From 42bea946cc5d4866bc97bc35664215ed8dbce7f3 Mon Sep 17 00:00:00 2001
From: "Aaron W. Storey" <astoreyai@gmail.com>
Date: Fri, 19 Jan 2024 11:56:59 -0600
Subject: [PATCH] Update documentation

---
 Week_03/003_Overview.html                     |    5 +
 Week_03/Lesson_11.html                        |   17 +-
 Week_03/Lesson_12.html                        |    7 +-
 Week_03/Lesson_14.html                        | 1391 +++++++++++++++++
 Week_04/004_Overview.html                     |  619 ++++++++
 ...84140ac7c9437ebe24db9b8ad291e8978b9328.png |  Bin 0 -> 46565 bytes
 ...acf15f2e1cad9cc381e14d970ba63acd2a5fe4.png |  Bin 0 -> 22405 bytes
 ...66565fe71ef8465389f89f42e53f08098cd8ac.png |  Bin 0 -> 91620 bytes
 ...a2daffb0d1f69aabd4336b481fd14bb38bee07.png |  Bin 0 -> 95410 bytes
 ...f0723dcdd0569b3e60d1bc65e1c45489ad8866.png |  Bin 0 -> 38766 bytes
 _sources/Week_03/Lesson_11.ipynb              |   13 +-
 _sources/Week_03/Lesson_12.ipynb              |    2 +-
 _sources/Week_03/Lesson_14.ipynb              |  873 +++++++++++
 _sources/Week_04/004_Overview.md              |   26 +
 genindex.html                                 |    5 +
 index.html                                    |    7 +
 objects.inv                                   |  Bin 795 -> 835 bytes
 search.html                                   |    5 +
 searchindex.js                                |    2 +-
 19 files changed, 2962 insertions(+), 10 deletions(-)
 create mode 100644 Week_03/Lesson_14.html
 create mode 100644 Week_04/004_Overview.html
 create mode 100644 _images/075fbf5c776cc58927915791bc84140ac7c9437ebe24db9b8ad291e8978b9328.png
 create mode 100644 _images/161bcbf5a6867251be614f19cfacf15f2e1cad9cc381e14d970ba63acd2a5fe4.png
 create mode 100644 _images/95a447794b5441d1481141789f66565fe71ef8465389f89f42e53f08098cd8ac.png
 create mode 100644 _images/bb774e75caba478f35149ac942a2daffb0d1f69aabd4336b481fd14bb38bee07.png
 create mode 100644 _images/f3800c3ae52d0ac0ae87145cb2f0723dcdd0569b3e60d1bc65e1c45489ad8866.png
 create mode 100644 _sources/Week_03/Lesson_14.ipynb
 create mode 100644 _sources/Week_04/004_Overview.md

diff --git a/Week_03/003_Overview.html b/Week_03/003_Overview.html
index 508302a..a77ff76 100644
--- a/Week_03/003_Overview.html
+++ b/Week_03/003_Overview.html
@@ -239,10 +239,15 @@
 <li class="toctree-l2"><a class="reference internal" href="Lesson_12solution.html">Day 12: In-Depth Exploration of Data Splitting Techniques - Solution</a></li>
 
 <li class="toctree-l2"><a class="reference internal" href="Lesson_13.html">Day 13 - Handling Missing Data in Python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Lesson_14.html">Day 14 - Data Normalization and Scaling using Python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Lesson_15.html">Day 15: Encoding Categorical Data in Python - Expanded with Mathematical Implications</a></li>
 
 </ul>
 </li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 4 - Data Preprocessing</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../Week_04/004_Overview.html">Week_04: Overview</a></li>
 </ul>
 
     </div>
diff --git a/Week_03/Lesson_11.html b/Week_03/Lesson_11.html
index 6887463..cf3bd93 100644
--- a/Week_03/Lesson_11.html
+++ b/Week_03/Lesson_11.html
@@ -241,10 +241,15 @@
 <li class="toctree-l2"><a class="reference internal" href="Lesson_12solution.html">Day 12: In-Depth Exploration of Data Splitting Techniques - Solution</a></li>
 
 <li class="toctree-l2"><a class="reference internal" href="Lesson_13.html">Day 13 - Handling Missing Data in Python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Lesson_14.html">Day 14 - Data Normalization and Scaling using Python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Lesson_15.html">Day 15: Encoding Categorical Data in Python - Expanded with Mathematical Implications</a></li>
 
 </ul>
 </li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 4 - Data Preprocessing</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../Week_04/004_Overview.html">Week_04: Overview</a></li>
 </ul>
 
     </div>
@@ -502,7 +507,7 @@ <h2> Contents </h2>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#basic-summary-statistics-in-python">3. Basic (Summary) Statistics in Python</a><ul class="nav section-nav flex-column">
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#setup-for-activities">Setup for Activities</a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#in-the-above-example-we-used-a-numeric-column-in-order-to-display-the-mode-could-you-use-a-non-numeric-column">In the above example we used a numeric column in order to display the mode? <strong>Could you use a non-numeric column?</strong></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#why-would-there-be-a-difference-in-the-variance-and-standard-deviation-between-the-two-different-methods"><strong>Why would there be a difference in the variance and standard deviation between the two different methods?</strong></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#why-would-there-be-a-difference-in-the-variance-and-standard-deviation-between-numpy-and-pandas"><strong>Why would there be a difference in the variance and standard deviation between NumPy and Pandas?</strong></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#why-are-quartiles-and-interquartile-range-important">Why are Quartiles and Interquartile Range Important?</a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#activity-hands-on"><strong>Activity - Hands-On</strong></a><ul class="nav section-nav flex-column">
 <li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#additional-resources"><strong>Additional Resources</strong></a></li>
@@ -1254,7 +1259,7 @@ <h3>In the above example we used a numeric column in order to display the mode?
 </div>
 <p><strong>Standard Deviation (σ)</strong></p>
 <ul class="simple">
-<li><p>Formula: <span class="math notranslate nohighlight">\((\sigma = \sqrt{\sigma^2}\)</span>)</p></li>
+<li><p>Formula: <span class="math notranslate nohighlight">\(\sigma = \sqrt{\frac{\sum_{i=1}^n (x_i-\bar{x})^2}{n}}\)</span></p></li>
 <li><p>Activity: Calculate the standard deviation for ‘quality’.</p></li>
 </ul>
 <div class="cell docutils container">
@@ -1286,8 +1291,10 @@ <h3>In the above example we used a numeric column in order to display the mode?
 </div>
 </div>
 </section>
-<section id="why-would-there-be-a-difference-in-the-variance-and-standard-deviation-between-the-two-different-methods">
-<h3><strong>Why would there be a difference in the variance and standard deviation between the two different methods?</strong><a class="headerlink" href="#why-would-there-be-a-difference-in-the-variance-and-standard-deviation-between-the-two-different-methods" title="Permalink to this heading">#</a></h3>
+<section id="why-would-there-be-a-difference-in-the-variance-and-standard-deviation-between-numpy-and-pandas">
+<h3><strong>Why would there be a difference in the variance and standard deviation between NumPy and Pandas?</strong><a class="headerlink" href="#why-would-there-be-a-difference-in-the-variance-and-standard-deviation-between-numpy-and-pandas" title="Permalink to this heading">#</a></h3>
+<p>The difference between the numpy var and pandas var methods are not dependent on the range of the data but on the degrees of freedom (ddof) set by package. pandas sets ddof=1 (unbiased estimator) while numpy sets ddof = 0 (mle).
