Enhance Day 6 Logistic Regression with Training and Test Set Visualizations

[PUROJITA-SINGH]

Description

This pull request enhances the Day 6 Logistic Regression.md file by incorporating visualizations of the Logistic Regression model's decision boundaries for both the training and test sets.

Motivation

• Clarity and Understanding: These visualizations are critical for understanding how the Logistic Regression model makes its predictions and separates data points into different classes. They clearly show the learned decision boundary.
• Model Evaluation: By comparing the decision boundaries on the training and test sets, users can gain insights into potential overfitting or how well the model generalizes to unseen data.
• Educational Value: For a resource aimed at learning machine learning, these visual aids significantly improve the pedagogical effectiveness of the Logistic Regression explanation.

Changes Made

• Added Python code to generate two distinct plots:
  • One illustrating the decision boundary and data points for the training set.
  • One illustrating the decision boundary and data points for the test set.
• The plots utilize matplotlib with ListedColormap to clearly distinguish between the predicted regions and actual data points.
• The actual data points are plotted using plt.scatter, enhancing visual readability with edgecolors='black' and s=50.
• Each plot includes appropriate titles and axis labels (Age (Scaled), Estimated Salary (Scaled)).

Related Issues (if applicable)

• [If this change is related to an existing issue in the original repository, link it here, e.g., Closes #123 or Fixes #456]