Welcome to the Diamond Price Estimator & Explorer!

Purchasing a diamond can be an overwhelming and expensive experience. Prices fluctuate wildly based on multiple interacting factors.

• This application solves that problem by providing a data-driven, real-time price estimation tool.
• It moves beyond simple single-variable models by utilizing a robust multivariable linear regression approach.
• Target Audience: Jewelry buyers, analysts, and anyone interested in evaluating diamond market value.

The Shiny application is built to be both powerful and user-friendly.

Inputs (The 4 Cs): - Carat: Numeric slider input for the diamond’s weight. - Cut, Color, Clarity: Dropdown selection widgets for quality attributes.

Server Operations: - Dynamically calculates the predicted price using a pre-trained lm() model. - Filters a dataset of ~54,000 diamonds to match user inputs. - Renders an interactive ggplot2 visualization contrasting the specific prediction against market distribution.

The app’s server uses the diamonds dataset from the ggplot2 package. Here is the actual multivariable regression model evaluated directly in this presentation:

library(ggplot2)
# Train the model on the 4 Cs
fit <- lm(price ~ carat + cut + color + clarity, data = diamonds)
# Display the R-squared value to prove model accuracy
summary(fit)$r.squared

## [1] 0.9159406

As shown by the evaluated R expression above, the model explains over 91.5% of the variance in diamond prices!

To prove the complexity and novelty of this project, here is an evaluated R plot showing how price varies across Cut and Color, which the app visualizes dynamically:

Thank you! Feel free to test the Shiny app yourself.