kennethbhunt Goto Github PK

auto-sklearn

census-data-set-

Data analysis for the UCI census data set

cpu_kmeans

K-means clustering example

credit

Data analysis with regression trees, decision trees, random forest, boosted trees, and bagging trees.

credit1

Data analysis using boosting, random forest, and decision trees for classification

education_hc

Hierarchical cluster analysis

forestfire

Data analysis with - k nearest neighbor, support vector machine, & neural networks models

housing_kmeans

K-means clustering example

kennethhunt.github.io

logreg1

#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?

ordinary-least-squares-regression

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.

ordinary-least-squares-regression2

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.)

penalized-regression

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.

penalized-regression_pt2

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.

penalized-regressionpt3

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

phone_churn

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".

principalcomonentanalysis

PCA example.

testgit

This is for testing gits

ulcer_recurrence

vehicles

Data analysis for logistic regression, linear discriminant analysis, naïve Bayes estimation, support vector machine, &neural networks models

wine-quality

Analysis of - logistic regression, lasso logistic regression, linear discriminant analysis, quadratic discriminant analysis, naïve Bayes estimation, K nearest neighbor, & support vector machine models

wine-quality2

Data analysis with boosted trees, random forest, and decision tree models