Welcome to the **DSI-TDA-02 Project 2**!! 

Project 2 is a **Regression Challenge** with the aim of having you review the regression concepts you learned in class while having you struggle with real (large and messy) data. 

In this challenge, you will use *Ames housing data* to create a fine regression model that predicts the price of houses in Ames. The data has a lot of features so your data science decision-making skills will be put to the test.

## Goal
- Be a thoughtful data scientist
- Make sound decisions
- Be an awesome teammate
- Compete with grace and dignity

## Data
- `train.csv` : training data that includes the column `SalePrice`
- `test.csv` : testing data for predicting price, without the column `SalePrice`
- `sample_submission.csv` : an example of correctly formatted submission for this challenge
- `sample_code.ipynb` : sample code for creating `submission.csv`

## Metric

The metric used to evaluate your model is **Root Mean Squared Error (RMSE)**. It is automatically calculated and shown in Leaderboard after submission.

$$ \text{RMSE} = \sqrt{\frac{1}{n}\sum^{n} (y-\hat{y})^2} $$

where *ŷ* is your prediction, *y* is the actual value

You can calculate the RMSE score like this...
~~~
from sklearn from metrics
metrics.mean_squared_error(y_true, y_pred, squared=False)
~~~

## Submission

The name of the file to submit must be `submission.csv`,  with a header `Order` `SalePrice`.

- column `Order` has the same values as `test.csv`
- column `SalePrice` is your predictions

`submission.csv`
~~~
Order,SalePrice
2127,100
193,100
2407,100
46,100
...
~~~

If you don't know how to format like this, refer to `sample_code.ipynb`

## Presentation

After submission, you'll prepare a presentation deck (slides). This deck must contain:

- Explanation of your final model
- What was the process of getting to that final model
- Which features you used and why you used those features
- How you split the data into train and test
- How you evaluated your model performance
- and so on and so on.