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  "source_url": "https://www.kaggle.com/competitions/eee-datathon-challenge-2020/data",
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      "name": "rules",
      "content": "# Rules to apply: \n\n###  1. Don't cheat.\n###  2. You may upload 5 submissions per day (UTC time).\n###  3. You may select max 2 eligible submissions for final judgement.\n### 4. Have fun!\n\nPlease note that only registered Induction Fiesta datathon participants are eligible for the final evaluation and prize giving. ",
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      "content": "<h3>Please refer to \"submission_sample.csv\" for your submission.</h3>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>submission_sample.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2> Data fields</h2>\n<ul>\n<li><strong>id</strong> - row that represent id of the diamond</li>\n<li><strong>price</strong> - known price of diamonds</li>\n<li><strong>carat</strong> - weight of the diamond </li>\n<li><strong>cut</strong> - quality of the cut (Fair, Good, Very Good, Premium, Ideal)</li>\n<li><strong>color</strong> - diamond colour, from D (best) to J (worst)</li>\n<li><strong>clarity</strong> - a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))</li>\n<li><strong>x</strong> - length in mm</li>\n<li><strong>y</strong> - width in mm</li>\n<li><strong>z</strong> - depth in mm</li>\n<li><strong>depth</strong> - total depth percentage = z / mean(x, y) = 2 * z / (x + y)</li>\n<li><strong>table</strong> - width of top of diamond relative to widest point</li>\n</ul>",
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    {
      "name": "Description",
      "content": "<p>Looking to learn about Machine Learning? Join in for a hands-on experience with EEE Datathon Challenge in collaboration with MLDA@EEE! 🤟🏼🥳 Code your own data model using skills picked up from MLDA's very own tutorial videos! 👩🏻‍💻🧑🏻‍💻</p>\n<p>Sign up as an individual or a team of not more than 3 via https://tinyurl.com/NTUEEEDatathon! 👈🏼👈🏼</p>\n<h3>Date: 9 May - 23 May 2020, 2359</h3>\n<h3>Submission Deadline: 05/23/2020 11:59 PM SGT</h3>\n<h3>✨ How to participate ✨</h3>\n<p>1. Follow the NTU EEE Instagram page @ntueee!</p>\n<p>2. Complete watching the series of the 5 introductory videos on MLDA@EEE youtube channel to get you started on your very own machine learning project 👾</p>\n<p>3. Upgrade your skills with 3 💫bonus videos 💫 on MLDA@EEE's youtube channel covering more advanced topics like CV, NLP and CNN!</p>\n<p>Watch them ALL in a playlist: https://www.youtube.com/watch?v=FgTpL-8gouE&list=PLXpIV63PRtvV0i00XYqfj2_mOjp8kt4ti</p>\n<p>On 15th May: \n✅ datathon challenge dataset\n✅ instructional video \nwill be released!\n✅ scoring board on Kaggle will also be updated daily \n👉🏼 Stay tuned for updates on @ntueee on Instagram!</p>\n<p>Watch them ALL in a playlist: https://www.youtube.com/watch?v=FgTpL-8gouE&list=PLXpIV63PRtvV0i00XYqfj2_mOjp8kt4ti</p>\n<h3>Start your coding:</h3>\n<h4>1. Find \"Example notebook\" created by MLDA@EEE under Notebooks.</h4>\n<h4>2. Click \"Copy and Edit\".</h4>\n<h4>3. Rename and work on your notebook!</h4>\n<h4>4. Name and submit your submission file!</h4>\n\n<p>Instruction video for a quick start: https://www.youtube.com/watch?v=7w7n0DGsOaM</p>\n\n<p>You may also work with Jupyter lab on a local computer.</p>\n\n<p>Submit your file onto Kaggle from 15 May to 23 May, 2359 to be evaluated by our testing data! 👩🏻‍⚖️🧑🏻‍⚖️</p>\n<p>Protip: You are allowed to submit your csv file onto Kaggle as many times as you'd like to get the highest accuracy results! 🤞🏼😉</p>\n<p>The top 3 teams with the highest accuracy scores will be crowned winners of EEE Datathon on 24 May! 🎉🥳</p>\n<p>For more resources & tips, head over to our Induction Fiesta telegram channel: https://t.me/inductionfiesta2020 🤓🤩</p>\n<p>Organised by MLDA@EEE and Induction Fiesta EEE</p>\n<p></p>",
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    {
      "name": "Evaluation",
      "content": "<h3>Evaluation metric: R2Score</h3>\n",
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    {
      "name": "Timeline",
      "content": "<p>Date: 9 May - 23 May 2020, 2359</p>\n<p>Submission Deadline: 05/23/2020 11:59 PM SGT</p>",
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    {
      "name": "Prizes",
      "content": "## 🎉 WIN ATTRACTIVE PRIZES! 🎉 \n1st prize: $200 CapitaVoucher \n2nd prize: $100 CapitaVoucher\n3rd prize: $50 CapitaVoucher\nConsolation: $40 x 10 CapitaVouchers\nWinners will be contacted via DM through email!",
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    {
      "name": "Kernels Requirements",
      "content": "Tested under Python 3.8, scikit-learn 0.22.2, numpy 1.18.1, pandas 1.0.3, matplotlib 3.1.3, seaborn 0.10.1.",
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