From b0b1c645adbb5f5263b00b6fe012f08f617169c2 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Tue, 2 Apr 2024 00:26:52 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                     |  2 +-
 Projects/project1.html        | 36 +++++++++++++--------------
 Projects/project2.html        | 38 ++++++++++++++---------------
 Projects/project3.html        | 26 ++++++++++----------
 Projects/project4.html        | 46 +++++++++++++++++------------------
 Projects/project5.html        | 42 ++++++++++++++++----------------
 Templates/DS250_Template.html | 14 +++++------
 projects.html                 | 14 +++++------
 search.json                   | 14 ++++++-----
 sitemap.xml                   | 32 ++++++++++++------------
 10 files changed, 133 insertions(+), 131 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 76c7fb0..44209d0 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-e6969d9b
\ No newline at end of file
+53c627bd
\ No newline at end of file
diff --git a/Projects/project1.html b/Projects/project1.html
index 54dfe76..112be74 100644
--- a/Projects/project1.html
+++ b/Projects/project1.html
@@ -4799,7 +4799,7 @@
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../projects.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">DS250 Projects</span></a>
+ <span class="menu-text"></span></a>
   </div>
 </li>
         <li class="px-0"><hr class="sidebar-divider hi "></li>
@@ -4810,7 +4810,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis the popularity of baby names over the years.</span>
+ <span class="menu-text">What’s in a name?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4819,7 +4819,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores airplane delays. It also involves a little data cleaning.</span>
+ <span class="menu-text">TLate flights and missing data (JSON files)</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4828,7 +4828,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores trending in baseball. It also uses JSON and sqlite as tool to complete the project.</span>
+ <span class="menu-text">TFinding relationships in baseball.</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4837,7 +4837,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project uses machine learning to predict if a house was built before 1980.</span>
+ <span class="menu-text">Can you predict that?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4846,7 +4846,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis data about star wars fans. It mostly focuses on data cleaning, although it dose involve some machine learning and visualizations.</span>
+ <span class="menu-text">The war with Star Wars.</span>
   </li>
     </ul>
     </div>
@@ -4887,7 +4887,7 @@ <h2 class="anchored" data-anchor-id="elevator-pitch">Elevator pitch</h2>
 <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <p><strong>How does your name at your birth year compare to its use historically?</strong></p>
 <p>My name, Bethany was most popular from 1980 to 2000. Before and after that there were only a couple hundred people were given that name. However, at its height about 2,000 babies were given that name. I was born in 2004, right when the popularity of the name was dropping off.</p>
-<div id="8846e0f7" class="cell" data-execution_count="3">
+<div id="cfa42aa9" class="cell" data-execution_count="3">
 <details class="code-fold">
 <summary>Show the code</summary>
 <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="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -4915,9 +4915,9 @@ <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a>bethany_plot.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
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@@ -4951,7 +4951,7 @@ <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <p><strong>If you talked to someone named Brittany on the phone, what is your guess of his or her age? What ages would you not guess?</strong></p>
 <p>I would guess based on probability that someone named Brittany would be born between 1985 and 1995, which would mean that as of 2024 they would be between the ages of 39 and 29. Before these years there were only about 500 babies given that name per year and after those years there were only about a thousand babies given that name per year. This may seem like a lot, but between 1985 and 1995 it was more like 30,000 per year.</p>
-<div id="893a3f83" class="cell" data-execution_count="4">
+<div id="9c4778ec" class="cell" data-execution_count="4">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -4976,9 +4976,9 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a>Brittany_plot.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -5013,7 +5013,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <p><strong>Mary, Martha, Peter, and Paul are all Christian names. From 1920 - 2000, compare the name usage of each of the four names. What trends do you notice?</strong></p>
 <p>The most apparent trend is that the popularity of all of these biblical names went down substantially in the 1970s. Another notable trend is that all of these names were most popular in the 1950s. Of these names Mary was the most popular at about 54,000 at its height of popularity, followed shortly by Paul, with about 25,000 at its height. However, both Martha and Peter were never as popular at about 11,000 at their height.</p>
-<div id="8d6d3f4c" class="cell" data-execution_count="5">
+<div id="72512c3e" class="cell" data-execution_count="5">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -5045,9 +5045,9 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <span id="cb4-27"><a href="#cb4-27" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -5083,7 +5083,7 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <h2 class="anchored" data-anchor-id="questiontask-4">QUESTION|TASK 4</h2>
 <p><strong>Think of a unique name from a famous movie. Plot the usage of that name and see how changes line up with the movie release. Does it look like the movie had an effect on usage?</strong></p>
 <p>It is hard to say if the movie had an effect on the name’s popularity. The popularity was already going up when the movie came out, but the overall trend of the name being more popular did stay higher. However, in 1997, the year average age of the children who watched that show when it first came out were having children there first children, it was a little lower than before the movie came out. However, in the early 2000s, it picked up significantly. It is hard to say without more information if the movie caused this spick or if it was something else.</p>
-<div id="7f2a6741" class="cell" data-execution_count="6">
+<div id="c828f188" class="cell" data-execution_count="6">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -5110,9 +5110,9 @@ <h2 class="anchored" data-anchor-id="questiontask-4">QUESTION|TASK 4</h2>
 <span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a>Elliott_plot.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
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diff --git a/Projects/project2.html b/Projects/project2.html
index 527d9ea..0896c0e 100644
--- a/Projects/project2.html
+++ b/Projects/project2.html
@@ -4799,7 +4799,7 @@
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../projects.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">DS250 Projects</span></a>
+ <span class="menu-text"></span></a>
   </div>
 </li>
         <li class="px-0"><hr class="sidebar-divider hi "></li>
@@ -4810,7 +4810,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis the popularity of baby names over the years.</span>
+ <span class="menu-text">What’s in a name?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4819,7 +4819,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores airplane delays. It also involves a little data cleaning.</span>
