C Program to Write a recursive C function to calculate the sum of digits of a number - IProgramX

by - IProgram X on - June 27, 2018
Q. Write a recursive C function to calculate the sum of digits of a number. Use this function in main to accept a number and print sum of its digits

Program

#include <stdio.h>
int sum (int a);
int main()
{
    int num, result;
    printf("Enter the number: ");
    scanf("%d", &num);
    result = sum(num);
    printf("Sum of digits in %d is %d\n", num, result);
    return 0;
}

int sum (int num)
{
    if (num != 0)
    {
        return (num % 10 + sum (num / 10));
    }
    else
    {
       return 0;
    }
}

Output:

Enter the number: 354
Sum of digits in 354 is 12

Tags
Reactions

Post a Comment

10 Comments

  1. AnonymousJune 16, 2026 at 11:49 PM

    1 csv, excel, mysql



    data.csv

    ID,Name,Age,Salary
    1,Raj,25,30000
    2,Amit,30,40000
    3,Ravi,28,35000
    4,Priya,32,45000
    5,Neha,26,32000
    6,Kiran,29,38000

    data.xlsx

    | ID | Name | Age | Salary |
    | -- | ----- | --- | ------ |
    | 1 | Raj | 25 | 30000 |
    | 2 | Amit | 30 | 40000 |
    | 3 | Ravi | 28 | 35000 |
    | 4 | Priya | 32 | 45000 |
    | 5 | Neha | 26 | 32000 |
    | 6 | Kiran | 29 | 38000 |



    CREATE DATABASE testdb;
    USE testdb;
    CREATE TABLE employees (
    emp_id INT,
    name VARCHAR(50),
    age INT,
    salary INT
    );
    INSERT INTO employees VALUES
    (1,'Raj',25,30000),
    (2,'Amit',30,40000),
    (3,'Ravi',28,35000),
    (4,'Priya',32,45000),
    (5,'Neha',26,32000),
    (6,'Kiran',29,38000);
    SELECT * FROM employees;


    getwd()

    setwd("C:/Users/abc/Desktop/MCA/Shardul/DA")




    if (!requireNamespace("readxl", quietly = TRUE)) install.packages("readxl")
    if (!requireNamespace("DBI", quietly = TRUE)) install.packages("DBI")
    if (!requireNamespace("RMySQL", quietly = TRUE)) install.packages("RMySQL")

    library(readxl)
    library(DBI)
    library(RMySQL)

    data_csv <- read.csv("data.csv")
    head(data_csv)

    data_excel <- read_excel("data.xlsx")
    head(data_excel)

    con <- dbConnect(RMySQL::MySQL(),
    dbname = "testdb",
    host = "localhost",
    user = "root",
    password = "your_password")

    data_db <- dbGetQuery(con, "SELECT * FROM employees")
    head(data_db)

    dbDisconnect(con)

    ReplyDelete
  2. AnonymousJune 16, 2026 at 11:50 PM

    2 Data Cleaning and Preprocessing



    data <- data.frame(
    ID = c(1, 2, 3, 4, 4, 5),
    Name = c("Raj", "Amit", "Ravi", "Priya", "Priya", NA),
    Age = c(25, NA, 28, 32, 32, 29),
    Salary = c(30000, 40000, NA, 45000, 45000, 38000)
    )

    data$Age[is.na(data$Age)] <- mean(data$Age, na.rm = TRUE)
    data$Salary[is.na(data$Salary)] <- mean(data$Salary, na.rm = TRUE)
    data$Name[is.na(data$Name)] <- "Unknown"

    data <- unique(data)

    data$Name <- toupper(data$Name)

    data$Salary_norm <- (data$Salary - min(data$Salary)) / (max(data$Salary) - min(data$Salary))

    print(data)

    ReplyDelete
  3. AnonymousJune 16, 2026 at 11:52 PM

    3 Sampling Techniques (Training & Testing Dataset)




    data <- data.frame(
    ID = 1:10,
    Name = c("Raj","Amit","Ravi","Priya","Neha","Kiran","Vijay","Pooja","Ajay","Rina"),
    Age = c(25,30,28,32,26,29,31,27,24,33),
    Salary = c(30000,40000,35000,45000,32000,38000,42000,36000,31000,47000)
    )

    set.seed(123)

    train_index <- sample(1:nrow(data), 0.7 * nrow(data))

    train_data <- data[train_index, ]
    test_data <- data[-train_index, ]

    print(train_data)
    print(test_data)

    install.packages("caTools")
    library(caTools)

    set.seed(123)

    split <- sample.split(data$Salary, SplitRatio = 0.7)

    train_data2 <- subset(data, split == TRUE)
    test_data2 <- subset(data, split == FALSE)

    print(train_data2)
    print(test_data2)

    ReplyDelete
  4. AnonymousJune 16, 2026 at 11:53 PM

    4 Linear Regression and Model Evaluation




    data <- data.frame(
    Experience = c(1,2,3,4,5,6,7,8,9,10),
    Salary = c(30000,35000,40000,45000,50000,55000,60000,65000,70000,75000)
    )

    model <- lm(Salary ~ Experience, data = data)

    summary(model)

    new_data <- data.frame(Experience = c(3.5, 7.5))

    predictions <- predict(model, new_data)

    print(predictions)

    pred <- predict(model, data)

    actual <- data$Salary

    mse <- mean((actual - pred)^2)

    rmse <- sqrt(mse)

    r2 <- summary(model)$r.squared

    print(mse)
    print(rmse)
    print(r2)

    plot(data$Experience,
    data$Salary,
    main = "Linear Regression",
    xlab = "Experience",
    ylab = "Salary",
    pch = 16)

    abline(model, col = "red", lwd = 2)

