I’ll help you convert this PowerPoint presentation into an RStudio presentation format. You can create this as either an R Markdown presentation (ioslides, slidy, or beamer) or a Quarto presentation. I’ll provide both options.
Option 1: R Markdown (ioslides) Presentation
Create a new file called Next_Word_Prediction.Rmd:
---
title: "Next Word Prediction Application"
author: "Deepak Varshney"
date: "2026-02-16"
output:
ioslides_presentation:
widescreen: true
smaller: true
---
```f3175f2a-e8a0-4436-be12-b33925b6d220
31b8e172-b470-440e-83d8-e6b185028602:dAB5AHAAZQA6AFoAZwBBAHoAQQBEAEUAQQBOAHcAQQAxAEEARwBZAEEATQBnAEIAaABBAEMAMABBAFoAUQBBADQAQQBHAEUAQQBNAEEAQQB0AEEARABRAEEATgBBAEEAegBBAEQAWQBBAEwAUQBCAGkAQQBHAFUAQQBNAFEAQQB5AEEAQwAwAEEAWQBnAEEAegBBAEQATQBBAE8AUQBBAHkAQQBEAFUAQQBZAGcAQQAyAEEARwBRAEEATQBnAEEAeQBBAEQAQQBBAAoAcABvAHMAaQB0AGkAbwBuADoATgBRAEEAeQBBAEQAUQBBAAoAcAByAGUAZgBpAHgAOgAKAHMAbwB1AHIAYwBlADoAWQBBAEIAZwBBAEcAQQBBAGUAdwBCAHkAQQBDAEEAQQBjAHcAQgBsAEEASABRAEEAZABRAEIAdwBBAEMAdwBBAEkAQQBCAHAAQQBHADQAQQBZAHcAQgBzAEEASABVAEEAWgBBAEIAbABBAEQAMABBAFIAZwBCAEIAQQBFAHcAQQBVAHcAQgBGAEEASAAwAEEAQwBnAEIAcgBBAEcANABBAGEAUQBCADAAQQBIAEkAQQBPAGcAQQA2AEEARwA4AEEAYwBBAEIAMABBAEgATQBBAFgAdwBCAGoAQQBHAGcAQQBkAFEAQgB1AEEARwBzAEEASgBBAEIAegBBAEcAVQBBAGQAQQBBAG8AQQBHAFUAQQBZAHcAQgBvAEEARwA4AEEASQBBAEEAOQBBAEMAQQBBAFIAZwBCAEIAQQBFAHcAQQBVAHcAQgBGAEEAQwBrAEEAQwBnAEIAZwBBAEcAQQBBAFkAQQBBAD0ACgBzAHUAZgBmAGkAeAA6AA==:31b8e172-b470-440e-83d8-e6b185028602Introduction
- Predictive Text Using N-Gram Modeling
- This project builds a Next Word Prediction App using statistical language modeling techniques
- The model was trained on English text data from:
- News
- Blogs
- The objective is to predict the most probable next word given a phrase
Problem & Data Processing
The Challenge
Given a phrase such as: > “The economy is expected to”
Predict the next most likely word
Data Preparation
- Converted text to lowercase
- Removed punctuation and numbers
- Tokenized words
Algorithm Design
N-Gram Backoff Strategy
Prediction logic:
- Use last two words → search Trigram
- If no match → search Bigram
- If no match → return most frequent Unigram
Benefits: - Always returns a prediction - Fast computation - Memory efficient
The word with the highest probability is selected.
The Shiny Application
How It Works
- User enters a phrase
- Clicks Predict
- Model processes input
- Displays a single predicted word
Features: - Simple interface - Fast response time - Deployed on shinyapps.io - Real-time prediction
Business Value & Future Improvements
Applications
- Messaging apps
- Email systems
- Search engines
- Customer support chatbots
Strengths
- Lightweight statistical model
- Low latency
Thanks!
Deepak Varshney
## Option 2: Quarto Presentation (More Modern)
Create a new file called `Next_Word_Prediction.qmd`:
```markdown
---
title: "Next Word Prediction Application"
author: "Deepak Varshney"
format:
revealjs:
theme: default
transition: slide
slide-number: true
---
## Introduction
- **Predictive Text Using N-Gram Modeling**
- This project builds a Next Word Prediction App using statistical language modeling techniques
- The model was trained on English text data from:
- Twitter
- News
- Blogs
- The objective is to predict the most probable next word given a phrase
---
## Problem & Data Processing
### The Challenge
Given a phrase such as:
> "The economy is expected to"
Predict the next most likely word
### Data Preparation
- Converted text to lowercase
- Removed punctuation and numbers
- Tokenized words
---
## Algorithm Design
### N-Gram Backoff Strategy
**Prediction logic:**
1. Use last two words → search **Trigram**
2. If no match → search **Bigram**
3. If no match → return most frequent **Unigram**
**Benefits:**
- Always returns a prediction
- Fast computation
- Memory efficient
The word with the highest probability is selected.
---
## The Shiny Application
### How It Works
1. User enters a phrase
2. Clicks Predict
3. Model processes input
4. Displays a single predicted word
**Features:**
- Simple interface
- Fast response time
- Deployed on shinyapps.io
- Real-time prediction
---
## Business Value & Future Improvements
### Applications
- Messaging apps
- Email systems
- Search engines
- Customer support chatbots
### Strengths
- Lightweight statistical model
- Low latency
---
## Thanks!
**Deepak Varshney**Option 3: Shiny App Code (If you want to include the actual app)
If you want to include the actual Shiny app code in your presentation, here’s a simple example:
---
title: "Next Word Prediction Application"
author: "Deepak Varshney"
output: ioslides_presentation
---
## Shiny App Demo
::: {.cell}
```{.r .cell-code}
library(shiny)
# Simple prediction function
predict_next_word <- function(input_text) {
# Simplified example - replace with your actual model
words <- strsplit(tolower(input_text), " ")[[1]]
last_word <- tail(words, 1)
# Basic trigram lookup (example)
trigrams <- list(
"the economy is" = "expected",
"economy is expected" = "to",
"is expected to" = "grow"
)
# Return prediction or default
if (length(words) >= 3) {
key <- paste(tail(words, 3), collapse = " ")
return(trigrams[[key]] %||% "the")
}
return("the")
}
# UI
ui <- fluidPage(
titlePanel("Next Word Prediction"),
sidebarLayout(
sidebarPanel(
textInput("phrase", "Enter your phrase:",
value = "The economy is expected to"),
actionButton("predict", "Predict Next Word")
),
mainPanel(
h3("Predicted next word:"),
verbatimTextOutput("prediction")
)
)
)
# Server
server <- function(input, output) {
observeEvent(input$predict, {
output$prediction <- renderPrint({
predict_next_word(input$phrase)
})
})
}
# Run the app
shinyApp(ui = ui, server = server)
```
:::How to Use:
- For R Markdown: Save the code as
.Rmdfile and click “Knit” to render - For Quarto: Save as
.qmdfile and click “Render” - Install required packages:
install.packages(c("rmarkdown", "quarto", "shiny"))The presentation will maintain all your original content while adding professional formatting suitable for RStudio. Choose the option that best fits your needs!