+ <span class="menu-text">TLate flights and missing data (JSON files)</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4828,7 +4828,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores trending in baseball. It also uses JSON and sqlite as tool to complete the project.</span>
+ <span class="menu-text">TFinding relationships in baseball.</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4837,7 +4837,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project uses machine learning to predict if a house was built before 1980.</span>
+ <span class="menu-text">Can you predict that?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4846,7 +4846,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis data about star wars fans. It mostly focuses on data cleaning, although it dose involve some machine learning and visualizations.</span>
+ <span class="menu-text">The war with Star Wars.</span>
   </li>
     </ul>
     </div>
@@ -4888,7 +4888,7 @@ <h2 class="anchored" data-anchor-id="elevator-pitch">Elevator pitch</h2>
 <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <p>Fix all of the varied missing data types in the data to be consistent (all missing values should be displayed as “NaN”). In your report include one record example (one row) from your new data, in the raw JSON format. Your example should display the “NaN” for at least one missing value.</p>
 <p>The data was corrected, per the specifications of the task.</p>
-<div id="838fb77f" class="cell" data-execution_count="3">
+<div id="3d731c40" class="cell" data-execution_count="3">
 <details class="code-fold">
 <summary>Show the code</summary>
 <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">#replaces missing or &quot;bad&quot; data with &quot;good&quot; data.</span></span>
@@ -5010,7 +5010,7 @@ <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <p>Which airport has the worst delays? Discuss the metric you chose, and why you chose it to determine the “worst” airport. Your answer should include a summary table that lists (for each airport) the total number of flights, total number of delayed flights, proportion of delayed flights, and average delay time in hours.</p>
 <p>From my analysis, the Chicago airport has the worst delays. It on average has the worst delays as far as average minutes delayed. It is about second when it is fairly comparable with the Atlanta airport when you compare the total number of flights to the number of delays no matter how long. Adding these two variables together clearly shows that the Chicago airport is the worst when it comes to delays.</p>
-<div id="e2b30f3d" class="cell" data-execution_count="4">
+<div id="49d3a112" class="cell" data-execution_count="4">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># mean of num_of_delays_total: 3437</span></span>
@@ -5053,9 +5053,9 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
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@@ -5125,7 +5125,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <p>What is the best month to fly if you want to avoid delays of any length? Discuss the metric you chose and why you chose it to calculate your answer. Include one chart to help support your answer, with the x-axis ordered by month. (To answer this question, you will need to remove any rows that are missing the Month variable.)</p>
 <p>The best month to travel if you want no delays is November, closely followed by September. To figure this out I found the average number of carrier delays compared it to each month. Finding the average for each month I found makes it much clearer and easier to see the full picture of what is happening. This also helps with reader bias when they see outliers.</p>
-<div id="6c9983bf" class="cell" data-execution_count="5">
+<div id="a2fd8234" class="cell" data-execution_count="5">
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 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Replace &#39;n/a&#39; with NaN</span></span>
@@ -5159,9 +5159,9 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <span id="cb4-29"><a href="#cb4-29" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -5200,7 +5200,7 @@ <h2 class="anchored" data-anchor-id="questiontask-4">QUESTION|TASK 4</h2>
 
 From April to August, 40% of delayed flights in the NAS category are due to weather. The rest of the months, the proportion rises to 65%.</code></pre>
 <p>All data has been corrected according to the desired rules given.</p>
-<div id="d8f42f73" class="cell" data-execution_count="6">
+<div id="af6eba09" class="cell" data-execution_count="6">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co">#cleans up data</span></span>
@@ -5369,7 +5369,7 @@ <h2 class="anchored" data-anchor-id="questiontask-4">QUESTION|TASK 4</h2>
 <h2 class="anchored" data-anchor-id="questiontask-5">QUESTION|TASK 5</h2>
 <p>Using the new weather variable calculated above, create a barplot showing the proportion of all flights that are delayed by weather at each airport. Discuss what you learn from this graph.</p>
 <p>This graph shows that overall, not many of the flights are delayed by weather. The highest percentage is SFO at a little under 0.03% of their flights are delayed by weather, and ATL as low as 0.018%. Overall weather doesn’t effect plane travel in the grand scheme of things.</p>
-<div id="c92da9ad" class="cell" data-execution_count="7">
+<div id="b2575564" class="cell" data-execution_count="7">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>lookup <span class="op">=</span> {</span>
@@ -5414,9 +5414,9 @@ <h2 class="anchored" data-anchor-id="questiontask-5">QUESTION|TASK 5</h2>
 <span id="cb7-40"><a href="#cb7-40" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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diff --git a/Projects/project3.html b/Projects/project3.html
index 2b131a1..5a487ab 100644
--- a/Projects/project3.html
+++ b/Projects/project3.html
@@ -4799,7 +4799,7 @@
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../projects.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">DS250 Projects</span></a>
+ <span class="menu-text"></span></a>
   </div>
 </li>
         <li class="px-0"><hr class="sidebar-divider hi "></li>
@@ -4810,7 +4810,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis the popularity of baby names over the years.</span>
+ <span class="menu-text">What’s in a name?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4819,7 +4819,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores airplane delays. It also involves a little data cleaning.</span>
+ <span class="menu-text">TLate flights and missing data (JSON files)</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4828,7 +4828,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores trending in baseball. It also uses JSON and sqlite as tool to complete the project.</span>
+ <span class="menu-text">TFinding relationships in baseball.</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4837,7 +4837,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project uses machine learning to predict if a house was built before 1980.</span>
+ <span class="menu-text">Can you predict that?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4846,7 +4846,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis data about star wars fans. It mostly focuses on data cleaning, although it dose involve some machine learning and visualizations.</span>
+ <span class="menu-text">The war with Star Wars.</span>
   </li>
     </ul>
     </div>
@@ -4888,7 +4888,7 @@ <h2 class="anchored" data-anchor-id="elevator-pitch">Elevator pitch</h2>
 <section id="questiontask-1" class="level2">
