diff --git a/Week_00/00_Overview.html b/Week_00/00_Overview.html index f32c6c4..32f5f78 100644 --- a/Week_00/00_Overview.html +++ b/Week_00/00_Overview.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_00/00a_DailyChallenge.html b/Week_00/00a_DailyChallenge.html index 0569420..28a4370 100644 --- a/Week_00/00a_DailyChallenge.html +++ b/Week_00/00a_DailyChallenge.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_00/00b_DailyResources.html b/Week_00/00b_DailyResources.html index fb10a30..7ab0785 100644 --- a/Week_00/00b_DailyResources.html +++ b/Week_00/00b_DailyResources.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_00/01_Errata.html b/Week_00/01_Errata.html index ba80109..0c625b8 100644 --- a/Week_00/01_Errata.html +++ b/Week_00/01_Errata.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_01/001_Overview.html b/Week_01/001_Overview.html index a02665a..10c38a0 100644 --- a/Week_01/001_Overview.html +++ b/Week_01/001_Overview.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_01/Lesson_01.html b/Week_01/Lesson_01.html index 10c8f08..2f606a3 100644 --- a/Week_01/Lesson_01.html +++ b/Week_01/Lesson_01.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_01/Lesson_02.html b/Week_01/Lesson_02.html index e987c1c..d11db79 100644 --- a/Week_01/Lesson_02.html +++ b/Week_01/Lesson_02.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_01/Lesson_03.html b/Week_01/Lesson_03.html index f5524d4..f42ef04 100644 --- a/Week_01/Lesson_03.html +++ b/Week_01/Lesson_03.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_01/Lesson_04.html b/Week_01/Lesson_04.html index 0a32004..613f4af 100644 --- a/Week_01/Lesson_04.html +++ b/Week_01/Lesson_04.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • diff --git a/Week_01/Lesson_05.html b/Week_01/Lesson_05.html index d3e83c0..bc03947 100644 --- a/Week_01/Lesson_05.html +++ b/Week_01/Lesson_05.html @@ -259,6 +259,7 @@
  • Day 21 - Introduction to Regression Analysis in Python
  • Day 22: Implementing Multiple Linear Regression in Python
    • +
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • @@ -778,7 +779,7 @@

    Standard Library Highlights -
    0.6508227309087278
+
    0.11290791626756647
    @@ -844,7 +845,7 @@

    Standard Library Highlights -
    2024-01-30 09:22:01.276254
+
    2024-01-31 08:52:49.083772
    @@ -1167,8 +1168,8 @@

    Basic Quiz Game 
    What is the capital of France?
-a. London
-b. Paris
+a. Paris
+b. London
 c. Rome
    @@ -1208,9 +1209,9 @@

    Basic Quiz Game 
    What is the capital of France?
-a. Rome
-b. Paris
-c. London
+a. London
+b. Rome
+c. Paris
 
    ---------------------------------------------------------------------------
@@ -1249,9 +1250,9 @@ 
    Basic Quiz Game
 
    What is the capital of France?
-a. Paris
-b. London
-c. Rome
+a. London
+b. Rome
+c. Paris
 
    
 
    
 
    ---------------------------------------------------------------------------
@@ -1291,8 +1292,8 @@ 
    Basic Quiz Game
 
    What is the capital of France?
 a. Rome
-b. Paris
-c. London
+b. London
+c. Paris
 
    
 
    
 
    ---------------------------------------------------------------------------
diff --git a/Week_02/002_Overview.html b/Week_02/002_Overview.html
index 3613209..1c7b0e7 100644
--- a/Week_02/002_Overview.html
+++ b/Week_02/002_Overview.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_02/Lesson_06.html b/Week_02/Lesson_06.html
index e6cc40b..ae7d1be 100644
--- a/Week_02/Lesson_06.html
+++ b/Week_02/Lesson_06.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_02/Lesson_07.html b/Week_02/Lesson_07.html
index 154c1f9..f75a03d 100644
--- a/Week_02/Lesson_07.html
+++ b/Week_02/Lesson_07.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -741,15 +742,15 @@ 
    Matrix Dot Product (Element-wise Product):Day 21 - Introduction to Regression Analysis in Python
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_02/Lesson_09.html b/Week_02/Lesson_09.html
index ff91e91..568f967 100644
--- a/Week_02/Lesson_09.html
+++ b/Week_02/Lesson_09.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_02/Lesson_10.html b/Week_02/Lesson_10.html
index c1ab9e8..57eafa7 100644
--- a/Week_02/Lesson_10.html
+++ b/Week_02/Lesson_10.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -595,7 +596,7 @@ 
    Step 1: Import Necessary Libraries
-
    /tmp/ipykernel_237581/584559657.py:3: DeprecationWarning: 
+
    /tmp/ipykernel_288453/584559657.py:3: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
@@ -662,7 +663,7 @@ 
    Step 3: Understanding Distributions
-../_images/9239cf149dff59ff38d351ff926223010fb8551c6f5fa5166e60f27a8a9386e7.png
+../_images/a298648d33b481f372b4498a7f50d6080ac2ff6cb7286d57d3c8a7e488319dab.png
 
    
 
    
 
    Binomial Distribution Example
    
@@ -682,7 +683,7 @@ 
    Step 3: Understanding Distributions
-../_images/95c79f53523d1b9783ae4165a7fd293f44bf5c1ec13b96e0683169676e70c07c.png
+../_images/596a853ea8f36fbbdd24acadc5c2d5007c23779e63fc1516ee16885d91138718.png
 
    
 
    
 
    Poisson Distribution Example
    
@@ -702,7 +703,7 @@ 
    Step 3: Understanding Distributions
-../_images/f471f48523b4b750801da5035d60e32b3d9fa7f967a86460fac3a817493a2910.png
+../_images/8c3a0d5ed06805763b5404ef2a3b932553e09a5d3a21332e387e2d8401ad344f.png
 
    
 
    
 
diff --git a/Week_03/003_Overview.html b/Week_03/003_Overview.html
index d40ecf5..ab0a82e 100644
--- a/Week_03/003_Overview.html
+++ b/Week_03/003_Overview.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_03/Lesson_11.html b/Week_03/Lesson_11.html
index cbab5d9..d5f4122 100644
--- a/Week_03/Lesson_11.html
+++ b/Week_03/Lesson_11.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -598,7 +599,7 @@ 
    Setup for Activities
 
    
 
    
-
    /tmp/ipykernel_237615/2223182689.py:3: DeprecationWarning: 
+
    /tmp/ipykernel_288486/2223182689.py:3: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
diff --git a/Week_03/Lesson_12.html b/Week_03/Lesson_12.html
index e043166..d8257d6 100644
--- a/Week_03/Lesson_12.html
+++ b/Week_03/Lesson_12.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_03/Lesson_12solution.html b/Week_03/Lesson_12solution.html
index e4a845c..b5c1723 100644
--- a/Week_03/Lesson_12solution.html
+++ b/Week_03/Lesson_12solution.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -603,7 +604,7 @@ 
    Objective
-
    /tmp/ipykernel_237648/2223182689.py:3: DeprecationWarning: 
+
    /tmp/ipykernel_288519/2223182689.py:3: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
diff --git a/Week_03/Lesson_13.html b/Week_03/Lesson_13.html
index 0f49dd0..935bdd9 100644
--- a/Week_03/Lesson_13.html
+++ b/Week_03/Lesson_13.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -649,7 +650,7 @@ 
    Step 1: Load and Explore the Dataset
    /tmp/ipykernel_237687/245541981.py:1: DeprecationWarning: 
+
    /tmp/ipykernel_288555/245541981.py:1: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
@@ -723,14 +724,14 @@ 
    General Best Practices
 
    
 
    
-
    /tmp/ipykernel_237687/684399944.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
+
    /tmp/ipykernel_288555/684399944.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
 The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
 
 For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
 
 
   titanic_data['Age'].fillna(titanic_data['Age'].median(), inplace=True)
-/tmp/ipykernel_237687/684399944.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
+/tmp/ipykernel_288555/684399944.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
 The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
 
 For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
diff --git a/Week_03/Lesson_14.html b/Week_03/Lesson_14.html
index 796e91c..9113cd1 100644
--- a/Week_03/Lesson_14.html
+++ b/Week_03/Lesson_14.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -871,7 +872,7 @@ 
    Best Practices and Considerations
    /tmp/ipykernel_237713/2131166956.py:1: DeprecationWarning: 
+
    /tmp/ipykernel_288580/2131166956.py:1: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
@@ -1127,7 +1128,7 @@ 
    Step 8: Statistical Summary Comparison
-
    /tmp/ipykernel_237713/1279951594.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
+
    /tmp/ipykernel_288580/1279951594.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
 The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
 
