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      "content": "# Overview\nThe data has been split into two groups: - training set (`train.csv`) - test set (`test.csv`)\n\nThe training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.\n\nThe test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.\n\nWe also include `gender_submission.csv`, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.\n\n## Data Dictionary\n`Survived` : Survival, 0 = No, 1 = Yes\n\n`Pclass` : Ticket class, 1 = 1st, 2 = 2nd, 3 = 3rd\n\n`Sex` : Sex\n\n`Age`: Age in years\n\n`SibSp`: Number of siblings / spouses aboard the Titanic\n\n`Parch`: Number of parents / children aboard the Titanic\n\n`Ticket`: Ticket number\n\n`Fare`: Passenger fare\n\n`Cabin`: Cabin number\n\n`Embarked`: Part of Embarkation, C = Cherbourg, Q = Queenstown, S = Southampton\n\n#### Variable Notes\n\n`Pclass`: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower\n\n`Age`: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n\n`SibSp`: The number of siblings/spouses\n\n`Parch`: The number of parents/children\n\nSome children travelled only with a nanny, therefore parch=0 for them.",
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      "name": "Description",
      "content": " ## **Welcome to DMA's Lab 5, the Kaggle Competition!**\nIn this lab, you will get a chance to use and apply all the concepts you have learned so far. This is also a very good opportunity to get your feet wet in data science competitions and familiarize yourself with Kaggle!\n\n## **Team Structure**\nIn this lab, you must work in groups, and they can be comprised of 2-5 DMA students. You are permitted to mix-and-match between DATA 144 and EDUC 244 students.\n\n## **The Challenge**\nThe sinking of the Titanic is one of the most infamous shipwrecks in history. On 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. Here is a [link](https://en.wikipedia.org/wiki/Titanic) if you would like to learn more. While 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?**” You will use passenger data to accomplish this, i.e. name, age, gender, socio-economic class, etc. of the passengers.\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\n- **Get to Work**\nDownload the data, build models on it locally or and generate a prediction file.\n\n- **Make a Submission**\nUpload your prediction as a submission on Kaggle and receive an accuracy score.\n\n- **Check the Leaderboard**\nSee how your model ranks against other Kagglers on our leaderboard.\n\n## What Data Will I Use in This Competition?\nIn this competition, you’ll gain access to two similar datasets that include passenger information like name, age, gender, socio-economic class, etc. One dataset is titled `train.csv` and the other is titled `test.csv`.\n\nThe `train.csv` will contain the details of a subset of the passengers on board (891 to be exact) and importantly, will reveal whether they survived or not, also known as the “ground truth”.\n\nThe `test.csv` dataset contains similar information but does not disclose the “ground truth” for each passenger. **It’s your job to predict these outcomes**.\n\nUsing the patterns you find in the `train.csv` data, predict whether the other 418 passengers on board (found in `test.csv`) survived.\n\nCheck out the “Data” tab to explore the datasets even further. Once you feel you’ve created a competitive model, submit your predictions to Kaggle to see where your model stands on our leaderboard against other Kagglers.\n\n## How to Submit your Prediction to Kaggle\nOnce you’re ready to make a submission and get on the leaderboard:\n\n- Click on the “Submit Predictions” button\n- Upload a CSV file in the submission file format. **You’re able to submit 5 submissions to Kaggle each day, so submit wisely!** Each \"day\" will refresh at **5:00pm PT** (Kaggle's rules, not ours).\n\n## Submission File Format:\nYou should submit a `.csv` file with exactly 418 entries plus a header row. Your submission will show an error if you have extra columns (beyond `PassengerId` and `Survived`) or rows.\nThe file should have exactly 2 columns:\n\n- `PassengerId` (sorted in any order)\n- `Survived` (contains your binary predictions: 1 for survived, 0 for deceased)",
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    {
      "name": "rules",
      "content": "Please reference the **Overview** for the Rules and constraints regarding this project.",
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      "name": "Evaluation",
      "content": "## Overarching Goal\nIt is your job to predict if a passenger survived the sinking of the Titanic or not.\nFor each in the test set, you must predict a 0 or 1 value for the variable.\n\n## Metric\nYour score is the percentage of passengers you correctly predict. This is known as accuracy.\n\n## Submission File Format for Predictions to Kaggle\nYou should submit a `.csv` file with exactly 418 entries plus a header row. Your submission will show an error if you have extra columns (beyond `PassengerId` and `Survived`) or rows.\n\nThe file should have exactly 2 columns:\n- `PassengerId` (sorted in any order)\n- `Survived` (contains your binary predictions: 1 for survived, 0 for deceased)\n\nFor example:\n```\nPassengerId, Survived\n892, 0\n893, 1\n894, 0\n...\n```\n\nYou can download an example submission file (gender_submission.csv) on the [Data](https://www.kaggle.com/competitions/dma2022-competition/data) page.