# Project Work

**PUT ALL TEAM MEMBERS' NAMES HERE**

```{r}
# Load data and required packages
```

```{r}
# Any code to clean the data
```

## Initial investigation 1: ignoring nonlinearity (for now)

Use ordinary least squares (OLS) regression and LASSO to build initial models for your quantitative outcome as a function of the predictors of interest. (As part of data cleaning, exclude any variables that you don't want to consider as predictors.)

- These models should not include any transformations to deal with nonlinearity. You'll explore this in the next investigation.

```{r}
# Your code
```

**PUT ANY RELEVANT TEXT/RESPONSES/INTERPRETATIONS HERE**


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Estimate test performance of the models from these different methods. Report and interpret (with units) these estimates along with a measure of uncertainty in the estimate.

- Compare estimated test performance across methods. Which method(s) might you prefer?


```{r}
# Your code
```

**PUT ANY RELEVANT TEXT/RESPONSES/INTERPRETATIONS HERE**




## Investigation 2: Accounting for nonlinearity

Update your LASSO model to use natural splines for the quantitative predictors. It's recommended to use few knots (e.g., 2 knots = 3 degrees of freedom).

```{r}
# Your code
```

**PUT ANY RELEVANT TEXT/RESPONSES/INTERPRETATIONS HERE**

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Fit a GAM using LOESS terms using the set of variables deemed to be most relevant based on your investigations so far.

- How does test performance of the GAM compare to other models you explored?
- Do you gain any insights from the GAM output plots for each predictor?

```{r}
# Your code
```

**PUT ANY RELEVANT TEXT/RESPONSES/INTERPRETATIONS HERE**


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## Summarize investigations

Decide on an overall best model based on your investigations so far. To do this, make clear your analysis goals. Predictive accuracy? Interpretability? A combination of both?


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## Societal impact

Are there any harms that may come from your analyses and/or how the data were collected? What cautions do you want to keep in mind when communicating your work?



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# Ethics in ML


**Ethics: (REQUIRED)** Read the article [Getting Past Identity to What You Really Want](https://weallcount.com/2021/02/19/getting-past-identity-to-what-you-really-want/). Write a short (roughly 250 words), thoughtful response about the ideas that the article brings forth. What skills do you think are essential for the leaders and data analysts of organizations to have to handle these issues with care?

**Response:** YOUR RESPONSE


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