Dr. Tossou's Projects
:bar_chart: Methods of Applied Statistics Course Textbook Repository
A curated list of awesome network analysis resources.
My solution to the Caesar Cipher challenge using basic Python lists and functions.
Data Science and Machine Learning Bootcamp with R: https://www.udemy.com/course/data-science-and-machine-learning-bootcamp-with-r/learn/lecture/5413116#overview by Jose Portilla, Head of Data Science, Pierian Data Inc.
Exercise for data science course using R
The Leek group guide to data sharing
An end-to-end decision tree classification tutorial, with theory and code.
Source code for my collection of articles on using pandas.
A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
Developing trading strategies as machine learning classification: Feature engineering, k-nearest neighbors, decision trees & random forests, and strategic quant trading
Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models are good for times series data that are very volatile. This sample project showcases how various GARCH models can be fitted and how predictions can be made. Apple stock prices are used for the application.
My first repository using R! I created this repository entirely using instructions at this location: https://guides.github.com/activities/hello-world/. Check it out!
We use the Statistical Analysis Software (SAS/SAT) to model home sale price. Codes and outputs are included in the project.
Implementation of Bayesian Hyperparameter Optimization of Machine Learning Algorithms
Book about interpretable machine learning
Introduction to Regression with statsmodels in Python. A DataCamp tutorial.
Capstone project for completion of Udacity's Machine Learning Engineering Nano Degree.
Machine Learning Experiments and Work
Collection of 11 projects: Sales (2 projects), Marketing (3 projects), Operations (1 projects), Finance (2 projects), Customer Support (1 project), Human Resources (1 project), and Information Technology (1 project)
SAS Codes for Markov Chain
The purpose of this project is to simulate the process of merger and acquisition (i.e., M&A) between three firms and project their ex-post market valuation, as well as the resulting margin from the operation. Merger simulation models predict post-merger prices based on information about a set of pre-merger market conditions and certain assumptions about the behavior of the firms in the relevant market. This project replicates the analysis in the seminal M&A paper by Rubinfeld and Epstein (2001).
Multioutput regression are regression problems that involve predicting two or more numerical values given an input example. This tutorial covers how to develop models for multi-output regression.
The purpose of this FUN project is to use machine learning to predict the probability that each customer would purchase an insurance product.
Pre-processes lending club loan data and concatenates into one large file.
Course material and links for Promotionskolleg module "Machine learning in applied economics"
Simple Moving Averages, Exponential Weighted Moving Averages, ETS (Error, Trend & Seasonality) Decomposition, ARIMA, and SARIMAX
Zero Intelligence Agent-Based Model of Modern Limit Order Book