 For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
diff --git a/Week_03/Lesson_15.html b/Week_03/Lesson_15.html
index bd48b9a..bd76991 100644
--- a/Week_03/Lesson_15.html
+++ b/Week_03/Lesson_15.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -751,7 +752,7 @@ 
    Step 1: Load the necessary libraries
-
    /tmp/ipykernel_237751/3916798240.py:2: DeprecationWarning: 
+
    /tmp/ipykernel_288616/3916798240.py:2: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
diff --git a/Week_04/004_Overview.html b/Week_04/004_Overview.html
index 2095142..f512db4 100644
--- a/Week_04/004_Overview.html
+++ b/Week_04/004_Overview.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_04/Lesson_16.html b/Week_04/Lesson_16.html
index a4f3666..7ce5fa3 100644
--- a/Week_04/Lesson_16.html
+++ b/Week_04/Lesson_16.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -581,7 +582,7 @@ 
    Prerequisites
-
    /tmp/ipykernel_237786/955872667.py:2: DeprecationWarning: 
+
    /tmp/ipykernel_288649/955872667.py:2: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
diff --git a/Week_04/Lesson_17.html b/Week_04/Lesson_17.html
index 0c5b0cf..14b57bc 100644
--- a/Week_04/Lesson_17.html
+++ b/Week_04/Lesson_17.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -583,7 +584,7 @@ 
    Prerequisites
-
    /tmp/ipykernel_237837/955872667.py:2: DeprecationWarning: 
+
    /tmp/ipykernel_288689/955872667.py:2: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
diff --git a/Week_04/Lesson_18.html b/Week_04/Lesson_18.html
index c7f2ab6..a96f880 100644
--- a/Week_04/Lesson_18.html
+++ b/Week_04/Lesson_18.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_04/Lesson_19.html b/Week_04/Lesson_19.html
index 9befee1..9719ec4 100644
--- a/Week_04/Lesson_19.html
+++ b/Week_04/Lesson_19.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_04/Lesson_20.html b/Week_04/Lesson_20.html
index 14449a6..7b2fd47 100644
--- a/Week_04/Lesson_20.html
+++ b/Week_04/Lesson_20.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_05/005_Overview.html b/Week_05/005_Overview.html
index c3b93de..abf1f8a 100644
--- a/Week_05/005_Overview.html
+++ b/Week_05/005_Overview.html
@@ -259,6 +259,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/Week_05/005_Overview_.html b/Week_05/005_Overview_.html
deleted file mode 100644
index 69b761d..0000000
--- a/Week_05/005_Overview_.html
+++ /dev/null
@@ -1,645 +0,0 @@
-
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-    Course Structure — 100 Days of Machine Learning
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    Module 3: Supervised Learning - Regression and Classification#
    
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    Weeks 5-6#
    
-
      
-
    • Focus: Key concepts and algorithms in supervised learning.
      • 
-
    • Topics include regression, classification algorithms, decision trees, SVM, and ensemble methods.
      • 
-
    
-
    
-
    Week 5: Supervised Learning - Regression#
    
-
      
-
    • Day 21: Introduction to Regression Analysis in Python
      
-
        
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      • Basics of regression analysis and simple linear regression.
        • 
-
      • Math Focus: Linear equation fundamentals and fitting models to data.
        • 
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      • 
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    • Day 22: Implementing Multiple Linear Regression in Python
      
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      • Understand and implement multiple linear regression.
        • 
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      • Math Focus: Multivariate calculus and regression coefficients interpretation.
        • 
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      • 
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    • Day 23: Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
      
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      • Explore advanced regression techniques and their applications.
        • 
-
      • Math Focus: Polynomial functions, Lasso and Ridge regularization techniques.
        • 
-
      
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      • 
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    • Day 24: Regression Model Evaluation Metrics in Python
      
-
        
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      • Key metrics for evaluating regression models.
        • 
-
      • Math Focus: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.
        • 
-
      
-
      • 
-
    • Day 25: Addressing Overfitting and Underfitting in Regression Models
      
-
        
-
      • Strategies to combat overfitting and underfitting in regression.
        • 
-
      • Math Focus: Bias-variance tradeoff and regularization methods.
        • 
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diff --git a/Week_05/Lesson_21.html b/Week_05/Lesson_21.html
index f40f37c..6b68de5 100644
--- a/Week_05/Lesson_21.html
+++ b/Week_05/Lesson_21.html
@@ -261,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -848,7 +849,7 @@ 
    Import and Analyze a Housing Dataset
    /tmp/ipykernel_237871/737031205.py:1: DeprecationWarning: 
+
    /tmp/ipykernel_288722/737031205.py:1: DeprecationWarning: 
 Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
 (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
 but was not found to be installed on your system.
diff --git a/Week_05/Lesson_22.html b/Week_05/Lesson_22.html
index cc17138..cdd05e3 100644
--- a/Week_05/Lesson_22.html
+++ b/Week_05/Lesson_22.html
@@ -69,6 +69,7 @@
     
     
     
+    
     
   
   
@@ -260,6 +261,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
@@ -574,7 +576,7 @@ 
    Key Concepts for Implementationimport matplotlib.pyplot as plt
 import pandas as pd
 
-df = pd.read_csv("customer_data2.csv")
+df = pd.read_csv("customer_data.csv")
 g = sns.pairplot(df, x_vars=['Age','Income'], y_vars='Spending_Score', height=7, aspect=0.7, kind='reg')
 g.fig.suptitle("Regression Lines: Age and Income versus Spending Score", y=1.02)
 plt.show()
@@ -582,7 +584,7 @@ 
    Key Concepts for Implementation
-../_images/92ac3250e6513c3b8472e0b18e967da18927d77722af5d165c07f194e84d7801.png
+../_images/bb0d0c6c15e5ffac9ec2fbb8b1013187538a784f2b9216a086c4291db4962b35.png
 
    
 
    
 
@@ -685,8 +687,8 @@ 
    Understanding Model Evaluation Metrics
-
    MSE: 732.3551245668029
-R-squared: -0.027105564211052702
+
    MSE: 3183.330047035957
+R-squared: 0.13751568541102366
 
    
 
    
 
    
@@ -710,8 +712,8 @@ 
    Understanding Model Evaluation Metrics
-../_images/1d0563321f6a2b86ded4cb8e6c1035aab0234f07e9db2f827b7c6ea403da2913.png
-
    Coefficients for each independent variable: [-0.24544393  0.72516579]
+../_images/9bd1031423bef1a2f61f1d89256148d1b71e943953636677f033cd71d2a243d2.png
+
    Coefficients for each independent variable: [34.34198373 55.53651751]
 
    
 
    
 
    
@@ -736,7 +738,7 @@ 
    Understanding Model Evaluation Metrics
-
    48.57488877753046
+
    -9.31964941598494
 
    
 
    
 
    
@@ -891,6 +893,10 @@ 
    Exercise For The Reader
 
    
 
    Additional Resources#
    
+
 
    https://www.statology.org/multiple-linear-regression-assumptions/
    
 
    
 
@@ -933,6 +939,15 @@ 
    Additional ResourcesDay 21 - Introduction to Regression Analysis in Python
    
       
    
     
+    
+      
    
+        
    next
    
+        
    Day 23: Advanced Regression Techniques
    
+      
    
+      
+        
 
    
                 
               
diff --git a/Week_05/Lesson_23.html b/Week_05/Lesson_23.html
index c0e5c7c..4cd5a10 100644
--- a/Week_05/Lesson_23.html
+++ b/Week_05/Lesson_23.html
@@ -9,7 +9,7 @@
     
     
 
-    Outline Only - Lesson 23: Advanced Regression Techniques — 100 Days of Machine Learning
+    Day 23: Advanced Regression Techniques — 100 Days of Machine Learning
   
   
   
@@ -62,13 +62,14 @@
 const thebe_selector_output = ".output, .cell_output"
 
     
+    
+    
     
     
     
     
     
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+    
   
   
   
@@ -238,11 +239,6 @@
 
 
 
-
-
    Week 5 - Data Preprocessing
    
-
 
    Week 4 - Data Preprocessing
    
 
      
@@ -258,10 +254,15 @@
 
    
 
 
-
    Course Overview
    
-
    
@@ -483,7 +484,9 @@
   
 `);
 
-
+
 
    
       
     
    
@@ -494,11 +497,27 @@
               
 
 
    
-    
    Outline Only - Lesson 23: Advanced Regression Techniques
    
+    
    Day 23: Advanced Regression Techniques
    
     
     
    
         
     
    
 
    
@@ -508,35 +527,1398 @@ 
    Outline Only - Lesson 23: Advanced Regression Techniques
    
 
                 
    
                   
-  
    
-
    Outline Only - Lesson 23: Advanced Regression Techniques#
    
-
    Polynomial, Lasso, and Ridge Regression - Explore advanced regression techniques and their applications. - Math Focus: Polynomial functions, Lasso and Ridge regularization techniques.
    
+  
    
+
    Day 23: Advanced Regression Techniques#
    
+
    
+
    Introduction#
    
+
    Astute readers may have noticed during the Day 22 lesson that some of the customer_data.csv plots were not great fits across the entire domain of the data. This is because there was a non-linear element (an \(x^2\) term) in the function I used to build that dataset out of random numbers. Most data we’ll encounter is not perfectly linear, but we can still use regression. A higher order function may describe the relationships between our independent variables and the dependent variable. We are not limited to \(y = mx + c\), or even \(y = \beta_0 + \beta_1x_1 + \beta_2x_2 + ... + \beta_nx_n + \epsilon\) – we can include \(x_n^2, x_n^3, ...\) as if they were new independent variables, and give our regression line the freedom to match any polynomial function.
    
