UNCW BAN 502 This course explores methods for model selection, parameter estimation, and validation. Focused on techniques and algorithms from the statistical and machine learning disciplines.

Tree-based data mining algorithmic model for body weight prediction in indigenous Nigerian goat breeds

Final project of course "Probability and statistics in data analysis" Fall-2019

Includes the R code and the supporting materials for our paper "Using machine learning to identify nontraditional spatial dependence in occupancy data"

Bike sharing is an alternative and sustainable mobility service that is always taking that is becoming increasingly popular in almost all major cities.

This is the repository for my R project on modeling historical weather data in Santa Barbara.

What factors influence runners

Statistical Learning in R

🤖🧠 Report and Knime workflow used to predict the King County houses' prices for 2021/22 Machine Learning project @ University of Milano-Bicocca

Analiza indicatorilor macroeconomici precum pretul, numarul de actiuni, venit, profit, cash flow etc pentru firmele din industria materiilor prime.

Overview of DS, Classification Tree, Regression Tree, Logistic Regression, Multiple Linear Regression, Random Forest Classifiers, p-value approach to Decision Tree, Disease Progression Models, Neural Networks and Deep Learning.

This is a presentation I gave to the Econometrics IV class. I described a paper by Patrick Bajari and coauthors. I also described some of the methods the applied in simple terms.

Predicting the Demand for Real Estate Listings in Austria

Fit four different neural networks: (a) Two distinct single hidden layer neural networks. (b) Two distinct neural networks with two hidden layers. Compare the accuracy of these four Neural networks among them. Also compare it to other classification methods.

Tuning Random Forests, comparing their classification accuracy to Regression Trees.

Various techniques applied for the prediction of median home value were- Generalized Linear Regression, Regression Tree, Generalized Additive Model and Neural Networks.

Bootcamp-Python Study

Using Python and the Natural Resources Canada Fuel Consumption Ratings to view and predict vehicle efficiency.