 <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <p>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.</p>
-<div id="5f2f3164" class="cell" data-execution_count="3">
+<div id="3c4b36ec" class="cell" data-execution_count="3">
 <details class="code-fold">
 <summary>Show the code</summary>
 <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"># Connect to the SQLite database</span></span>
@@ -4970,7 +4970,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <p>This three-part question requires you to calculate batting average (number of hits divided by the number of at-bats)</p>
 <p><strong>QUESTION|TASK 2.1</strong></p>
 <p>Write an SQL query that provides playerID, yearID, and batting average for players with at least 1 at bat that year. Sort the table from highest batting average to lowest, and then by playerid alphabetically. Show the top 5 results in your report.</p>
-<div id="683173f5" class="cell" data-execution_count="4">
+<div id="f43738d9" class="cell" data-execution_count="4">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Connect to the SQLite database</span></span>
@@ -5014,7 +5014,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 </ul>
 <p><strong>QUESTION|TASK 2.2</strong></p>
 <p>Use the same query as above, but only include players with at least 10 at bats that year. Print the top 5 results.</p>
-<div id="503d5e36" class="cell" data-execution_count="5">
+<div id="c0b5460c" class="cell" data-execution_count="5">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Connect to the SQLite database</span></span>
@@ -5059,7 +5059,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 </ul>
 <p><strong>QUESTION|TASK 2.3</strong></p>
 <p>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>
-<div id="ec0be205" class="cell" data-execution_count="6">
+<div id="0ab830f5" class="cell" data-execution_count="6">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Connect to the SQLite database</span></span>
@@ -5110,7 +5110,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <section id="questiontask-3" class="level2">
 <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <p>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 Plotly Express to visualize the comparison. What do you learn?</p>
-<div id="227f0cfa" class="cell" data-execution_count="7">
+<div id="e42f071e" class="cell" data-execution_count="7">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sqlite3</span>
@@ -5198,9 +5198,9 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <span id="cb10-83"><a href="#cb10-83" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
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diff --git a/Projects/project4.html b/Projects/project4.html
index b548e1f..4280a1d 100644
--- a/Projects/project4.html
+++ b/Projects/project4.html
@@ -4799,7 +4799,7 @@
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../projects.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">DS250 Projects</span></a>
+ <span class="menu-text"></span></a>
   </div>
 </li>
         <li class="px-0"><hr class="sidebar-divider hi "></li>
@@ -4810,7 +4810,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis the popularity of baby names over the years.</span>
+ <span class="menu-text">What’s in a name?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4819,7 +4819,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores airplane delays. It also involves a little data cleaning.</span>
+ <span class="menu-text">TLate flights and missing data (JSON files)</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4828,7 +4828,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores trending in baseball. It also uses JSON and sqlite as tool to complete the project.</span>
+ <span class="menu-text">TFinding relationships in baseball.</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4837,7 +4837,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project uses machine learning to predict if a house was built before 1980.</span>
+ <span class="menu-text">Can you predict that?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4846,7 +4846,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis data about star wars fans. It mostly focuses on data cleaning, although it dose involve some machine learning and visualizations.</span>
+ <span class="menu-text">The war with Star Wars.</span>
   </li>
     </ul>
     </div>
@@ -4887,7 +4887,7 @@ <h2 class="anchored" data-anchor-id="elevator-pitch">Elevator pitch</h2>
 <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <p>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.</p>
 <p>Only one of these charts was very useful at helping find if the house was built before 1980, and that was the ‘Year Built’ chart, witch makes seance. Some of the other charts could help but wouldn’t be that accrate.</p>
-<div id="9c40d435" class="cell" data-execution_count="3">
+<div id="3c03983f" class="cell" data-execution_count="3">
 <details class="code-fold">
 <summary>Show the code</summary>
 <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">#chart one sell price </span></span>
@@ -4911,9 +4911,9 @@ <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
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-var gd = document.getElementById('aa07b9f5-1121-4ebe-b48a-27144f144782');
+var gd = document.getElementById('6a872d87-c947-4d0a-8604-1950d66bc119');
 var x = new MutationObserver(function (mutations, observer) {{
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@@ -4938,9 +4938,9 @@ <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
                         })                };                });            </script>        </div>
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+var gd = document.getElementById('855314c2-741e-410c-a2bf-693cb81df9e0');
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@@ -5004,7 +5004,7 @@ <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <p>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.</p>
 <p>I chose to use the RandomForestClassifier for my model. I used it becouse out of the twentey or so models I tried it had the highest accuracy, without overfiting. I also chhose to use a 30/70 for my perameter, this seemed to work the best and is very commonly recoginized a being genreally a good ratio in the inductry.</p>
-<div id="91fb8806" class="cell" data-execution_count="4">
+<div id="dd07f594" class="cell" data-execution_count="4">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -5073,7 +5073,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <span id="cb3-64"><a href="#cb3-64" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">&quot;Accuracy:&quot;</span>, accuracy)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
-<pre><code>Accuracy: 0.9502473086994472</code></pre>
+<pre><code>Accuracy: 0.9503927844050044</code></pre>
 </div>
 </div>
 <p><strong>Summery/Findings</strong></p>
@@ -5094,7 +5094,7 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <li><p>abstrprd</p></li>
 <li><p>gartype_Att</p></li>
 </ol>
-<div id="7a6fc59e" class="cell" data-execution_count="5">
+<div id="856ba01f" class="cell" data-execution_count="5">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co">#%%</span></span>
@@ -5198,19 +5198,19 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <span id="cb5-99"><a href="#cb5-99" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(feature_importance_df.head())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
-<pre><code>Accuracy: 0.9349723596159442
+<pre><code>Accuracy: 0.9316264183881292
 Top 5 features:
                Feature  Importance
-0               parcel    0.268134
-38  arcstyle_ONE-STORY    0.233912
-23           quality_C    0.153616
-1             abstrprd    0.063377
