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  "source_url": "https://www.kaggle.com/competitions/autism-prediction/data",
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      "content": "The details of the files provided, as well as the column information for domain knowledge, is given below:\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns\n\n*   `ID` - ID of the patient\n*   `A1_Score to A10_Score` - Score based on Autism Spectrum Quotient (AQ) 10 item screening tool\n*  `age` - Age of the patient in years\n*  `gender` - Gender of the patient\n*  `ethnicity` - Ethnicity of the patient\n*  `jaundice` - Whether the patient had jaundice at the time of birth\n*  `autism` - Whether an immediate family member has been diagnosed with autism\n*  `contry_of_res` - Country of residence of the patient\n*  `used_app_before` - Whether the patient has undergone a screening test before\n*  `result` - Score for AQ1-10 screening test\n*  `age_desc` - Age of the patient\n*  `relation` - Relation of patient who completed the test\n*  `Class/ASD` - Classified result as 0 or 1. Here 0 represents No and 1 represents Yes. This is the target column, and during submission submit the values as 0 or 1 only.",
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      "name": "Description",
      "content": "Welcome to ML Olympiad hosted by TFUG Chennai and TFUG Mysuru .\n\n### What is ML Olympiad?\n\nAn associated Kaggle Community Competitions hosted by ML GDEs or TFUGs, sponsored by Google Developers. \n\n[Source](https://www.kaggle.com/andrewmvd/autism-screening-on-adults)\n\n### Abstract\nImprove Autism Screening by creating predicting the likelihood of having this condition.\n\n### About this dataset\nWhat is Autism\nAutism, or autism spectrum disorder (ASD), refers to a broad range of conditions characterized by challenges with social skills, repetitive behaviors, speech and nonverbal communication.\n\n### Causes and Challenges\nIt is mostly influenced by a combination of genetic and environmental factors. Because autism is a spectrum disorder, each person with autism has a distinct set of strengths and challenges. The ways in which people with autism learn, think and problem-solve can range from highly skilled to severely challenged.\nResearch has made clear that high quality early intervention can improve learning, communication and social skills, as well as underlying brain development. Yet the diagnostic process can take several years.\n\n### The Role of Machine Learning\nThis dataset is composed of survey results for more than 700 people who filled an app form. There are labels portraying whether the person received a diagnosis of autism, allowing machine learning models to predict the likelihood of having autism, therefore allowing healthcare professionals prioritize their resources.\n\n## Acknowledgements\n\n**Citations**\n\nTabtah, F. (2017). Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment. Proceedings of the 1st International Conference on Medical and Health Informatics 2017, pp.1-6. Taichung City, Taiwan, ACM.\nThabtah, F. (2017). Machine Learning in Autistic Spectrum Disorder Behavioural Research: A Review. To Appear in Informatics for Health and Social Care Journal. December, 2017\n\n[Image Source](https://www.verdict.co.uk/machine-learning-autism/)\n\n**License**\n\nPublic Domain\n\n**Data Source**\n\nThe data files for this competition was developed synthetically by using [CTGan](https://github.com/sdv-dev/CTGAN) on [Autism Screening on Adults Dataset](https://www.kaggle.com/andrewmvd/autism-screening-on-adults). \nThe dataset creation, as well as the competition setup, was done by [Suryansu Dash](https://www.linkedin.com/in/suryansu-dash-a71b9712b/) and [Laxman Naik](https://www.linkedin.com/in/laxman-naik/) of [GDSC CET-Bhubaneswar](https://gdsc.community.dev/college-of-engineering-technology-bhubaneswar/)\n\n#MLOlympiad",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [AUC-ROC Score](https://en.wikipedia.org/wiki/Receiver_operating_characteristic). The AUC-ROC Score is commonly used for classification tasks. It measures the degree or measure of separability between the classes. The ROC curve is plotted with TPR against the FPR where TPR is on the y-axis and FPR is on the x-axis.\n\nTPR or True Positive Rate can be defined as (True Positives / (True Positives + False Negatives))\nSimilarly FPR, or False Positive Rate, can be defined as (False Positives / (False Positives + True Negatives))\n\nAUC - ROC curve is a performance measurement for the classification problems at various threshold settings\n\n## Submission Format\n\nEvery Submission file should have the the following format:\n```\nID,Class/ASD\n1,1\n```\nYou can refer to the sample_submission.csv file to get an idea on how to frame the submission file.",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!\n - A max team of 3 members is allowed.",
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