+
    We’ll just need to steer clear of overfitting: with unlimited terms, it’s arbitrarily simple to draw a polynomial function that passes through every data point perfectly. This is unlikely to translate into predictive power outside of training, though. Lasso and Ridge are specific forms of regularization designed to address overfitting by penalizing the size of the coefficients. Keep an eye out for the penalty term (\(\lambda\)) which controls the complexity of the model.
    
+
    A general polynomial regression model can be represented as:
    
+
    \( y = \beta_0 + \beta_1x_1 + \beta_2x_1^2 + \ldots + \beta_nx_1^n + \epsilon \)
    
+
    Where:
    
 
      
-
    • Theoretical Concepts:
      
-
        
-
      • Overview of polynomial regression and its applications.
        • 
-
      • Introduction to regularization techniques: Lasso and Ridge regression.
        • 
+
      • \(y\) is the dependent variable.
        • 
+
      • \(x_1, x_1^2, \ldots, x_1^n\) are the predictor variables and their polynomial terms up to degree (n).
        • 
+
      • \(\beta_0, \beta_1, \ldots, \beta_n\) are the coefficients.
        • 
+
      • \(\epsilon\) represents the model error.
        • 
 
      
-
      • 
-
    • Mathematical Foundation:
      
-
        
-
      • Polynomial functions and their role in regression.
        • 
-
      • Lasso (L1 regularization) and Ridge (L2 regularization) concepts.
        • 
+
        To get acquainted with the topic, let’s take a peek at what adding additional degrees to the polynomial can do:
        
+
        
+
        
+
        # overview plot
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.metrics import r2_score
+
+# Generating demo data
+np.random.seed(42)
+x = np.random.normal(0, 1, 20)
+
+# This is the actual equation, so we could check exactly what coefficients our regression found.
+y= -3.8 * x**4 + 3.4 * x**3 + 6.6 * x**2 - 2.5 * x + np.random.normal(0, 1, 20)
+
+# Reshape x for sklearn
+x = x[:, np.newaxis]
+y = y[:, np.newaxis]
+
+# Simple linear regression
+linear_regressor = LinearRegression()
+linear_regressor.fit(x, y)
+y_pred_linear = linear_regressor.predict(x)
+
+# Polynomial regression (underfit)
+poly_features2 = PolynomialFeatures(degree=2)
+x_poly2 = poly_features2.fit_transform(x)
+poly_regressor2 = LinearRegression()
+poly_regressor2.fit(x_poly2, y)
+y_pred_poly2 = poly_regressor2.predict(x_poly2)
+
+# Polynomial regression (well-fit)
+poly_features3 = PolynomialFeatures(degree=3)
+x_poly3 = poly_features3.fit_transform(x)
+poly_regressor3 = LinearRegression()
+poly_regressor3.fit(x_poly3, y)
+y_pred_poly3 = poly_regressor3.predict(x_poly3)
+
+# Polynomial regression (overfit)
+poly_features5 = PolynomialFeatures(degree=5)
+x_poly5 = poly_features5.fit_transform(x)
+poly_regressor5 = LinearRegression()
+poly_regressor5.fit(x_poly5, y)
+y_pred_poly5 = poly_regressor5.predict(x_poly5)
+
+# R^2 Scores
+r2_linear = r2_score(y, y_pred_linear)
+r2_poly2 = r2_score(y, y_pred_poly2)
+r2_poly3 = r2_score(y, y_pred_poly3)
+r2_poly5 = r2_score(y, y_pred_poly5)
+
+# Plotting
+plt.figure(figsize=(12, 10))
+
+# Plot simple linear regression
+plt.subplot(2, 2, 1)
+plt.scatter(x, y, color='blue', label='Actual response, yi')
+plt.plot(x, y_pred_linear, color='red', label='Estimated regression line, f(x)')
+plt.title(f'Degree: 1, R^2 = {r2_linear:.2f}')
+plt.legend()
+
+# Plot underfit polynomial regression
+plt.subplot(2, 2, 2)
+plt.scatter(x, y, color='blue', label='Actual response, yi')
+sorted_axis = np.argsort(x[:, 0])
+plt.plot(x[sorted_axis], y_pred_poly2[sorted_axis], color='red', label='Estimated regression line, f(x)')
+plt.title(f'Degree: 2, R^2 = {r2_poly2:.2f}')
+plt.legend()
+
+# Plot well-fit polynomial regression
+plt.subplot(2, 2, 3)
+plt.scatter(x, y, color='blue', label='Actual response, yi')
+sorted_axis = np.argsort(x[:, 0])
+plt.plot(x[sorted_axis], y_pred_poly3[sorted_axis], color='red', label='Estimated regression line, f(x)')
+plt.title(f'Degree: 3, R^2 = {r2_poly3:.2f}')
+plt.legend()
+
+# Plot overfit polynomial regression
+plt.subplot(2, 2, 4)
+plt.scatter(x, y, color='blue', label='Actual response, yi')
+sorted_axis = np.argsort(x[:, 0])
+plt.plot(x[sorted_axis], y_pred_poly5[sorted_axis], color='red', label='Estimated regression line, f(x)')
+plt.title(f'Degree: 5, R^2 = {r2_poly5:.2f}')
+plt.legend()
+
+# Show the plots
+plt.tight_layout()
+plt.show()
+
        
+
        
+
        
+
        
+../_images/5a3b207bfabe58ccdfda75f84946a10cdf87d302d66c3b6d1746e2c56a800111.png
+
        
+
        
+
    
+
    
+
    Polynomial Regression#
    
+
    In terms of execution, we can use sklearn’s sklearn.preprocessing.PolynomialFeatures functionality to perform linear regression with a higher degree. This is exactly what was used in the overview plot above, but I wanted to isolate the code for easier comparison to Lasso and Ridge regression below.
    
+
    Some additional things to keep in mind:
    
+
      
+
    1. Choice of Polynomial Degree: Determining the appropriate degree of the polynomial is critical. A higher-degree polynomial can fit the training data very well but might perform poorly on unseen data due to overfitting. Various model selection techniques, such as cross-validation, can be used to choose a polynomial degree that balances bias and variance.
      2. 
+
    2. Feature Scaling: Polynomial terms can have very different scales, especially for higher degrees, which can make the regression model sensitive to the scale of the input features. Normalizing or standardizing the features before applying polynomial regression can help with model convergence and interpretation.
      3. 
+
    3. Multivariate Polynomial Regression: While your introduction focuses on polynomial regression with a single independent variable (\(x_1\)), it’s important to note that polynomial regression can be extended to multiple independent variables, allowing for interaction terms between different variables (e.g., \(x_1x_2\), \(x_1^2x_2\), etc.). This introduces complexity in model interpretation but can capture interactions between predictors that are not apparent in single-variable analyses.
      4. 
+
    4. Computational Complexity: As the degree of the polynomial and the number of independent variables increase, the computational complexity of fitting the regression model also increases. This is due to the larger number of terms and interactions that need to be calculated and optimized. It’s important to balance the model’s complexity with computational constraints.
      5. 
+
    5. Analyzing Residuals: When using polynomial regression, it becomes even more important to analyze residuals to ensure that the assumptions of linear regression are still met. This includes checking for homoscedasticity, normality of residuals, and absence of autocorrelation. If these assumptions are violated, the results of the regression, including any inference drawn from the coefficients, may not be valid.
      6. 
+
    
+
    
+
    On make_pipeline#
    
+
    make_pipeline from sklearn.pipeline is a utility function that simplifies the process of creating a pipeline of transformations with a final estimator. In machine learning workflows, it’s often necessary to chain together multiple steps such as preprocessing (like scaling features or applying polynomial expansions) and then applying a model (like LinearRegression, Lasso, or Ridge). A pipeline bundles these steps into a single object that behaves like a compound estimator.
    
+
    When you use make_pipeline, you can pass it a series of transformations followed by an estimator, and it automatically names each step based on its class. The steps are executed in sequence: each step’s fit_transform() method is called on the input data (except for the last step, where only fit() is called), transforming the data along the way, until it finally fits the model on the transformed data. This streamlines the code, making it cleaner and easier to read, and reduces the risk of mistakes (like applying transformations to the training data but forgetting to do so on the test data).
    
+
    The intro plot above does not use a pipeline, but Lasso and Ridge specifically benefit from it. In this next example, we will pipeline from PolynomialFeatures (a preprocessor) to LinearRegression (a model).
    
+
    This allows us to convert this block of code:
    
+
    # Polynomial regression (well-fit)
+poly_features3 = PolynomialFeatures(degree=3)
+x_poly3 = poly_features3.fit_transform(x)
+poly_regressor3 = LinearRegression()
+poly_regressor3.fit(x_poly3, y)
+y_pred_poly3 = poly_regressor3.predict(x_poly3)
+
    
+
    
+
    into this:
    
+
    poly = make_pipeline(PolynomialFeatures(degree), LinearRegression())
+poly.fit(X, y)
+
    
+
    
+
    You can imagine that as you add additional steps to transform your data, the first style of code will grow from 6 lines, to 9, to 12… while the second example simply adds more “machinery” to the sequence described in make_pipeline(...).
    
