## **Welcome to DMA's Lab 5, the Kaggle Competition!**
In 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!

## **Team Structure**
In 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.

## **The Challenge**
The 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.

In 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.

## **Overview of How Kaggle’s Competitions Work**
- **Join the Competition**
Read about the challenge description, accept the Competition Rules and gain access to the competition dataset.

- **Get to Work**
Download the data, build models on it locally or and generate a prediction file.

- **Make a Submission**
Upload your prediction as a submission on Kaggle and receive an accuracy score.

- **Check the Leaderboard**
See how your model ranks against other Kagglers on our leaderboard.

## What Data Will I Use in This Competition?
In 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`.

The `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”.

The `test.csv` dataset contains similar information but does not disclose the “ground truth” for each passenger. **It’s your job to predict these outcomes**.

Using the patterns you find in the `train.csv` data, predict whether the other 418 passengers on board (found in `test.csv`) survived.

Check 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.

## How to Submit your Prediction to Kaggle
Once you’re ready to make a submission and get on the leaderboard:

- Click on the “Submit Predictions” button
- 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).

## Submission File Format:
You 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.
The file should have exactly 2 columns:

- `PassengerId` (sorted in any order)
- `Survived` (contains your binary predictions: 1 for survived, 0 for deceased)