+RE: <a class="reference external" href="https://stackoverflow.com/questions/62938495/difference-between-numpy-var-and-pandas-var">https://stackoverflow.com/questions/62938495/difference-between-numpy-var-and-pandas-var</a></p>
 <p><strong>Max and Min Range</strong></p>
 <p>The range has a significant role in describing the variability of a data set, as long as there are no outliers. An outlier is an extreme high or low value that stands alone from the other values. If an outlier exist, the value of the range by itself can be misleading.</p>
 <div class="cell docutils container">
@@ -1457,7 +1464,7 @@ <h4><strong>Additional Resources</strong><a class="headerlink" href="#additional
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#basic-summary-statistics-in-python">3. Basic (Summary) Statistics in Python</a><ul class="nav section-nav flex-column">
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#setup-for-activities">Setup for Activities</a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#in-the-above-example-we-used-a-numeric-column-in-order-to-display-the-mode-could-you-use-a-non-numeric-column">In the above example we used a numeric column in order to display the mode? <strong>Could you use a non-numeric column?</strong></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#why-would-there-be-a-difference-in-the-variance-and-standard-deviation-between-the-two-different-methods"><strong>Why would there be a difference in the variance and standard deviation between the two different methods?</strong></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#why-would-there-be-a-difference-in-the-variance-and-standard-deviation-between-numpy-and-pandas"><strong>Why would there be a difference in the variance and standard deviation between NumPy and Pandas?</strong></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#why-are-quartiles-and-interquartile-range-important">Why are Quartiles and Interquartile Range Important?</a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#activity-hands-on"><strong>Activity - Hands-On</strong></a><ul class="nav section-nav flex-column">
 <li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#additional-resources"><strong>Additional Resources</strong></a></li>
diff --git a/Week_03/Lesson_12.html b/Week_03/Lesson_12.html
index ae01882..953fa3d 100644
--- a/Week_03/Lesson_12.html
+++ b/Week_03/Lesson_12.html
@@ -241,10 +241,15 @@
 <li class="toctree-l2"><a class="reference internal" href="Lesson_12solution.html">Day 12: In-Depth Exploration of Data Splitting Techniques - Solution</a></li>
 
 <li class="toctree-l2"><a class="reference internal" href="Lesson_13.html">Day 13 - Handling Missing Data in Python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Lesson_14.html">Day 14 - Data Normalization and Scaling using Python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Lesson_15.html">Day 15: Encoding Categorical Data in Python - Expanded with Mathematical Implications</a></li>
 
 </ul>
 </li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 4 - Data Preprocessing</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../Week_04/004_Overview.html">Week_04: Overview</a></li>
 </ul>
 
     </div>
@@ -552,7 +557,7 @@ <h2>1. Theoretical Background and Mathematical Principles<a class="headerlink" h
 <li><p><strong>Statistical Sampling</strong>: Importance in understanding how well samples represent populations.</p></li>
 <li><p><strong>Central Limit Theorem (CLT)</strong>:</p>
 <ul>
-<li><p><strong>Formula</strong>: <span class="math notranslate nohighlight">\((\bar{X} \approx N(\mu, \frac{\sigma^2}{n})\)</span>) for large <span class="math notranslate nohighlight">\((n\)</span>).</p></li>
+<li><p><strong>Formula</strong>: <span class="math notranslate nohighlight">\((\bar{X} \sim N(\mu, \frac{\sigma^2}{n})\)</span>) for large <span class="math notranslate nohighlight">\((n\)</span>).</p></li>
 <li><p><strong>Explanation</strong>: <span class="math notranslate nohighlight">\((\bar{X}\)</span>) is the sample mean; <span class="math notranslate nohighlight">\((N\)</span>) indicates a normal distribution; <span class="math notranslate nohighlight">\((\mu\)</span>) is the population mean; <span class="math notranslate nohighlight">\((\sigma^2\)</span>) is the population variance; <span class="math notranslate nohighlight">\((n\)</span>) is the sample size.</p></li>
 <li><p><strong>Importance</strong>: Foundation for statistical methods, applicable even with unknown population distribution.</p></li>
 </ul>
diff --git a/Week_03/Lesson_14.html b/Week_03/Lesson_14.html
new file mode 100644
index 0000000..1914ebd
--- /dev/null
+++ b/Week_03/Lesson_14.html
@@ -0,0 +1,1391 @@
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+        <p aria-level="2" class="caption" role="heading"><span class="caption-text">Preface</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="../Week_00/00_Overview.html">Welcome: Overview</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../Week_00/00a_DailyChallenge.html">Daily Challenge Curriculum</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../Week_00/00b_DailyResources.html"><strong>Daily Curriculum Resources</strong></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../Week_00/01_Errata.html">Errata: Corrections History</a></li>
+</ul>
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+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 1 - Introduction to Python Basics</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="../Week_01/001_Overview.html">Week_01: Overview</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_01.html">Day 1 - Python Basics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_02.html">Day 2 - Python Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_03.html">Day 3 - Control Structures in Python: Loops</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_04.html">Day 4 - Control Structures in Python: Conditional Statements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_05.html">Day 5 - Functions and Modules</a></li>
+</ul>
+</li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 2 - Introduction to Machine Learning Mathematics</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="../Week_02/002_Overview.html">Week_02: Overview</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../Week_02/Lesson_06.html">Day 6 - Linear Algebra - Vectors</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_02/Lesson_07.html">Day 7 - Linear Algebra - Matrices and Matrix Operations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_02/Lesson_08.html">Day 8 - Calculus - Derivatives, Concept and Application</a></li>
+</ul>
+</li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 3 - Data Preprocessing</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="Lesson_11.html">Day 11 - Introduction to Data Preprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Lesson_12.html">Day 12: In-Depth Exploration of Data Splitting Techniques in Python with Cross-Validation</a></li>
+
+<li class="toctree-l2"><a class="reference internal" href="Lesson_12solution.html">Day 12: In-Depth Exploration of Data Splitting Techniques - Solution</a></li>
+
+<li class="toctree-l2"><a class="reference internal" href="Lesson_13.html">Day 13 - Handling Missing Data in Python</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Day 14 - Data Normalization and Scaling using Python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Lesson_15.html">Day 15: Encoding Categorical Data in Python - Expanded with Mathematical Implications</a></li>
+
+</ul>
+</li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 4 - Data Preprocessing</span></p>
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+    <h1>Day 14: Data Normalization and Scaling using Python</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#objective">Objective</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#prerequisites">Prerequisites</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#introduction-to-data-normalization-and-scaling">Introduction to Data Normalization and Scaling</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#understanding-scaling-types">Understanding Scaling Types</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#comprehensive-mathematical-foundations">Comprehensive Mathematical Foundations</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#z-score-normalization-standardization">Z-Score Normalization (Standardization)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#min-max-scaling">Min-Max Scaling</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#hands-on-activities-with-the-iris-dataset">Hands-On Activities with the Iris Dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#implementing-z-score-normalization">Implementing Z-Score Normalization</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#implementing-min-max-scaling">Implementing Min-Max Scaling</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#visualization-and-analysis-post-data-normalization-and-scaling">Visualization and Analysis Post Data Normalization and Scaling</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id1">Objective</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#importance-of-visualization">Importance of Visualization</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id2">Implementing Z-Score Normalization</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id3">Visualization and Analysis Post Data Normalization and Scaling</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#visualization-techniques">Visualization Techniques</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#analytical-assessment-post-scaling">Analytical Assessment Post-Scaling</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#best-practices-and-considerations">Best Practices and Considerations</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#additional-resources-train-test-splitting-data-leakage"><strong>Additional Resources (Train/Test Splitting | Data Leakage)</strong></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#day-14-activity">Day 14: Activity</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-1-import-necessary-libraries">Step 1: Import necessary libraries</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-2-load-and-explore-the-dataset">Step 2: Load and Explore the Dataset</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-3-preprocess-the-data">Step 3: Preprocess the Data</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-4-split-the-data-into-training-and-testing-sets">Step 4: Split the Data into Training and Testing Sets</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-5-apply-z-score-normalization-standardization">Step 5: Apply Z-Score Normalization (Standardization)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-6-apply-min-max-scaling">Step 6: Apply Min-Max Scaling</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-7-visualize-and-analyze-the-effect-of-scaling">Step 7: Visualize and Analyze the Effect of Scaling</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-8-statistical-summary-comparison">Step 8: Statistical Summary Comparison</a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section class="tex2jax_ignore mathjax_ignore" id="day-14-data-normalization-and-scaling-using-python">
+<h1>Day 14: Data Normalization and Scaling using Python<a class="headerlink" href="#day-14-data-normalization-and-scaling-using-python" title="Permalink to this heading">#</a></h1>
+<section id="objective">
+<h2>Objective<a class="headerlink" href="#objective" title="Permalink to this heading">#</a></h2>
+<p>Explore data normalization and feature scaling in Python, emphasizing the complete mathematical principles underlying these techniques, and understand their practical implications in data preprocessing for machine learning models.</p>
+</section>
+<section id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this heading">#</a></h2>
+<ul class="simple">
+<li><p>Intermediate Python, Pandas, NumPy, and Scikit-Learn skills.</p></li>
+<li><p>Basic understanding of statistical concepts and linear algebra.</p></li>
+<li><p>Dataset: <a class="github reference external" href="https://github.com/100daysofml/100daysofml_notebooks/blob/main/content/Week_03/Iris.csv">100daysofml/100daysofml_notebooks</a></p></li>
+</ul>
+</section>
+<section id="introduction-to-data-normalization-and-scaling">
+<h2>Introduction to Data Normalization and Scaling<a class="headerlink" href="#introduction-to-data-normalization-and-scaling" title="Permalink to this heading">#</a></h2>
+<p>Data normalization and scaling are essential preprocessing techniques in data analysis and machine learning. They ensure consistent data representation across features, improving the performance and interpretability of machine learning algorithms. By modifying feature scales, these techniques help in stabilizing model training and enhancing algorithm convergence speed.</p>
+<section id="understanding-scaling-types">
+<h3>Understanding Scaling Types<a class="headerlink" href="#understanding-scaling-types" title="Permalink to this heading">#</a></h3>
+<ol class="arabic simple">
+<li><p><strong>Z-Score Normalization (Standardization)</strong></p></li>
+<li><p><strong>Min-Max Scaling</strong></p></li>
+</ol>
+</section>
+</section>
+<section id="comprehensive-mathematical-foundations">
+<h2>Comprehensive Mathematical Foundations<a class="headerlink" href="#comprehensive-mathematical-foundations" title="Permalink to this heading">#</a></h2>
+<section id="z-score-normalization-standardization">