+
    For instance, when used with LinearRegression, Lasso, or Ridge in the context of polynomial regression, you would typically create a pipeline that first expands your features into a polynomial feature space (using PolynomialFeatures) and then scales them (using StandardScaler, although not in these basic examples, it’s a common practice), before finally applying the regression model. This ensures that the feature expansion and scaling are part of the model fitting process, which is particularly important for cross-validation and deploying the model for predictions on new data.
    
+
    To get the coefficients and intercept out of a pipeline that ends in linear regression, you’ll have to reach inside the pipeline via the name it generates for its different steps. poly_regressor3.coef_ becomes poly['linearregression'].coef, where the string 'linearregression' is generated from the LinearRegression object that was passed into the pipeline.
    
+
    
+
    
+
    import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import Lasso
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import make_pipeline
+# Generating demo data
+np.random.seed(42)
+x = np.random.normal(0, 1, 20)
+X = x[:, np.newaxis]
+
+# This is the actual equation, so we could check exactly what coefficients our regression found.
+y = -3.8 * x**4 + 3.4 * x**3 + 6.6 * x**2 - 2.5 * x + np.random.normal(0, 1, 20)
+
+# Reshape x for sklearn
+x = x[:, np.newaxis]
+y = y[:, np.newaxis]
+
+degree = 4  # choosing the same degree as the true model
+poly = make_pipeline(PolynomialFeatures(degree), LinearRegression())
+poly.fit(X, y)
+
+# Generating points for plotting the regression line
+x_plot = np.linspace(min(X), max(X), 100)
+y_plot = poly.predict(x_plot)
+
+plt.scatter(x, y, label='Data points')
+sorted_axis = np.argsort(x[:, 0])
+plt.plot(x_plot, y_plot, label='Polynomial regression line', color='red')
+plt.legend()
+plt.xlabel('x')
+plt.ylabel('y')
+plt.title('Polynomial (degree = 4) regression')
+plt.show()
+
    
+
    
+
    
+
    
+../_images/efe2803e3c407dd4463ca9959dfcf6bfa2b0b72cc864062d127916b2c28a2c0e.png
+
    
+
    
+
    
+
    
+
    poly
+
    
+
    
+
    
+
    
+
    Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=4)),
+                ('linearregression', LinearRegression())])
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    
+
    
+
    
+
    
+
    poly['linearregression'].coef_
+
    
+
    
+
    
+
    
+
    array([[ 0.        , -2.00193104,  7.3848762 ,  3.00349368, -4.16571691]])
+
    
+
    
+
    
+
    
+
    
+
    
+
    poly['linearregression'].intercept_
+
    
+
    
+
    
+
    
+
    array([-0.32345315])
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    Lasso Regression#
    
+
    Lasso regression adds a penalty equal to the absolute value of the magnitude of coefficients. This can lead not only to small coefficients but can actually shrink some of them to zero, effectively performing variable selection. Using that feature of Lasso regression, you don’t have to interpret the usefulness of a variable - if it’s not important to the model, then its coefficients will drop to zero. However, to achieve this result, you’ll have to tune your lambda (\(\lambda\)) value. If \(\lambda\) is too small, the penalty effect might be negligible, leading to little improvement over ordinary least squares regression. If \(\lambda\) is too large, too many variables might be eliminated, resulting in underfitting. Techniques such as cross-validation can be used to select an optimal \(\lambda\).
    
+
    The objective function for Lasso regression is:
    
+
    \( \text{Minimize: } \frac{1}{2N} \sum_{i=1}^{N} (y_i - \sum_{j=1}^{n} \beta_j x_{ij})^2 + \lambda \sum_{j=1}^{n} |\beta_j| \)
    
+
    Where:
    
+
      
+
    • \(N\) is the number of observations.
      • 
+
    • \(\lambda\) is the regularization parameter controlling the strength of the penalty.
      • 
+
    • The first term is the Mean Squared Error, and the second term is the L1 penalty.
      • 
 
    
-
-
  • Python Implementation:
    
-
      
-
    • Implementing polynomial regression with numpy and scikit-learn.
      • 
-
    • Demonstrating Lasso and Ridge regression using scikit-learn.
      • 
-
    • Comparing models using visualizations in matplotlib.
      • 
+
      Other things to keep in mind:
      
+
        
+
      • Lasso regression is sensitive to the scale of the input variables, so standardizing the data (to have 0 mean and unit variance) before applying Lasso regression is a common practice.
        • 
+
      • While Lasso can lead to sparse solutions, Ridge regression is preferred when multicollinearity is present among the features.
        • 
 
      
-
-
    • Example Dataset:
      
-
        
-
      • Dataset requiring a non-linear fit (e.g., environmental data).
        • 
+
        The introduction plot used sklearn.linear_model.LinearRegression to perform regression with different degrees of polynomials. Although Lasso might not perfectly capture the relationship in polynomial terms without specifically including polynomial features, this code will illustrate the process:
        
+
        
+
        
+
        import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import Lasso
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import make_pipeline
+
+# Generating demo data
+np.random.seed(42)
+x = np.random.normal(0, 1, 20)
+y = -3.8 * x**4 + 3.4 * x**3 + 6.6 * x**2 - 2.5 * x + np.random.normal(0, 1, 20)
+X = x[:, np.newaxis]
+
+degree = 4  # choosing the same degree as the true model
+lasso_poly = make_pipeline(PolynomialFeatures(degree), Lasso(alpha=0.1, max_iter=10000))
+lasso_poly.fit(X, y)
+
+# Generating points for plotting the regression line
+x_plot = np.linspace(min(x), max(x), 100)
+X_plot = x_plot[:, np.newaxis]
+y_plot = lasso_poly.predict(X_plot)
+
+# Plotting the data points and the regression line
+plt.scatter(x, y, label='Data points')
+plt.plot(x_plot, y_plot, label='Lasso Regression Line', color='red')
+plt.legend()
+plt.xlabel('x')
+plt.ylabel('y')
+plt.title('Lasso Regression with Polynomial Features')
+plt.show()
+
        
+
        
+
        
+
        
+../_images/cf2893ef3bdd7bf0354019761a0360b80dbe08cc759e847d38b3cd7a4c87129a.png
+
        
+
        
+
        
+
        
+
        lasso_poly['lasso'].coef_
+
        
+
        
+
        
+
        
+
        array([ 0.        , -1.55260439,  5.72240286,  2.90811749, -3.66350745])
+
        
+
        
+
        
+
        
+
        
+
        
+
        lasso_poly['lasso'].intercept_
+
        
+
        
+
        
+
        
+
        0.19943027115730638
+
        
+
        
+
        
+
        
+
          
+
        • Choosing Degree and \(\lambda\): The choice of degree=4 for the polynomial features and alpha=0.1 for the Lasso regression penalty ($\lambda$) is somewhat arbitrary here and might need adjustment based on cross-validation to find the optimal model complexity and regularization strength.
          • 
+
        • Max Iterations: Increasing max_iter in Lasso() might be necessary for the algorithm to converge, especially for higher degrees of polynomials or smaller values of \(\alpha\) (lambda).
          • 
 
        
-
+
    • 
+
    
+
    Ridge Regression#
    
+
    Ridge regression adds a penalty equal to the square of the magnitude of coefficients. All coefficients are shrunk by the same factor (none are eliminated).
    
+
    The objective function for Ridge regression is:
    
+
    \( \text{Minimize: } \frac{1}{2N} \sum_{i=1}^{N} (y_i - \sum_{j=1}^{n} \beta_j x_{ij})^2 + \lambda \sum_{j=1}^{n} \beta_j^2 \)
    
+
      
+
    • Similarly, \(N\) and \(\lambda\) have the same definitions as in Lasso.
      • 
+
    • The first term again represents the Mean Squared Error, and the second term is the L2 penalty.
      • 
 
    
+
      
+
    1. Effect of the Penalty: Ridge regression is particularly useful when dealing with multicollinearity or when you have more predictors than observations.
      2. 
+
    2. Scaling Importance: standardizing the features in Ridge regression is important due to the square of the coefficients being included in the penalty term. Features on larger scales can have disproportionately large effects on the formulation.
      3. 
+
    3. Choosing \(\lambda\) for the bias-variance trade-off: A higher \(\lambda\) increases bias but reduces variance, whereas a lower \(\lambda\) does the opposite. The optimal \(\lambda\) minimizes the mean squared error of predictions.
      4. 
+
    4. Computational Aspects: Ridge regression tends to be computationally more efficient than Lasso for a large number of features, mainly because the solution is obtained through matrix operations that have computationally efficient implementations.
      5. 
+
    
+
    
+
    
+
    import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import Ridge
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import make_pipeline
+
+# Generating demo data using the same snippet
+np.random.seed(42)
+x = np.random.normal(0, 1, 20)
+y = -3.8 * x**4 + 3.4 * x**3 + 6.6 * x**2 - 2.5 * x + np.random.normal(0, 1, 20)
+X = x[:, np.newaxis]
+
+# Using polynomial features again since our relationship is non-linear
+degree = 4
+ridge_poly = make_pipeline(PolynomialFeatures(degree), Ridge(alpha=0.1))
+ridge_poly.fit(X, y)
+
+# Generating points for plotting
+x_plot = np.linspace(min(x), max(x), 100)
+X_plot = x_plot[:, np.newaxis]
+y_plot = ridge_poly.predict(X_plot)
+
+# Plotting
+plt.scatter(x, y, label='Data points')
+plt.plot(x_plot, y_plot, color='red', label='Ridge Regression Line')
+plt.legend()
+plt.xlabel('x')
+plt.ylabel('y')
+plt.title('Ridge Regression with Polynomial Features')
+plt.show()
+
    
+
    
+
    
+
    
+../_images/0db9f24dd130ecb95bde386604ac8a3a2c4540ea1d11b2edf145ecd048c39112.png
+
    
+
    
+
    
+
    
+
    ridge_poly['ridge'].coef_
+
    
+
    
+
    
+
    
+
    array([ 0.        , -2.05626925,  6.75667935,  3.06069885, -3.96459941])
+
    
+
    
+
    
+
    
+
    
+
    
+
    ridge_poly['ridge'].intercept_
+
    
+
    
+
    
+
    
+
    -0.14654769263455236
+
    
+
    
+
    
+
    
+
    
+
    
+
    Exercise For The Reader#
    
+
    Lasso and Ridge regression could be applied to sklearn’s included California housing data set.
    
