---
title: "Client Report - Can You Predict That?"
subtitle: "Course DS 250"
author: "[STUDENT NAME]"
format:
  html:
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    page-layout: full
    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"
    code-overflow: wrap
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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

__Create 2-3 charts that evaluate potential relationships between the home variables and `before1980`.__ Explain what you learn from the charts that could help a machine learning algorithm. 

_type your results and analysis here_

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


```


## QUESTION|TASK 2

__Build a classification model labeling houses as being built “before 1980” or “during or after 1980”.__ Your goal is to reach or exceed 90% accuracy. Explain your final model choice (algorithm, tuning parameters, etc) and describe what other models you tried.  

_type your results and analysis here_

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


```


## QUESTION|TASK 3

__Justify your classification model by discussing the most important features selected by your model.__ This discussion should include a feature importance chart and a description of the features. 

_type your results and analysis here_

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

```


## QUESTION|TASK 4

__Describe the quality of your classification model using 2-3 different evaluation metrics.__ You also need to explain how to interpret each of the evaluation metrics you use.  

_type your results and analysis here_

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

```

---

## STRETCH QUESTION|TASK 1

__Repeat the classification model using 3 different algorithms.__ Display their Feature Importance, and Decision Matrix. Explian the differences between the models and which one you would recommend to the Client.   

_type your results and analysis here_

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


```


## STRETCH QUESTION|TASK 2

__Join the `dwellings_neighborhoods_ml.csv` data to the `dwelling_ml.csv` on the `parcel` column to create a new dataset. Duplicate the code for the stretch question above and update it to use this data.__ Explain the differences and if this changes the model you recomend to the Client.   

_type your results and analysis here_

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


```


## STRETCH QUESTION|TASK 3

__Can you build a model that predicts the year a house was built?__ Explain the model and the evaluation metrics you would use to determine if the model is good.  

_type your results and analysis here_

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


```

---