+<h3>Z-Score Normalization (Standardization)<a class="headerlink" href="#z-score-normalization-standardization" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p><strong>Concept</strong>: Transforms data to have zero mean <span class="math notranslate nohighlight">\((μ = 0)\)</span> and unit variance <span class="math notranslate nohighlight">\((σ² = 1)\)</span>.</p></li>
+<li><p><strong>Formula</strong>: <span class="math notranslate nohighlight">\(Z=(X−μ)σZ=σ(X−μ)\)</span></p>
+<ul>
+<li><p>Where XX is the original value, μμ is the mean, and σσ is the standard deviation.</p></li>
+</ul>
+</li>
+<li><p><strong>Mathematical Rationale</strong>:</p>
+<ul>
+<li><p>By subtracting the mean, the data is centered around zero.</p></li>
+<li><p>Dividing by the standard deviation scales the data so that the variance is normalized.</p></li>
+<li><p>Ideal for features following a Gaussian distribution or when the algorithm assumes Gaussian distribution (e.g., Linear Regression, Logistic Regression, Linear Discriminant Analysis).</p></li>
+<li><p>Pros:</p>
+<ul>
+<li><p>Less affected by outliers.</p></li>
+<li><p>Keeps useful information about outliers.</p></li>
+</ul>
+</li>
+<li><p>Cons:</p>
+<ul>
+<li><p>Not bound to a specific range.</p></li>
+</ul>
+</li>
+<li><p>Use Case: Algorithms that assume data is Gaussian (e.g., Linear Regression, Gaussian Naive Bayes).</p></li>
+</ul>
+</li>
+</ul>
+</section>
+<section id="min-max-scaling">
+<h3>Min-Max Scaling<a class="headerlink" href="#min-max-scaling" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p><strong>Concept</strong>: Rescales the range of features to [0, 1].</p></li>
+<li><p><strong>Formula</strong>: <span class="math notranslate nohighlight">\(Xnorm=(X−Xmin)(Xmax−Xmin)Xnorm\)</span>=(Xmax<span class="math notranslate nohighlight">\(−Xmin\)</span>)(X−Xmin<span class="math notranslate nohighlight">\()\)</span></p>
+<ul>
+<li><p>Where XX is the original value, <span class="math notranslate nohighlight">\(XminXmin\)</span> and <span class="math notranslate nohighlight">\(XmaxXmax\)</span> are the minimum and maximum values.</p></li>
+</ul>
+</li>
+<li><p><strong>Mathematical Rationale</strong>:</p>
+<ul>
+<li><p>Ensures that all features contribute equally to the result.</p></li>
+<li><p>Useful when algorithms are sensitive to the scale of input data (e.g., K-Nearest Neighbors, Neural Networks).</p></li>
+<li><p>Prevents features with larger ranges from overpowering those with smaller ranges.</p></li>
+<li><p>Pros:</p>
+<ul>
+<li><p>Bound to a specific range.</p></li>
+<li><p>Preserves relationships in data.</p></li>
+</ul>
+</li>
+<li><p>Cons:</p>
+<ul>
+<li><p>Sensitive to outliers.</p></li>
+</ul>
+</li>
+<li><p>Use Case: Algorithms sensitive to input scale (e.g., Neural Networks, KNN).</p></li>
+</ul>
+</li>
+</ul>
+</section>
+</section>
+<section id="hands-on-activities-with-the-iris-dataset">
+<h2>Hands-On Activities with the Iris Dataset<a class="headerlink" href="#hands-on-activities-with-the-iris-dataset" title="Permalink to this heading">#</a></h2>
+<section id="implementing-z-score-normalization">
+<h3>Implementing Z-Score Normalization<a class="headerlink" href="#implementing-z-score-normalization" title="Permalink to this heading">#</a></h3>
+<p>Implement Z-Score Normalization and Min-Max Scaling using the <code class="docutils literal notranslate"><span class="pre">StandardScaler</span></code> and <code class="docutils literal notranslate"><span class="pre">MinMaxScaler</span></code> from Scikit-Learn, respectively. Tasks include examining the distribution of features before and after scaling, and comparing how scaling alters the range of values in features.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">iris</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
+<span class="n">iris_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)</span>
+<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+<span class="n">iris_standardized</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">iris_data</span><span class="p">)</span> 
+</pre></div>
+</div>
+<ul class="simple">
+<li><p><strong>Task</strong>: Examine the distribution of any feature before and after standardization.</p></li>
+</ul>
+</section>
+<section id="implementing-min-max-scaling">
+<h3>Implementing Min-Max Scaling<a class="headerlink" href="#implementing-min-max-scaling" title="Permalink to this heading">#</a></h3>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+
+<span class="n">min_max_scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">iris_min_max_scaled</span> <span class="o">=</span> <span class="n">min_max_scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">iris_data</span><span class="p">)</span> 
+</pre></div>
+</div>
+<ul class="simple">
+<li><p><strong>Task</strong>: Compare how min-max scaling alters the range of values in a feature.</p></li>
+</ul>
+</section>
+</section>
+<section id="visualization-and-analysis-post-data-normalization-and-scaling">
+<h2>Visualization and Analysis Post Data Normalization and Scaling<a class="headerlink" href="#visualization-and-analysis-post-data-normalization-and-scaling" title="Permalink to this heading">#</a></h2>
+<section id="id1">
+<h3>Objective<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h3>
+<p>To visually and analytically assess the impact of normalization and scaling on the data’s distribution and inter-feature relationships.</p>
+</section>
+<section id="importance-of-visualization">
+<h3>Importance of Visualization<a class="headerlink" href="#importance-of-visualization" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p><strong>Understanding Data Transformation</strong>: Visualization helps in comprehending how normalization and scaling alter the data.</p></li>
+<li><p><strong>Model Readiness Check</strong>: It ensures that the data is appropriately scaled or normalized for specific machine learning models.</p></li>
+</ul>
+</section>
+</section>
+<section id="id2">
+<h2>Implementing Z-Score Normalization<a class="headerlink" href="#id2" title="Permalink to this heading">#</a></h2>
+<ol class="arabic simple">
+<li><p><strong>Load the dataset</strong>:</p></li>
+</ol>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="n">iris_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Iris.csv&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">iris_data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="2">
+<li><p><strong>Implement Z-Score Normalization</strong>:</p></li>
+</ol>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+<span class="n">iris_standardized</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">iris_data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>  <span class="c1"># Assuming last column is target</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="3">
+<li><p><strong>Tasks</strong>:</p>
+<ul class="simple">
+<li><p>Examine distribution of features before and after standardization.</p></li>
+</ul>
+</li>
+</ol>
+<ol class="arabic simple">
+<li><p><strong>Implement Min-Max Scaling</strong>:</p></li>
+</ol>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScalermin_max_scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">iris_min_max_scaled</span> <span class="o">=</span> <span class="n">min_max_scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">iris_data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="2">
+<li><p><strong>Tasks</strong>:</p>
+<ul class="simple">
+<li><p>Compare how min-max scaling alters the range of values in features.</p></li>
+</ul>
+</li>
+</ol>
+</section>
+<section id="id3">
+<h2>Visualization and Analysis Post Data Normalization and Scaling<a class="headerlink" href="#id3" title="Permalink to this heading">#</a></h2>
+<section id="visualization-techniques">
+<h3>Visualization Techniques<a class="headerlink" href="#visualization-techniques" title="Permalink to this heading">#</a></h3>
+<ol class="arabic simple">
+<li><p><strong>Histograms and Density Plots</strong>:</p>
+<ul class="simple">
+<li><p>Show distribution of features before and after normalization or scaling.</p></li>
+<li><p>Example:</p></li>
+</ul>
+</li>
+</ol>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+        
+<span class="n">feature</span> <span class="o">=</span> <span class="s1">&#39;SepalLengthCm&#39;</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">iris_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Original </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">)</span>  <span class="c1"># Adjust index accordingly</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Standardized </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> 
+</pre></div>
+</div>
+<ol class="arabic simple">
+<li><p><strong>Histograms and Density Plots</strong>:</p>
+<ul class="simple">
+<li><p>Show distribution of features before and after normalization or scaling.</p></li>
+<li><p>Example:</p></li>
+</ul>
+</li>
+</ol>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+        
+<span class="n">feature</span> <span class="o">=</span> <span class="s1">&#39;SepalLengthCm&#39;</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">iris_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Original </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">)</span>  <span class="c1"># Adjust index accordingly</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Standardized </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> 
+</pre></div>
+</div>
+<ul class="simple">
+<li><p><strong>Box Plots</strong>:</p>
+<ul>
+<li><p>Show spread and central tendency of the data.</p></li>
+<li><p>Example:</p></li>
+</ul>
+</li>
+</ul>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">iris_data</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">feature</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Original </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> 
+        
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>  <span class="c1"># Adjust index accordingly</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Standardized </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> 
+</pre></div>
+</div>
+<ul class="simple">
+<li><p><strong>Scatter Plots</strong>:</p>
+<ul>
+<li><p>Observe relationship between two features.</p></li>
+<li><p>Example:</p></li>
+</ul>
+</li>
+</ul>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">iris_data</span><span class="p">[</span><span class="s1">&#39;SepalLengthCm&#39;</span><span class="p">],</span> <span class="n">iris_data</span><span class="p">[</span><span class="s1">&#39;SepalWidthCm&#39;</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Original&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Standardized&#39;</span><span class="p">)</span>  <span class="c1"># Adjust indices accordingly</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Sepal Length&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Sepal Width&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Sepal Length vs. Width: Original vs. Standardized&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> 
+</pre></div>
+</div>
+</section>
+<section id="analytical-assessment-post-scaling">
+<h3>Analytical Assessment Post-Scaling<a class="headerlink" href="#analytical-assessment-post-scaling" title="Permalink to this heading">#</a></h3>
+<ol class="arabic simple">
+<li><p><strong>Statistical Summary</strong>:</p>
+<ul class="simple">
+<li><p>Compare key statistics (mean, standard deviation, range) before and after scaling.</p></li>
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">describe()</span></code> function in Pandas.</p></li>
+</ul>
+</li>
+<li><p><strong>Correlation Analysis</strong>:</p>
+<ul class="simple">
+<li><p>Assess changes in inter-feature correlations post-scaling.</p></li>
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">corr()</span></code> function in Pandas.</p></li>
+</ul>
+</li>
+<li><p><strong>Model Performance Evaluation</strong>:</p>
+<ul class="simple">
+<li><p>Compare accuracy, precision, and recall before and after scaling.</p></li>
+<li><p>Train models on original, standardized, and min-max scaled data.</p></li>
+</ul>
+</li>
+</ol>
+</section>
+<section id="best-practices-and-considerations">
+<h3>Best Practices and Considerations<a class="headerlink" href="#best-practices-and-considerations" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p><strong>Data Leakage</strong>: Fit scalers on the training set only to prevent information leakage.</p></li>
+<li><p><strong>Train/Test Splitting</strong>: Split data before preprocessing to avoid data snooping.</p></li>
+<li><p><strong>Choosing the Right Method</strong>: Consider data distribution, algorithm requirements, and presence of outliers when choosing a scaling method.</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="n">iris_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Iris.csv&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">iris_data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
+0   1            5.1           3.5            1.4           0.2  Iris-setosa
+1   2            4.9           3.0            1.4           0.2  Iris-setosa
+2   3            4.7           3.2            1.3           0.2  Iris-setosa
+3   4            4.6           3.1            1.5           0.2  Iris-setosa
+4   5            5.0           3.6            1.4           0.2  Iris-setosa
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+<span class="n">iris_standardized</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">iris_data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>  <span class="c1"># Assuming last column is target</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+<span class="n">min_max_scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">iris_min_max_scaled</span> <span class="o">=</span> <span class="n">min_max_scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">iris_data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+        