+
      
+
    • Don’t forget to do a test train split.
      • 
+
    • It’s probably best to use a scaler.
      • 
+
    • selecting variables is important, but can be thought of as part science and part art. Try a few and see what helps.
      • 
+
    
+
    
+
    
+
    # starter code
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+fetched = fetch_california_housing(as_frame=True)
+X = fetched['data']
+Y = fetched['target']
+
+# Splitting dataset
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
+
    
+
    
+
    
+
    
+
    
+
    
+
    X.head()
+
    
+
    
+
    
+
    
+
    
+
+
+  
+    
+      
+      
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+      
+    
+  
+
    MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitude
    08.325241.06.9841271.023810322.02.55555637.88-122.23
    18.301421.06.2381370.9718802401.02.10984237.86-122.22
    27.257452.08.2881361.073446496.02.80226037.85-122.24
    35.643152.05.8173521.073059558.02.54794537.85-122.25
    43.846252.06.2818531.081081565.02.18146737.85-122.25
    
+
    
+
    
+
    
+
    
+
    # model configuration
+from sklearn.preprocessing import StandardScaler
+
+degrees = 8
+ridge_poly = make_pipeline(PolynomialFeatures(degrees), StandardScaler(), Ridge(alpha=0.1))
+ridge_poly.fit(X_train, Y_train)
+
    
+
    
+
    
+
    
+
    Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=8)),
+                ('standardscaler', StandardScaler()),
+                ('ridge', Ridge(alpha=0.1))])
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    
+
    
+
    
+
    
+
    from sklearn.metrics import mean_squared_error, r2_score
+
+# Making predictions
+predictions = ridge_poly.predict(X_test)
+
+# Evaluation
+mse = mean_squared_error(Y_test, predictions)
+r2 = r2_score(Y_test, predictions)
+
+print(f'MSE: {mse}')
+print(f'R-squared: {r2}')
+
    
+
    
+
    
+
    
+
    MSE: 620.2980353830641
+R-squared: -472.3621683634644
+
    
+
    
+
    
+
    
+
    
+
    
+
    # examine the concrete results of the model - prediction vs test data's label
+# the dataset provides home values in fractions of $100,000, so we convert the prediction to real dollars.
+print(f"""Input data:
+{X_test[:1]}
+
+Median house value: ${Y_test.iloc[0] * 100:.2f}k
+Predicted value: ${predictions[0] * 100:.2f}k""")
+
    
+
    
+
    
+
    
+
    Input data:
+       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \
+20046  1.6812      25.0  4.192201   1.022284      1392.0  3.877437     36.06   
+
+       Longitude  
+20046    -119.01  
+
+Median house value: $47.70k
+Predicted value: $70.53k
+
    
+
    
+
    
+
    
+
    Can you improve it with data preprocessing or altering the model parameters?
    
+
    Have fun!
    
+
    
+
    
+
    Additional Resources#
    
+
+
    
 
    
 
     
