{
  "fetched_at": "2026-10-20T08:31:31Z",
  "source_url": "https://www.kaggle.com/competitions/dataplayground/data",
  "pages": [
    {
      "name": "data-description",
      "content": "**Dataset Titanic untuk Percobaan**\n\n###Overview\nDataset yang tersedia kini merupakan mockup untuk menguji dan mempelajari bagaimana caranya dataset di desain untuk Data Playground Series ini. Deskripsi data untuk kasus yang sebenarnya akan disusun ulang sesuai dengan kondisi data yang sebenarnya.\n\n###Data Dictionary\n\n| Variabel |Defenisi | Key |\n| -- | -- | -- |\n| survival | Survive | 0=No, 1=Yes\n| pclass | Ticket Class | 1 = 1st, 2 = 2nd, 3 = 3rd\n| sex | Gender |\n\n###Variable Notes\n**pclass**: A proxy for socio-economic status (SES)\n1st = Upper\n2nd = Middle\n3rd = Lower",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "**💥Hai teman-teman DSI, Anda berada di Data Playground💥** \n\nData Playground merupakan series studi kasus data untuk mengasah analisa, visualisasi dan prespektif data. Hasil submisi teman-teman DSI akan melewati proses fiscreening para ahli data di DSI. Seluruh submisi akan mendapatkan penilaian tersendir dan teman DSI yang beruntung akan mendapatkan eksposure dari tim DSI.\n\nCara ikutan Data Playground series sangat sederhana berikan analisa, visualisasi, prediksi atau kesimpulan apapun menurut teman-teman DSI. \n\nData Playground terdiri dari beberapa series studi kasus, kerjakan studi kasus yang kamu suka dan komitmen untuk terus mengembangkan skill data mu bersama DSI.\n\n👋🛳️ Ahoy, welcome to Kaggle! You’re in the right place.\nThis is the legendary Titanic ML competition – the best, first challenge for you to dive into ML competitions and familiarize yourself with how the Kaggle platform works.\n\nThe competition is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.\n\nRead on or watch the video below to explore more details. Once you’re ready to start competing, click on the \"Join Competition button to create an account and gain access to the competition data. Then check out Alexis Cook’s Titanic Tutorial that walks you through step by step how to make your first submission!\n\n###The Challenge\nThe sinking of the Titanic is one of the most infamous shipwrecks in history.\n\nOn April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.\n\nWhile there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.\n\nIn this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).\n\n###Overview of How Kaggle’s Competitions Work\n**Join the Competition**\nRead about the challenge description, accept the Competition Rules and gain access to the competition dataset.\n**Get to Work**\nDownload the data, build models on it locally or on Kaggle Notebooks (our no-setup, customizable Jupyter Notebooks environment with free GPUs) and generate a prediction file.\n**Make a Submission**\nUpload your prediction as a submission on Kaggle and receive an accuracy score.\n**Check the Leaderboard**\nSee how your model ranks against other Kagglers on our leaderboard.\n**Improve Your Score**\nCheck out the discussion forum to find lots of tutorials and insights from other competitors.\n\n###Acknowledgements\n\nTim DSI menyampaikan penghargaan dan terima kasih yang sebesar-bearnya kepada PT. X sebagai penyedia usecase data ini. ",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "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###Satu Akun untuk Setiap Peserta\n\n###Analisa Dikerjakan Sendiri\n\n###Timeline Kegiatan\n\n###Website DSI Playground\nhttp://www.kaggle.com/competitions/dsi-playground\n\n### Akses Data dan Kegunaan\nHanya untuk kepentingan akademik dan DSI Playground, data ini tidak untuk dikomersialisasikan dengan tujuan apapun. Pihak yang melakukan penggandaan tanpa ijin tertulis dari DSI dapat dikenakan hukuman sesuai ketentuan perundang-undangan yang berlaku.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Waktu Pelaksanaan",
      "content": "📢 Join \"**Data Playground**\" Competition on Kaggle and Unleash Your Analytical Skills! 🎉\n\n🗓️ Mark your calendars! The highly anticipated \"Data Playground\" competition is set to kick off on July 15, 2023, and will run for two exhilarating months! 🚀\n\n🌐 Are you ready to dive into the world of data analysis and showcase your talent? This competition is your chance to harness the power of datasets and unveil remarkable insights. 📊💡\n\n🤔 How does it work? It's simple! Participants will have access to diverse and intriguing datasets, handpicked to challenge your analytical prowess. Engage in rigorous analysis, employ cutting-edge techniques, and develop innovative models to tackle real-world problems. 