-26         gartype_Att    0.057019</code></pre>
+0               parcel    0.276088
+38  arcstyle_ONE-STORY    0.213434
+23           quality_C    0.147960
+1             abstrprd    0.070798
+26         gartype_Att    0.061357</code></pre>
 </div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
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iYQEREREREREfk/ok+fPvj5+eW6/cGDB439CRMm5Kr8e0aRkZFZHj916hRvv/02K1eu5NatWzmOExcXl6d5H5QaNWpkez41NZUjR44AkJycbCRL5FZqaiqxsbF39Tb43ahQoUK25y2l5/Pa9vr16znOnfHhsS3Vq1fH2dmZpKQkrly5Qnx8vLGMwalTp4x2jzzySK4SMBo0aGAkE2Tsn5Wcvuv82rRpE++++y5bt261SrCxJTd/Iz4+Pjm2sVQmAEhMTLRKJshYEaBevXo5VtWwJeO/KwEBAQQEBOSpv61/V0RERESk4GmZAxERERERERERyVJ0dHS++mf10Hj9+vU8+uijLFmyJFeJBABJSUn5iuN+8fT0zPZ8bGysscTB3crNg/iC4uLiku15k8l0V21zcw+8vb1zbGNnZ4eXl5fxOTY2Nsv93CZfZGyX8a3/rOT0XefHxIkTadeuHX/88UeuEgmAXP0t5fQdQfbfU8Z/D0qUKJGruLJyL/5dEREREZF7Q5UJREREREREREQkS5YlDgC6d+9O8+bN89T/zjLxkZGR9O3bl5s3bwLw+OOPM2zYMBo3bkzZsmVxd3fH2dnZaO/j48P58+fzcQX3V05LHGS8n25ubkycODHPc2R8eC4PTk7f9d3atGkTQUFBQPqD/b59+9KrVy/q1KlDqVKlcHV1NSpahIaGWi018U+R8e9gyJAheV6yoFmzZgUdkoiIiIjYoGQCERERERERERHJUsa3wxs1asSrr76ar/HmzZtnlGPv2bMny5Yty/ah7LVr1/I1X1YyvnltNputPt/pxo0bBTp3xvt5+/btfN/Pf7PcvL2elpZmVYEgY6JFxv3cvgkfFRVl7Gcs738/ffnll8b+Z599xksvvWSz7b34+8hOxt9vREREgYzz5JNPMmDAgHzFJSIiIiL3jpY5EBERERERERGRLNWsWdPYz7jO+d3auXOnsT9mzJhsEwkuXbp0Tx6Wuru7G/uJiYnZti3oqgiOjo5UrlwZSE8myLgGvVjLze/txIkTxhIYpUqVwsPDwzhXtWpVY//vv//O1dIK+/fvN/arVauWl3ALjOVvxMXFhf/85z/Ztj18+PD9CMlQu3ZtY//AgQMkJyff1TgF/e+KiIiIiNw7SiYQEREREREREZEsPfnkk8ab++vWrbN6C/xuZOyf05vfy5Yty3E8S7l3gNTU1FzFULJkSWP/5MmT2bZdv359rsbMi3bt2hn7ixYtKvDx/y3WrVtnLIdhy08//WTs31n6vmLFipQpUwZIf4N/48aN2Y6VkJBg9X3ndUkPW/L6G7X8jbi7u+Pk5JRt2+XLl+cvuDwqXbq0kQhw8+ZNli5delfjZPwbWLZsmdWyByIiIiLycFEygYiIiIiIiIiIZKl06dL06tULSH/Y+sorr+S6r9lsznQsY3nzvXv32uwbERHB5MmTc5zD09PT2I+JiclVXA0aNDD2f/zxR5vtzp07xw8//JCrMfPi+eefNxI0Pv/8c44cOZLrvlnd03+ra9euMWXKFJvnY2Ji+Oyzz4zPgwcPztQm47G3334724fWkyZN4vr16wA0bNiQOnXq3E3YmeT1N2r5G4mKisq2Msbvv//OqlWr8h9gHj333HPG/vjx462WhsitJk2a0LhxYwDCwsJ4//33c933/9LfgIiIiMjDQMkEIiIiIiIiIiJi0wcffGCUjw8ODmbgwIHZrpceERHB119/Tf369TOda9mypbE/fvx4zpw5k6nNyZMnefLJJ4mIiDAeutvi7u5uvH1+4sQJwsPDc7yenj17Gvuff/45O3bsyDKGjh07kpCQkON4eVWnTh2jfH1iYiKtW7dm5cqVNtunpaWxbds2Bg4cyJdfflng8TysTCYTEydOZNasWZnOXblyhc6dOxsPsuvVq0fnzp0ztXvppZeMChh//fUXAwYMyPSdms1mPvvsM6ZOnWocmzhxYoFdR/Xq1Y39LVu25LjcQsa/kWHDhmX5G1y7dq3V7/h+GjZsGLVq1QLSlyJp2bKl1fIQGaWmprJmzRqrChIWn332mVG1ISgoiLFjxxIfH29z3vPnzzN58mQ6dOhQAFchIiIiIrnl8KADEBERERERERGRh1eVKlVYsmQJPXv25ObNmyxYsIAVK1bQunVr6tWrh6enJ9evX+fy5cvs37+f/fv3k5aWRqFChTKNNXjwYN5//33Cw8O5fPkytWrVonv37tSoUQOz2cz+/ftZv349t2/fZvDgwWzZsoVz585lG1+nTp2YOXMmqampPPHEE/Tp04fSpUtjZ5f+Dk3NmjV58sknjfZNmzalY8eOrFu3jhs3btCyZUu6d+9OvXr1uH37Nvv27WPt2rWkpKTw1ltv8d577xXsDSU9ieHMmTOsX7+eyMhIunfvTtWqVWndujXlypXD3t6e2NhYjh8/zu7du43kjXr16hV4LA+r559/nhkzZjBs2DC+/fZb2rVrh7u7OydPnuTnn382Hjy7u7sTHByMvb19pjFKlSrFDz/8QK9evUhNTWXp0qWEhITQrVs3fH19iY2NZe3atVbVIcaMGUOnTp0K7DqqVq1K1apVOXXqFEeOHKFFixb4+/tTpEgRo03btm2pUaMGAOPGjePHH38kNTWVTZs2UalSJbp3746vry/x8fFs2bKFXbt2AfDOO+8wadKkAos1N1xdXVmxYgWtWrUiIiKCY8eO0bBhQ5o3b87jjz9O0aJFSUxM5MSJE2zZsoXIyEgmTJhAjx49rMZp3rw506dP59lnnyU1NZVPP/2U77//nieffJJatWpRuHBhEhISuHDhAvv27ePw4cMA1K5d+75er4iIiMj/dUomEBERERERERGRbHXs2JE///yTgIAA/v77b27dusXatWtZu3atzT5169bNdMzd3Z2VK1fSqVMnIiIiSE5OznLd9X79+jF9+nTjAWt23nnnHVatWsXVq1cJDQ3lww8/tDofEBBglUwA6RUW2rVrx6FDh0hJSWH58uVW6887ODgwZcoUnn766XuSTODo6MiaNWt45513mDp1KklJSZw6dYpTp07Z7OPu7o6vr2+Bx/KwatiwIfPmzWPIkCHs3bs3y2UxSpQowfLly3n00UdtjtO9e3dWrlxJYGAgUVFRREREMHPmzEztHB0d+e9//0tQUFBBXgaQnjzSvXt3bt++zZ9//smff/5pdX727NnGb71x48bMmDGDUaNGcfv2baKiovj+++8zxfrBBx/Qq1ev+55MAFCjRg12795N//79+fPPPzGbzWzbto1t27Zl2d5SgeBOQ4YMwcfHh+HDhxMaGkp8fDw//fRTlpUMLApq+QkRERERyR0lE4iIiIiIiIiISI7q1avHgQMH+PXXX/n555/5888/uXz5MvHx8bi6ulKqVClq1qzJE088wVNPPWXzoV+jRo04dOgQn3/+Ob/88oux1EHJkiV57LHHGDx4ME899VSu4ypXrhwHDhzgiy++YOPGjZw+fZrExERSU1Nt9ilZsiS7d+/mm2++YdmyZRw/fpykpCTKlClD69atef7556lfvz5hYWF5ukd5YW9vz/vvv88LL7zA7Nmz+f333zl27BjR0dGkpaXh6elJpUqVqFevHu3ataNjx45ZVnv4N3vmmWeoV68e3377LevXr+fixYsAxtv6GZcxyE6nTp04c+YMM2fO5JdffuHYsWPExMTg7u6Oj48P7dq1Y8SIEVSpUuWeXMdTTz3F7t27+eqrr/jzzz+5cOECN27cwGw2Z9l+6NChNG7cmM8++4zNmzcTHh6Oq6srZcqUoW3btgwfPpxHHnnknv4+c+Lj48P27dtZt24dS5cuZfv27Vy5coXr16/j4eFBpUqVaNKkCd27d6dt27Y2x2nTpg0nT55k+fLlrFmzhl27dhEREUFiYiKFChWibNmy1KpVi5YtW9K5c2cqVap0H69SRERERExmW/+vVUREREREREREROQ+CQoKYuLEiUD62/qBgYEPNiARERERkf/j7B50ACIiIiIiIiIiIiIiIiIiIvJwUTKBiIiIiIiIiIiIiIiIiIiIWFEygYiIiIiIiIiIiIiIiIiIiFhRMoGIiIiIiIiIiIiIiIiIiIhYUTKBiIiIiIiIiIiIiIiIiIiIWFEygYiIiIiIiIiIiIiIiIiIiFgxmc1m84MOQkRERERERERERERERERERB4eqkwgIiIiIiIiIiIiIiIiIiIiVpRMICIiIiIiIiIiIiIiIiIiIlaUTCAiIiIiIiIiIiIiIiIiIiJWlEwgIiIiIiIiIiIiIiIiIiIiVpRMICIiIiIiIiIiIiIiIiIiIlYcHnQAIiIiInmRkpLChQsXAPDw8MDOTrmRIiIiIiIiIiIiIiJpaWnEx8cDUL58eRwc8pcOoGQCERER+Ue5cOEClSpVetBhiIiIiIiIiIiIiIg8tM6ePUvFihXzNYZe5RMRERERERERERERERERERErqkwgIiIi/ygeHh7G/tmzZylSpMiDC0ZERERERERERERE5CERFxdnVPbN+N/S75aSCUREROQfxc7uf4WVihQpgpeX1wOMRkRERERERERERETk4ZPxv6Xf9RgFEIeIiIiIiIiIiIiIiIiIiIj8iyiZQERERERERERERERERERERKwomUBERERERERERERERERERESsKJlARERERERERERERERERERErCiZQERERERERERERERERERERKwomUBERERERERERERERERERESsKJlARERERERERERERERERERErCiZQERERERERERERERERERERKwomUBERERERERERERERERERESsKJlARERERERERERERERERERErCiZQERERERERERERERERERERKwomUBERERERERERERERERERESsKJlARERERERERERERERERERErCiZQERERERERERERERERERERKwomUBERERERERERERERERERESsKJlARERERERERERERERERERErCiZQERERERERERER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width="1034" height="523" class="figure-img"></p>
 </figure>
 </div>
 </div>
diff --git a/Projects/project5.html b/Projects/project5.html
index 1b15c04..283cac8 100644
--- a/Projects/project5.html
+++ b/Projects/project5.html
@@ -4799,7 +4799,7 @@
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../projects.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">DS250 Projects</span></a>
+ <span class="menu-text"></span></a>
   </div>
 </li>
         <li class="px-0"><hr class="sidebar-divider hi "></li>
@@ -4810,7 +4810,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis the popularity of baby names over the years.</span>
+ <span class="menu-text">What’s in a name?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4819,7 +4819,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores airplane delays. It also involves a little data cleaning.</span>
+ <span class="menu-text">TLate flights and missing data (JSON files)</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4828,7 +4828,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores trending in baseball. It also uses JSON and sqlite as tool to complete the project.</span>
+ <span class="menu-text">TFinding relationships in baseball.</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4837,7 +4837,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project uses machine learning to predict if a house was built before 1980.</span>
+ <span class="menu-text">Can you predict that?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -4846,7 +4846,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis data about star wars fans. It mostly focuses on data cleaning, although it dose involve some machine learning and visualizations.</span>
+ <span class="menu-text">The war with Star Wars.</span>
   </li>
     </ul>
     </div>
@@ -4896,7 +4896,7 @@ <h2 class="anchored" data-anchor-id="elevator-pitch">Elevator pitch</h2>
 <h2 class="anchored" data-anchor-id="questiontask-1">QUESTION|TASK 1</h2>
 <p>Shorten the column names and clean them up for easier use with pandas. Provide a table or list that exemplifies how you fixed the names.</p>
 <p>I cleaned the data so that it is much easier to understand and makes more seance. This included renaming all columns, convverting yeses to 1 and nos to 0 and many more little things to make it cleaner and earier for pandas.</p>