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diff --git a/_sources/Week_05/005_Overview_.md b/_sources/Week_05/005_Overview_.md
deleted file mode 100644
index eb841cb..0000000
--- a/_sources/Week_05/005_Overview_.md
+++ /dev/null
@@ -1,27 +0,0 @@
-  # Course Structure
-
-  # Module 3: Supervised Learning - Regression and Classification
- ## Weeks 5-6
-- Focus: Key concepts and algorithms in supervised learning.
-- Topics include regression, classification algorithms, decision trees, SVM, and ensemble methods.
-
-### Week 5: Supervised Learning - Regression
-- **Day 21:** Introduction to Regression Analysis in Python
-  - Basics of regression analysis and simple linear regression.
-  - Math Focus: Linear equation fundamentals and fitting models to data.
-
-- **Day 22:** Implementing Multiple Linear Regression in Python
-  - Understand and implement multiple linear regression.
-  - Math Focus: Multivariate calculus and regression coefficients interpretation.
-
-- **Day 23:** Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
-  - Explore advanced regression techniques and their applications.
-  - Math Focus: Polynomial functions, Lasso and Ridge regularization techniques.
-
-- **Day 24:** Regression Model Evaluation Metrics in Python
-  - Key metrics for evaluating regression models.
-  - Math Focus: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.
-
-- **Day 25:** Addressing Overfitting and Underfitting in Regression Models
-  - Strategies to combat overfitting and underfitting in regression.
-  - Math Focus: Bias-variance tradeoff and regularization methods.
\ No newline at end of file
diff --git a/_sources/Week_05/Lesson_22.ipynb b/_sources/Week_05/Lesson_22.ipynb
index ac1a21f..fa12c56 100644
--- a/_sources/Week_05/Lesson_22.ipynb
+++ b/_sources/Week_05/Lesson_22.ipynb
@@ -76,7 +76,7 @@
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "\n",
-    "df = pd.read_csv(\"customer_data2.csv\")\n",
+    "df = pd.read_csv(\"customer_data.csv\")\n",
     "g = sns.pairplot(df, x_vars=['Age','Income'], y_vars='Spending_Score', height=7, aspect=0.7, kind='reg')\n",
     "g.fig.suptitle(\"Regression Lines: Age and Income versus Spending Score\", y=1.02)\n",
     "plt.show()"
@@ -511,6 +511,9 @@
    "source": [
     "# Additional Resources\n",
     "\n",
+    "-   **Resource 1:** [Multiple Linear Regression in Python](https://www.nickmccullum.com/multiple-linear-regression-python/) (Detailed guide on multiple linear regression, including data preparation and model building)\n",
+    "-   **Resource 2:** [Multivariate Linear Regression Tutorial with Real Python](https://realpython.com/linear-regression-in-python/#multiple-linear-regression) (Explanation of multiple linear regression with an example using Python)\n",
+    "\n",
     "https://www.statology.org/multiple-linear-regression-assumptions/"
    ]
   },
diff --git a/_sources/Week_05/Lesson_23.ipynb b/_sources/Week_05/Lesson_23.ipynb
index 0b93193..e2831ad 100644
--- a/_sources/Week_05/Lesson_23.ipynb
+++ b/_sources/Week_05/Lesson_23.ipynb
@@ -5,26 +5,1647 @@
    "id": "210315fa-bf4a-4892-9e66-e42ee9b5d804",
    "metadata": {},
    "source": [
-    "# Outline Only - **Lesson 23:** Advanced Regression Techniques\n",
-    "Polynomial, Lasso, and Ridge Regression - Explore advanced regression techniques and their applications. - Math Focus: Polynomial functions, Lasso and Ridge regularization techniques.\n",
-    "- **Theoretical Concepts:**\n",
-    "    - Overview of polynomial regression and its applications.\n",
-    "    - Introduction to regularization techniques: Lasso and Ridge regression.\n",
-    "- **Mathematical Foundation:**\n",
-    "    - Polynomial functions and their role in regression.\n",
-    "    - Lasso (L1 regularization) and Ridge (L2 regularization) concepts.\n",
-    "- **Python Implementation:**\n",
-    "    - Implementing polynomial regression with numpy and scikit-learn.\n",
-    "    - Demonstrating Lasso and Ridge regression using scikit-learn.\n",
-    "    - Comparing models using visualizations in matplotlib.\n",
-    "- **Example Dataset:**\n",
-    "    - Dataset requiring a non-linear fit (e.g., environmental data)."
+    "# Day 23: Advanced Regression Techniques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "242f5383-181e-459b-895d-aebad6bf0aa6",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "Astute readers may have noticed during the Day 22 lesson that some of the `customer_data.csv` plots were not great fits across the entire domain of the data. This is because there was a non-linear element (an $x^2$ term) in the function I used to build that dataset out of random numbers. Most data we'll encounter is not perfectly linear, but we can still use regression. A higher order function may describe the relationships between our independent variables and the dependent variable. We are not limited to $y = mx + c$, or even $y = \\beta_0 + \\beta_1x_1 + \\beta_2x_2 + ... + \\beta_nx_n + \\epsilon$ -- we can include $x_n^2, x_n^3, ...$ as if they were new independent variables, and give our regression line the freedom to match any polynomial function.\n",
+    "\n",
+    "We'll just need to steer clear of overfitting: with unlimited terms, it's arbitrarily simple to draw a polynomial function that passes through *every* data point perfectly. This is unlikely to translate into predictive power outside of training, though. Lasso and Ridge are specific forms of regularization designed to address overfitting by penalizing the size of the coefficients. Keep an eye out for the penalty term ($\\lambda$) which controls the complexity of the model.\n",
+    "\n",
+    "A general polynomial regression model can be represented as:\n",
+    "\n",
+    "$ y = \\beta_0 + \\beta_1x_1 + \\beta_2x_1^2 + \\ldots + \\beta_nx_1^n + \\epsilon $\n",
+    "\n",
+    "Where:\n",
+    "- $y$ is the dependent variable.\n",
+    "- $x_1, x_1^2, \\ldots, x_1^n$ are the predictor variables and their polynomial terms up to degree \\(n\\).\n",
+    "- $\\beta_0, \\beta_1, \\ldots, \\beta_n$ are the coefficients.\n",
+    "- $\\epsilon$ represents the model error.\n",
+    "\n",
+    "To get acquainted with the topic, let's take a peek at what adding additional degrees to the polynomial can do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "19e3f075-176d-4dff-89a0-88b71613af31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# overview plot\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "# Generating demo data\n",
+    "np.random.seed(42)\n",
+    "x = np.random.normal(0, 1, 20)\n",
+    "\n",
+    "# This is the actual equation, so we could check exactly what coefficients our regression found.\n",
+    "y= -3.8 * x**4 + 3.4 * x**3 + 6.6 * x**2 - 2.5 * x + np.random.normal(0, 1, 20)\n",
+    "\n",
+    "# Reshape x for sklearn\n",
+    "x = x[:, np.newaxis]\n",
+    "y = y[:, np.newaxis]\n",
+    "\n",
+    "# Simple linear regression\n",
+    "linear_regressor = LinearRegression()\n",
+    "linear_regressor.fit(x, y)\n",
+    "y_pred_linear = linear_regressor.predict(x)\n",
+    "\n",
+    "# Polynomial regression (underfit)\n",
+    "poly_features2 = PolynomialFeatures(degree=2)\n",
+    "x_poly2 = poly_features2.fit_transform(x)\n",
+    "poly_regressor2 = LinearRegression()\n",
+    "poly_regressor2.fit(x_poly2, y)\n",
+    "y_pred_poly2 = poly_regressor2.predict(x_poly2)\n",
+    "\n",
+    "# Polynomial regression (well-fit)\n",
+    "poly_features3 = PolynomialFeatures(degree=3)\n",
+    "x_poly3 = poly_features3.fit_transform(x)\n",
+    "poly_regressor3 = LinearRegression()\n",
+    "poly_regressor3.fit(x_poly3, y)\n",
+    "y_pred_poly3 = poly_regressor3.predict(x_poly3)\n",
+    "\n",
+    "# Polynomial regression (overfit)\n",
+    "poly_features5 = PolynomialFeatures(degree=5)\n",
+    "x_poly5 = poly_features5.fit_transform(x)\n",
+    "poly_regressor5 = LinearRegression()\n",
+    "poly_regressor5.fit(x_poly5, y)\n",
+    "y_pred_poly5 = poly_regressor5.predict(x_poly5)\n",
+    "\n",
+    "# R^2 Scores\n",
+    "r2_linear = r2_score(y, y_pred_linear)\n",
+    "r2_poly2 = r2_score(y, y_pred_poly2)\n",
+    "r2_poly3 = r2_score(y, y_pred_poly3)\n",
+    "r2_poly5 = r2_score(y, y_pred_poly5)\n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "\n",
+    "# Plot simple linear regression\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.scatter(x, y, color='blue', label='Actual response, yi')\n",
+    "plt.plot(x, y_pred_linear, color='red', label='Estimated regression line, f(x)')\n",
+    "plt.title(f'Degree: 1, R^2 = {r2_linear:.2f}')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Plot underfit polynomial regression\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.scatter(x, y, color='blue', label='Actual response, yi')\n",
+    "sorted_axis = np.argsort(x[:, 0])\n",
+    "plt.plot(x[sorted_axis], y_pred_poly2[sorted_axis], color='red', label='Estimated regression line, f(x)')\n",
+    "plt.title(f'Degree: 2, R^2 = {r2_poly2:.2f}')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Plot well-fit polynomial regression\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.scatter(x, y, color='blue', label='Actual response, yi')\n",
+    "sorted_axis = np.argsort(x[:, 0])\n",
+    "plt.plot(x[sorted_axis], y_pred_poly3[sorted_axis], color='red', label='Estimated regression line, f(x)')\n",
+    "plt.title(f'Degree: 3, R^2 = {r2_poly3:.2f}')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Plot overfit polynomial regression\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.scatter(x, y, color='blue', label='Actual response, yi')\n",
+    "sorted_axis = np.argsort(x[:, 0])\n",
+    "plt.plot(x[sorted_axis], y_pred_poly5[sorted_axis], color='red', label='Estimated regression line, f(x)')\n",
+    "plt.title(f'Degree: 5, R^2 = {r2_poly5:.2f}')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plots\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51bb7f38-423d-468b-bac5-052d1aa0d6b1",
+   "metadata": {},
+   "source": [
+    "## Polynomial Regression\n",
+    "\n",
+    "In terms of execution, we can use `sklearn`'s `sklearn.preprocessing.PolynomialFeatures` functionality to perform linear regression with a higher degree. This is exactly what was used in the overview plot above, but I wanted to isolate the code for easier comparison to Lasso and Ridge regression below.\n",
+    "\n",
+    "Some additional things to keep in mind:\n",
+    "\n",
+    "1. **Choice of Polynomial Degree**: Determining the appropriate degree of the polynomial is critical. A higher-degree polynomial can fit the training data very well but might perform poorly on unseen data due to overfitting. Various model selection techniques, such as cross-validation, can be used to choose a polynomial degree that balances bias and variance.\n",
+    "\n",
+    "2. **Feature Scaling**: Polynomial terms can have very different scales, especially for higher degrees, which can make the regression model sensitive to the scale of the input features. Normalizing or standardizing the features before applying polynomial regression can help with model convergence and interpretation.\n",
+    "\n",
+    "3. **Multivariate Polynomial Regression**: While your introduction focuses on polynomial regression with a single independent variable ($x_1$), it's important to note that polynomial regression can be extended to multiple independent variables, allowing for interaction terms between different variables (e.g., $x_1x_2$, $x_1^2x_2$, etc.). This introduces complexity in model interpretation but can capture interactions between predictors that are not apparent in single-variable analyses.\n",
+    "\n",
