Next Word Prediction App
Srikanth S
2025-01-19
Introduction
- Purpose: Predict the next word given a phrase.
- Why it matters:
- Useful in text prediction (e.g., search engines, chatbots).
- Enhances user experience in applications requiring typing.
Algorithm Overview
- Data: Example sentences used to build a simple
bigram model.
- Process:
- Preprocess input text (lowercase, remove punctuation, etc.).
- Build a bigram frequency table from sample
text.
- Predict the next word based on the most frequent bigram match.
App Functionality
- Inputs:
- A text box to input a phrase (e.g., “Shiny apps are”).
- Outputs:
- A single predicted next word based on the last word in the
phrase.
- How it works:
- The app identifies the last word of the input and matches it to the
most frequent bigram.
Experience Using the App
- Ease of Use:
- Enter a phrase and get a prediction instantly.
- Reliability:
- Predictions are based on bigram frequency from training data.
- Limitations:
- Accuracy depends on the quality and size of the training data.
Why It Stands Out
- Novel Approach:
- A lightweight and straightforward implementation of next-word
prediction.
- Extendable:
- Can be expanded to use larger datasets and more advanced N-gram
models.
- Hireability:
- Demonstrates understanding of Shiny, N-grams, and text
processing.