-<div id="474e4b99" class="cell" data-execution_count="3">
+<div id="765fcf49" class="cell" data-execution_count="3">
 <details class="code-fold">
 <summary>Show the code</summary>
 <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">#%%</span></span>
@@ -5079,7 +5079,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <li>2.1 Filter the dataset to respondents that have seen at least one film.</li>
 </ul>
 <p>Completed</p>
-<div id="8f6a0215" class="cell" data-execution_count="4">
+<div id="c73fa57d" class="cell" data-execution_count="4">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> data[data[<span class="st">&#39;Seen_any_SW&#39;</span>] <span class="op">!=</span> <span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -5089,7 +5089,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <li>2.2 Create a new column that converts the age ranges to a single number. Drop the age range categorical column.</li>
 </ul>
 <p>Completed</p>
-<div id="90244672" class="cell" data-execution_count="5">
+<div id="b8cb3ce9" class="cell" data-execution_count="5">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co">#make into int-able</span></span>
@@ -5140,7 +5140,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <li>2.3 Create a new column that converts the education groupings to a single number. Drop the school categorical column</li>
 </ul>
 <p>Completed</p>
-<div id="3b6372ea" class="cell" data-execution_count="6">
+<div id="cccec3df" class="cell" data-execution_count="6">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>edu_dic <span class="op">=</span> {</span>
@@ -5185,7 +5185,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <li>2.4 Create a new column that converts the income ranges to a single number. Drop the income range categorical column.</li>
 </ul>
 <p>Completed</p>
-<div id="c892ac7b" class="cell" data-execution_count="7">
+<div id="e56e9e8a" class="cell" data-execution_count="7">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>income_dic <span class="op">=</span> {</span>
@@ -5214,7 +5214,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <li>2.5 Create your target (also known as “y” or “label”) column based on the new income range column.</li>
 </ul>
 <p>Completed</p>
-<div id="08727386" class="cell" data-execution_count="8">
+<div id="53adda52" class="cell" data-execution_count="8">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -5243,7 +5243,7 @@ <h2 class="anchored" data-anchor-id="questiontask-2">QUESTION|TASK 2</h2>
 <span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">&quot;Accuracy:&quot;</span>, accuracy)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
-<pre><code>Accuracy: 0.2597864768683274</code></pre>
+<pre><code>Accuracy: 0.2704626334519573</code></pre>
 </div>
 </div>
 <ul>
@@ -5256,7 +5256,7 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <p>Validate that the data provided on GitHub lines up with the article by recreating 2 of the visuals from the article.</p>
 <p>The graphs I made is not exactly like the ones in the ardicle, but they are very close in looks and in the numbers, althought they are not perfect matches, they are very close, and the discresamcys are propbily due to slitly differant clean ‘styles’, and the software used to make the graphs.</p>
 <p>Graph 1</p>
-<div id="40b7dca1" class="cell" data-execution_count="9">
+<div id="3f3bf531" class="cell" data-execution_count="9">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co">#%%</span></span>
@@ -5315,9 +5315,9 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <span id="cb10-54"><a href="#cb10-54" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
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@@ -5343,7 +5343,7 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
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 <p>Graph 2</p>
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@@ -5411,9 +5411,9 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
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@@ -5443,7 +5443,7 @@ <h2 class="anchored" data-anchor-id="questiontask-3">QUESTION|TASK 3</h2>
 <h2 class="anchored" data-anchor-id="questiontask-4">QUESTION|TASK 4</h2>
 <p>Build a machine learning model that predicts whether a person makes more than $50k. Describe your model and report the accuracy.</p>
 <p>I tried like 20 differant classifers, however the highest Accuracy I ever got was abouut 28%. I ended up useing the MLPClassifier classifier. This probibly means that the corralation isn’t very high between these facts about liking, watching etc. about starwars and our income. It is also posible the the data set isn’t big enough, but I kind of doubt it.</p>
-<div id="09f17d7b" class="cell" data-execution_count="11">
+<div id="b88ee52e" class="cell" data-execution_count="11">
 <details class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co">#%%</span></span>
@@ -5498,7 +5498,7 @@ <h2 class="anchored" data-anchor-id="questiontask-4">QUESTION|TASK 4</h2>
 <span id="cb12-50"><a href="#cb12-50" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">&quot;Accuracy:&quot;</span>, accuracy)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
-<pre><code>Accuracy: 0.11743772241992882</code></pre>
+<pre><code>Accuracy: 0.06761565836298933</code></pre>
 </div>
 </div>
 
diff --git a/Templates/DS250_Template.html b/Templates/DS250_Template.html
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+var gd = document.getElementById('7995123c-f9b2-4bc0-aa6c-c2e267034000');
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diff --git a/projects.html b/projects.html
index 98896e7..bbee54a 100644
--- a/projects.html
+++ b/projects.html
@@ -115,7 +115,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./projects.html">DS250 Projects</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./projects.html"></a></li></ol></nav>
         <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -130,7 +130,7 @@
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./projects.html" class="sidebar-item-text sidebar-link active">
- <span class="menu-text">DS250 Projects</span></a>
+ <span class="menu-text"></span></a>
   </div>
 </li>
         <li class="px-0"><hr class="sidebar-divider hi "></li>
@@ -141,7 +141,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis the popularity of baby names over the years.</span>
+ <span class="menu-text">What’s in a name?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -150,7 +150,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores airplane delays. It also involves a little data cleaning.</span>
+ <span class="menu-text">TLate flights and missing data (JSON files)</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -159,7 +159,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project explores trending in baseball. It also uses JSON and sqlite as tool to complete the project.</span>
+ <span class="menu-text">TFinding relationships in baseball.</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -168,7 +168,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project uses machine learning to predict if a house was built before 1980.</span>
+ <span class="menu-text">Can you predict that?</span>
   </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -177,7 +177,7 @@
   </div>
 </li>
         <li class="sidebar-item">
- <span class="menu-text">This project analysis data about star wars fans. It mostly focuses on data cleaning, although it dose involve some machine learning and visualizations.</span>
+ <span class="menu-text">The war with Star Wars.</span>
   </li>
     </ul>
     </div>
diff --git a/search.json b/search.json
index d900762..6e91ca2 100644
--- a/search.json
