zixiao-wu / machine-learning-tools-fall-2022

This is DAT 500S: Machine Learning Tools for Prediction of Business Outcomes course in Fall 2022 of WashU Olin Business School.

Machine Learning Tools for Prediction of Business Outcomes Fall 2022

This repository contains the Module assignments and its solution contained in the book An Introduction to Statistical Learning. Also the Python version of the sloution.

An-Introduction-to-Statistical-Learning is one of the most popular books among data scientists to learn the conepts and intuitions behind machine learning algorithms, however, the exercises are implemented in R language. To overcome this and build my Python ability I have tried solving all the questions in practical exerices in Python language, so people using python language can also get the most our of this amazing book.

An Introduction to Statistical Learning

• Chapter_2_Statistical_Learning
• Chapter_3_Linear_Regression
• Chapter_4_Classification
• Chapter_5_Resampling_Methods
• Chapter_6_Linear_Model_Selection_and_Regularization
• Chapter_7_Moving_Beyond_Linearity
• Chapter_8_Tree_Based_Methods
• Chapter_8_Tree_Based_Methods Cont.
• Chapter_9_Neural Network
• Chapter_10_Unsupervised_Learning

Course Objectives

1. Understand the language of predictive analytics to communicate effectively with both experts and colleagues
2. Understand the bias-variance tradeoff and impact of it on predictive models
3. Master predictive analytics tools, applying them correctly to solve business problems. Among these tools are • Classification Trees& Regression Trees • Random Forest (Committee of Trees) • Boosted Trees (This is a good competitor for Neural Network) • Neural Network • Logistic Regression • Multi Adaptive Regression Splines • Association Rules • K-Nearest Neighbors • Principal Component Analysis • K-means Clustering
4. Develop Proficiency in utilizing software to build models
5. Analyze current business problems using the above tools

Course Designer and Instructor Dr. Durai Sundaramoorthi