<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>Colour</th>
      <th>Type</th>
      <th>Origin</th>
      <th>Stolen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>red</td>
      <td>sports</td>
      <td>domestic</td>
      <td>yes</td>
    </tr>
    <tr>
      <th>1</th>
      <td>red</td>
      <td>sports</td>
      <td>domestic</td>
      <td>no</td>
    </tr>
    <tr>
      <th>2</th>
      <td>red</td>
      <td>sports</td>
      <td>domestic</td>
      <td>yes</td>
    </tr>
    <tr>
      <th>3</th>
      <td>yellow</td>
      <td>sports</td>
      <td>domestic</td>
      <td>no</td>
    </tr>
    <tr>
      <th>4</th>
      <td>yellow</td>
      <td>sports</td>
      <td>imported</td>
      <td>yes</td>
    </tr>
  </tbody>
</table>
</div>

   Colour    Type    Origin Stolen
0     red  sports  domestic    yes
1     red  sports  domestic     no
2     red  sports  domestic    yes
3  yellow  sports  domestic     no
4  yellow  sports  imported    yes

(array([0]), array([[0.66666667, 0.33333333]]))

Notebook

3. Use naive Bayes algorithm to determine whether a red domestic SUV car is a stolen car or not using the following data: