File size: 2,869 Bytes
021a69e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | library(shiny)
library(bslib)
library(tidymodels)
library(tidyverse)
library(ranger)
library(xgboost)
# Load Model (Single Best Model)
# Ensure 'model.rds' exists in the same directory (src/)
model <- readRDS("model.rds")
# UI Definition
ui <- page_sidebar(
theme = bs_theme(bootswatch = "flatly"),
title = "Bank Marketing AI (Interactive)",
sidebar = sidebar(
title = "Client Profile",
numericInput("age", "Age", 35, 18, 100),
selectInput("job", "Job", choices = c("admin.", "blue-collar", "technician", "services", "management", "retired", "entrepreneur", "self-employed", "housemaid", "unemployed", "student", "unknown")),
selectInput("marital", "Marital Status", choices = c("married", "single", "divorced", "unknown")),
selectInput("education", "Education", choices = c("university.degree", "high.school", "basic.9y", "professional.course", "basic.4y", "basic.6y", "unknown", "illiterate")),
selectInput("default", "Has Credit in Default?", choices = c("no", "yes", "unknown")),
selectInput("housing", "Has Housing Loan?", choices = c("no", "yes", "unknown")),
selectInput("loan", "Has Personal Loan?", choices = c("no", "yes", "unknown")),
hr(),
actionButton("predict_btn", "Run Prediction", class = "btn-success w-100")
),
layout_columns(
col_widths = c(12),
card(
card_header("Prediction Output"),
tableOutput("pred_results")
)
)
)
# Server Logic
server <- function(input, output) {
predictions <- eventReactive(input$predict_btn, {
# Construct Input DataFrame
# Note: 'duration' is deliberately excluded as per model inference hygiene
input_df <- data.frame(
age = input$age,
job = input$job,
marital = input$marital,
education = input$education,
default = input$default,
housing = input$housing,
loan = input$loan,
# Default mock values for other features to allow prediction
contact = "cellular",
month = "may",
day_of_week = "mon",
campaign = 1,
pdays = 999,
previous = 0,
poutcome = "nonexistent",
emp.var.rate = -1.8,
cons.price.idx = 92.8,
cons.conf.idx = -46.2,
euribor3m = 1.2,
nr.employed = 5099
)
# Predict Class
pred_class <- tryCatch({
predict(model, input_df) %>% pull(.pred_class)
}, error = function(e) "Error")
# Predict Probability
pred_prob <- tryCatch({
predict(model, input_df, type = "prob") %>% pull(.pred_Yes)
}, error = function(e) 0)
data.frame(
Result = c("Predicted Class", "Probability (Subscribes)"),
Value = c(as.character(pred_class), paste0(round(pred_prob * 100, 2), "%"))
)
})
output$pred_results <- renderTable({
predictions()
}, striped = TRUE, hover = TRUE, colnames = FALSE)
}
shinyApp(ui, server)
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