+<span class="n">feature</span> <span class="o">=</span> <span class="s1">&#39;SepalLengthCm&#39;</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">iris_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Original </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">)</span>  <span class="c1"># Adjust index accordingly</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Standardized </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> 
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/f3800c3ae52d0ac0ae87145cb2f0723dcdd0569b3e60d1bc65e1c45489ad8866.png" src="../_images/f3800c3ae52d0ac0ae87145cb2f0723dcdd0569b3e60d1bc65e1c45489ad8866.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+        
+<span class="n">feature</span> <span class="o">=</span> <span class="s1">&#39;SepalLengthCm&#39;</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">iris_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Original </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">)</span>  <span class="c1"># Adjust index accordingly</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Standardized </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> 
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/f3800c3ae52d0ac0ae87145cb2f0723dcdd0569b3e60d1bc65e1c45489ad8866.png" src="../_images/f3800c3ae52d0ac0ae87145cb2f0723dcdd0569b3e60d1bc65e1c45489ad8866.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">iris_data</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">feature</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Original </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> 
+        
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>  <span class="c1"># Adjust index accordingly</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Standardized </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> 
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/161bcbf5a6867251be614f19cfacf15f2e1cad9cc381e14d970ba63acd2a5fe4.png" src="../_images/161bcbf5a6867251be614f19cfacf15f2e1cad9cc381e14d970ba63acd2a5fe4.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">iris_data</span><span class="p">[</span><span class="s1">&#39;SepalLengthCm&#39;</span><span class="p">],</span> <span class="n">iris_data</span><span class="p">[</span><span class="s1">&#39;SepalWidthCm&#39;</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Original&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iris_standardized</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Standardized&#39;</span><span class="p">)</span>  <span class="c1"># Adjust indices accordingly</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Sepal Length&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Sepal Width&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Sepal Length vs. Width: Original vs. Standardized&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> 
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/075fbf5c776cc58927915791bc84140ac7c9437ebe24db9b8ad291e8978b9328.png" src="../_images/075fbf5c776cc58927915791bc84140ac7c9437ebe24db9b8ad291e8978b9328.png" />
+</div>
+</div>
+<section id="additional-resources-train-test-splitting-data-leakage">
+<h4><strong>Additional Resources (Train/Test Splitting | Data Leakage)</strong><a class="headerlink" href="#additional-resources-train-test-splitting-data-leakage" title="Permalink to this heading">#</a></h4>
+<p><a class="reference external" href="https://machinelearningmastery.com/data-preparation-without-data-leakage/">https://machinelearningmastery.com/data-preparation-without-data-leakage/</a></p>
+<p><a class="reference external" href="https://medium.com/&#64;dancerworld60/detecting-and-preventing-data-leakage-in-machine-learning-4bb910900ab7">https://medium.com/&#64;dancerworld60/detecting-and-preventing-data-leakage-in-machine-learning-4bb910900ab7</a></p>
+<p><a class="reference external" href="https://towardsdatascience.com/five-hidden-causes-of-data-leakage-you-should-be-aware-of-e44df654f185">https://towardsdatascience.com/five-hidden-causes-of-data-leakage-you-should-be-aware-of-e44df654f185</a></p>
+<p><a class="reference external" href="https://www.analyticsvidhya.com/blog/2023/11/train-test-validation-split/">https://www.analyticsvidhya.com/blog/2023/11/train-test-validation-split/</a></p>
+<p><a class="reference external" href="https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/">https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/</a></p>
+</section>
+</section>
+</section>
+<section id="day-14-activity">
+<h2>Day 14: Activity<a class="headerlink" href="#day-14-activity" title="Permalink to this heading">#</a></h2>
+<section id="step-1-import-necessary-libraries">
+<h3>Step 1: Import necessary libraries<a class="headerlink" href="#step-1-import-necessary-libraries" title="Permalink to this heading">#</a></h3>
+</section>
+<section id="step-2-load-and-explore-the-dataset">
+<h3>Step 2: Load and Explore the Dataset<a class="headerlink" href="#step-2-load-and-explore-the-dataset" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Dataset: <a class="github reference external" href="https://github.com/100daysofml/100daysofml_notebooks/blob/main/content/Week_03/titanic.csv">100daysofml/100daysofml_notebooks</a></p></li>
+</ul>
+</section>
+<section id="step-3-preprocess-the-data">
+<h3>Step 3: Preprocess the Data<a class="headerlink" href="#step-3-preprocess-the-data" title="Permalink to this heading">#</a></h3>
+</section>
+<section id="step-4-split-the-data-into-training-and-testing-sets">
+<h3>Step 4: Split the Data into Training and Testing Sets<a class="headerlink" href="#step-4-split-the-data-into-training-and-testing-sets" title="Permalink to this heading">#</a></h3>
+</section>
+<section id="step-5-apply-z-score-normalization-standardization">
+<h3>Step 5: Apply Z-Score Normalization (Standardization)<a class="headerlink" href="#step-5-apply-z-score-normalization-standardization" title="Permalink to this heading">#</a></h3>
+</section>
+<section id="step-6-apply-min-max-scaling">
+<h3>Step 6: Apply Min-Max Scaling<a class="headerlink" href="#step-6-apply-min-max-scaling" title="Permalink to this heading">#</a></h3>
+</section>
+<section id="step-7-visualize-and-analyze-the-effect-of-scaling">
+<h3>Step 7: Visualize and Analyze the Effect of Scaling<a class="headerlink" href="#step-7-visualize-and-analyze-the-effect-of-scaling" title="Permalink to this heading">#</a></h3>
+</section>
+<section id="step-8-statistical-summary-comparison">
+<h3>Step 8: Statistical Summary Comparison<a class="headerlink" href="#step-8-statistical-summary-comparison" title="Permalink to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Step 1: Import Necessary Libraries</span>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">MinMaxScaler</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Step 2: Load and Explore the Dataset</span>
+
+<span class="c1"># Load the dataset</span>
+<span class="n">titanic_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;titanic.csv&#39;</span><span class="p">)</span>
+
+<span class="c1"># Display the first few rows of the dataset</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">titanic_data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+
+<span class="c1"># Display summary statistics</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Summary Statistics:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">titanic_data</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+
+
+<span class="c1"># Check for missing values</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Missing Values:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">titanic_data</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> 
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>   PassengerId  Pclass                                          Name     Sex  \
+0          892       3                              Kelly, Mr. James    male   
+1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   
+2          894       2                     Myles, Mr. Thomas Francis    male   
+3          895       3                              Wirz, Mr. Albert    male   
+4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   
+
+    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Survived  
+0  34.5      0      0   330911   7.8292   NaN        Q         0  
+1  47.0      1      0   363272   7.0000   NaN        S         1  
+2  62.0      0      0   240276   9.6875   NaN        Q         0  
+3  27.0      0      0   315154   8.6625   NaN        S         0  
+4  22.0      1      1  3101298  12.2875   NaN        S         1  
+
+Summary Statistics:
+       PassengerId      Pclass         Age       SibSp       Parch  \
+count   418.000000  418.000000  332.000000  418.000000  418.000000   
+mean   1100.500000    2.265550   30.272590    0.447368    0.392344   
+std     120.810458    0.841838   14.181209    0.896760    0.981429   
+min     892.000000    1.000000    0.170000    0.000000    0.000000   
+25%     996.250000    1.000000   21.000000    0.000000    0.000000   
+50%    1100.500000    3.000000   27.000000    0.000000    0.000000   
+75%    1204.750000    3.000000   39.000000    1.000000    0.000000   
+max    1309.000000    3.000000   76.000000    8.000000    9.000000   
+
+             Fare    Survived  
+count  417.000000  418.000000  
+mean    35.627188    0.385167  
+std     55.907576    0.487218  
+min      0.000000    0.000000  
+25%      7.895800    0.000000  
+50%     14.454200    0.000000  
+75%     31.500000    1.000000  
+max    512.329200    1.000000  
+
+Missing Values:
+PassengerId      0
+Pclass           0
+Name             0
+Sex              0
+Age             86
+SibSp            0
+Parch            0
+Ticket           0
+Fare             1
+Cabin          327
+Embarked         0
+Survived         0
+dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Step 3: Preprocess the Data</span>
+
+<span class="c1"># Handle missing values, encode categorical variables if necessary, and select features for scaling.</span>
+
+<span class="c1"># Handling missing values (simple example by filling with median or mode)titanic_data[&#39;Age&#39;].fillna(titanic_data[&#39;Age&#39;].median(), inplace=True)</span>
+<span class="n">titanic_data</span><span class="p">[</span><span class="s1">&#39;Embarked&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">titanic_data</span><span class="p">[</span><span class="s1">&#39;Embarked&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mode</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Select features for scaling (numeric features)</span>
+<span class="n">features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Pclass&#39;</span><span class="p">,</span> <span class="s1">&#39;Age&#39;</span><span class="p">,</span> <span class="s1">&#39;SibSp&#39;</span><span class="p">,</span> <span class="s1">&#39;Parch&#39;</span><span class="p">,</span> <span class="s1">&#39;Fare&#39;</span><span class="p">]</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">titanic_data</span><span class="p">[</span><span class="n">features</span><span class="p">]</span>
+
+<span class="c1"># If &#39;Survived&#39; is the target variable</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">titanic_data</span><span class="p">[</span><span class="s1">&#39;Survived&#39;</span><span class="p">]</span> 
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Step 4: Split the Data into Training and Testing Sets</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span> 
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Step 5: Apply Z-Score Normalization (Standardization)</span>
+
+<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+<span class="n">X_train_standardized</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
+<span class="n">X_test_standardized</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Step 6: Apply Min-Max Scaling</span>
+
+<span class="n">min_max_scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">X_train_min_max</span> <span class="o">=</span> <span class="n">min_max_scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
+<span class="n">X_test_min_max</span> <span class="o">=</span> <span class="n">min_max_scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> 
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Step 7: Visualize and Analyze the Effect of Scaling</span>
+
+<span class="c1">#Visualize the effect of Z-Score Normalization and Min-Max Scaling on the feature distributions.</span>
+
+<span class="c1">#Visualize the distribution of features before and after Z-Score Normalization</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">feature</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">features</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">),</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Original&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Original </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">)</span><span class="o">+</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">X_train_standardized</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Standardized&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Standardized </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># Visualize the distribution of features before and after Min-Max Scaling</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">feature</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">features</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">),</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Original&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Original </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">)</span><span class="o">+</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">X_train_min_max</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Min-Max Scaled&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Min-Max Scaled </span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/bb774e75caba478f35149ac942a2daffb0d1f69aabd4336b481fd14bb38bee07.png" src="../_images/bb774e75caba478f35149ac942a2daffb0d1f69aabd4336b481fd14bb38bee07.png" />
