📊 R You Ready for Regression?

This project is my submission for the "R You Ready for Regression" AI/ML challenge. It demonstrates how to perform simple linear regression using R on two classic built-in datasets: mtcars and iris.

---

📁 Files

• regression_analysis.R – R script for data analysis and plotting
• plot_mtcars.png – Regression of MPG vs Weight (mtcars)
• plot_iris.png – Regression of Petal Length vs Petal Width (iris)

---

📌 Dataset 1: mtcars

Model: mpg ~ wt

• Predicts Miles per Gallon (mpg) from Weight (wt)
• Shows a negative relationship: as weight increases, MPG decreases

Sample Output:

Call: lm(formula = mpg ~ wt, data = mtcars)

Coefficients: (Intercept) wt 37.285 -5.344

---

📌 Dataset 2: iris

Model: Petal.Length ~ Petal.Width

• Predicts Petal Length based on Petal Width
• Strong positive correlation: wider petals tend to be longer

Sample Output:

Call: lm(formula = Petal.Length ~ Petal.Width, data = iris)

Coefficients: (Intercept) Petal.Width 1.084 2.229

---

🖼️ Plots

MPG vs Weight (mtcars)

Petal Length vs Petal Width (iris)

---

▶️ How to Run

Make sure R is installed on your system. Then run the following command in your terminal or RStudio:

Rscript regression_analysis.R

This will:

• Print model summaries in the console
• Save the plots as:
  • plot_mtcars.png
  • plot_iris.png

---

📚 Libraries Used

• ggplot2 – for creating regression plots

---