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diff --git a/R_LogR/main.r b/R_LogR/main.r
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+#!/usr/bin/env Rscript
+
+# https://www.r-bloggers.com/2015/09/how-to-perform-a-logistic-regression-in-r/
+
+data <- read.csv("survey.csv")
+
+
+str(data)
+head(data)
+
+data$price20 <- ifelse(data$Price == 20, 1, 0)
+data$price30 <- ifelse(data$Price == 30, 1, 0)
+head(data)
+
+model <- glm(
+  MYDEPV ~ Income + Age + price20 + price30,
+  family = binomial(link = "logit"),
+  data = data
+)
+summary(model)
+
+coef(model)
+
+plot(data$Income, data$MYDEPV)
+
+
+test_dat <- data.frame(Income = seq(20, 100, 1), Age = 20, price20 = 1, price30 = 0)
+pred <- predict(model, newdata = test_dat, type = "response")
+
+lines(test_dat$Income, pred, col = "blue", lwd = 2)
+
+
+new_data3 <- data.frame(
+  Income = c(58),
+  Age = c(25),
+  price20 = c(1),
+  price30 = c(0)
+)
+
+predicted <- predict(model, newdata = new_data3)
+print(1 / (1 + exp(-predicted)))