First commit

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+data/*

CONTRIBUTION.md

+# To be filled in later with an consistent contribution guide
+- Q : Who writes that ?
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README.md

+# 🕵️ Introduction
+
+**🛠 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 [aicrowd_CRDIO_challenge].(https://gitlab.aicrowd.com/aicrowd/practice-challenges/aicrowd_CRDIO_challenge
+![](https://www.researchgate.net/profile/Svetlana_Jankovic-Raznatovic/publication/303555090/figure/fig1/AS:457790276804611@1486157115977/Sinusoidal-cardiotocography-CTG-of-the-patient-with-oscillation-amplitude-which-is_W640.jpg)
+
+`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`. 
+
+Understand with code! Here is [getting started code](https://discourse.aicrowd.com/t/baseline-mnist/2757) 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` on the basis of fetal state.<br/>
+To know about given attributes click [here](https://gitlab.aicrowd.com/aicrowd/practice-challenges/aicrowd_CRDIO_challenge/blob/master/dataset_info.md).
+
+# 📁 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.
+
+# 🚀 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`.   
+
+**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,
+
+<img src="https://latex.codecogs.com/gif.latex?%24%24F1%20%3D%20%7Bprecision%20*%20recall%20%5Cover%20precision%20&plus;%20recall%7D%24%24"/> </br>
+
+<img src="http://latex.codecogs.com/gif.latex?%24%24%20Log%20Loss%20%3D%20-log%20P%28yt%7Cyp%29%20%3D%20-%28yt%20log%28yp%29%20&plus;%20%281%20-%20yt%29%20log%281%20-%20yp%29%29%20%24%24"/>
+
+
+# 🔗 Links
+* 💪 Challenge Page : https://www.aicrowd.com/challenges/crdio-classify-fetal-state
+* 🗣️ Discussion Forum : https://www.aicrowd.com/challenges/crdio-classify-fetal-state/discussion
+* 🏆 leaderboard : https://www.aicrowd.com/challenges/crdio-classify-fetal-state/leaderboards
+
+# 📱 Contact
+- [Shubham Sharma](shubham@ext.aicrowd.com)
+
+# 📚 References
+
+- Source:
+  Marques de Sá, J.P., jpmdesa@gmail.com, Biomedical Engineering Institute, Porto, Portugal.
+  Bernardes, J., joaobern@med.up.pt, Faculty of Medicine, University of Porto, Portugal.
+  Ayres de Campos, D., sisporto@med.up.pt, Faculty of Medicine, University of Porto, Portugal.
+
+- 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.
+
+- [Image source](https://www.researchgate.net/publication/303555090_Massive_fetomaternal_hemorrhage_as_a_cause_of_severe_fetal_anemia/figures?lo=1)
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aicrowd.yaml

+---
+challenge_name: aicrowd_CRDIO_challenge
+evaluation_repo: git@gitlab.aicrowd.com:aicrowd/practice-challenges/aicrowd_CRDIO_challenge_evaluator.git
+data_url: https://s3.wasabisys.com/aicrowd-practice-challenges/public/crdio/v0.1/test_ground_truth.csv
+official_baseline: CRDIO_baseline.ipynb
+authors:
+- name: Shubham Sharma
+  email: shubham@ext.aicrowd.com
+version: '0.1'

dataset_info.md

+| Index  | Attribute-Name | Details |
+| ------------- | ------------- | -------------  |
+1  |LBE |FHR baseline value (medical expert) (beats per minute) |
+2  |LB |FHR baseline (SisPorto) (beats per minute) |
+3  |AC |Number of accelerations per second  |  
+4  |FM |Number of fetal movements per second  |  
+5  |UC |Number of uterine contractions per second  |  
+6  |ASTV |percentage of time with abnormal short term variability  |  
+7  |MSTV |mean value of short term variability  |  
+8  |ALTV |percentage of time with abnormal long term variability |  
+9  |MLTV |mean value of long term variability |
+10  |DL |Number of light decelerations per second  |  
+11  |DS |Number of severe decelerations per second  |  
+12  |DP |Number of prolonged decelerations per second  |  
+13  |DR |Number of repetitive decelerations per second  |  
+14  |Width |width of FHR histogram |
+15  |Min |minimum of FHR histogram |
+16  |Max |maximum of FHR histogram |
+17  |Nmax |Number of histogram peaks |
+18  |Nzeros |Number of histogram zeros |
+19  |Mode |histogram mode  |
+20  |Mean |histogram mean  |
+21  |Median |histogram median |
+22  |Variance |histogram variance |
+23  |Tendency |histogram tendency |
+24  |NSP |Normal=1; Suspect=2; Pathologic=3 |
+
+
+
+