# Report Title
### Team Ford


```python
import pandas as pd 
import numpy as np
from scipy.spatial import distance_matrix
import sympy as sym
import seaborn as sns
import matplotlib.pyplot as plt
```

## Abstract
Short summary about the data and what it tells you about your project. 

## Data input
In this section include code that reads in the csv file


```python
data = pd.read_csv('2022_Project_distance_Matrix.csv',header='infer')
data.head()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ARFL</th>
      <th>Argonne</th>
      <th>Boeing</th>
      <th>Delta Dental</th>
      <th>Ford</th>
      <th>Hope Village</th>
      <th>Kellogg's</th>
      <th>Neogen</th>
      <th>Old Nation</th>
      <th>Qside</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>7</td>
      <td>4</td>
      <td>9</td>
      <td>8</td>
      <td>3</td>
      <td>0</td>
      <td>6</td>
      <td>2</td>
      <td>5</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>4</td>
      <td>8</td>
      <td>6</td>
      <td>5</td>
      <td>7</td>
      <td>9</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>2</td>
      <td>1</td>
      <td>6</td>
      <td>0</td>
      <td>8</td>
      <td>5</td>
      <td>4</td>
      <td>7</td>
      <td>9</td>
    </tr>
    <tr>
      <th>3</th>
      <td>8</td>
      <td>7</td>
      <td>3</td>
      <td>9</td>
      <td>4</td>
      <td>5</td>
      <td>0</td>
      <td>6</td>
      <td>1</td>
      <td>2</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0</td>
      <td>2</td>
      <td>1</td>
      <td>8</td>
      <td>7</td>
      <td>3</td>
      <td>9</td>
      <td>6</td>
      <td>4</td>
      <td>5</td>
    </tr>
  </tbody>
</table>
</div>



## Data Cleaning
In this section provide code for converting the raw data into clean and usable data structures (if needed)

## Data Modeling

This section that builds a model.  This doesn't have to be all that advanced. You should probably start with some simple statistical models such as an average distance matrix.


```python
dist_mat = distance_matrix(data.T,data.T,p=2)
fig, ax = plt.subplots(figsize = (15,15))
ax = sns.heatmap(dist_mat,annot=True)
```


```python
np.dot(data.T,data)
```




    array([[ 656,  567,  628,  785,  679,  465,  717,  500,  406,  447],
           [ 567,  791,  740,  848,  805,  598,  798,  550,  493,  515],
           [ 628,  740, 1024,  825,  868,  485,  890,  644,  460,  456],
           [ 785,  848,  825, 1476, 1121, 1017, 1177,  802,  841,  918],
           [ 679,  805,  868, 1121, 1432,  797, 1131,  804,  594,  679],
           [ 465,  598,  485, 1017,  797, 1283, 1106,  660,  988, 1106],
           [ 717,  798,  890, 1177, 1131, 1106, 1649,  904,  921, 1102],
           [ 500,  550,  644,  802,  804,  660,  904,  747,  568,  616],
           [ 406,  493,  460,  841,  594,  988,  921,  568,  930,  909],
           [ 447,  515,  456,  918,  679, 1106, 1102,  616,  909, 1127]],
          dtype=int64)



## Data Visualization

This section make some graphs visualizing your results.  A distance matrix and/or network graph may be cool.  Think though the best way to show what you learned.

## Conclusion

This should be similar to the abstract but with more details. What can you conclude about your project from this data?

5b4225357e164662f39083f2bc128bd26a618888
ipynb
Jupyter Notebook
Ford-Analysis/Survey-Analysis-Nick.ipynb
pathakis/CMSE495_2022_Distance_Measures
dce078764afead5154b313b2607f02de5f0e680e
MIT
Ford-Analysis/Survey-Analysis-Nick.ipynb
pathakis/CMSE495_2022_Distance_Measures
dce078764afead5154b313b2607f02de5f0e680e
MIT
Ford-Analysis/Survey-Analysis-Nick.ipynb
pathakis/CMSE495_2022_Distance_Measures
dce078764afead5154b313b2607f02de5f0e680e
MIT
Qwen/Qwen-72B
1. YES
2. YES