+<img alt="../_images/95a447794b5441d1481141789f66565fe71ef8465389f89f42e53f08098cd8ac.png" src="../_images/95a447794b5441d1481141789f66565fe71ef8465389f89f42e53f08098cd8ac.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Step 8: Statistical Summary Comparison</span>
+
+<span class="c1">#Compare the statistical summary before and after scaling.</span>
+
+<span class="c1"># Statistical summary before scalingprint(&quot;Statistical Summary before scaling:&quot;)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+
+<span class="c1"># Statistical summary after Z-Score Normalization</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Statistical Summary after Z-Score Normalization:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_train_standardized</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">features</span><span class="p">)</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+
+<span class="c1"># Statistical summary after Min-Max Scaling</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Statistical Summary after Min-Max Scaling:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_train_min_max</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">features</span><span class="p">)</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>           Pclass         Age       SibSp       Parch        Fare
+count  334.000000  262.000000  334.000000  334.000000  333.000000
+mean     2.269461   30.115763    0.470060    0.404192   36.909135
+std      0.844961   14.655775    0.944719    0.937113   58.054690
+min      1.000000    0.330000    0.000000    0.000000    0.000000
+25%      1.000000   21.000000    0.000000    0.000000    7.887500
+50%      3.000000   27.000000    0.000000    0.000000   14.454200
+75%      3.000000   39.000000    1.000000    0.000000   32.500000
+max      3.000000   76.000000    8.000000    9.000000  512.329200
+
+Statistical Summary after Z-Score Normalization:
+             Pclass           Age         SibSp         Parch          Fare
+count  3.340000e+02  2.620000e+02  3.340000e+02  3.340000e+02  3.330000e+02
+mean   2.233742e-16  2.711995e-17  2.127373e-17 -3.722904e-17 -4.267524e-17
+std    1.001500e+00  1.001914e+00  1.001500e+00  1.001500e+00  1.001505e+00
+min   -1.504644e+00 -2.036246e+00 -4.983123e-01 -4.319630e-01 -6.367217e-01
+25%   -1.504644e+00 -6.231816e-01 -4.983123e-01 -4.319630e-01 -5.006539e-01
+50%    8.658800e-01 -2.130032e-01 -4.983123e-01 -4.319630e-01 -3.873714e-01
+75%    8.658800e-01  6.073537e-01  5.617915e-01 -4.319630e-01 -7.606225e-02
+max    8.658800e-01  3.136788e+00  7.982518e+00  9.186412e+00  8.201500e+00
+
+Statistical Summary after Min-Max Scaling:
+           Pclass         Age       SibSp       Parch        Fare
+count  334.000000  262.000000  334.000000  334.000000  333.000000
+mean     0.634731    0.393627    0.058757    0.044910    0.072042
+std      0.422481    0.193680    0.118090    0.104124    0.113315
+min      0.000000    0.000000    0.000000    0.000000    0.000000
+25%      0.000000    0.273160    0.000000    0.000000    0.015395
+50%      1.000000    0.352451    0.000000    0.000000    0.028213
+75%      1.000000    0.511035    0.125000    0.000000    0.063436
+max      1.000000    1.000000    1.000000    1.000000    1.000000
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+</section>
+
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+    <i class="fa-solid fa-list"></i> Contents
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#objective">Objective</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#prerequisites">Prerequisites</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#introduction-to-data-normalization-and-scaling">Introduction to Data Normalization and Scaling</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#understanding-scaling-types">Understanding Scaling Types</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#comprehensive-mathematical-foundations">Comprehensive Mathematical Foundations</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#z-score-normalization-standardization">Z-Score Normalization (Standardization)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#min-max-scaling">Min-Max Scaling</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#hands-on-activities-with-the-iris-dataset">Hands-On Activities with the Iris Dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#implementing-z-score-normalization">Implementing Z-Score Normalization</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#implementing-min-max-scaling">Implementing Min-Max Scaling</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#visualization-and-analysis-post-data-normalization-and-scaling">Visualization and Analysis Post Data Normalization and Scaling</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id1">Objective</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#importance-of-visualization">Importance of Visualization</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id2">Implementing Z-Score Normalization</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id3">Visualization and Analysis Post Data Normalization and Scaling</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#visualization-techniques">Visualization Techniques</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#analytical-assessment-post-scaling">Analytical Assessment Post-Scaling</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#best-practices-and-considerations">Best Practices and Considerations</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#additional-resources-train-test-splitting-data-leakage"><strong>Additional Resources (Train/Test Splitting | Data Leakage)</strong></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#day-14-activity">Day 14: Activity</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-1-import-necessary-libraries">Step 1: Import necessary libraries</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-2-load-and-explore-the-dataset">Step 2: Load and Explore the Dataset</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-3-preprocess-the-data">Step 3: Preprocess the Data</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-4-split-the-data-into-training-and-testing-sets">Step 4: Split the Data into Training and Testing Sets</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-5-apply-z-score-normalization-standardization">Step 5: Apply Z-Score Normalization (Standardization)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-6-apply-min-max-scaling">Step 6: Apply Min-Max Scaling</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-7-visualize-and-analyze-the-effect-of-scaling">Step 7: Visualize and Analyze the Effect of Scaling</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#step-8-statistical-summary-comparison">Step 8: Statistical Summary Comparison</a></li>
+</ul>
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+</ul>
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+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 1 - Introduction to Python Basics</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="../Week_01/001_Overview.html">Week_01: Overview</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_01.html">Day 1 - Python Basics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_02.html">Day 2 - Python Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_03.html">Day 3 - Control Structures in Python: Loops</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_04.html">Day 4 - Control Structures in Python: Conditional Statements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_01/Lesson_05.html">Day 5 - Functions and Modules</a></li>
+</ul>
+</li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 2 - Introduction to Machine Learning Mathematics</span></p>
+<ul class="nav bd-sidenav">
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+<li class="toctree-l2"><a class="reference internal" href="../Week_02/Lesson_06.html">Day 6 - Linear Algebra - Vectors</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_02/Lesson_07.html">Day 7 - Linear Algebra - Matrices and Matrix Operations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_02/Lesson_08.html">Day 8 - Calculus - Derivatives, Concept and Application</a></li>
+</ul>
+</li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 3 - Data Preprocessing</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="../Week_03/003_Overview.html">Week_03: Overview</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../Week_03/Lesson_11.html">Day 11 - Introduction to Data Preprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_03/Lesson_12.html">Day 12: In-Depth Exploration of Data Splitting Techniques in Python with Cross-Validation</a></li>
+
+<li class="toctree-l2"><a class="reference internal" href="../Week_03/Lesson_12solution.html">Day 12: In-Depth Exploration of Data Splitting Techniques - Solution</a></li>
+
+<li class="toctree-l2"><a class="reference internal" href="../Week_03/Lesson_13.html">Day 13 - Handling Missing Data in Python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_03/Lesson_14.html">Day 14 - Data Normalization and Scaling using Python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Week_03/Lesson_15.html">Day 15: Encoding Categorical Data in Python - Expanded with Mathematical Implications</a></li>
+
+</ul>
+</li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 4 - Data Preprocessing</span></p>
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+    <h1>Course Structure</h1>
+    <!-- Table of contents -->
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-2-data-preprocessing-and-exploratory-data-analysis-weeks-3-4">Module 2: Data Preprocessing and Exploratory Data Analysis (Weeks 3-4)</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#week-4-exploratory-data-analysis-eda">Week 4: Exploratory Data Analysis (EDA)</a></li>
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+  <section class="tex2jax_ignore mathjax_ignore" id="course-structure">
+<h1>Course Structure<a class="headerlink" href="#course-structure" title="Permalink to this heading">#</a></h1>
+<section id="module-2-data-preprocessing-and-exploratory-data-analysis-weeks-3-4">
+<h2>Module 2: Data Preprocessing and Exploratory Data Analysis (Weeks 3-4)<a class="headerlink" href="#module-2-data-preprocessing-and-exploratory-data-analysis-weeks-3-4" title="Permalink to this heading">#</a></h2>
+<ul class="simple">
+<li><p>Focus: Data preprocessing methods and exploratory data analysis.</p></li>
+<li><p>Topics: Data preprocessing, visualization, descriptive statistics.</p></li>
+</ul>
+<section id="week-4-exploratory-data-analysis-eda">
+<h3>Week 4: Exploratory Data Analysis (EDA)<a class="headerlink" href="#week-4-exploratory-data-analysis-eda" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p><strong>Day 16:</strong> Introduction to EDA and Data Visualization in Python</p>
+<ul>
+<li><p>Basics of exploratory data analysis and data visualization techniques.</p></li>
+<li><p>Math Focus: Descriptive statistics and graphical representation of data.</p></li>
+</ul>
+</li>
+<li><p><strong>Day 17:</strong> Implementing Descriptive Statistics for EDA in Python</p>
+<ul>
+<li><p>Practical implementation of descriptive statistics in Python.</p></li>
+<li><p>Math Focus: Measures of central tendency and dispersion.</p></li>
+</ul>
+</li>
+<li><p><strong>Day 18:</strong> Visualization Techniques for Data Distribution in Python</p>
+<ul>
+<li><p>Create various types of plots to visualize data distributions.</p></li>
+<li><p>Math Focus: Histograms, box plots, and understanding data distributions.</p></li>
+</ul>
+</li>
+<li><p><strong>Day 19:</strong> Correlation Analysis using Python</p>
+<ul>
+<li><p>Explore correlation analysis and its implementation.</p></li>
+<li><p>Math Focus: Correlation coefficients and interpreting correlation in data.</p></li>
+</ul>
+</li>
+<li><p><strong>Day 20:</strong> Feature Selection and Importance in Python</p>
+<ul>
+<li><p>Techniques for feature selection and understanding feature importance.</p></li>
+<li><p>Math Focus: Information gain, Gini impurity, and feature importance metrics.</p></li>
+</ul>
+</li>
+</ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-2-data-preprocessing-and-exploratory-data-analysis-weeks-3-4">Module 2: Data Preprocessing and Exploratory Data Analysis (Weeks 3-4)</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#week-4-exploratory-data-analysis-eda">Week 4: Exploratory Data Analysis (EDA)</a></li>
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HcmV?d00001

diff --git a/_sources/Week_03/Lesson_11.ipynb b/_sources/Week_03/Lesson_11.ipynb
index 815eef1..ad1859a 100644
--- a/_sources/Week_03/Lesson_11.ipynb
+++ b/_sources/Week_03/Lesson_11.ipynb
@@ -986,7 +986,7 @@
    "metadata": {},
    "source": [
     "**Standard Deviation (σ)**\n",
-    "  * Formula: $(\\sigma = \\sqrt{\\sigma^2}$)\n",
+    "  * Formula: $\\sigma = \\sqrt{\\frac{\\sum_{i=1}^n (x_i-\\bar{x})^2}{n}}$\n",
     "  * Activity: Calculate the standard deviation for 'quality'."
