## R Coding

Coding in R A few snipits of code that are always useful: Finding which column has NA values: unlist(lapply(dataframe , function(x) any(is.na(x))))

Coding in R A few snipits of code that are always useful: Finding which column has NA values: unlist(lapply(dataframe , function(x) any(is.na(x))))

Machine Learning: Charity Donor Analysis Introduction A charitable organization wishes to develop a machine learning model to improve the cost effectiveness of their direct marketing campaigns to previous donors. The recent mailing records reflect an overall 10% response rate with an average donation of $14.50. The cost to produce and send each mail is $2.

+ Read More

Factor Analysis Introduction Utilizing a stock portfolio data set and a factor analysis to identify sectors in the stock market, we will transform the variables into log values to explain the variation in the log-returns of the stocks and market index. We will begin the factor analysis by performing a Principal Factor Analysis without a

+ Read More

Principal Components Analysis Utilizing a stock portfolio data set and the Principal Components Analysis as a method in reducing dimension and as a remedial measure for multicollinearity in Ordinary Least Squares regression. Beginning with the data, we will transform the variables into log values to explain the variation in the log-returns of the stocks and

+ Read More

Automated Variable Selection The Amex, Iowa housing data set build has been utilized to develop various iterative regression models to determine the mean sales price of a house based on numerous variables. The variables range correlated, continuous variables to categorical variables. In this installment, we continue building the model using raw categories and later, the

+ Read More

Data Variables and Analytical Models Before diving in to a statistical analysis of any dataset, spending the requisite time to understand the data, checking the quality and taking a look ‘under the dash’ is essential. Below, we will examine the data variables and analytical models on a housing prices as a first step in predicting

+ Read More

Assessing Regression Models Using Numerous Variables Regression model on the Amex, Iowa housing data set builds regression models for the house sale price with numerous variables. Some of which are highly correlated, continuous variables along to the other side of the continuum by evaluating categorical, low correlated variables. An assessment of each model will be

+ Read More

Variable Transformations The Amex, Iowa housing data set build has been utilized to develop various regression models to determine the sales price of a house based on numerous variables. The variables range from highly correlated, continuous variables to categorical variables with low correlations. In this assessment, variable transformations and comparisons of Y versus Log(Y) will

+ Read More

Cluster Analysis on Transformed Predictor Variables Cluster analysis is grouping a set of objects in a way that objects in the same group are more similar in some sense to each other than those in other groups. Clusters are identified by assessing the relative distances between points, the relative homogeneity of each cluster and the degree

+ Read More