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🛠 Contribute : Found a typo? Or any other change in the description that you would like to see ? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

You may know that doctors use Sonograms to ‘see’ the fetus, evaluate its health. Did you know, they also use a technique called Cardiotocography to record the fetal heartbeat during the pregnancy?

A large number of infants die even before they are a month old. Cardiotocography(CTG) is widely used to assess fetal wellbeing and identify high-risk fetuses.

For this puzzle, your goal is to develop a machine learning model which can use CTG data for identifying high-risk fetuses.

Understand with code! Here is getting started code for you.`😄`

## 💾 Dataset

The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians.

These `fetal cardiotocograms (CTGs)` were automatically processed and the respective diagnostic features measured. The `CTGs` were also classified by three expert obstetricians and a consensus classification label assigned to each of them. The dataset consists of `24` attributes out of which first `23` attributes describes details of `CTGs` features and last attribute called `NSP` is used to classify these `CTGs` in `1` for `normal`, `2` for`suspect` and `3` for `pathologic` on the basis of fetal state.

## 📁 Files

Following files are available in the `resources` section:

• `train.csv` - (`1700` samples) This csv contains the features from the cardiotocograph along with the risk state of the featus as `[1-3]` denoting `normal` ,`suspect` and `pathologic` respectively.

• `test.csv` - (`426` samples) This csv contains the features from the cardiotocograph but not the risk state of the featus.

## 🚀 Submission

• Prepare a csv containing header as `NSP` and predicted value as digit `[1-3]` with name as `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 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score and accuracy will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$

The score of only 60% of the test data will be revealed during the competition.

• 💪 Challenge Page: https://www.aicrowd.com/challenges/crdio
• 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/crdio/discussion