kennethbhunt Goto Github PK
Name: Kenneth B. Hunt MBA,
Type: User
Bio: Kenneth is a passionate business minded individual, with a strong entrepreneurial spirit.
Location: Southwest, US
Name: Kenneth B. Hunt MBA,
Type: User
Bio: Kenneth is a passionate business minded individual, with a strong entrepreneurial spirit.
Location: Southwest, US
Data analysis for the UCI census data set
K-means clustering example
Data analysis with regression trees, decision trees, random forest, boosted trees, and bagging trees.
Data analysis using boosting, random forest, and decision trees for classification
Hierarchical cluster analysis
Data analysis with - k nearest neighbor, support vector machine, & neural networks models
K-means clustering example
#Data set: credit.csv #Your task is to predict the customers credit score (rating) knowing the following #variables: age, income, cars, education and carloans. Use the following machine #learning techniques: # - logistic regression #- naïve Bayes estimation #- neural networks #Which technique gives us the best prediction accuracy in the test set?
Data set: cpuperform.csv Create an OLS regression model to predict the relative CPU performance (prp) based on the following variables: myct, mmin, mmax, cach, chmin, chmax. Validate your model using both the validation set method and the k-fold cross-validation method.
Data set: education.csv Create an OLS regression model to predict the expenditure on public education (expend) using the following predictors: urban, income and teen. Validate your model with the validation set approach. (Retain 30-35 cases for the training set and the others for the test set.)
Data set: winequality.csv Your task is to find the best predictors for the wines quality (quality) from the following 11 variables: fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates and alcohol. To that effect, use all of the following techniques: - best subset selection regression - forward and backward stepwise regression - ridge regression - lasso regression - PLS regression Identify the model that provides the best prediction accuracy in the test set.
Data set: housedata.csv You are supposed to find the best predictors for a house price (price) out of the following variables: bedrooms, bathrooms, sqft_living, sqft_lot, floors, grade, sqft_basement and old. Use all of the following techniques: - best subset selection regression - forward and backward stepwise regression - ridge regression - lasso regression - PLS regression Discover the model that ensures the best prediction accuracy in the test set.
Data set: bostonhousing.csv You have to predict the median house value (medv) using the following variables: crim, zn, indus, nox, rm, age, dis, rad, tax, ptratio and lstat. Identify the model with the highest prediction accuracy using these methods: - best subset selection regression - forward and backward stepwise regression - ridge regression - lasso regression - PLS regression
This is an analysis of a data set containing 6 variables, and 1000 observations. The response variable of this dataset is "churn", which describes whether a customer will leave the company based on the other variables which are "predictors".
PCA example.
This is for testing gits
Data analysis for logistic regression, linear discriminant analysis, naïve Bayes estimation, support vector machine, &neural networks models
Analysis of - logistic regression, lasso logistic regression, linear discriminant analysis, quadratic discriminant analysis, naïve Bayes estimation, K nearest neighbor, & support vector machine models
Data analysis with boosted trees, random forest, and decision tree models
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.