dataset. The expected outcome is predicting the outcome of pregnant women labeled with the "type" feature.
- Number of Instances: 14354 for csv
- Number of Attributes: 27 + output attribute.
- Missing Attribute Values: 58985 (represented by 999)
- The dataset is divided into two parts: training and testing. Files available for download:
- csv - contains the type (class label). This is the dataset that you will use to train your model.
- csv- resembles Train.csv but without the type (output-related) columns. This is the dataset on which you will apply your model to predict pregnancy outcome.
- You have the option of competing as an individual or as a team. Each team should have no more than three members.
- Participate in the data challenge by registering on IndabaXEthiopia2022.
- At the end of the competition, you will be able to submit your best score.
- If two solutions earn identical scores, the tiebreaker will be the date and time in which the submission was made (the earlier solution will win).
- Your submission file should look like this:
Registration ID type(outcome) Recall Precision F1-Score ID_000YI58E 0.97
- The top three Data Challenge solutions will be presented, and the winning individuals and/or teams will receive a special prize.
- A brief report detailing the methods, tools employed and the results obtained.
- The accuracy of your perdition model in predicting pregnancy outcomes for separate test data.