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  "fetched_at": "2026-10-19T20:56:18Z",
  "source_url": "https://www.kaggle.com/competitions/ctrl-shift-intelligence-2k22/data",
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      "content": "## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_sub.csv** - a sample submission file in the correct format\n\n## Columns\n\n*   `userID` -  serial number\n*   `title` - name \n*   `kind` - telecasting\n*   `genre` - genre\n*   `vote` - user voting\n*   `country` - release country \n*   `language` - language\n*   `cast` - actors involved\n*   `director` - director\n*   `composer` - composer\n*   `writter` - writter\n*   `runtime` - screen time\n*   `rating` - rating which will be predicted",
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      "name": "Description",
      "content": "In today’s technology driven world, recommender systems are socially and economically critical for ensuring that individuals can make appropriate choices surrounding the content they engage with on a daily basis. One application where this is especially true surrounds movie content recommendations; where intelligent algorithms can help viewers find great titles from tens of thousands of options.\n\nMovies and webseries data is one of today's popular datasets for OTT Recommendation. Dataset contains multiples columns like title, userID, year, kind, genre, vote, country, language, etc, of rating as the attributes for recommendation . \n\n\n#About\n\nClub of Programmers IIT(BHU) Varanasi is back with Ctrl Shift Intelligence, the Machine Learning event of COPS Week 2022 exclusively for machine learning enthusiasts. In this year's competition, the task will be to construct a recommendation algorithm based on content or collaborative filtering, capable of accurately predicting how a user will rate a movie they have not yet viewed based on their historical preferences.\n\n###Some general guidelines:\n\nThere will be a public leader board and a private leader board.\nAfter the competition the top performers will be asked to submit the code of their machine learning algorithm. Final standing will be evaluated only after the code evaluation.\nUse of any external dataset is not allowed. The participants must only work with the dataset provided.\nNote that predictions have to be made on test data.\nYou can participate in teams having max size of 2 members.",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is Root Mean Square Error. Root Mean Square Error (RMSE) is commonly used in regression analysis and forecasting, and measures the standard deviation of the residuals arising between predicted and actual observed values for a modelling process.\n\n##Submission Format\nFor every author in the dataset, submission files should contain two columns: userID and rating. 'rating' is the predicted rating for a given user-movie pair.\nThe file should contain a header and have the following format:\n\n```\nuserID,rating\n1,1.0\n2,2.0\n3,3.0\n```",
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    {
      "name": "rules",
      "content": "\n - Don't cheat!\n - Apply yourself!\n - Have fun!\n - First year wale cheat kie to tmhara branch change nhi hoga\n - Second year wale cheat kie to tmhara intern nhi lgega\n - Third year wale tum yaha kya kr rhe ho?\n ",
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