Problem Statement

• Diamond pricing is complex and often opaque to consumers
• Multiple factors affect price: carat, cut, color, clarity
• Need for transparent, data-driven price estimation

Solution: Shiny Web Application

• Interactive Interface: Easy-to-use sliders and dropdowns
• Real-time Predictions: Instant price estimates based on characteristics
  
• Data Visualization: Charts showing price distributions and comparisons
• Educational Tool: Learn about diamond characteristics and pricing

Target Users

• Jewelry buyers and sellers
• Students learning data science
• Anyone curious about diamond pricing factors

Key Features

• Input Widgets: Carat slider, cut/color/clarity dropdowns
• Price Prediction: Linear regression model with confidence intervals
• Visualization: Interactive price distribution charts
• Similar Diamonds: Table showing comparable diamonds from dataset

Technical Implementation

# Model creation example
diamonds$cut <- factor(diamonds$cut, 
                      levels = c("Fair", "Good", "Very Good", "Premium", "Ideal"))
price_model <- lm(price ~ carat + cut + color + clarity, data = diamonds)
summary(price_model)$r.squared

## [1] 0.9159406

R-squared: 0.916 - Model explains ~92% of price variation!

Diamond Dataset Overview

# Dataset summary
cat("Dataset size:", nrow(diamonds), "diamonds\n")

## Dataset size: 53940 diamonds

cat("Price range: $", min(diamonds$price), "-", max(diamonds$price), "\n")

## Price range: $ 326 - 18823

# Price distribution by key factors
ggplot(diamonds, aes(x = carat, y = price, color = cut)) +
  geom_point(alpha = 0.5, size = 0.8) +
  geom_smooth(method = "lm", se = FALSE) +
  scale_y_continuous(labels = scales::dollar_format()) +
  labs(title = "Price vs Carat by Cut Quality", 
       x = "Carat Weight", y = "Price ($)") +
  theme_minimal() + 
  theme(legend.position = "bottom")

Live Prediction Example

# Example prediction for a 1.5 carat, Ideal cut, F color, VS1 clarity diamond
new_diamond <- data.frame(
  carat = 1.5,
  cut = factor("Ideal", levels = c("Fair", "Good", "Very Good", "Premium", "Ideal")),
  color = factor("F", levels = c("D", "E", "F", "G", "H", "I", "J")),
  clarity = factor("VS1", levels = c("FL", "IF", "VVS1", "VVS2", "VS1", "VS2", "SI1", "SI2", "I1"))
)

prediction <- predict(price_model, new_diamond, interval = "prediction")
cat("Predicted Price: $", round(prediction[1], 0), "\n")

## Predicted Price: $ 11196

cat("95% Confidence Interval: $", round(prediction[2], 0), " - $", round(prediction[3], 0))

## 95% Confidence Interval: $ 8928  - $ 13464

Model Validation

# Model residuals plot
plot(price_model$fitted.values, price_model$residuals, 
     main = "Model Residuals vs Fitted Values", 
     xlab = "Fitted Values", ylab = "Residuals",
     pch = 16, alpha = 0.5)
abline(h = 0, col = "red", lwd = 2)

Educational Value

• Data Science Learning: Demonstrates regression modeling, R Shiny development
• Domain Knowledge: Teaches diamond characteristics and pricing factors
• Interactive Analysis: Users can explore “what-if” scenarios

Business Applications

• Consumer Tool: Help buyers understand fair pricing
• Market Analysis: Identify undervalued or overpriced diamonds
• Inventory Management: Assist dealers in pricing decisions

Future Enhancements

• Additional factors: depth, table, fluorescence
• Advanced ML models: Random Forest, XGBoost
• Real-time market data integration
• Mobile-responsive design

Access the Application

🔗 Shiny App: [Coming Soon - Will be deployed to shinyapps.io]
📊 Source Code: [GitHub Repository - Will be created]
📈 This Presentation: [RPubs Link - Will be published]

Thank you for exploring the Diamond Price Predictor!