`Fetal Development` the instructions sets that doesn't make mistakes as they build. Time to look into a problem related to this.<br/>
- Background: A major contributor to under-five mortality is the death of children in the 1st month of life. `Intrapartum complications` are one of the major causes of perinatal mortality. `Fetal cardiotocograph (CTGs)` can be used as a monitoring tool to identify high-risk women during labor.
- Aim: The objective of this study was to study the precision of machine learning algorithm techniques on `CTG` data in identifying high-risk `fetuses`.
Can you help save infant lives? A large number of infants die even before they are a month old. Majority of these deaths could be avoided through early diagnosis using monitoring tools such as Fetal cardiotocograph (CTGs). The goal is to develop a machine learning model which can use [`CTG`](https://geekymedics.com/how-to-read-a-ctg/) data for identifying high-risk `fetuses`.
Understand with code! Here is [getting started code](https://discourse.aicrowd.com/t/baseline-mnist/2757) for you.`😄`
# 💾 Dataset
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# 📁 Files
Following files are available in the `resources` section:
-`./data/train.csv` - (`1700` samples) File that should be used for training and validation purposes by the user.
-`./data/test.csv` - (`426` samples) File that will be used for actual evaluation for the leaderboard score.
-`train.csv` - (`1700` samples) File that should be used for training and validation purposes by the user.
-`test.csv` - (`426` samples) File that will be used for actual evaluation for the leaderboard score.
# 🚀 Submission
- Prepare a csv containing header as `NSP` and predicted value as digit `[1-3]` with name as `submission.csv`.
- Sample submission format available at `./data/sample_submission.csv`.
- Name of the above file should be `submission.csv`.
- Sample submission format available at `sample_submission.csv` in the resorces section.
**Make your first submission [here](https://www.aicrowd.com/challenges/crdio-classify-fetal-state/submissions/new) 🚀 !!**
# 🖊 Evaluation Criteria
During evaluation [F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) and [Log Loss](http://wiki.fast.ai/index.php/Log_Loss) will be used to test the efficiency of the model where,
During evaluation [F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) will be used to test the efficiency of the model where,