+++ b/search.json
@@ -271,7 +271,7 @@
     "href": "Projects/project4.html#questiontask-2",
     "title": "Project 4: Can you predict that?",
     "section": "QUESTION|TASK 2",
-    "text": "QUESTION|TASK 2\nBuild 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.\nI chose to use the RandomForestClassifier for my model. I used it becouse out of the twentey or so models I tried it had the highest accuracy, without overfiting. I also chhose to use a 30/70 for my perameter, this seemed to work the best and is very commonly recoginized a being genreally a good ratio in the inductry.\n\n\nShow the code\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn import metrics\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.tree import DecisionTreeRegressor\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\n\n\n# Remove year built, and before 1980 from data to be trained.\n\n# number bedrooms and number of bathrooms\n\n# corr()\n# df = df.drop('parcel', axis = 1)\n# \n\n\n\n# Read the CSV file into a DataFrame\ndf = pd.read_csv('dwellings_ml.csv')\n\n\n# Remove the '-' characters from the 'parcel' column\ndf['parcel'] = df['parcel'].str.replace('-', '')\n\n\n# Convert the 'parcel' column to integer\ndf['parcel'] = df['parcel'].astype(int)\n\ndf = df.drop('yrbuilt', axis = 1)\n\ndf.columns\n\n# Define features and target\nfeatures = df.drop('before1980', axis=1)  # Drop the target column\ntarget = df['before1980']\n\n# Split data into training and testing sets\ntrain_data, test_data, train_targets, test_targets = train_test_split(features, target, test_size = .3)\n                                                                      \n\n# Instantiate Gaussian Naive Bayes classifier\nclassifier = RandomForestClassifier()\n# classifier = DecisionTreeClassifier()\n# classifier = GradientBoostingClassifier()\n# classifier = tree(max_depth=10, n_estimators=500)\n\n# Train the classifier\nclassifier.fit(train_data, train_targets)\n\n# Predict using the trained classifier\ntargets_predicted = classifier.predict(test_data)\n\n# Calculate accuracy\naccuracy = metrics.accuracy_score(test_targets, targets_predicted)\nprint(\"Accuracy:\", accuracy)\n\n\nAccuracy: 0.9502473086994472\n\n\nSummery/Findings\n\nthis model is 93-95% accurate\nIt uses the GradientBoostingClassifier formula\nIt seporates it 30%-70% training and checking",
+    "text": "QUESTION|TASK 2\nBuild 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.\nI chose to use the RandomForestClassifier for my model. I used it becouse out of the twentey or so models I tried it had the highest accuracy, without overfiting. I also chhose to use a 30/70 for my perameter, this seemed to work the best and is very commonly recoginized a being genreally a good ratio in the inductry.\n\n\nShow the code\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn import metrics\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.tree import DecisionTreeRegressor\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\n\n\n# Remove year built, and before 1980 from data to be trained.\n\n# number bedrooms and number of bathrooms\n\n# corr()\n# df = df.drop('parcel', axis = 1)\n# \n\n\n\n# Read the CSV file into a DataFrame\ndf = pd.read_csv('dwellings_ml.csv')\n\n\n# Remove the '-' characters from the 'parcel' column\ndf['parcel'] = df['parcel'].str.replace('-', '')\n\n\n# Convert the 'parcel' column to integer\ndf['parcel'] = df['parcel'].astype(int)\n\ndf = df.drop('yrbuilt', axis = 1)\n\ndf.columns\n\n# Define features and target\nfeatures = df.drop('before1980', axis=1)  # Drop the target column\ntarget = df['before1980']\n\n# Split data into training and testing sets\ntrain_data, test_data, train_targets, test_targets = train_test_split(features, target, test_size = .3)\n                                                                      \n\n# Instantiate Gaussian Naive Bayes classifier\nclassifier = RandomForestClassifier()\n# classifier = DecisionTreeClassifier()\n# classifier = GradientBoostingClassifier()\n# classifier = tree(max_depth=10, n_estimators=500)\n\n# Train the classifier\nclassifier.fit(train_data, train_targets)\n\n# Predict using the trained classifier\ntargets_predicted = classifier.predict(test_data)\n\n# Calculate accuracy\naccuracy = metrics.accuracy_score(test_targets, targets_predicted)\nprint(\"Accuracy:\", accuracy)\n\n\nAccuracy: 0.9503927844050044\n\n\nSummery/Findings\n\nthis model is 93-95% accurate\nIt uses the GradientBoostingClassifier formula\nIt seporates it 30%-70% training and checking",
     "crumbs": [
       "DS250 Projects",
       "Project 4"
@@ -282,7 +282,7 @@
     "href": "Projects/project4.html#questiontask-3",
     "title": "Project 4: Can you predict that?",
     "section": "QUESTION|TASK 3",
-    "text": "QUESTION|TASK 3\nJustify your classification model by discussing the most important features selected by your model. This discussion should include a chart and a description of the features.\nAcording to my caculations the top five features are as follows:\n\nParcel\narcstyle_TWO-STORY\nquality_C\nabstrprd\ngartype_Att\n\n\n\nShow the code\n#%%\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn import metrics\nfrom sklearn.tree import DecisionTreeClassifier\n\nfrom sklearn.tree import DecisionTreeRegressor\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\n\n#%%\n# Remove year built, and before 1980 from data to be trained.\n\n# number bedrooms and number of bathrooms\n\n# corr()\n# df = df.drop('parcel', axis = 1)\n# \n\n\n#%%\n# Read the CSV file into a DataFrame\ndf = pd.read_csv('dwellings_ml.csv')\n\n#%%\n# Remove the '-' characters from the 'parcel' column\ndf['parcel'] = df['parcel'].str.replace('-', '')\n\n#%%\n# Convert the 'parcel' column to integer\ndf['parcel'] = df['parcel'].astype(int)\n\n\n\n#%%\n\ndf = df.drop('yrbuilt', axis = 1)\n\ndf.columns\n\n#%%\n# Define features and target\nfeatures = df.drop('before1980', axis=1)  # Drop the target column\ntarget = df['before1980']\n\n# Split data into training and testing sets\ntrain_data, test_data, train_targets, test_targets = train_test_split(features, target, test_size = .3)\n                                                                      \n\n# Instantiate Gaussian Naive Bayes classifier\nclassifier = GradientBoostingClassifier()\n# classifier = tree(max_depth=10, n_estimators=500)\n\n# Train the classifier\nclassifier.fit(train_data, train_targets)\n\n# Predict using the trained classifier\ntargets_predicted = classifier.predict(test_data)\n\n# Calculate accuracy\naccuracy = metrics.accuracy_score(test_targets, targets_predicted)\nprint(\"Accuracy:\", accuracy)\n\n\n\n#%%\n\nimport matplotlib.pyplot as plt\n\n# Get feature importances\nfeature_importances = classifier.feature_importances_\n\n# Get the names of the features\nfeature_names = features.columns\n\n# Create a DataFrame to hold feature names and their importances\nfeature_importance_df = pd.DataFrame({\n    'Feature': feature_names,\n    'Importance': feature_importances\n})\n\n# Sort the DataFrame by importance\nfeature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n\n# Plotting the feature importance\nplt.figure(figsize=(10, 6))\nplt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'])\nplt.xlabel('Importance')\nplt.ylabel('Feature')\nplt.title('Feature Importance')\nplt.show()\n\n# Display top 5 features\nprint(\"Top 5 features:\")\nprint(feature_importance_df.head())\n\n\nAccuracy: 0.9349723596159442\nTop 5 features:\n               Feature  Importance\n0               parcel    0.268134\n38  arcstyle_ONE-STORY    0.233912\n23           quality_C    0.153616\n1             abstrprd    0.063377\n26         gartype_Att    0.057019",