\n\n## Evaluation\nThe grading for Lab 5 is out of 20 points: twice the points for a lab twice the length.\n\nThe grading for this lab is comprised of two parts:\n- **due 10/9:** Checkpoint (Lab 5a), 2 pts possible\n- **due 10/16:** Optimization (Lab 5b), 18 pts possible\n\nPlease note that there are two leaderboards, a **public** leaderboard and a **private** leaderboard. You will only have access to the public leaderboard. The public leaderboard is comprised of 40% of the data provided in `test.csv`, while the private leaderboard is comprised of the complement (the other 60%). It is possible for your team to receive a 100% on this lab while only considering the public leaderboard. However, any Extra Credit will rely on your model performing well on the private leaderboard, which you won't have access to.\n\n## Checkpoint (due 10/9)\nIn the **Data** section of the lab, you were introduced to the baseline public score produced by `gender_submission.csv`. **In the first week, your assignment is to beat the public score of that algorithm.** In other words, the first half of this lab will entail the initial construction of your algorithm and your optimization of that algorithm to predict survival better than one that only accounts for gender.\n\n### Checkpoint Scoring\nThe breakdown for the scores you may receive are as follows:\n- **2 pts:** You have submitted predictions by an algorithm that beats the baseline public score.\n- **1 pt:** You have submitted predictions by an algorithm, but it does not beat the baseline public score.\n- **0 pts:** You did not submit anything.\n\n### Checkpoint Submission\nTo submit the checkpoint, you will submit the following:\n- **Kaggle:** Upload the `.csv` file with your predictions and confirm that you appear on the leaderboard.\n- **Gradescope:** Submit a screenshot of your team's name, rank, and % accuracy from the leaderboard to Gradescope. The assignment will be called \"Lab 5a: Baseline Checkpoint\". **Include ALL members of your group in Gradescope.** While one person needs to submit for your whole group, it is vital that you include all members in your group in Gradescope.\n\n## Optimization (due 10/16)\nOnce you have completed the baseline requirement, you will turn to perfecting your model. **In the second week, your assignment is to optimize your model such that you are in the top 70% of either the public or private leaderboard.** In other words, the \"competitive\" aspect of the lab takes place in the second week, and you should dedicate yourself to discovering better ways to refine your model.\n\n### Optimization Scoring\nThe breakdown for the scores you may receive are as follows:\n- **18 pts:** The predictions from your algorithm rank your group in the top 70% of the class, either in the public or private leaderboard.\n- **15 pts:** The predictions from your algorithm do not rank your group in the top 70% of the class, either in the public or private leaderboard, but your predictions do beat the baseline.\n- **TA Discretion:** The predictions from your algorithm do not rank your group in the top 70% of the class, either in the public or private leaderboard, and your predictions do not beat the baseline.\n- **0 pts:** You did not submit anything.\n\nThere will also be an opportunity for **extra credit**:\n- **+4 pts EC:** Your team ranks amongst the top five (rank 1-5) on the private leaderboard, and at least one member of your group presents your group's approach to the project.\n- **+2 pts EC:** Your team ranks amongst the next five (rank 6-10) on the private leaderboard, and at least one member of your group presents your group's approach to the project.\n\n### Optimization Submission\nTo submit the optimization part, you will submit the following:\n- **Kaggle:** Upload the `.csv` file with your predictions and confirm that you appear on the leaderboard. Your rank on the public and private leaderboards will be accessed by the TA team for grading.\n- **Gradescope:** Upload all files you used for your algorithm to Gradescope, ideally in a `.zip` file. The assignment will be called \"Lab 5b: Code for Kaggle Project\". **Include ALL members of your group in Gradescope.** While one person needs to submit for your whole group, it is vital that you include all members in your group in Gradescope. This is also a mandatory prerequisite to be considered for the extra credit.",
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      "name": "Reminders & Tips",
      "content": "## Reminders\n- You need to submit as a team. \n- Make sure your submission has 418 rows.\n- You need to beat the Baseline Prediction to get credit.\n- Each team is allowed 5 submissions per day. Days refresh at **5:00pm PT** (Kaggle's rules, not ours).\n- You have 2 weeks to complete the lab but must submit at least 1 prediction and finalize group membership before Wednesday, October 9th at 12:00, to receive full credit. Teams without a submission will receive a 0 on Lab 5a.\n- Please note that DSP accommodations **cannot** be applied to this lab, as this is a synchronous competition that works actively on a timeline.\n- See [Evaluation](www.kaggle.com/competitions/dma-24-kaggle-competition/overview/evaluation) for grading specifics. \n\n\n## Tips\n- Make sure to do your research!\n- A helpful website for collaboratively working on Notebooks together is [Deepnote](https://deepnote.com/). \n- Start working early so you have as much time as possible to develop your approach!\n- Good luck!",
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