** RESEARCHES / DONE**

Develop several projects in Data Science, Visualization and / or Applied Statistics. Here are only some listed.

Machine Learning

■ Machine Learning: Unsupervised learning applied to Multiple Selection Test data -2013/2017- in Health Careers of the Universidad Mayor■ Python-MLearning: Portuguese Banking Data using Logistic Regression (LR) and PCA Reduced Dimension

■ Python-MLearning: A comparison of Machine Learning Models for Digits Recognition using PCA Reduced Dimension, and Sklearn Library

■ Python-MLearning: Digits Recognition using Logistic Regression (LR), PCA Reduced Dimension, and Sklearn Library

■ R-Project: Exploratory Data Analysis of the Digits recognition dataset (MNIST) using R

■ Python-MLearning: Digits Recognition using Support Vector Machine (SVM), PCA Reduced Dimension, and Sklearn Library

■ Python-MLearning: Digits Recognition using Gradient Boosting Machine (GBM), PCA Reduced Dimension, and Sklearn Library

■ Python-MLearning: Digits Recognition using Logistic Regression (LR), Optimal Number of Variales, and Sklearn Library

■ Python-MLearning: Digits Recognition using Random Forest, PCA Reduced Dimension, and Sklearn Library

■ Python-MLearning: Digits Recognition using K-Nearest Neighbors (KNN), PCA Reduced Dimension, and Sklearn Library

■ Python-MLearning: Classification using Gradient Boosting Machine (GBM) and Sklearn Library

■ Python-MLearning: Classification using Extreme Gradient Boosting (XGBoost) and Sklearn Library

■ Python-MLearning: Classification using Random Forest and Sklearn Library

■ Python-MLearning: Classification using Decision Trees and Sklearn Library

■ Python-MLearning: Classification using Naive Bayes and Sklearn Library

■ Python-MLearning: Classification using Support Vector Machine (SVM) and Sklearn Library

■ Python-MLearning: Classification using K-Nearest Neighbors (KNN) and Sklearn Library

■ Machine Learning: Comparing MLR, NN, GBM and XGBoost regression models with R

■ Machine Learning: Portugal Wine. Two Class approach for Quality classification with XGBoost using R

■ Machine Learning: Portugal Wine. Neural Network and MLR Regression Comparison to model Quality using R, Caret, and K fold cross-validation

■ Machine Learning: Portugal Wine. Multiple Linear Regression to model Quality using R and K fold cross-validation

■ Machine Learning: Portugal Wine under Two Class approach for Red and White classification with Logistic Regression using R and K fold cross-validation

■ Machine Learning: Portugal Wine under Two Class approach for Quality classification with Logistic Regression using R and K fold cross-validation

■ Machine Learning: Portugal Wine under Two Class approach for Red and White classification using R and K fold cross-validation

■ Machine Learning: A comparison of supervised learning algorithms applied to the classification problem in R with MLR Package and K fold cross validation

■ Machine Learning: A comparison of supervised learning algorithms applied to the classification problem in R with MLR Package

■ Machine Learning: A general way to run and compare most common supervised learning algorithms with R-project

■ Machine Learning: A comparison of supervised learning algorithms applied to the classification problem with Scikit-Learn Python library

■ Machine Learning: A comparison of supervised learning algorithms applied to the classification problem with caret R-project library

Deep Learning

■ Python-MLearning: Digits recognition using PCA, Neural Network (NN) and Sklearn Library■ Python-MLearning: Digits recognition using Neural Network (NN) and Sklearn Library

■ Python-MLearning: Classification using Neural Network (NN) and Sklearn Library

■ Machine Learning: Portugal Wine. Two Class approach for Red and White classification with Neural Network using R, NEURALNET Library and K fold cross-validation

■ Machine Learning: Portugal Wine. Two Class approach for Red and White classification with Neural Network using R and K fold cross-validation

■ Machine Learning: Portugal Wine. Two Class approach for Quality classification with Neural Network using R and K fold cross-validation

Text Mining and NLP

■ Word Cloud Examples■ Simple Chatbot using Python

■ Sentiment Analysis Application with R

Time Series

■ Data Science: Using ARIMA and ARIMAX modeling to Forecast Time Series with R■ Data Science: A comparison of models to Forecast Time Series with R

■ Data Science: Using R and ARIMA to Forecast Industrial production in the USA

■ Data Science: ARIMA Modeling Application to Domestic Autos Sales using R

■ ARIMA Modeling Application using the Bike-Sharing dataset and Python

■ ARIMA Modeling Application to the AirPassengers dataset using Python

■ ARIMA Modeling Application to the AirPassengers dataset using R

Geospatial

■ Chicago Crime Incidents HeatMap■ Worlwide Cities

■ Mapping Chicagoland Motor Vehicle Theft Crime Incidents Jan/2001-August/2016

■ Geospatial Visualization of San Francisco Police Department Incidents Year 2015

■ Geospatial Visualization of Chilean Undergraduate Institutions by Year 2015

Data Science Others

■ Data Science: Analysis with multivariate approach of the Multiple Selection Tests data -2013/2017- in Health Careers of the Universidad Mayor■ Data Science: Analysis of Multiple Selection Tests -2013/2017- in Health Careers of the Universidad Mayor

■ Python-Pandas: Movie Films Data Analysis Using SQLite Database Platform and Pandas

■ A More Realistic View of the Chilean Undergraduate Educational System Results

■ Study: Prediction model based on multiple regression for undergraduate chilean educational institutions 2014

■ Study: Cluster analysis over undergraduate chilean educational institutions 2014

■ Study: Descriptive profile od some important indicators for undergraduate chilean educational institutions 2014

■ Data Analysis for the Chicagoland Motor Vehicle Theft Crime Incidents Jan/2001-Aug/2016

■ Cluster analysis application to the data results of the project "Evaluation of speed and reading comprehension in university students of first year of the health area of Universidad Mayor"

■ Evaluation of speed and reading comprehension in university students of first year of the health area of Universidad Mayor

■ Comparison of Discriminant Analysis and Logistic Regression as predictive models for credit risks

■ Knowledge level, perceptions and information that students of Faculty of Medicine of Universidad Mayor have on violence against women in Chile

■ Vocational motivation, labor perceptions and value aspects in medicine students of Universidad Mayor

■ Comparison of medicine faculty students lifestyle versus students from other faculties in Universidad Mayor

■ Sampling simulation (considering different conditions) for the epidemiological study over the determination of the mutational state of oncogen BRAF in malignant melanoma in stages III and IV

Tutorials

■ Data Science: Dynamic Pivot Tables with R, Python, and Online■ Python: Data Management Tips Over Connectivity

■ Python-Pandas: Input Data into Python with Pandas

■ Python: Generalities and Introduction to Python

■ Python Pandas: Pandas Functions Frequently Used

■ R-project Data Management TIPS over Connectivity: Intermediate-Advanced Level

■ Some tips over database management supported by R-project

■ R-Project Support: Confidence intervals and Test of hypothesis

Hector Alvaro Rojas / Data Science, Visualizations and Applied Statistics.