Enhance documentation for machine learning algorithms

machine_learning/decision_tree.py

@@ -1,11 +1,52 @@
+import numpy as np
+
+"""
+Decision Tree Regression Implementation
+
+This module implements a basic regression decision tree using Python and NumPy.
+
+📄 Overview:
+Decision Trees are a type of supervised learning algorithm used for both
+classification and regression tasks. In this implementation, the tree
+is specifically designed for regression: mapping continuous input features
+to continuous outputs.
+
+📌 Features:
+- Supports one-dimensional datasets with continuous labels.
+- Splits data recursively based on minimizing mean squared error.
+- Can specify maximum tree depth and minimum leaf size.
+- Predicts outputs for unseen data using the trained tree.
+
+🌟 Educational Value:
+This implementation is lightweight and designed for learning purposes.
+It demonstrates:
+- The core concept of recursive partitioning in decision trees.
+- How mean squared error guides splitting decisions.
+- The trade-off between tree depth and model complexity.
+
+💡 Use Cases:
+- Understanding how regression trees work internally.
+- Testing and experimenting with small datasets.
+- Educational projects, tutorials, or self-learning exercises.
+
+Audience:
+- Beginners learning machine learning concepts
+- Students studying regression algorithms
+- Anyone interested in understanding decision tree internals
+
+📦 Dependencies:
+- numpy
+
+Note: This implementation is meant for demonstration and learning purposes
+and may not be optimized for large-scale production datasets.
+"""
+
 """
 Implementation of a basic regression decision tree.
 Input data set: The input data set must be 1-dimensional with continuous labels.
 Output: The decision tree maps a real number input to a real number output.
 """
 
-import numpy as np
-
 
 class DecisionTree:
     def __init__(self, depth=5, min_leaf_size=5):

machine_learning/linear_regression.py

@@ -1,3 +1,33 @@
+import httpx
+import numpy as np
+
+"""
+Linear Regression Algorithm - Predictive Analysis (Enhanced Documentation)
+
+This file implements linear regression using gradient descent to predict
+values based on a dataset. The example dataset here is from CSGO (ADR vs Rating).
+
+Purpose:
+- Demonstrates basic linear regression with a small dataset.
+- Shows how to collect data from an online source.
+- Illustrates calculation of errors (sum of squares and mean absolute error).
+- Provides a step-by-step gradient descent implementation.
+
+Audience:
+- Beginners learning machine learning algorithms.
+- Students who want to understand linear regression implementation in Python.
+
+Dependencies:
+- Python >= 3.13
+- httpx
+- numpy
+
+Notes:
+- The algorithm iteratively updates feature weights to minimize error.
+- Output includes the feature vector representing the best-fit line.
+- Designed for educational purposes; can be adapted for other datasets.
+"""
+
 """
 Linear regression is the most basic type of regression commonly used for
 predictive analysis. The idea is pretty simple: we have a dataset and we have
@@ -16,9 +46,6 @@
 # ]
 # ///
 
-import httpx
-import numpy as np
-
 
 def collect_dataset():
     """Collect dataset of CSGO

machine_learning/logistic_regression.py

@@ -9,10 +9,29 @@
 # importing all the required libraries
 
 """
-Implementing logistic regression for classification problem
+Implementation of Logistic Regression from scratch.
+
+Audience:
+- Beginners exploring supervised learning and classification algorithms
+- Students learning how logistic regression works mathematically and programmatically
+- Developers wanting to understand how to implement gradient descent for classification
+
+Dependencies:
+- numpy
+- matplotlib
+- scikit-learn (for dataset demonstration)
+
+Notes:
+- This implementation is for educational purposes and demonstrates logistic regression
+  without using high-level libraries.
+- The training is done with gradient descent; for large datasets, optimization
+  techniques like stochastic gradient descent may be preferable.
+- Visualization is based on the Iris dataset, focusing only on two features
+  for simplicity.
+
 Helpful resources:
-Coursera ML course
-https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
+- Coursera Machine Learning course
+- https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
 """
 
 import numpy as np
@@ -21,7 +40,6 @@
 
 # get_ipython().run_line_magic('matplotlib', 'inline')
 
-
 # In[67]:
 
 # sigmoid function or logistic function is used as a hypothesis function in