    ]
   },
@@ -1038,7 +1038,16 @@
    "id": "b5afa9fc-c862-49f2-bc56-4f283761e59d",
    "metadata": {},
    "source": [
-    "### **Why would there be a difference in the variance and standard deviation between the two different methods?**"
+    "### **Why would there be a difference in the variance and standard deviation between NumPy and Pandas?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c55d04c3-0ea6-437e-b850-ee733666c71e",
+   "metadata": {},
+   "source": [
+    "The difference between the numpy var and pandas var methods are not dependent on the range of the data but on the degrees of freedom (ddof) set by package. pandas sets ddof=1 (unbiased estimator) while numpy sets ddof = 0 (mle). \n",
+    "RE: https://stackoverflow.com/questions/62938495/difference-between-numpy-var-and-pandas-var"
    ]
   },
   {
diff --git a/_sources/Week_03/Lesson_12.ipynb b/_sources/Week_03/Lesson_12.ipynb
index 805fd50..4c58920 100644
--- a/_sources/Week_03/Lesson_12.ipynb
+++ b/_sources/Week_03/Lesson_12.ipynb
@@ -35,7 +35,7 @@
     "**Understanding Data Distribution**\n",
     "- **Statistical Sampling**: Importance in understanding how well samples represent populations.\n",
     "- **Central Limit Theorem (CLT)**:\n",
-    "  - **Formula**: $(\\bar{X} \\approx N(\\mu, \\frac{\\sigma^2}{n})$) for large $(n$).\n",
+    "  - **Formula**: $(\\bar{X} \\sim N(\\mu, \\frac{\\sigma^2}{n})$) for large $(n$).\n",
     "  - **Explanation**: $(\\bar{X}$) is the sample mean; $(N$) indicates a normal distribution; $(\\mu$) is the population mean; $(\\sigma^2$) is the population variance; $(n$) is the sample size.\n",
     "  - **Importance**: Foundation for statistical methods, applicable even with unknown population distribution.\n",
     "\n",
diff --git a/_sources/Week_03/Lesson_14.ipynb b/_sources/Week_03/Lesson_14.ipynb
new file mode 100644
index 0000000..914cabe
--- /dev/null
+++ b/_sources/Week_03/Lesson_14.ipynb
@@ -0,0 +1,873 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7b0edda6-c273-434a-bf4d-826dfc6b0023",
+   "metadata": {},
+   "source": [
+    "# Day 14: Data Normalization and Scaling using Python\n",
+    "\n",
+    "## Objective\n",
+    "\n",
+    "Explore data normalization and feature scaling in Python, emphasizing the complete mathematical principles underlying these techniques, and understand their practical implications in data preprocessing for machine learning models.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "-   Intermediate Python, Pandas, NumPy, and Scikit-Learn skills.\n",
+    "-   Basic understanding of statistical concepts and linear algebra.\n",
+    "-   Dataset: https://github.com/100daysofml/100daysofml_notebooks/blob/main/content/Week_03/Iris.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "798eae1d-f231-424d-8833-e552a97b4346",
+   "metadata": {},
+   "source": [
+    "## Introduction to Data Normalization and Scaling\n",
+    "\n",
+    "Data normalization and scaling are essential preprocessing techniques in data analysis and machine learning. They ensure consistent data representation across features, improving the performance and interpretability of machine learning algorithms. By modifying feature scales, these techniques help in stabilizing model training and enhancing algorithm convergence speed.\n",
+    "\n",
+    "### Understanding Scaling Types\n",
+    "\n",
+    "1.  **Z-Score Normalization (Standardization)**\n",
+    "2.  **Min-Max Scaling**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20cee6b5-5418-402e-b918-0c30dbf38b24",
+   "metadata": {},
+   "source": [
+    "## Comprehensive Mathematical Foundations\n",
+    "\n",
+    "### Z-Score Normalization (Standardization)\n",
+    "\n",
+    "-   **Concept**: Transforms data to have zero mean $(μ = 0)$ and unit variance $(σ² = 1)$.\n",
+    "-   **Formula**: $Z=(X−μ)σZ=σ(X−μ)$\n",
+    "    -   Where XX is the original value, μμ is the mean, and σσ is the standard deviation.\n",
+    "-   **Mathematical Rationale**:\n",
+    "    -   By subtracting the mean, the data is centered around zero.\n",
+    "    -   Dividing by the standard deviation scales the data so that the variance is normalized.\n",
+    "    -   Ideal for features following a Gaussian distribution or when the algorithm assumes Gaussian distribution (e.g., Linear Regression, Logistic Regression, Linear Discriminant Analysis).\n",
+    "    -   Pros:\n",
+    "        -   Less affected by outliers.\n",
+    "        -   Keeps useful information about outliers.\n",
+    "    -   Cons:\n",
+    "        -   Not bound to a specific range.\n",
+    "    -   Use Case: Algorithms that assume data is Gaussian (e.g., Linear Regression, Gaussian Naive Bayes).\n",
+    "\n",
+    "### Min-Max Scaling\n",
+    "\n",
+    "-   **Concept**: Rescales the range of features to [0, 1].\n",
+    "-   **Formula**: $Xnorm=(X−Xmin)(Xmax−Xmin)Xnorm$=(Xmax$−Xmin$)(X−Xmin$)$\n",
+    "    -   Where XX is the original value, $XminXmin$ and $XmaxXmax$ are the minimum and maximum values.\n",
+    "-   **Mathematical Rationale**:\n",
+    "    -   Ensures that all features contribute equally to the result.\n",
+    "    -   Useful when algorithms are sensitive to the scale of input data (e.g., K-Nearest Neighbors, Neural Networks).\n",
+    "    -   Prevents features with larger ranges from overpowering those with smaller ranges.\n",
+    "    -   Pros:\n",
+    "        -   Bound to a specific range.\n",
+    "        -   Preserves relationships in data.\n",
+    "    -   Cons:\n",
+    "        -   Sensitive to outliers.\n",
+    "    -   Use Case: Algorithms sensitive to input scale (e.g., Neural Networks, KNN)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9822bcb4-312f-481c-bdb6-3106ed5d6392",
+   "metadata": {},
+   "source": [
+    "## Hands-On Activities with the Iris Dataset\n",
+    "\n",
+    "### Implementing Z-Score Normalization\n",
+    "\n",
+    "Implement Z-Score Normalization and Min-Max Scaling using the `StandardScaler` and `MinMaxScaler` from Scikit-Learn, respectively. Tasks include examining the distribution of features before and after scaling, and comparing how scaling alters the range of values in features.\n",
+    "\n",
+    "```python\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import pandas as pd\n",
+    "\n",
+    "iris = load_iris()\n",
+    "iris_data = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
+    "scaler = StandardScaler()\n",
+    "iris_standardized = scaler.fit_transform(iris_data) \n",
+    "```\n",
+    "-   **Task**: Examine the distribution of any feature before and after standardization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbef4e5c-1d35-4d47-9f14-98592ec777a3",
+   "metadata": {},
+   "source": [
+    "### Implementing Min-Max Scaling\n",
+    "\n",
+    "```python\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "min_max_scaler = MinMaxScaler()\n",
+    "iris_min_max_scaled = min_max_scaler.fit_transform(iris_data) \n",
+    "```\n",
+    "-   **Task**: Compare how min-max scaling alters the range of values in a feature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b68b37c-c8bb-4072-aaec-20ef928b3b1c",
+   "metadata": {},
+   "source": [
+    "## Visualization and Analysis Post Data Normalization and Scaling\n",
+    "\n",
+    "### Objective\n",
+    "\n",
+    "To visually and analytically assess the impact of normalization and scaling on the data's distribution and inter-feature relationships.\n",
+    "\n",
+    "### Importance of Visualization\n",
+    "\n",
+    "-   **Understanding Data Transformation**: Visualization helps in comprehending how normalization and scaling alter the data.\n",
+    "-   **Model Readiness Check**: It ensures that the data is appropriately scaled or normalized for specific machine learning models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71caf4b1-144a-473f-8ce1-5b2991818ae7",
+   "metadata": {},
+   "source": [
+    "## Implementing Z-Score Normalization\n",
+    "\n",
+    "1.  **Load the dataset**:\n",
+    "```python\n",
+    "import pandas as pd\n",
+    "iris_data = pd.read_csv('Iris.csv')\n",
+    "print(iris_data.head())\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12a07da8-c189-40b0-a272-85496a2a099c",
+   "metadata": {},
+   "source": [
+    "2.  **Implement Z-Score Normalization**:\n",
+    "```python\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "iris_standardized = scaler.fit_transform(iris_data.iloc[:, :-1])  # Assuming last column is target\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2f944f3-ca28-4a4f-84e2-9a929492c506",
+   "metadata": {},
+   "source": [
+    "3.  **Tasks**:\n",
+    "    -   Examine distribution of features before and after standardization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e879efbc-5a5a-46df-b923-9e77bb11a496",
+   "metadata": {},
+   "source": [
+    "1.  **Implement Min-Max Scaling**:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.preprocessing import MinMaxScalermin_max_scaler = MinMaxScaler()\n",
+    "iris_min_max_scaled = min_max_scaler.fit_transform(iris_data.iloc[:, :-1])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee9f5e4f-0652-4f54-a338-4c8bb68edbf9",
+   "metadata": {},
+   "source": [
+    "2.  **Tasks**:\n",
+    "    -   Compare how min-max scaling alters the range of values in features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d534359-32ed-443e-ad0d-f18480c3ddb1",
+   "metadata": {},
+   "source": [
+    "## Visualization and Analysis Post Data Normalization and Scaling\n",
+    "\n",
+    "### Visualization Techniques\n",
+    "\n",
+    "1.  **Histograms and Density Plots**:\n",
+    "    \n",
+    "    -   Show distribution of features before and after normalization or scaling.\n",
+    "    -   Example:\n",
+    "        \n",
+    "```python\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "        \n",
+    "feature = 'SepalLengthCm'\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(iris_data[feature], kde=True, color='blue')\n",
+    "plt.title(f'Original {feature}')\n",
