How to implement Binary Logistic Regression in R Language?

 Implementing Binary Logistic Regression in R is straightforward using the built-in functions and libraries. Here is a step-by-step guide to implementing Binary Logistic Regression in R:

1. Load the necessary libraries:

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library(stats)
library(glm)

2. Load the data:

arduino
data <- read.csv("data.csv", header = TRUE)

3. Split the data into training and testing datasets:

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set.seed(123) 
train_index <- sample(1:nrow(data), round(0.7 * nrow(data)), replace = FALSE) 
train_data <- data[train_index,] 
test_data <- data[-train_index,]

4. Fit the Binary Logistic Regression model using the glm() function:

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model <- glm(formula = outcome ~ predictor1 + predictor2 + predictor3, family = binomial(link = "logit"), data = train_data)

5. Make predictions on the testing dataset using the predict() function:

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predictions <- predict(model, newdata = test_data, type = "response")

6. Evaluate the model's performance using evaluation metrics such as accuracy, precision, recall, and F1 score:

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actual <- test_data$outcome
predicted <- ifelse(predictions > 0.5, 1, 0)
accuracy <- sum(predicted == actual) / length(actual)
precision <- sum(predicted == 1 & actual == 1) / sum(predicted == 1)
recall <- sum(predicted == 1 & actual == 1) / sum(actual == 1)
f1_score <- 2 * (precision * recall) / (precision + recall)

7. Visualize the results using plots such as a ROC curve and a confusion matrix:

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library(pROC)
roc <- roc(test_data$outcome, predictions)
plot(roc)
confusionMatrix(predicted, actual)

Note that in Step 4, the formula argument specifies the model formula, where the outcome variable is on the left-hand side of the tilde (~) and the independent variables are on the right-hand side, separated by a plus (+) sign. The family argument specifies the type of model to be fitted, in this case, binomial for Binary Logistic Regression with logit link function.

Also note that in Step 5, the type argument in the predict() function specifies the type of prediction, where "response" returns the predicted probabilities of the outcome variable.