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  "source_url": "https://www.kaggle.com/competitions/car-becho-paisa-paao/data",
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    {
      "name": "rules",
      "content": "### One account per participant\n \nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n \n### No private sharing outside teams\n \nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n \n### Team Mergers\n \nTeam mergers are not allowed and can be performed by the team leader.\n \n### Submission Limits\n \nYou may submit a maximum of 100 entries per day.\n \nYou may select up to 1 final submissions for judging.\n \n### Competition Timeline\n \nCompetition Timeline dates (including Entry Deadline, Final Submission Deadline, Start Date, and Team Merger Deadline, as applicable) are reflected on the competition’s [Overview > Timeline](https://www.kaggle.com/competitions/playground-series-s4e9/overview/timeline) page.\n \n ",
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      "name": "Evaluation",
      "content": "## Root Mean Squared Error (RMSE)\nMr. Shreyansh keeping everything in a very classic way wants you to have the evaluation metrics as root mean squared error. RMSE is defined as:\n\n$$\n\\textrm{RMSE} =  \\left( \\frac{1}{N} \\sum\\_{i=1}^{N} (y\\_i - \\widehat{y}\\_i)^2 \\right)^{\\frac{1}{2}}\n$$\n\nwhere \\\\( \\widehat{y}\\_i \\\\) is the predicted value and \\\\( y_i \\\\) is the original value for each instance \\\\(i\\\\).\n\n## Submission File\nFor each `id` in the test set, you must predict the `price` of the car. The file should contain a header and have the following format:\n\n    id,price\n    188533,43878.016\n    188534,43878.016\n    188535,43878.016\n    etc.",
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      "name": "Timeline",
      "content": "* **Start Date** - April 18, 2025\n* **Entry Deadline** - Same as the Final Submission Deadline\n* **Team Merger Deadline** - Same as the Final Submission Deadline\n* **Final Submission Deadline** -  September 30, 2024\n",
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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 [Used Car Price Prediction Dataset](https://www.kaggle.com/datasets/taeefnajib/used-car-price-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; `price` is the continuous target\n*   **test.csv** - the test dataset; your objective is to predict the value of `price` for each row\n*   **sample_submission.csv** - a sample submission file in the correct format",
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    {
      "name": "abstract",
      "content": "Mr. Shreyansh a multimillioner of a company which buys the car depending upon some of the features . Being into this buisness for past 20 years and with the increasing age Mr. Shreyansh wants to automate the process of price prediction . He called a group of engineers Aayush , Aryan , Tanishka and Trishul to make the ML model which can predict the car price accurately . Now help these group of engineers and Mr. Shreyansh to do so .",
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