---
title: "Client Report - The War with Star Wars"
subtitle: "Course DS 250"
author: "[STUDENT NAME]"
format:
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    number-sections: false
    html-math-method: katex
    code-fold: true
    code-summary: "Show the code"
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execute: 
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---

```{python}
import pandas as pd 
import numpy as np
from lets_plot import *
# add the additional libraries you need to import for ML here

LetsPlot.setup_html(isolated_frame=True)
```


```{python}
# Learn morea about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html

# Include and execute your code here

# import your data here using pandas and the URL


```

## Elevator pitch
_A SHORT (2-3 SENTENCES) PARAGRAPH THAT `DESCRIBES KEY INSIGHTS` TAKEN FROM METRICS IN THE PROJECT RESULTS THINK TOP OR MOST IMPORTANT RESULTS._ (Note: this is not a summary of the project, but a summary of the results.)

_A Client has requested this analysis and this is your one shot of what you would say to your boss in a 2 min elevator ride before he takes your report and hands it to the client._

## QUESTION|TASK 1

__Shorten the column names and clean them up for easier use with pandas.__ Provide a table or list that exemplifies how you fixed the names. 

_type your results and analysis here_

```{python}
# Include and execute your code here


```


## QUESTION|TASK 2

__Clean and format the data so that it can be used in a machine learning model.__ As you format the data, you should complete each item listed below. In your final report provide example(s) of the reformatted data with a short description of the changes made.  
    a. Filter the dataset to respondents that have seen at least one film  
    a. Create a new column that converts the age ranges to a single number. Drop the age range categorical column  
    a. Create a new column that converts the education groupings to a single number. Drop the school categorical column  
    a. Create a new column that converts the income ranges to a single number. Drop the income range categorical column  
    a. Create your target (also known as “y” or “label”) column based on the new income range column  
    a. One-hot encode all remaining categorical columns   

_type your results and analysis here_

```{python}
# Include and execute your code here


```

```{python}
# Include and execute your code here


```

```{python}
# Include and execute your code here


```

```{python}
# Include and execute your code here


```

```{python}
# Include and execute your code here


```

```{python}
# Include and execute your code here


```

## QUESTION|TASK 3

__Validate that the data provided on GitHub lines up with the article by recreating 2 of the visuals from the article.__  

_type your results and analysis here_

```{python}
# Include and execute your code here

```

```{python}
# Include and execute your code here

```

## QUESTION|TASK 4

__Build a machine learning model that predicts whether a person makes more than $50k. Describe your model and report the accuracy.__ 

_type your results and analysis here_

```{python}
# Include and execute your code here

```

---

## STRETCH QUESTION|TASK 1

__Build a machine learning model that predicts whether a person makes more than $50k. With accuracy of at least 65%. Describe your model and report the accuracy.__

_type your results and analysis here_

```{python}
# Include and execute your code here


```


## STRETCH QUESTION|TASK 2

__Validate the data provided on GitHub lines up with the article by recreating a 3rd visual from the article.__

_type your results and analysis here_

```{python}
# Include and execute your code here


```


## STRETCH QUESTION|TASK 3

__Create a new column that converts the location groupings to a single number. Drop the location categorical column.__  

_type your results and analysis here_

```{python}
# Include and execute your code here


```

---