+    "        \n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.histplot(iris_standardized[:, 0], kde=True, color='green')  # Adjust index accordingly\n",
+    "plt.title(f'Standardized {feature}')\n",
+    "plt.show() \n",
+    "```      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3725b37c-fcc6-42f4-b909-cb02c741017f",
+   "metadata": {},
+   "source": [
+    "1.  **Histograms and Density Plots**:\n",
+    "    \n",
+    "    -   Show distribution of features before and after normalization or scaling.\n",
+    "    -   Example:\n",
+    "        \n",
+    "```python\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "        \n",
+    "feature = 'SepalLengthCm'\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(iris_data[feature], kde=True, color='blue')\n",
+    "plt.title(f'Original {feature}')\n",
+    "        \n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.histplot(iris_standardized[:, 0], kde=True, color='green')  # Adjust index accordingly\n",
+    "plt.title(f'Standardized {feature}')\n",
+    "plt.show() \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "989ed0a5-3db5-4555-8332-16284330a125",
+   "metadata": {},
+   "source": [
+    "-   **Box Plots**:\n",
+    "    \n",
+    "    -   Show spread and central tendency of the data.\n",
+    "    -   Example:\n",
+    "        \n",
+    "```python\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.boxplot(data=iris_data, y=feature)\n",
+    "plt.title(f'Original {feature}') \n",
+    "        \n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(y=iris_standardized[:, 0])  # Adjust index accordingly\n",
+    "plt.title(f'Standardized {feature}')\n",
+    "plt.show() \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2415782d-3c34-4398-ba77-68b91542eb0e",
+   "metadata": {},
+   "source": [
+    "-   **Scatter Plots**:\n",
+    "    \n",
+    "    -   Observe relationship between two features.\n",
+    "    -   Example:\n",
+    "        \n",
+    "```python\n",
+    "plt.scatter(iris_data['SepalLengthCm'], iris_data['SepalWidthCm'], alpha=0.5, label='Original')\n",
+    "plt.scatter(iris_standardized[:, 0], iris_standardized[:, 1], alpha=0.5, label='Standardized')  # Adjust indices accordingly\n",
+    "plt.xlabel('Sepal Length')\n",
+    "plt.ylabel('Sepal Width')\n",
+    "plt.title('Sepal Length vs. Width: Original vs. Standardized')\n",
+    "plt.legend()\n",
+    "plt.show() \n",
+    "``` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9744e147-71ff-426c-9429-57cfb8d00c38",
+   "metadata": {},
+   "source": [
+    "### Analytical Assessment Post-Scaling\n",
+    "\n",
+    "1.  **Statistical Summary**:\n",
+    "    \n",
+    "    -   Compare key statistics (mean, standard deviation, range) before and after scaling.\n",
+    "    -   Use `describe()` function in Pandas.\n",
+    "2.  **Correlation Analysis**:\n",
+    "    \n",
+    "    -   Assess changes in inter-feature correlations post-scaling.\n",
+    "    -   Use `corr()` function in Pandas.\n",
+    "3.  **Model Performance Evaluation**:\n",
+    "    \n",
+    "    -   Compare accuracy, precision, and recall before and after scaling.\n",
+    "    -   Train models on original, standardized, and min-max scaled data.\n",
+    "\n",
+    "### Best Practices and Considerations\n",
+    "\n",
+    "-   **Data Leakage**: Fit scalers on the training set only to prevent information leakage.\n",
+    "-   **Train/Test Splitting**: Split data before preprocessing to avoid data snooping.\n",
+    "-   **Choosing the Right Method**: Consider data distribution, algorithm requirements, and presence of outliers when choosing a scaling method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7c9f07a3-584d-4a44-9ee9-d6588e68899b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
+      "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
+      "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
+      "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
+      "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
+      "4   5            5.0           3.6            1.4           0.2  Iris-setosa\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "iris_data = pd.read_csv('Iris.csv')\n",
+    "print(iris_data.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "93f19351-6b58-4305-8caa-e50c05788faa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "iris_standardized = scaler.fit_transform(iris_data.iloc[:, :-1])  # Assuming last column is target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "05e0057c-af18-477f-8b89-76c5e5d54ade",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "min_max_scaler = MinMaxScaler()\n",
+    "iris_min_max_scaled = min_max_scaler.fit_transform(iris_data.iloc[:, :-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "49c2f633-7d7e-4b10-8c7f-37414be55376",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "        \n",
+    "feature = 'SepalLengthCm'\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(iris_data[feature], kde=True, color='blue')\n",
+    "plt.title(f'Original {feature}')\n",
+    "        \n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.histplot(iris_standardized[:, 0], kde=True, color='green')  # Adjust index accordingly\n",
+    "plt.title(f'Standardized {feature}')\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e0da6099-15c4-4b8c-b945-cd5925fe9cc5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "        \n",
+    "feature = 'SepalLengthCm'\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(iris_data[feature], kde=True, color='blue')\n",
+    "plt.title(f'Original {feature}')\n",
+    "        \n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.histplot(iris_standardized[:, 0], kde=True, color='green')  # Adjust index accordingly\n",
+    "plt.title(f'Standardized {feature}')\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fe0f0fac-c5b8-4bd2-9980-7d16bed5f3d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.boxplot(data=iris_data, y=feature)\n",
+    "plt.title(f'Original {feature}') \n",
+    "        \n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(y=iris_standardized[:, 0])  # Adjust index accordingly\n",
+    "plt.title(f'Standardized {feature}')\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2cf35662-9468-4df5-9219-0f9728b17594",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(iris_data['SepalLengthCm'], iris_data['SepalWidthCm'], alpha=0.5, label='Original')\n",
+    "plt.scatter(iris_standardized[:, 0], iris_standardized[:, 1], alpha=0.5, label='Standardized')  # Adjust indices accordingly\n",
+    "plt.xlabel('Sepal Length')\n",
+    "plt.ylabel('Sepal Width')\n",
+    "plt.title('Sepal Length vs. Width: Original vs. Standardized')\n",
+    "plt.legend()\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc38147f-1d80-4a36-aac8-469695da84e0",
+   "metadata": {},
+   "source": [
+    "#### **Additional Resources (Train/Test Splitting | Data Leakage)**\n",
+    "\n",
+    "https://machinelearningmastery.com/data-preparation-without-data-leakage/\n",
+    "\n",
+    "https://medium.com/@dancerworld60/detecting-and-preventing-data-leakage-in-machine-learning-4bb910900ab7\n",
+    "\n",
+    "https://towardsdatascience.com/five-hidden-causes-of-data-leakage-you-should-be-aware-of-e44df654f185\n",
+    "\n",
+    "https://www.analyticsvidhya.com/blog/2023/11/train-test-validation-split/\n",
+    "\n",
+    "https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8651918-11b1-44b8-b809-73425c18d369",
+   "metadata": {},
+   "source": [
+    "## Day 14: Activity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7886e629-ffe5-4e5d-a75b-7e6edcf71c16",
+   "metadata": {},
+   "source": [
+    "### Step 1: Import necessary libraries\n",
+    "\n",
+    "### Step 2: Load and Explore the Dataset\n",
+    "- Dataset: https://github.com/100daysofml/100daysofml_notebooks/blob/main/content/Week_03/titanic.csv\n",
+    "\n",
+    "### Step 3: Preprocess the Data\n",
+    "\n",
+    "### Step 4: Split the Data into Training and Testing Sets\n",
+    "\n",
+    "### Step 5: Apply Z-Score Normalization (Standardization)\n",
+    "\n",
+    "### Step 6: Apply Min-Max Scaling\n",
+    "\n",
+    "### Step 7: Visualize and Analyze the Effect of Scaling\n",
+    "\n",
+    "### Step 8: Statistical Summary Comparison\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "328febd5-15a9-4849-b87a-ec6710a2d294",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Step 1: Import Necessary Libraries\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9621f1eb-94f4-45bb-8a8f-c15b069c1228",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   PassengerId  Pclass                                          Name     Sex  \\\n",
+      "0          892       3                              Kelly, Mr. James    male   \n",
+      "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
+      "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
+      "3          895       3                              Wirz, Mr. Albert    male   \n",
+      "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
+      "\n",
+      "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Survived  \n",
+      "0  34.5      0      0   330911   7.8292   NaN        Q         0  \n",
+      "1  47.0      1      0   363272   7.0000   NaN        S         1  \n",
+      "2  62.0      0      0   240276   9.6875   NaN        Q         0  \n",
+      "3  27.0      0      0   315154   8.6625   NaN        S         0  \n",
+      "4  22.0      1      1  3101298  12.2875   NaN        S         1  \n",
+      "\n",
+      "Summary Statistics:\n",
+      "       PassengerId      Pclass         Age       SibSp       Parch  \\\n",
+      "count   418.000000  418.000000  332.000000  418.000000  418.000000   \n",
+      "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   \n",
+      "std     120.810458    0.841838   14.181209    0.896760    0.981429   \n",
+      "min     892.000000    1.000000    0.170000    0.000000    0.000000   \n",
+      "25%     996.250000    1.000000   21.000000    0.000000    0.000000   \n",
+      "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   \n",
