Various techniques applied for the prediction of median home value were- Generalized Linear Regression, Regression Tree, Generalized Additive Model and Neural Networks.
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Updated
Mar 29, 2018 - R
Various techniques applied for the prediction of median home value were- Generalized Linear Regression, Regression Tree, Generalized Additive Model and Neural Networks.
This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
This repository contains the script and figures of the conference paper selected for presentation at the Latin American Conference of Computationa Intelligence 2018. The abstract of the paper is as follows: Crime is an important social and economic problem of South Africa. Though certain categories of crimes are of serious proportions, yet on ag…
ggplot-based graphics and useful functions for GAMs fitted using the mgcv package
solving nonlinear scoring problems where linear regression doesn't fit well using techniques like Generalized Additive Models (GAM) and Support Vector Regression (SVR).
Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions, precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, W…
structured additive regression models talk
A workshop on using generalized additive models and the mgcv package.
Code and materials to reproduce graphics in "A generalized additive model approach to time-to-event analysis"
Resolution-independent normalization of Hi-C data
Thesis Chatper 3: This repository compares Spatial Interpolation methods to predict Seoul's air pollution
Website for Biostat 200C (Methods in Biostatistics C)
MSc_dissertation_code
biostatistical workflows in R covering regression and classification models
Exploration of generalized additive models.
Analysis of data from the Framingham Heart Study using generalized linear models.
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
Negative Binomial Additive Model for RNASeq Data
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