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  "source_url": "https://www.kaggle.com/competitions/data-vision-2025/data",
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      "name": "rules",
      "content": "**Data Vision 2025 – Competition Rules**  \n\n**Eligibility:**  \n- Open to all students of SR University.  \n- Participants can compete individually or in teams (maximum of 3 members).  \n\n**Competition Structure:**  \n1. **Problem Statement Release:** Participants will receive a real-world data science challenge.  \n2. **Data Preprocessing & Model Building:** Participants must clean, analyze, and build predictive models using provided datasets.  \n3. **Submission Deadline:** All solutions, code, and reports must be submitted before the specified deadline.  \n\n**Evaluation Criteria:**  \nWinners will be evaluated based on the Leaderboard score.\n\n**Prizes & Recognition:**  \n- Winners will receive cash prizes, certificates .\n\n",
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      "name": "Evaluation",
      "content": "Submissions are evaluated on [area under the ROC curve](http://en.wikipedia.org/wiki/Receiver_operating_characteristic) between the predicted probability and the observed target.\n\n\n## Submission File\nFor each `id` in the test set, you must predict the probability for the target variable `Exited`. The file should contain a header and have the following format:\n\n    id,Exited\n    165034,0.9\n    165035,0.1\n    165036,0.5\n    etc.",
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      "content": "* **Start Date** - Feb 12, 2025\n* **Entry Deadline** - Same as the Final Submission Deadline\n* **Team Merger Deadline** - Same as the Final Submission Deadline\n* **Final Submission Deadline** -  Feb 21, 2025\n\nThe competition organizers reserve the right to update the contest timeline if they deem it necessary.",
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      "name": "data-description",
      "content": "The dataset for this competition (both train and test) was generated from a deep learning model trained on the [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shubhammeshram579/bank-customer-churn-prediction) dataset. Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.\n\n## Files\n\n*   **train.csv** - the training dataset; `Exited` is the binary target\n*   **test.csv** - the test dataset; your objective is to predict the probability of `Exited`\n*   **sample_submission.csv** - a sample submission file in the correct format",
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    {
      "name": "About Data Vision 2025",
      "content": "### Welcome to Data Vision 2025 – A Competition by the Data Science Club @ SR University  \n\nThe goal of **Data Vision 2025** is to provide data science enthusiasts with hands-on challenges to enhance their skills in machine learning and data science. This competition is designed for students and practitioners to experiment with real-world data scenarios, refine their analytical abilities, and apply various modeling and feature engineering techniques.  \n\nThe competition consists of two exciting parts:  \n📊 **Visualize It** – Showcase your data visualization skills and uncover hidden insights.  \n🔮 **Predict It** – Apply machine learning models to make accurate predictions.  \n\n### Real-World Inspired Datasets  \n\nTo ensure a dynamic and engaging experience, the competition features **real-world inspired datasets** that allow participants to explore data patterns, build models, and iterate quickly. This is a unique opportunity to **test your skills, learn from peers, and improve your problem-solving abilities** in a competitive yet collaborative environment.  \n\nWe encourage all participants to experiment, **play with data, and push their boundaries!** Your journey in data science starts here good luck and happy analyzing! 🚀",
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    {
      "name": "abstract",
      "content": "**Welcome, Data Science Enthusiasts!**\n\nThe **Data Science Club at SR University** is thrilled to present the **Data Vision 2025 Competition**. This is your opportunity to showcase your expertise in data science methodologies, challenge yourself, and gauge your current skill level. Dive into the data, unleash your creativity, and demonstrate your prowess. Let the data games begin!\n\n**Your Goal:** for this part of competition your task is to predict whether a customer continues with their account or closes it (e.g., churns). Good luck!",
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