+      "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   \n",
+      "max    1309.000000    3.000000   76.000000    8.000000    9.000000   \n",
+      "\n",
+      "             Fare    Survived  \n",
+      "count  417.000000  418.000000  \n",
+      "mean    35.627188    0.385167  \n",
+      "std     55.907576    0.487218  \n",
+      "min      0.000000    0.000000  \n",
+      "25%      7.895800    0.000000  \n",
+      "50%     14.454200    0.000000  \n",
+      "75%     31.500000    1.000000  \n",
+      "max    512.329200    1.000000  \n",
+      "\n",
+      "Missing Values:\n",
+      "PassengerId      0\n",
+      "Pclass           0\n",
+      "Name             0\n",
+      "Sex              0\n",
+      "Age             86\n",
+      "SibSp            0\n",
+      "Parch            0\n",
+      "Ticket           0\n",
+      "Fare             1\n",
+      "Cabin          327\n",
+      "Embarked         0\n",
+      "Survived         0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Step 2: Load and Explore the Dataset\n",
+    "\n",
+    "# Load the dataset\n",
+    "titanic_data = pd.read_csv('titanic.csv')\n",
+    "\n",
+    "# Display the first few rows of the dataset\n",
+    "\n",
+    "print(titanic_data.head())\n",
+    "\n",
+    "# Display summary statistics\n",
+    "\n",
+    "print(\"\\nSummary Statistics:\")\n",
+    "print(titanic_data.describe())\n",
+    "\n",
+    "\n",
+    "# Check for missing values\n",
+    "print(\"\\nMissing Values:\")\n",
+    "print(titanic_data.isnull().sum()) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "53f02aa4-6eb5-47b3-b128-451855cf322f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Step 3: Preprocess the Data\n",
+    "\n",
+    "# Handle missing values, encode categorical variables if necessary, and select features for scaling.\n",
+    "\n",
+    "# Handling missing values (simple example by filling with median or mode)titanic_data['Age'].fillna(titanic_data['Age'].median(), inplace=True)\n",
+    "titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0], inplace=True)\n",
+    "\n",
+    "# Select features for scaling (numeric features)\n",
+    "features = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
+    "X = titanic_data[features]\n",
+    "\n",
+    "# If 'Survived' is the target variable\n",
+    "y = titanic_data['Survived'] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b8748324-c390-4b91-ba8a-0b8ffe44d29b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Step 4: Split the Data into Training and Testing Sets\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a4534a91-90ef-46ac-b0a7-841f9762c85e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Step 5: Apply Z-Score Normalization (Standardization)\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_train_standardized = scaler.fit_transform(X_train)\n",
+    "X_test_standardized = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fdf19605-c2ac-45fb-ace2-348b58d675f8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Step 6: Apply Min-Max Scaling\n",
+    "\n",
+    "min_max_scaler = MinMaxScaler()\n",
+    "X_train_min_max = min_max_scaler.fit_transform(X_train)\n",
+    "X_test_min_max = min_max_scaler.transform(X_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8849100a-6984-4dd5-b185-f33c93507bee",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Step 7: Visualize and Analyze the Effect of Scaling\n",
+    "\n",
+    "#Visualize the effect of Z-Score Normalization and Min-Max Scaling on the feature distributions.\n",
+    "\n",
+    "#Visualize the distribution of features before and after Z-Score Normalization\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "for i, feature in enumerate(features):\n",
+    "    plt.subplot(2, len(features), i+1)\n",
+    "    sns.histplot(X_train[feature], kde=True, color='blue', label='Original')\n",
+    "    plt.title(f'Original {feature}')\n",
+    "    plt.subplot(2, len(features), len(features)+i+1)\n",
+    "    sns.histplot(X_train_standardized[:, i], kde=True, color='green', label='Standardized')\n",
+    "    plt.title(f'Standardized {feature}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Visualize the distribution of features before and after Min-Max Scaling\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "for i, feature in enumerate(features):\n",
+    "    plt.subplot(2, len(features), i+1)\n",
+    "    sns.histplot(X_train[feature], kde=True, color='blue', label='Original')\n",
+    "    plt.title(f'Original {feature}')\n",
+    "    plt.subplot(2, len(features), len(features)+i+1)\n",
+    "    sns.histplot(X_train_min_max[:, i], kde=True, color='red', label='Min-Max Scaled')\n",
+    "    plt.title(f'Min-Max Scaled {feature}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "793d7c93-5b76-40f5-a822-00a296372a76",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Pclass         Age       SibSp       Parch        Fare\n",
+      "count  334.000000  262.000000  334.000000  334.000000  333.000000\n",
+      "mean     2.269461   30.115763    0.470060    0.404192   36.909135\n",
+      "std      0.844961   14.655775    0.944719    0.937113   58.054690\n",
+      "min      1.000000    0.330000    0.000000    0.000000    0.000000\n",
+      "25%      1.000000   21.000000    0.000000    0.000000    7.887500\n",
+      "50%      3.000000   27.000000    0.000000    0.000000   14.454200\n",
+      "75%      3.000000   39.000000    1.000000    0.000000   32.500000\n",
+      "max      3.000000   76.000000    8.000000    9.000000  512.329200\n",
+      "\n",
+      "Statistical Summary after Z-Score Normalization:\n",
+      "             Pclass           Age         SibSp         Parch          Fare\n",
+      "count  3.340000e+02  2.620000e+02  3.340000e+02  3.340000e+02  3.330000e+02\n",
+      "mean   2.233742e-16  2.711995e-17  2.127373e-17 -3.722904e-17 -4.267524e-17\n",
+      "std    1.001500e+00  1.001914e+00  1.001500e+00  1.001500e+00  1.001505e+00\n",
+      "min   -1.504644e+00 -2.036246e+00 -4.983123e-01 -4.319630e-01 -6.367217e-01\n",
+      "25%   -1.504644e+00 -6.231816e-01 -4.983123e-01 -4.319630e-01 -5.006539e-01\n",
+      "50%    8.658800e-01 -2.130032e-01 -4.983123e-01 -4.319630e-01 -3.873714e-01\n",
+      "75%    8.658800e-01  6.073537e-01  5.617915e-01 -4.319630e-01 -7.606225e-02\n",
+      "max    8.658800e-01  3.136788e+00  7.982518e+00  9.186412e+00  8.201500e+00\n",
+      "\n",
+      "Statistical Summary after Min-Max Scaling:\n",
+      "           Pclass         Age       SibSp       Parch        Fare\n",
+      "count  334.000000  262.000000  334.000000  334.000000  333.000000\n",
+      "mean     0.634731    0.393627    0.058757    0.044910    0.072042\n",
+      "std      0.422481    0.193680    0.118090    0.104124    0.113315\n",
+      "min      0.000000    0.000000    0.000000    0.000000    0.000000\n",
+      "25%      0.000000    0.273160    0.000000    0.000000    0.015395\n",
+      "50%      1.000000    0.352451    0.000000    0.000000    0.028213\n",
+      "75%      1.000000    0.511035    0.125000    0.000000    0.063436\n",
+      "max      1.000000    1.000000    1.000000    1.000000    1.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Step 8: Statistical Summary Comparison\n",
+    "\n",
+    "#Compare the statistical summary before and after scaling.\n",
+    "\n",
+    "# Statistical summary before scalingprint(\"Statistical Summary before scaling:\")\n",
+    "print(X_train.describe())\n",
+    "\n",
+    "# Statistical summary after Z-Score Normalization\n",
+    "print(\"\\nStatistical Summary after Z-Score Normalization:\")\n",
+    "print(pd.DataFrame(X_train_standardized, columns=features).describe())\n",
+    "\n",
+    "# Statistical summary after Min-Max Scaling\n",
+    "print(\"\\nStatistical Summary after Min-Max Scaling:\")\n",
+    "print(pd.DataFrame(X_train_min_max, columns=features).describe())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/Week_04/004_Overview.md b/_sources/Week_04/004_Overview.md
new file mode 100644
index 0000000..9bb93e4
--- /dev/null
+++ b/_sources/Week_04/004_Overview.md
@@ -0,0 +1,26 @@
+  # Course Structure
+
+ ## Module 2: Data Preprocessing and Exploratory Data Analysis (Weeks 3-4)
+- Focus: Data preprocessing methods and exploratory data analysis.
+- Topics: Data preprocessing, visualization, descriptive statistics.
+
+### Week 4: Exploratory Data Analysis (EDA)
+- **Day 16:** Introduction to EDA and Data Visualization in Python
+  - Basics of exploratory data analysis and data visualization techniques.
+  - Math Focus: Descriptive statistics and graphical representation of data.
+
+- **Day 17:** Implementing Descriptive Statistics for EDA in Python
+  - Practical implementation of descriptive statistics in Python.
+  - Math Focus: Measures of central tendency and dispersion.
+
+- **Day 18:** Visualization Techniques for Data Distribution in Python
+  - Create various types of plots to visualize data distributions.
+  - Math Focus: Histograms, box plots, and understanding data distributions.
+
+- **Day 19:** Correlation Analysis using Python
+  - Explore correlation analysis and its implementation.
+  - Math Focus: Correlation coefficients and interpreting correlation in data.
+
+- **Day 20:** Feature Selection and Importance in Python
+  - Techniques for feature selection and understanding feature importance.
+  - Math Focus: Information gain, Gini impurity, and feature importance metrics.
\ No newline at end of file
diff --git a/genindex.html b/genindex.html
index 327244f..bcb1730 100644
--- a/genindex.html
+++ b/genindex.html
@@ -236,10 +236,15 @@
 <li class="toctree-l2"><a class="reference internal" href="Week_03/Lesson_12solution.html">Day 12: In-Depth Exploration of Data Splitting Techniques - Solution</a></li>
 
 <li class="toctree-l2"><a class="reference internal" href="Week_03/Lesson_13.html">Day 13 - Handling Missing Data in Python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Week_03/Lesson_14.html">Day 14 - Data Normalization and Scaling using Python</a></li>
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