Data Science — Underfitting

R basics

# Vectors
x <- c(1, 2, 3, 4, 5)
mean(x)
sd(x)

# Data frames
df <- data.frame(name = c("A", "B"), value = c(1, 2))

# Read CSV
df <- read.csv("data.csv")

ggplot2

library(ggplot2)

# Scatter plot
ggplot(df, aes(x = x, y = y)) +
  geom_point()

# Line plot
ggplot(df, aes(x = date, y = value)) +
  geom_line()

# Bar plot
ggplot(df, aes(x = category)) +
  geom_bar()

dplyr

library(dplyr)

# Filter
df %>% filter(value > 5)

# Select
df %>% select(name, value)

# Arrange
df %>% arrange(desc(value))

# Summarize
df %>% group_by(category) %>% summarize(mean_value = mean(value))

Statistics

# T-test
t.test(group1, group2)

# Linear regression
model <- lm(y ~ x, data = df)
summary(model)

Mini Practice

Create data frames
Visualize with ggplot2
Manipulate with dplyr
Perform statistical tests

Up Next

Continue with Tableau - Data visualization tool.

Frequently Asked Questions about Underfitting

What is Underfitting in Data Science?

Underfitting is a fundamental concept in Data Science. This lesson explains it step by step with clear examples, making it easy for beginners to understand.

How do I learn Underfitting?

Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Underfitting.

Why is Underfitting important in Data Science?

Underfitting is essential for Data Science development. Understanding this concept will help you write better code and solve real-world problems more effectively.