📈🔬\n\n⚡️ With \"Data Playground,\" the possibilities are endless. Explore, experiment, and push the boundaries of data science to uncover hidden patterns and valuable information. The competition encourages creativity, collaboration, and out-of-the-box thinking. 🤝💡\n\n💼 Aspire to be recognized for your skills and expertise? \"Data Playground\" offers a platform to showcase your abilities to a vibrant community of data enthusiasts, experts, and industry professionals. Build your reputation and establish connections that can shape your future career in the data science realm. 🏆🔍\n\n🌟 Don't miss this incredible opportunity to embark on a thrilling data journey! Join \"Data Playground\" on Kaggle, starting July 15, 2023. Sharpen your skills, compete with the best, and make a lasting impact in the world of data analysis. 🎯💻\n\n🔗 For more information and to participate, visit the competition page on Kaggle. Get ready to unleash your analytical genius and let the \"Data Playground\" competition begin! 🚀💪\n\n#DataPlayground #KaggleCompetition #DataScience #Analytics #UnleashYourPotential",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Petunjuk Teknis",
      "content": "🔎 Our esteemed panel of judges will meticulously assess each participant's submissions based on the following key criteria:\n\n1️⃣ Accuracy: How well does your solution predict the target variable? The closer your predictions align with the actual values, the higher your accuracy score will be. 📈📉\n\n2️⃣ Creativity: We value innovative approaches! Did you explore unique methodologies, novel feature engineering techniques, or inventive modeling strategies? Showcase your creativity and stand out from the competition. 🌟💡\n\n3️⃣ Robustness: Does your solution demonstrate stability and reliability? A robust model should perform consistently across different datasets and show resilience to outliers and noise. 🧱💪\n\n4️⃣ Interpretability: Can you explain the reasoning behind your choices? Clear documentation and insightful explanations of your model's decisions will earn you extra points. 📝🧐\n\n5️⃣ Efficiency: How well does your solution scale? Consider the computational resources required and the time taken to generate results. Optimized models that strike a balance between accuracy and efficiency will be highly regarded. ⏱️⚡\n\n🔍 The judging process will be conducted with utmost fairness and transparency. Each submission will be rigorously evaluated, and the top performers will be identified based on their overall scores across these evaluation criteria.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Acknowledgement",
      "content": "🌟 So, keep refining your models, fine-tuning your algorithms, and impressing us with your data analysis prowess! The grand prize awaits the individual or team that demonstrates exceptional performance in the \"Data Playground\" competition. 🎉🚀\n\nGood luck to all participants, and may the best data scientist win! 🏆💻🔬\n\n#DataPlayground #KaggleCompetition #DataScience #Analytics #CompetitionEvaluations",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluasi",
      "content": "**Bagaimana cara evaluasinya:**\n\nSubmission Score: Setiap peserta akan mengirimkan prediksi mereka menggunakan dataset validasi yang dirahasiakan. Kemudian, skor submission akan dihitung berdasarkan tingkat akurasi prediksi terhadap nilai sebenarnya.\n\nLeaderboard: Peserta dapat melihat peringkat mereka di leaderboard, yang akan menampilkan skor submission tertinggi hingga terendah. Leaderboard ini memberikan gambaran komparatif mengenai kinerja peserta dan memotivasi peserta lain untuk meningkatkan performa mereka.\n\nEvaluation Metrics: Terdapat beberapa metrik evaluasi yang relevan dengan jenis masalah yang ada dalam kompetisi. Metrik ini dapat berupa rasio akurasi, error rate, atau metrik kustom sesuai dengan tujuan kompetisi.\n\nPublic dan Private Score: Kaggle biasanya membagi dataset menjadi dua bagian: public dan private. Skor publik digunakan untuk menampilkan peringkat peserta di leaderboard selama kompetisi berlangsung. Namun, skor pribadi (private score) baru terungkap setelah kompetisi berakhir. Tujuannya adalah untuk mencegah overfitting pada dataset publik dan menguji kehandalan solusi pada data baru.\n\nEvaluation Guidelines: Setiap kompetisi di Kaggle biasanya memiliki pedoman evaluasi yang jelas. Peserta harus memperhatikan panduan tersebut untuk memastikan pemahaman yang benar tentang cara evaluasi dilakukan dan mematuhi aturan kompetisi.\n\nPenting untuk diingat bahwa setiap kompetisi Kaggle memiliki aturan dan metrik evaluasi yang unik. Pastikan untuk membaca dengan cermat instruksi dan pedoman yang disediakan oleh penyelenggara kompetisi \"Data Playground\" agar dapat memahami evaluasi secara menyeluruh.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}