+    "4. **Computational Complexity**: As the degree of the polynomial and the number of independent variables increase, the computational complexity of fitting the regression model also increases. This is due to the larger number of terms and interactions that need to be calculated and optimized. It's important to balance the model's complexity with computational constraints.\n",
+    "\n",
+    "5. **Analyzing Residuals**: When using polynomial regression, it becomes even more important to analyze residuals to ensure that the assumptions of linear regression are still met. This includes checking for homoscedasticity, normality of residuals, and absence of autocorrelation. If these assumptions are violated, the results of the regression, including any inference drawn from the coefficients, may not be valid.\n",
+    "\n",
+    "### On `make_pipeline`\n",
+    "\n",
+    "`make_pipeline` from `sklearn.pipeline` is a utility function that simplifies the process of creating a pipeline of transformations with a final estimator. In machine learning workflows, it's often necessary to chain together multiple steps such as preprocessing (like scaling features or applying polynomial expansions) and then applying a model (like LinearRegression, Lasso, or Ridge). A pipeline bundles these steps into a single object that behaves like a compound estimator. \n",
+    "\n",
+    "When you use `make_pipeline`, you can pass it a series of transformations followed by an estimator, and it automatically names each step based on its class. The steps are executed in sequence: each step's `fit_transform()` method is called on the input data (except for the last step, where only `fit()` is called), transforming the data along the way, until it finally fits the model on the transformed data. This streamlines the code, making it cleaner and easier to read, and reduces the risk of mistakes (like applying transformations to the training data but forgetting to do so on the test data).\n",
+    "\n",
+    "The intro plot above does not use a pipeline, but Lasso and Ridge specifically benefit from it. In this next example, we will pipeline from [PolynomialFeatures](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html) (a preprocessor) to [LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) (a model).\n",
+    "\n",
+    "This allows us to convert this block of code:\n",
+    "\n",
+    "```python\n",
+    "# Polynomial regression (well-fit)\n",
+    "poly_features3 = PolynomialFeatures(degree=3)\n",
+    "x_poly3 = poly_features3.fit_transform(x)\n",
+    "poly_regressor3 = LinearRegression()\n",
+    "poly_regressor3.fit(x_poly3, y)\n",
+    "y_pred_poly3 = poly_regressor3.predict(x_poly3)\n",
+    "```\n",
+    "\n",
+    "into this:\n",
+    "\n",
+    "```python\n",
+    "poly = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n",
+    "poly.fit(X, y)\n",
+    "```\n",
+    "\n",
+    "You can imagine that as you add additional steps to transform your data, the first style of code will grow from 6 lines, to 9, to 12... while the second example simply adds more \"machinery\" to the sequence described in `make_pipeline(...)`.\n",
+    "\n",
+    "For instance, when used with `LinearRegression`, `Lasso`, or `Ridge` in the context of polynomial regression, you would typically create a pipeline that first expands your features into a polynomial feature space (using `PolynomialFeatures`) and then scales them (using `StandardScaler`, although not in these basic examples, it's a common practice), before finally applying the regression model. This ensures that the feature expansion and scaling are part of the model fitting process, which is particularly important for cross-validation and deploying the model for predictions on new data.\n",
+    "\n",
+    "To get the coefficients and intercept out of a pipeline that ends in linear regression, you'll have to reach inside the pipeline via the name it generates for its different steps. `poly_regressor3.coef_` becomes `poly['linearregression'].coef`, where the string `'linearregression'` is generated from the `LinearRegression` object that was passed into the pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0ff250a3-56ab-49aa-a03b-2e13b64922b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import Lasso\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "# Generating demo data\n",
+    "np.random.seed(42)\n",
+    "x = np.random.normal(0, 1, 20)\n",
+    "X = x[:, np.newaxis]\n",
+    "\n",
+    "# This is the actual equation, so we could check exactly what coefficients our regression found.\n",
+    "y = -3.8 * x**4 + 3.4 * x**3 + 6.6 * x**2 - 2.5 * x + np.random.normal(0, 1, 20)\n",
+    "\n",
+    "# Reshape x for sklearn\n",
+    "x = x[:, np.newaxis]\n",
+    "y = y[:, np.newaxis]\n",
+    "\n",
+    "degree = 4  # choosing the same degree as the true model\n",
+    "poly = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n",
+    "poly.fit(X, y)\n",
+    "\n",
+    "# Generating points for plotting the regression line\n",
+    "x_plot = np.linspace(min(X), max(X), 100)\n",
+    "y_plot = poly.predict(x_plot)\n",
+    "\n",
+    "plt.scatter(x, y, label='Data points')\n",
+    "sorted_axis = np.argsort(x[:, 0])\n",
+    "plt.plot(x_plot, y_plot, label='Polynomial regression line', color='red')\n",
+    "plt.legend()\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('Polynomial (degree = 4) regression')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "75134492-a181-4364-bca2-f25babb8fb71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
    Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=4)),\n",
+       "                ('linearregression', LinearRegression())])
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    "
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=4)),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "poly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "82785ac3-c1ab-4411-84eb-7ac688452ece",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.        , -2.00193104,  7.3848762 ,  3.00349368, -4.16571691]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "poly['linearregression'].coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3cee9f6f-2317-43f4-9e32-99d9433266bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.32345315])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "poly['linearregression'].intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f413714d-8027-48cc-8abc-1abb98e4a4bd",
+   "metadata": {},
+   "source": [
+    "## Lasso Regression\n",
+    "\n",
+    "Lasso regression adds a penalty equal to the absolute value of the magnitude of coefficients. This can lead not only to small coefficients but can actually shrink some of them to zero, effectively performing variable selection. Using that feature of Lasso regression, you don't have to interpret the usefulness of a variable - if it's not important to the model, then its coefficients will drop to zero. However, to achieve this result, you'll have to tune your lambda ($\\lambda$) value. If $\\lambda$ is too small, the penalty effect might be negligible, leading to little improvement over ordinary least squares regression. If $\\lambda$ is too large, too many variables might be eliminated, resulting in underfitting. Techniques such as cross-validation can be used to select an optimal $\\lambda$.\n",
+    "\n",
+    "The objective function for Lasso regression is:\n",
+    "\n",
+    "$ \\text{Minimize: } \\frac{1}{2N} \\sum_{i=1}^{N} (y_i - \\sum_{j=1}^{n} \\beta_j x_{ij})^2 + \\lambda \\sum_{j=1}^{n} |\\beta_j| $\n",
+    "\n",
+    "Where:\n",
+    "- $N$ is the number of observations.\n",
+    "- $\\lambda$ is the regularization parameter controlling the strength of the penalty.\n",
+    "- The first term is the Mean Squared Error, and the second term is the L1 penalty.\n",
+    "\n",
+    "\n",
+    "\n",
+    "Other things to keep in mind:\n",
+    "\n",
+    "- Lasso regression is sensitive to the scale of the input variables, so standardizing the data (to have 0 mean and unit variance) before applying Lasso regression is a common practice.\n",
+    "- While Lasso can lead to sparse solutions, Ridge regression is preferred when multicollinearity is present among the features.\n",
+    "\n",
+    "The introduction plot used `sklearn.linear_model.LinearRegression` to perform regression with different degrees of polynomials. Although Lasso might not perfectly capture the relationship in polynomial terms without specifically including polynomial features, this code will illustrate the process:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c57477e2-507e-438e-82ee-28ffbc7eac3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import Lasso\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "# Generating demo data\n",
+    "np.random.seed(42)\n",
+    "x = np.random.normal(0, 1, 20)\n",
+    "y = -3.8 * x**4 + 3.4 * x**3 + 6.6 * x**2 - 2.5 * x + np.random.normal(0, 1, 20)\n",
+    "X = x[:, np.newaxis]\n",
+    "\n",
+    "degree = 4  # choosing the same degree as the true model\n",
+    "lasso_poly = make_pipeline(PolynomialFeatures(degree), Lasso(alpha=0.1, max_iter=10000))\n",
+    "lasso_poly.fit(X, y)\n",
+    "\n",
+    "# Generating points for plotting the regression line\n",
+    "x_plot = np.linspace(min(x), max(x), 100)\n",
+    "X_plot = x_plot[:, np.newaxis]\n",
+    "y_plot = lasso_poly.predict(X_plot)\n",
+    "\n",
+    "# Plotting the data points and the regression line\n",
+    "plt.scatter(x, y, label='Data points')\n",
+    "plt.plot(x_plot, y_plot, label='Lasso Regression Line', color='red')\n",
+    "plt.legend()\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('Lasso Regression with Polynomial Features')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e7fa142d-4ff9-42e6-9858-8e8156852e49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        , -1.55260439,  5.72240286,  2.90811749, -3.66350745])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lasso_poly['lasso'].coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "80726256-28a9-4326-8d9d-520162c999a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.19943027115730638"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lasso_poly['lasso'].intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afcf86f0-ac8c-42f8-9842-ee4e9471c888",
+   "metadata": {},
+   "source": [
+    "- **Choosing Degree and $\\lambda$**: The choice of `degree=4` for the polynomial features and `alpha=0.1` for the Lasso regression penalty (`$\\lambda$`) is somewhat arbitrary here and might need adjustment based on cross-validation to find the optimal model complexity and regularization strength.\n",
+    "- **Max Iterations**: Increasing `max_iter` in `Lasso()` might be necessary for the algorithm to converge, especially for higher degrees of polynomials or smaller values of $\\alpha$ (`lambda`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddf35035-e970-4a46-969a-74eebeda569e",
+   "metadata": {},
+   "source": [
+    "## Ridge Regression\n",
+    "\n",
+    "Ridge regression adds a penalty equal to the square of the magnitude of coefficients. All coefficients are shrunk by the same factor (none are eliminated).\n",
+    "\n",
+    "The objective function for Ridge regression is:\n",
+    "\n",
+    "$ \\text{Minimize: } \\frac{1}{2N} \\sum_{i=1}^{N} (y_i - \\sum_{j=1}^{n} \\beta_j x_{ij})^2 + \\lambda \\sum_{j=1}^{n} \\beta_j^2 $\n",
+    "\n",
+    "- Similarly, $N$ and $\\lambda$ have the same definitions as in Lasso.\n",
+    "- The first term again represents the Mean Squared Error, and the second term is the L2 penalty.\n",
