ML 4 IB with R
Motivation
1
Introduction
1.1
Course Material
1.1.1
Linear Regression
1.1.2
Logistic Regression
1.1.3
K-Nearest Neighbors
1.1.4
Naive Bayes
1.1.5
Support Vector Machines
1.1.6
Decision Tree
1.1.7
Random Forest
1.1.8
AdaBoost
1.1.9
XGBoost
1.1.10
LightGBM
1.1.11
CatBoost
2
Reproducible Research with R
3
Exploratory Data Analysis I: Data Wrangling
4
Exploratory Data analysis II: Descriptive Statistics, API Usage and Global Risk Analysis
5
Web Scrapping and Visualizations: An Application in International Strategy
6
Supervised Learning Principles: An Application in International Strategy
7
Linear Regression, Logistic Regression and Regularization: An Application in International Strategy
8
Unsupervised Learning: An Application in International Trade
9
Decision Trees and support Vector Machines: An Application in International Finance
10
Random Forest and Gradient Boosting Models: An Application in Finance
11
Natural Language Processing: An Application in Finance
12
Neural Networks: An Application in Finance
13
Parallel computing: An application in Finance
13.1
An application to portfolio optimisation
14
NLP: An application in Finance
15
Which machine learning algorithm for which question?
15.1
Providing a decision framework for hiring new employees
15.2
Understanding and Predicting product attributes that make a product most likely to be purchased
15.3
Analyzing sentiment to assess product perception in the market.
15.4
When you are working with time-series data or sequences (eg, audio recordings or text)
15.5
Predicting the Housing Prices
15.6
Exploring customer demographic data to identify patterns
15.7
Predicting Loan Repayment
15.8
Predicting if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc)
15.9
Predicting client churn
15.10
Creating classification system to filter out spam emails
15.11
Predicting how likely someone is to click on an online ad
15.12
Detecting fraudulent activity in credit-card transactions.
15.13
Predicting the price of cars based on their characteristics
15.14
Predicting the probability that a patient joins a healthcare program
15.15
Predicting whether registered users will be willing or not to pay a particular price for a product.
15.16
Segmenting customers into groups by distinct charateristics (eg, age group)
15.17
Featuring extraction from speech data for use in speech recognition systems
15.18
Object tracking of multiple objects, where the number of mixture components and their means Predicting object locations at each frame in a video sequence.
15.19
Organizing the genes and samples from a set of microarray experiments so as to reveal biologically interesting patterns.
15.20
Recommending what movies consumers should view based on preferences of other customers with similar attributes.
15.21
Recommending news articles a reader might want to read based on the article she or he is reading.
15.22
Recommending news articles a reader might want to read based on the article she or he is reading.
15.23
Optimizing the driving behavior of self-driving cars
15.24
Diagnosing health diseases from medical scans.
15.25
Balancing the load of electricity grids in varying demand cycles
15.26
Providing language translation
15.27
Generating captions for images
15.28
Powering chatbots that can address more nuanced customer needs and inquiries
References
www.warin.ca
Machine Learning for International Business with R
Chapter 14
NLP: An application in Finance
https://towardsdatascience.com/loan-risk-nlp-d98021613ff3