Logistic Regression 2- Titanic.Rmd

---
title: "Logistic Regression"
author: "SRIRAM VIVEK"
output: pdf_document
date: "2025-02-26"
---


Cleaning the data

```{r }


library(tidyverse)
library(caret)


titanic_data <- read.csv('/Users/sriram/Desktop/SEMESTER 2/AMS 580/Logistic Regression/Titanic2.csv')


titanic_cleaned <- titanic_data %>%
  select(-PassengerId, -Name, -Ticket, -Cabin)

titanic_cleaned <- titanic_cleaned %>%
  filter(!is.na(Age))

n_passengers <- nrow(titanic_cleaned)
cat("Number of passengers after cleaning:", n_passengers, "\n")

```

```{r }



titanic_cleaned <- titanic_cleaned %>%
  mutate(Survived = as.factor(Survived),
         Pclass = as.factor(Pclass))

str(titanic_cleaned)

```

Using the random seed 123 to divide the cleaned data into 80%
training and 20% testing.

```{r }

set.seed(123)
training_samples <- createDataPartition(titanic_cleaned$Survived, p = 0.8, list = FALSE)
train_data <- titanic_cleaned[training_samples, ]
test_data <- titanic_cleaned[-training_samples, ]

```

Fitting a logistic regression model with all the other 7 predictors using
the training data.

```{r }

model <- glm(Survived ~ ., data = train_data, family = binomial)
summary(model)

```

Predicting the response variable "Survived" (whether the subject survived or not) for the testing data based on the fitted model followed by generating a confusion matrix, reporting overall accuracy,  sensitivity and specificity.


```{r }

probabilities <- predict(model, test_data, type = "response")
predicted_classes <- ifelse(probabilities > 0.5, 1, 0)

confusion_matrix <- table(Predicted = predicted_classes, Actual = test_data$Survived)
print(confusion_matrix)

accuracy <- mean(predicted_classes == test_data$Survived)
sensitivity <- sum(predicted_classes == 1 & test_data$Survived == 1) / sum(test_data$Survived == 1)
specificity <- sum(predicted_classes == 0 & test_data$Survived == 0) / sum(test_data$Survived == 0)

cat("Overall Accuracy:", accuracy, "\n")
cat("Sensitivity:", sensitivity, "\n")
cat("Specificity:", specificity, "\n")

```

Testing the above model to predict the survival of additional passengers.

```{r }

additional_passengers <- data.frame(
  Pclass = as.factor(c(3, 1, 2)),
  Sex = c("male", "female", "male"),
  Age = c(24, 68, 41),
  SibSp = c(1, 0, 1),
  Parch = c(0, 0, 2),
  Fare = c(8.42, 24.34, 41.93),
  Embarked = c("Q", "C", "S")
)

additional_probabilities <- predict(model, additional_passengers, type = "response")
additional_predicted_classes <- ifelse(additional_probabilities > 0.5, 1, 0)

cat("Predicted survival for additional passengers:", additional_predicted_classes, "\n")
cat("Passenger 892: ", "Not Survived\n")
cat("Passenger 893: ", "Survived\n")
cat("Passenger 894: ", "Not Survived")
```