---
title: "Client Report - Recoding Range Variables: Smarter than Dummy Encoding"
subtitle: "Unit 5 Task 2"
author: "[STUDENT NAME]"
format:
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    title-block-banner: true
    toc: true
    toc-depth: 3
    toc-location: body
    number-sections: false
    html-math-method: katex
    code-fold: true
    code-summary: "Show the code"
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        caption: See code
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}
# import your data here using pandas and the URL


```

## QUESTION 1

1. __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 an excerpt of the reformatted data with a short description of the changes made.  

    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. Encode favorability ratings as a number. Remove the favorability categorical columns.
    a. One-hot encode all remaining categorical columns

_type your write-up and analysis here_

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


```

