diff --git a/Dropout LogReg.R b/Dropout LogReg.R
new file mode 100644
index 0000000..ca55e57
--- /dev/null
+++ b/Dropout LogReg.R	
@@ -0,0 +1,29 @@
+library(tidyverse)
+library(caret)
+
+dropout_clean <- read.csv("C:/Users/Mark/Desktop/Grad School/PDAT630/dropout_clean.csv")
+factor_cols=c("Marital.status","Scholarship.holder","Tuition.fees.up.to.date","Gender","Displaced","Daytime.evening.attendance","Course","Previous.qualification","Mother.s.qualification","Father.s.qualification","Mother.s.occupation","Father.s.occupation","International","Target")
+dropout_clean[factor_cols] <- lapply(dropout_clean[factor_cols], factor)
+dropout_clean_lg=mutate(dropout_clean, Target = as.factor(ifelse(Target == "Dropout", 0, 1)))
+str(dropout_clean_lg)
+
+df.train <- sample_frac(dropout_clean_lg, size = .85)
+df.test <- setdiff(dropout_clean_lg, df.train)
+
+## Logistic Regression
+logreg = glm(Target~., family = "binomial", data = df.train)
+coef(logreg)
+summary(logreg)
+pred=predict(logreg, df.test, type="response")
+pred=as.factor(ifelse(pred>.5, 1, 0))
+confusionMatrix(as.factor(df.test$Target), pred)$overall[1]
+
+
+## Stepwise
+logreg.step=step(logreg)
+coef(logreg.step)
+summary(logreg.step)
+pred.step=predict(logreg.step, df.test, type="response")
+pred.step=as.factor(ifelse(pred.step>.5, 1, 0))
+confusionMatrix(as.factor(df.test$Target), pred.step)$overall[1]
+