+    "\n",
+    "1. **Effect of the Penalty**: Ridge regression is particularly useful when dealing with multicollinearity or when you have more predictors than observations.\n",
+    "\n",
+    "2. **Scaling Importance**: standardizing the features in Ridge regression is important due to the square of the coefficients being included in the penalty term. Features on larger scales can have disproportionately large effects on the formulation.\n",
+    "\n",
+    "3. **Choosing $\\lambda$ for the bias-variance trade-off**: A higher $\\lambda$ increases bias but reduces variance, whereas a lower $\\lambda$ does the opposite. The optimal $\\lambda$ minimizes the mean squared error of predictions.\n",
+    "\n",
+    "4. **Computational Aspects**: Ridge regression tends to be computationally more efficient than Lasso for a large number of features, mainly because the solution is obtained through matrix operations that have computationally efficient implementations.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5c103fb1-7d95-4441-b00a-51035ea53e27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "# Generating demo data using the same snippet\n",
+    "np.random.seed(42)\n",
+    "x = np.random.normal(0, 1, 20)\n",
+    "y = -3.8 * x**4 + 3.4 * x**3 + 6.6 * x**2 - 2.5 * x + np.random.normal(0, 1, 20)\n",
+    "X = x[:, np.newaxis]\n",
+    "\n",
+    "# Using polynomial features again since our relationship is non-linear\n",
+    "degree = 4\n",
+    "ridge_poly = make_pipeline(PolynomialFeatures(degree), Ridge(alpha=0.1))\n",
+    "ridge_poly.fit(X, y)\n",
+    "\n",
+    "# Generating points for plotting\n",
+    "x_plot = np.linspace(min(x), max(x), 100)\n",
+    "X_plot = x_plot[:, np.newaxis]\n",
+    "y_plot = ridge_poly.predict(X_plot)\n",
+    "\n",
+    "# Plotting\n",
+    "plt.scatter(x, y, label='Data points')\n",
+    "plt.plot(x_plot, y_plot, color='red', label='Ridge Regression Line')\n",
+    "plt.legend()\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('Ridge Regression with Polynomial Features')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ec10b1ed-2cd4-4e2f-856b-dc95c093b7eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        , -2.05626925,  6.75667935,  3.06069885, -3.96459941])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ridge_poly['ridge'].coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "950a5b0b-afeb-4899-8c0a-74bc989e464a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.14654769263455236"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ridge_poly['ridge'].intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6605fd80-9410-46d4-b054-b06fcd85f4d3",
+   "metadata": {},
+   "source": [
+    "## Exercise For The Reader\n",
+    "\n",
+    "Lasso and Ridge regression could be applied to sklearn's included [California housing data set](https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset).\n",
+    "\n",
+    "\n",
+    "\n",
+    "* Don't forget to do a test train split.\n",
+    "* It's probably best to use a scaler.\n",
+    "* selecting variables is important, but can be thought of as part science and part art. Try a few and see what helps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0a63bf90-e251-4807-9ca6-0952a43d26c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# starter code\n",
+    "from sklearn.datasets import fetch_california_housing\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "fetched = fetch_california_housing(as_frame=True)\n",
+    "X = fetched['data']\n",
+    "Y = fetched['target']\n",
+    "\n",
+    "# Splitting dataset\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": 29,
+   "id": "0fd6514b-3031-41a2-b2da-9aef3cb508ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "
    MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitude
    08.325241.06.9841271.023810322.02.55555637.88-122.23
    18.301421.06.2381370.9718802401.02.10984237.86-122.22
    27.257452.08.2881361.073446496.02.80226037.85-122.24
    35.643152.05.8173521.073059558.02.54794537.85-122.25
    43.846252.06.2818531.081081565.02.18146737.85-122.25
    \n",
+       "
    "
+      ],
+      "text/plain": [
+       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
+       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
+       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
+       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
+       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
+       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
+       "\n",
+       "   Longitude  \n",
+       "0    -122.23  \n",
+       "1    -122.22  \n",
+       "2    -122.24  \n",
+       "3    -122.25  \n",
+       "4    -122.25  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "52b11cb7-455a-4a6d-935b-33d801df9ad0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
    Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=8)),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('ridge', Ridge(alpha=0.1))])
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    "
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=8)),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('ridge', Ridge(alpha=0.1))])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# model configuration\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "degrees = 8\n",
+    "ridge_poly = make_pipeline(PolynomialFeatures(degrees), StandardScaler(), Ridge(alpha=0.1))\n",
+    "ridge_poly.fit(X_train, Y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "4d97f8a1-f718-40f6-90b8-c1ab3a477662",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE: 620.2980353830641\n",
+      "R-squared: -472.3621683634644\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# Making predictions\n",
+    "predictions = ridge_poly.predict(X_test)\n",
+    "\n",
+    "# Evaluation\n",
+    "mse = mean_squared_error(Y_test, predictions)\n",
+    "r2 = r2_score(Y_test, predictions)\n",
+    "\n",
+    "print(f'MSE: {mse}')\n",
+    "print(f'R-squared: {r2}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "e4df6f54-b6a6-40ff-8849-f5a9ca2ff290",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input data:\n",
+      "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
+      "20046  1.6812      25.0  4.192201   1.022284      1392.0  3.877437     36.06   \n",
+      "\n",
+      "       Longitude  \n",
+      "20046    -119.01  \n",
+      "\n",
+      "Median house value: $47.70k\n",
+      "Predicted value: $70.53k\n"
+     ]
+    }
+   ],
+   "source": [
+    "# examine the concrete results of the model - prediction vs test data's label\n",
+    "# the dataset provides home values in fractions of $100,000, so we convert the prediction to real dollars.\n",
+    "print(f\"\"\"Input data:\n",
+    "{X_test[:1]}\n",
+    "\n",
+    "Median house value: ${Y_test.iloc[0] * 100:.2f}k\n",
+    "Predicted value: ${predictions[0] * 100:.2f}k\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65e80f87-06a3-4416-83e0-cd06078c6703",
+   "metadata": {},
+   "source": [
+    "Can you improve it with data preprocessing or altering the model parameters?\n",
+    "\n",
+    "Have fun!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27de40d2-56fd-482f-bc79-6c31ab056d3c",
+   "metadata": {},
+   "source": [
+    "## Additional Resources\n",
+    "\n",
+    "-   **Resource 1:** [Advanced Linear Regression With Python](https://realpython.com/linear-regression-in-python/#polynomial-regression) (Guide on polynomial regression, Lasso, and Ridge regression in Python)\n",
+    "-   **Resource 2:** [Machine Learning: Polynomial Regression with Python](https://towardsdatascience.com/machine-learning-polynomial-regression-with-python-5328e4e8a386) (Tutorial on polynomial regression and its application in Python)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "780f47fd-0028-4f64-8cd3-cef867bcb2b2",
+   "id": "6328b773-d5f0-4c16-88e5-201cdf8068e4",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/_sources/Week_05/Lesson_24.ipynb b/_sources/Week_05/Lesson_24.ipynb
deleted file mode 100644
index 56cf18e..0000000
--- a/_sources/Week_05/Lesson_24.ipynb
+++ /dev/null
@@ -1,53 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d042b09f-5dbb-4492-b635-efaf72f2430a",
-   "metadata": {},
-   "source": [
-    "# Outline Only - **Lesson 24:** Regression Model Evaluation Metrics in Python - Key metrics for evaluating regression models.\n",
-    "Math Focus: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.\n",
-    "- **Theoretical Concepts:**\n",
-    "    - Importance of model evaluation in regression analysis.\n",
-    "    - Overview of key metrics: MSE, RMSE, and R-squared.\n",
-    "- **Mathematical Foundation:**\n",
-    "    - Formulas and interpretation of MSE, RMSE, and R-squared.\n",
-    "    - Understanding the significance of these metrics in model performance.\n",
-    "- **Python Implementation:**\n",
-    "    - Calculating MSE, RMSE, and R-squared using scikit-learn.\n",
-    "    - Visualizing residuals to understand model performance.\n",
-    "- **Example Dataset:**\n",
-    "    - Use datasets from previous lessons for consistency in evaluation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1004f48-41e2-48b4-9803-6b5d575d639b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/_sources/Week_05/Lesson_25.ipynb b/_sources/Week_05/Lesson_25.ipynb
deleted file mode 100644
index 8d4973d..0000000
--- a/_sources/Week_05/Lesson_25.ipynb
+++ /dev/null
@@ -1,54 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3f3fb1e7-ba4e-4da8-b4ec-d8a9688f1ae3",
-   "metadata": {},
-   "source": [
-    "# Outline Only - **Lesson 25:** Addressing Overfitting and Underfitting in Regression Models \n",
-    "Strategies to combat overfitting and underfitting in regression. - Math Focus: Bias-variance tradeoff and regularization methods.\n",
-    "- **Theoretical Concepts:**\n",
-    "    - Identifying symptoms of overfitting and underfitting in regression models.\n",
-    "    - Strategies to combat overfitting and underfitting.\n",
-    "- **Mathematical Foundation:**\n",
-    "    - Bias-variance tradeoff.\n",
-    "    - Regularization methods and their mathematical basis.\n",
-    "- **Python Implementation:**\n",
-    "    - Demonstrating overfitting and underfitting using matplotlib.\n",
-    "    - Implementing regularization techniques in Python.\n",
-    "    - Using validation curves and learning curves for model diagnostics.\n",
-    "- **Example Dataset:**\n",
-    "    - A dataset with a clear overfitting/underfitting tendency (e.g., high-dimensional data)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fd7b23be-578e-456b-a1ad-0cd8f56cceef",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/genindex.html b/genindex.html
index a26bae6..db4e119 100644
--- a/genindex.html
+++ b/genindex.html
@@ -256,6 +256,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/index.html b/index.html
index b8e7e1e..bedc41d 100644
--- a/index.html
+++ b/index.html
@@ -258,6 +258,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/objects.inv b/objects.inv
index f79226f..71c03a9 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/search.html b/search.html
index 9e1e5c4..1b6aedb 100644
--- a/search.html
+++ b/search.html
@@ -258,6 +258,7 @@
 
  • Day 21 - Introduction to Regression Analysis in Python
    • 
 
  • Day 22: Implementing Multiple Linear Regression in Python
    • 
 
+
  • Day 23 - Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression
    • 
 
 
 
diff --git a/searchindex.js b/searchindex.js
index a8bba31..e976b77 100644
--- a/searchindex.js
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