+    "text": "QUESTION|TASK 3\nJustify your classification model by discussing the most important features selected by your model. This discussion should include a chart and a description of the features.\nAcording to my caculations the top five features are as follows:\n\nParcel\narcstyle_TWO-STORY\nquality_C\nabstrprd\ngartype_Att\n\n\n\nShow the code\n#%%\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn import metrics\nfrom sklearn.tree import DecisionTreeClassifier\n\nfrom sklearn.tree import DecisionTreeRegressor\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\n\n#%%\n# Remove year built, and before 1980 from data to be trained.\n\n# number bedrooms and number of bathrooms\n\n# corr()\n# df = df.drop('parcel', axis = 1)\n# \n\n\n#%%\n# Read the CSV file into a DataFrame\ndf = pd.read_csv('dwellings_ml.csv')\n\n#%%\n# Remove the '-' characters from the 'parcel' column\ndf['parcel'] = df['parcel'].str.replace('-', '')\n\n#%%\n# Convert the 'parcel' column to integer\ndf['parcel'] = df['parcel'].astype(int)\n\n\n\n#%%\n\ndf = df.drop('yrbuilt', axis = 1)\n\ndf.columns\n\n#%%\n# Define features and target\nfeatures = df.drop('before1980', axis=1)  # Drop the target column\ntarget = df['before1980']\n\n# Split data into training and testing sets\ntrain_data, test_data, train_targets, test_targets = train_test_split(features, target, test_size = .3)\n                                                                      \n\n# Instantiate Gaussian Naive Bayes classifier\nclassifier = GradientBoostingClassifier()\n# classifier = tree(max_depth=10, n_estimators=500)\n\n# Train the classifier\nclassifier.fit(train_data, train_targets)\n\n# Predict using the trained classifier\ntargets_predicted = classifier.predict(test_data)\n\n# Calculate accuracy\naccuracy = metrics.accuracy_score(test_targets, targets_predicted)\nprint(\"Accuracy:\", accuracy)\n\n\n\n#%%\n\nimport matplotlib.pyplot as plt\n\n# Get feature importances\nfeature_importances = classifier.feature_importances_\n\n# Get the names of the features\nfeature_names = features.columns\n\n# Create a DataFrame to hold feature names and their importances\nfeature_importance_df = pd.DataFrame({\n    'Feature': feature_names,\n    'Importance': feature_importances\n})\n\n# Sort the DataFrame by importance\nfeature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n\n# Plotting the feature importance\nplt.figure(figsize=(10, 6))\nplt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'])\nplt.xlabel('Importance')\nplt.ylabel('Feature')\nplt.title('Feature Importance')\nplt.show()\n\n# Display top 5 features\nprint(\"Top 5 features:\")\nprint(feature_importance_df.head())\n\n\nAccuracy: 0.9316264183881292\nTop 5 features:\n               Feature  Importance\n0               parcel    0.276088\n38  arcstyle_ONE-STORY    0.213434\n23           quality_C    0.147960\n1             abstrprd    0.070798\n26         gartype_Att    0.061357",
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@@ -392,7 +392,7 @@
     "href": "Projects/project5.html#questiontask-2",
     "title": "Client Report - Project 5: The war with Star Wars",
     "section": "QUESTION|TASK 2",
-    "text": "QUESTION|TASK 2\nClean 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 example(s) of the reformatted data with a short description of the changes made.\n\n2.1 Filter the dataset to respondents that have seen at least one film.\n\nCompleted\n\n\nShow the code\ndata = data[data['Seen_any_SW'] != 0]\n\n\n\n2.2 Create a new column that converts the age ranges to a single number. Drop the age range categorical column.\n\nCompleted\n\n\nShow the code\n#make into int-able\nage_dic = {\n    '18-29': 1,\n    '30-44': 2,\n    '45-60': 3,\n    '&gt; 60': 4  \n}\n\ndata['Age'] = data['Age'].replace('Response', 1)\n# Fill NaN values in the 'Age' column with 0\ndata['Age'] = data['Age'].fillna(0)\n\ndata['Age'] = data['Age'].replace(age_dic)\n#cast as int\ndata['Age'] = data['Age'].astype(int)\n\nage_dummies = pd.get_dummies(data['Age'])\n\nage_dummies.columns = ['NaN','18-29', '30-44', '45-60', '&gt; 60']\n\ndata = pd.concat([data, age_dummies], axis=1)\n\n# YN_dic = {'True': 1, 'False': 0}\n# age_dic = {'NaN','18-29','30-44','45-60','&gt; 60'}\n\n# data[age_dic] = data[age_dic].replace(YN_dic)\n\nTF_dic = {'True': 1, 'False': 0}\n\ndata.replace(TF_dic, inplace=True)\n\nTF_dic = {'True': 1, 'False': 0}\n\nTrue_False_columns = ['NaN', '18-29', '30-44', '45-60', '&gt; 60']\n\ndata[True_False_columns] = data[True_False_columns].astype(int)\n\ndata[True_False_columns] = data[True_False_columns].replace(TF_dic)\n\ndata.drop('Age', axis=1, inplace=True)\n\ndata.to_csv(\"cleaned_data.csv\", index=False)\n\n\n\n2.3 Create a new column that converts the education groupings to a single number. Drop the school categorical column\n\nCompleted\n\n\nShow the code\nedu_dic = {\n    'Response': 1,\n    'nan': 2,\n    'Less than high school degree': 3,\n    'High school degree': 4,\n    'Some college or Associate degree': 5,\n    'Bachelor degree': 6,\n    'Graduate degree': 7,\n}\n\ndata['education'] = data['education'].fillna(0)\n\ndata['education'] = data['education'].replace(edu_dic)\n#cast as int\ndata['education'] = data['education'].astype(int)\n\nage_dummies = pd.get_dummies(data['education'])\n\nage_dummies.columns = ['Response', 'edu_NaN','edu_less_HS', 'edu_HS', 'edu_Some_collage/AD', 'edu_BD', 'edu_GD']\n\ndata = pd.concat([data, age_dummies], axis=1)\n\n\ndata.replace(TF_dic, inplace=True)\n\nTF_dic = {'True': 1, 'False': 0}\n\nnames_columns = ['Response','edu_NaN','edu_less_HS', 'edu_HS', 'edu_Some_collage/AD', 'edu_BD', 'edu_GD']\n\ndata[names_columns] = data[names_columns].astype(int)\n\ndata[names_columns] = data[names_columns].replace(TF_dic)\n\ndata.drop('education', axis=1, inplace=True)\n\ndata.to_csv(\"cleaned_data.csv\", index=False)\n\n\n\n2.4 Create a new column that converts the income ranges to a single number. Drop the income range categorical column.\n\nCompleted\n\n\nShow the code\nincome_dic = {\n    'Response': 0,\n    'nan': 1,\n    '$0 - $24,999': 2,\n    '$100,000 - $149,999': 3,\n    '$25,000 - $49,999': 4,\n    '$50,000 - $99,999': 5,\n    '$150,000+': 6,\n}\n\n# Fill missing values in 'income' column with 0\ndata['income'] = data['income'].fillna(0)\n\n# Replace income categories with numerical values\ndata['income'] = data['income'].replace(income_dic)\n\n# Cast 'income' column as int\ndata['income'] = data['income'].astype(int)\n\ndata.to_csv(\"cleaned_data.csv\", index=False)\n\n\n\n2.5 Create your target (also known as “y” or “label”) column based on the new income range column.\n\nCompleted\n\n\nShow the code\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier  # For Neural Networks (Multi-layer Perceptron)\nfrom sklearn import metrics\n\ndf = pd.read_csv('cleaned_data.csv', encoding='latin-1')\ndf.drop(['1st_shot', 'gender', 'location', 'NaN', 'Response'], axis=1, inplace=True)\ndf.fillna(0, inplace=True)  \ndf.drop(0, inplace=True)\n\n# Split the data into features (X) and target (y)\nX = df.drop(['income'], axis=1)\ny = df['income']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n\nclassifier = MLPClassifier()\nclassifier.fit(X_train, y_train)\n\n# Predict on the test set\ny_pred = classifier.predict(X_test)\n\n# Calculate accuracy\naccuracy = metrics.accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)\n\n\nAccuracy: 0.2597864768683274\n\n\n\n2.6 One-hot encode all remaining categorical columns.\n\nCompleted above.",
