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250_Projects/project3.html

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   <li><a href="#stretch-questiontask-2" id="toc-stretch-questiontask-2">STRETCH QUESTION|TASK 2</a></li>
   </ul>
 <div class="toc-actions"><ul><li><a href="https://github.com/byuidatascience/course_work_portfolio/issues/new" class="toc-action"><i class="bi bi-github"></i>Report an issue</a></li></ul></div></nav>
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 <span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>LetsPlot.setup_html(isolated_frame<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Learn morea about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html</span></span>
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 <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <p><strong>Write an SQL query to create a new dataframe about baseball players who attended BYU-Idaho. The new table should contain five columns: playerID, schoolID, salary, and the yearID/teamID associated with each salary. Order the table by salary (highest to lowest) and print out the table in your report.</strong></p>
 <p><em>type your results and analysis here</em></p>
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 a. Use the same query as above, but only include players with at least 10 at bats that year. Print the top 5 results.<br>
 a. Now calculate the batting average for players over their entire careers (all years combined). Only include players with at least 100 at bats, and print the top 5 results.</p>
 <p><em>type your results and analysis here</em></p>
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 <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <p><strong>Pick any two baseball teams and compare them using a metric of your choice (average salary, home runs, number of wins, etc). Write an SQL query to get the data you need, then make a graph using Lets-Plot to visualize the comparison. What do you learn?</strong></p>
 <p><em>type your results and analysis here</em></p>
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 * Order the table by average salary in descending order.
 * Print the top 10 rows of this summary table.  </code></pre>
 <p><em>type your results and analysis here</em></p>
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 * The career_length should be calculated as the difference between the maximum and minimum yearID for each player. </code></pre>
 <p><em>type your results and analysis here</em></p>
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250_Projects/project4.html

@@ -4878,7 +4878,7 @@ <h2 id="toc-title">On this page</h2>
   <li><a href="#stretch-questiontask-3" id="toc-stretch-questiontask-3">STRETCH QUESTION|TASK 3</a></li>
   </ul>
 <div class="toc-actions"><ul><li><a href="https://github.com/byuidatascience/course_work_portfolio/issues/new" class="toc-action"><i class="bi bi-github"></i>Report an issue</a></li></ul></div></nav>
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 <span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>LetsPlot.setup_html(isolated_frame<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <p><strong>Create 2-3 charts that evaluate potential relationships between the home variables and <code>before1980</code>.</strong> Explain what you learn from the charts that could help a machine learning algorithm.</p>
 <p><em>type your results and analysis here</em></p>
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 <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <p><strong>Build a classification model labeling houses as being built “before 1980” or “during or after 1980”.</strong> 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.</p>
 <p><em>type your results and analysis here</em></p>
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 <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <p><strong>Justify your classification model by discussing the most important features selected by your model.</strong> This discussion should include a feature importance chart and a description of the features.</p>
 <p><em>type your results and analysis here</em></p>
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 <h2 class="anchored" data-anchor-id="questiontask-4">QUESTION|TASK 4</h2>
 <p><strong>Describe the quality of your classification model using 2-3 different evaluation metrics.</strong> You also need to explain how to interpret each of the evaluation metrics you use.</p>
 <p><em>type your results and analysis here</em></p>
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 <h2 class="anchored" data-anchor-id="stretch-questiontask-1">STRETCH QUESTION|TASK 1</h2>
 <p><strong>Repeat the classification model using 3 different algorithms.</strong> Display their Feature Importance, and Decision Matrix. Explian the differences between the models and which one you would recommend to the Client.</p>
 <p><em>type your results and analysis here</em></p>
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 <h2 class="anchored" data-anchor-id="stretch-questiontask-2">STRETCH QUESTION|TASK 2</h2>
 <p><strong>Join the <code>dwellings_neighborhoods_ml.csv</code> data to the <code>dwelling_ml.csv</code> on the <code>parcel</code> column to create a new dataset. Duplicate the code for the stretch question above and update it to use this data.</strong> Explain the differences and if this changes the model you recomend to the Client.</p>
 <p><em>type your results and analysis here</em></p>
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 <h2 class="anchored" data-anchor-id="stretch-questiontask-3">STRETCH QUESTION|TASK 3</h2>
 <p><strong>Can you build a model that predicts the year a house was built?</strong> Explain the model and the evaluation metrics you would use to determine if the model is good.</p>
 <p><em>type your results and analysis here</em></p>
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