packages used: Amelia, ROCR, aod, pscl and kernlab

package used: stats

Using association rules to find out particular purchased patterns and association between items in the grocery store. Package used: arules.

Using SVM to build model and make prediction of alphabetical letter classes (26 letters) for unseen data. Package used: kernlab

Using ANN to model the concrete strength. packages used include neuralnet and NeuralNetTools.

random forest analysis is applied to the credit data to make prediction for the bank loan default status. improvement is done by adjusting parameters of the model.

Apply logistic regression on a credit dataset and predict the bank loan applicant's default status. Packages included Amelia, ROCR, caret

logistic regression analysis on the distress conditions for the Challenger space shuttle. Model improvement include bootstrapping and k-fold cross validation. packages used in clude ROCR, Amelia and caret

package highlight: rpart, rpart.plot, Rweka

Multiple Linear regression analysis is made based on individual's personal/family information and their medical expenses. The model is made for prediction on medical expenses of future clients to aid medical insurance on charging the premium. Packages highlight: psych

Using both the One R and RIPPER rules learner algorithm to identify poisonous mushrooms. The dataset contains about 8000 observations and 23 features in the raw dataset. The packages used here are: Rweka, gmodels, C50

Packages used are C50 and gmodels.

Using Naived Bayes to prediction the house of representatives in the test dataset using trained dataset and its lebels. The packages used here are e1071, mlbench and gmodels.

Using the Naived Bayes Classification to predict incoming spam SMS in high accuracy. The packages used are: e1071, tm, SnowballC, wordcloud, and gmodels. (This is an exercise from Chapter 3 in Machine Learning)