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  RESEARCHES  /  DONE


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


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
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
LDA Topic Modeling Application on Amazon Reviews with Topicmodels R library
Data Science: Comparison of binary text classification models with tidytext and caret R library
Data Science: Comparison of binary text classification models with pca and the caret R library
Data Science: Complement Project on Text Mining and Sentiment Analysis on Amazon Reviews
Text Mining and Sentiment Analysis on Amazon Reviews
Simple Chatbot using Python
Word Cloud Examples
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
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: Comparison of binary text classification models with tidytext and caret R library
Data Science: Comparison of binary text classification models with pca and the caret R library
Data Science: Complement Project on Text Mining and Sentiment Analysis on Amazon Reviews
Data Science: Text Mining and Sentiment Analysis on Amazon Reviews
Data Science: IBM Employment Jobs Offers
Data Science: Data Mining on Movie's Budgets and Revenues Business
Data Science: Data Mining on Top 250 Best Rating Movies of 20/21 Centuries from IMDb
Data Science: Artificial Intelligence (AI), Machine Learning (L), Big Data, and Data Science
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
R Web Scraping Examples
Python Web Scraping Examples
Power BI Web Scraping Examples
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.