**Logistic regression with R:** Logistic regression is a type of statistical model used to analyze the relationship between a binary outcome variable (such as yes/no or true/false) and one or more predictor variables. It estimates the probability of the binary outcome based on the values of the predictor variables. The model outputs a logistic function, transforming the input values into a probability range between 0 and 1. Logistic regression is commonly used in fields such as medicine, social sciences, and business to predict the likelihood of a certain outcome based on given input variables. To perform logistic regression in the R programming language, you can follow the following steps:

Step 1: Load the required packages

```
library(tidyverse)
library(caret)
```

Step 2: Load the data

```
data <- read.csv("path/to/your/data.csv")
```

Step 3: Split the data into training and testing sets

```
set.seed(123)
training_index <- createDataPartition(data$target_variable, p = 0.8, list = FALSE)
training_data <- data[training_index, ]
testing_data <- data[-training_index, ]
```

Step 4: Build the logistic regression model

```
log_model <- train(target_variable ~ .,
data = training_data,
method = "glm",
family = "binomial")
```

Step 5: Predict using the model

```
predictions <- predict(log_model, newdata = testing_data)
```

Step 6: Evaluate the model’s performance

```
confusionMatrix(predictions, testing_data$target_variable)
```

This is a basic logistic regression model building and evaluation process. You can modify the code according to your specific use case.

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