Analyzing and Visualizing Data - Residency Individual Project

setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures") getwd() # Import test data data=read.csv("Admission.csv") head(data) # display the first 6 observations print(summary(data)) # Check distribution between the 2 binary classes xtabs(~admit + rank, data = data) xtabs(~admit + gpa, data = data) #First, we convert rank to a factor to indicate that rank should be treated as a categorical variable. data$rank <- factor(data$rank) logit <- glm(admit ~ gre + gpa + rank, data = data, family = "binomial") summary(logit) # We can use the confint function to obtain confidence intervals for the coefficient estimates. ## CIs using profiled log-likelihood confint(logit) ## CIs using standard errors confint.default(logit) #To get the exponentiated coefficients, you tell R that you want to exponentiate (exp) ## odds ratios only exp(coef(logit)) ## odds ratios and 95% CI exp(cbind(OR = coef(logit), confint(logit))) #Now for example, we can say that for a one unit increase in gpa, the odds of #being admitted to graduate school (versus not being admitted) increase by a factor of 2.17. with(logit, null.deviance - deviance) #The chi-square of 40.53 with 5 degrees of freedom and an associated p-value of less than 0.001 tells us that our model #as a whole fits significantly better than an empty model. data=read.csv("smarket.csv") head(data) # display the first 6 observations print(summary(data)) names(data) # Histogram of variables par(mfrow=c(1,8)) for(i in 1:8) { hist(data[,i], main=names(data)[i]) } install.packages("Amelia") library(Amelia) library(mlbench) # Check missing data missmap(data, col=c("blue", "red"), legend=FALSE) #featutes/target distribution pairs(data, col=data$Direction) # Logistics Regression glm.fit <- glm(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume, data = data, family = binomial) summary(glm.fit) glm.probs <- predict(glm.fit,type = "response") glm.probs[1:5] train.raw <- read.csv('train.csv',header=T,na.strings=c("")) #Now we need to check for missing values and look how many unique values there are for each variable using the sapply() function which applies the #function passed as argument to each column of the dataframe. sapply(train.raw,function(x) mean(is.na(x))) missmap(train.raw, main = "Missing values vs observed")