Statistics and Machine Learning Handbook

A Collection of Case Studies and Analyses

Welcome to the Statistics and Machine Learning Handbook. This collection of documents serves as a practical guide and a series of case studies in statistical analysis and machine learning. Each section explores a different dataset and a different set of techniques, providing a hands-on approach to learning and applying these methods.

Project Overview

This project is divided into several sections, each focusing on a specific area of data analysis:

Binary Classification: We explore how to predict binary outcomes, such as survival on the Titanic, using both R and Python.
Regression: We delve into predicting continuous values, such as housing prices, using both R and Python.
Statistical Analysis: We conduct a case study on whisky ratings to demonstrate various statistical tests and concepts.
Unsupervised Learning: We explore clustering techniques (K-Means and Hierarchical Clustering) and Principal Component Analysis (PCA) using both R and Python, applied to both the Iris and Income datasets.

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Welcome to the Statistics and Machine Learning Handbook. This collection of documents serves as a practical guide and a series of case studies in statistical analysis and machine learning. Each section explores a different dataset and a different set of techniques, providing a hands-on approach to learning and applying these methods.

# Project Overview

This project is divided into several sections, each focusing on a specific area of data analysis:

-   **Binary Classification:** We explore how to predict binary outcomes, such as survival on the Titanic, using both R and Python.
-   **Regression:** We delve into predicting continuous values, such as housing prices, using both R and Python.
-   **Statistical Analysis:** We conduct a case study on whisky ratings to demonstrate various statistical tests and concepts.
-   **Unsupervised Learning:** We explore clustering techniques (K-Means and Hierarchical Clustering) and Principal Component Analysis (PCA) using both R and Python, applied to both the Iris and Income datasets.