    ReplyDelete
  5. AnonymousJune 16, 2026 at 11:54 PM

    5 Naive Bayes Classification




    install.packages("e1071")
    library(e1071)

    data(iris)

    set.seed(123)

    index <- sample(1:nrow(iris), 0.7 * nrow(iris))

    train <- iris[index, ]
    test <- iris[-index, ]

    model <- naiveBayes(Species ~ ., data = train)

    print(model)

    pred <- predict(model, test)

    print(head(pred))

    cm <- table(Predicted = pred, Actual = test$Species)

    print(cm)

    accuracy <- mean(pred == test$Species)

    print(accuracy)

    ReplyDelete
  6. AnonymousJune 16, 2026 at 11:55 PM

    6 K-Nearest Neighbors (KNN)




    install.packages("class")
    library(class)

    data(iris)

    set.seed(123)

    normalize <- function(x) {
    (x - min(x)) / (max(x) - min(x))
    }

    iris_norm <- as.data.frame(lapply(iris[1:4], normalize))

    iris_norm$Species <- iris$Species

    sample_index <- sample(1:nrow(iris_norm), 0.7 * nrow(iris_norm))

    train_data <- iris_norm[sample_index, 1:4]
    train_label <- iris_norm[sample_index, 5]

    test_data <- iris_norm[-sample_index, 1:4]
    test_label <- iris_norm[-sample_index, 5]

    predicted <- knn(
    train = train_data,
    test = test_data,
    cl = train_label,
    k = 5
    )

    cm <- table(Predicted = predicted, Actual = test_label)

    print(cm)

    accuracy <- sum(diag(cm)) / sum(cm)

    cat("Accuracy:", accuracy * 100, "%\n")

    ReplyDelete
  7. AnonymousJune 16, 2026 at 11:56 PM

    7 K-Means Clustering and Visualization




    install.packages("ggplot2")
    library(ggplot2)

    data(iris)

    iris_data <- iris[, 1:4]

    set.seed(123)

    kmeans_result <- kmeans(
    iris_data,
    centers = 3,
    nstart = 25
    )

    print(kmeans_result)

    iris$Cluster <- as.factor(kmeans_result$cluster)

    ggplot(
    iris,
    aes(
    x = Sepal.Length,
    y = Sepal.Width,
    color = Cluster
    )
    ) +
    geom_point(size = 3) +
    labs(
    title = "K-Means Clustering on Iris Dataset",
    x = "Sepal Length",
    y = "Sepal Width"
    ) +
    theme_minimal()

    ReplyDelete
  8. AnonymousJune 16, 2026 at 11:57 PM

    8 Exploratory Data Analysis (EDA)




    data(iris)

    str(iris)

    summary(iris)

    head(iris)

    hist(
    iris$Sepal.Length,
    col = "lightblue",
    main = "Histogram of Sepal Length",
    xlab = "Sepal Length"
    )

    boxplot(
    iris[,1:4],
    col = c("red","green","blue","yellow"),
    main = "Boxplot of Iris Features"
    )

    plot(
    iris$Sepal.Length,
    iris$Sepal.Width,
    col = iris$Species,
    pch = 19,
    main = "Sepal Length vs Sepal Width",
    xlab = "Sepal Length",
    ylab = "Sepal Width"
    )

    legend(
    "topright",
    legend = unique(iris$Species),
    col = 1:3,
    pch = 19
    )

    ReplyDelete
  9. AnonymousJune 16, 2026 at 11:58 PM

    9 Principal Component Analysis (PCA)




    data(iris)

    iris_data <- iris[, 1:4]

    pca_result <- prcomp(
    iris_data,
    scale. = TRUE
    )

    summary(pca_result)

    print(pca_result$rotation)

    head(pca_result$x)

    plot(
    pca_result,
    type = "l",
    main = "Scree Plot - PCA"
    )

    ReplyDelete
  10. AnonymousJune 16, 2026 at 11:59 PM

    10 Classification Tree using rpart




    install.packages("rpart")
    install.packages("rpart.plot")

    library(rpart)
    library(rpart.plot)

    data(iris)

    set.seed(123)

    sample_index <- sample(1:nrow(iris), 0.7 * nrow(iris))

    train_data <- iris[sample_index, ]
    test_data <- iris[-sample_index, ]

    model <- rpart(
    Species ~ .,
    data = train_data,
    method = "class"
    )

    print(model)

    rpart.plot(
    model,
    main = "Decision Tree - Iris Dataset",
    type = 2,
    extra = 104,
    fallen.leaves = TRUE
    )

    predicted <- predict(
    model,
    test_data,
    type = "class"
    )

    cm <- table(
    Predicted = predicted,
    Actual = test_data$Species
    )

    print(cm)

    accuracy <- sum(diag(cm)) / sum(cm)

    cat("Accuracy:", accuracy * 100, "%\n")

    ReplyDelete