+    "text": "QUESTION|TASK 2\nClean 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 example(s) of the reformatted data with a short description of the changes made.\n\n2.1 Filter the dataset to respondents that have seen at least one film.\n\nCompleted\n\n\nShow the code\ndata = data[data['Seen_any_SW'] != 0]\n\n\n\n2.2 Create a new column that converts the age ranges to a single number. Drop the age range categorical column.\n\nCompleted\n\n\nShow the code\n#make into int-able\nage_dic = {\n    '18-29': 1,\n    '30-44': 2,\n    '45-60': 3,\n    '&gt; 60': 4  \n}\n\ndata['Age'] = data['Age'].replace('Response', 1)\n# Fill NaN values in the 'Age' column with 0\ndata['Age'] = data['Age'].fillna(0)\n\ndata['Age'] = data['Age'].replace(age_dic)\n#cast as int\ndata['Age'] = data['Age'].astype(int)\n\nage_dummies = pd.get_dummies(data['Age'])\n\nage_dummies.columns = ['NaN','18-29', '30-44', '45-60', '&gt; 60']\n\ndata = pd.concat([data, age_dummies], axis=1)\n\n# YN_dic = {'True': 1, 'False': 0}\n# age_dic = {'NaN','18-29','30-44','45-60','&gt; 60'}\n\n# data[age_dic] = data[age_dic].replace(YN_dic)\n\nTF_dic = {'True': 1, 'False': 0}\n\ndata.replace(TF_dic, inplace=True)\n\nTF_dic = {'True': 1, 'False': 0}\n\nTrue_False_columns = ['NaN', '18-29', '30-44', '45-60', '&gt; 60']\n\ndata[True_False_columns] = data[True_False_columns].astype(int)\n\ndata[True_False_columns] = data[True_False_columns].replace(TF_dic)\n\ndata.drop('Age', axis=1, inplace=True)\n\ndata.to_csv(\"cleaned_data.csv\", index=False)\n\n\n\n2.3 Create a new column that converts the education groupings to a single number. Drop the school categorical column\n\nCompleted\n\n\nShow the code\nedu_dic = {\n    'Response': 1,\n    'nan': 2,\n    'Less than high school degree': 3,\n    'High school degree': 4,\n    'Some college or Associate degree': 5,\n    'Bachelor degree': 6,\n    'Graduate degree': 7,\n}\n\ndata['education'] = data['education'].fillna(0)\n\ndata['education'] = data['education'].replace(edu_dic)\n#cast as int\ndata['education'] = data['education'].astype(int)\n\nage_dummies = pd.get_dummies(data['education'])\n\nage_dummies.columns = ['Response', 'edu_NaN','edu_less_HS', 'edu_HS', 'edu_Some_collage/AD', 'edu_BD', 'edu_GD']\n\ndata = pd.concat([data, age_dummies], axis=1)\n\n\ndata.replace(TF_dic, inplace=True)\n\nTF_dic = {'True': 1, 'False': 0}\n\nnames_columns = ['Response','edu_NaN','edu_less_HS', 'edu_HS', 'edu_Some_collage/AD', 'edu_BD', 'edu_GD']\n\ndata[names_columns] = data[names_columns].astype(int)\n\ndata[names_columns] = data[names_columns].replace(TF_dic)\n\ndata.drop('education', axis=1, inplace=True)\n\ndata.to_csv(\"cleaned_data.csv\", index=False)\n\n\n\n2.4 Create a new column that converts the income ranges to a single number. Drop the income range categorical column.\n\nCompleted\n\n\nShow the code\nincome_dic = {\n    'Response': 0,\n    'nan': 1,\n    '$0 - $24,999': 2,\n    '$100,000 - $149,999': 3,\n    '$25,000 - $49,999': 4,\n    '$50,000 - $99,999': 5,\n    '$150,000+': 6,\n}\n\n# Fill missing values in 'income' column with 0\ndata['income'] = data['income'].fillna(0)\n\n# Replace income categories with numerical values\ndata['income'] = data['income'].replace(income_dic)\n\n# Cast 'income' column as int\ndata['income'] = data['income'].astype(int)\n\ndata.to_csv(\"cleaned_data.csv\", index=False)\n\n\n\n2.5 Create your target (also known as “y” or “label”) column based on the new income range column.\n\nCompleted\n\n\nShow the code\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier  # For Neural Networks (Multi-layer Perceptron)\nfrom sklearn import metrics\n\ndf = pd.read_csv('cleaned_data.csv', encoding='latin-1')\ndf.drop(['1st_shot', 'gender', 'location', 'NaN', 'Response'], axis=1, inplace=True)\ndf.fillna(0, inplace=True)  \ndf.drop(0, inplace=True)\n\n# Split the data into features (X) and target (y)\nX = df.drop(['income'], axis=1)\ny = df['income']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n\nclassifier = MLPClassifier()\nclassifier.fit(X_train, y_train)\n\n# Predict on the test set\ny_pred = classifier.predict(X_test)\n\n# Calculate accuracy\naccuracy = metrics.accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)\n\n\nAccuracy: 0.2704626334519573\n\n\n\n2.6 One-hot encode all remaining categorical columns.\n\nCompleted above.",
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@@ -414,7 +414,7 @@
     "href": "Projects/project5.html#questiontask-4",
     "title": "Client Report - Project 5: The war with Star Wars",
     "section": "QUESTION|TASK 4",
-    "text": "QUESTION|TASK 4\nBuild a machine learning model that predicts whether a person makes more than $50k. Describe your model and report the accuracy.\nI tried like 20 differant classifers, however the highest Accuracy I ever got was abouut 28%. I ended up useing the MLPClassifier classifier. This probibly means that the corralation isn’t very high between these facts about liking, watching etc. about starwars and our income. It is also posible the the data set isn’t big enough, but I kind of doubt it.\n\n\nShow the code\n#%%\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn import metrics\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.tree import DecisionTreeRegressor\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier #31\nfrom sklearn.svm import SVC  # For Support Vector Machines\nfrom sklearn.ensemble import GradientBoostingClassifier # 32\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn import metrics\n\n# Read the cleaned data\ndata = pd.read_csv('cleaned_data.csv', encoding='latin-1')\n# data.drop(['1st_shot', 'gender', 'location', 'NaN', 'Response'], axis=1, inplace=True)\ndata.drop(['1st_shot', 'gender', 'location'], axis=1, inplace=True)\ndata.fillna(0, inplace=True)  \n\ndata.drop(0, inplace=True)\n\n# Split the data into features (X) and target (y)\nX = data.drop(['income'], axis=1)\ny = data['income']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n\nclassifier = MLPClassifier()\nclassifier.fit(X_train, y_train)\n\n# Predict on the test set\ny_pred = classifier.predict(X_test)\n\n# Calculate accuracy\naccuracy = metrics.accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)\n\n\nAccuracy: 0.11743772241992882",
+    "text": "QUESTION|TASK 4\nBuild a machine learning model that predicts whether a person makes more than $50k. Describe your model and report the accuracy.\nI tried like 20 differant classifers, however the highest Accuracy I ever got was abouut 28%. I ended up useing the MLPClassifier classifier. This probibly means that the corralation isn’t very high between these facts about liking, watching etc. about starwars and our income. It is also posible the the data set isn’t big enough, but I kind of doubt it.\n\n\nShow the code\n#%%\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn import metrics\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.tree import DecisionTreeRegressor\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier #31\nfrom sklearn.svm import SVC  # For Support Vector Machines\nfrom sklearn.ensemble import GradientBoostingClassifier # 32\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn import metrics\n\n# Read the cleaned data\ndata = pd.read_csv('cleaned_data.csv', encoding='latin-1')\n# data.drop(['1st_shot', 'gender', 'location', 'NaN', 'Response'], axis=1, inplace=True)\ndata.drop(['1st_shot', 'gender', 'location'], axis=1, inplace=True)\ndata.fillna(0, inplace=True)  \n\ndata.drop(0, inplace=True)\n\n# Split the data into features (X) and target (y)\nX = data.drop(['income'], axis=1)\ny = data['income']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n\nclassifier = MLPClassifier()\nclassifier.fit(X_train, y_train)\n\n# Predict on the test set\ny_pred = classifier.predict(X_test)\n\n# Calculate accuracy\naccuracy = metrics.accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)\n\n\nAccuracy: 0.06761565836298933",
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