Notebook Contents

1. K-Means Clustering (stp_kmeans.ipynb):

   • Demonstrates the implementation of the K-Means clustering algorithm for customer segmentation.
   • Visualizes how data points are grouped based on shared characteristics, providing actionable insights for marketing strategies.

2. Linear Discriminant Analysis (stp_lda.ipynb):

   • Explores Linear Discriminant Analysis (LDA) for classifying data points.
   • Focuses on GPA and GMAT scores to predict college admission status (Admit/Reject).
   • Explains the step-by-step process of using LDA for binary classification problems.

3. Multidimensional Scaling (MDS) (stp_nmds.ipynb):

   • Showcases Multidimensional Scaling (MDS) for visualizing data in reduced dimensions.
   • Highlights how MDS can be used to reveal underlying patterns in complex datasets.

4. Regression Analysis (stp_regression.ipynb):

   • Provides an overview of regression techniques using the scikit-learn and statsmodels libraries.
   • Discusses the differences between the libraries and their use cases for prediction versus statistical inference.
   • Includes examples of linear and multiple regression to model relationships between variables.

5. Logistic Regression (logit.ipynb):

   • Implements logistic regression using the Statsmodels library to analyze college admissions data.
   • Explains how logistic regression can predict binary outcomes and interpret feature weights.
   • Emphasizes statistical inference with p-values, confidence intervals, and detailed output for better decision-making.