A real-time predictive text system that suggests the most likely next word in a sentence.

Built using text data from:

• Blogs
• News articles
• Twitter posts

The goal is to improve typing speed and user experience.

The model is based on an n-gram language model.

It predicts the next word using historical word patterns:

1. Trigram model (best context: 3-word sequence)
2. Bigram model (2-word sequence fallback)
3. Unigram model (most frequent words overall)

If no match is found, the model falls back to common words in the dataset.

1. Enter a phrase into the text box
2. Click “Predict”
3. The model returns the most likely next word

Example:

Input: “I love”

Output: you

thank you → for
what is → the
going to → be
how are → you
I love → you

The model generates context-aware predictions using learned language patterns.

This system demonstrates how language models can improve typing efficiency.

Key Benefits

• Fast and lightweight
• Works in real time
• Simple and scalable

Future Improvements

• Larger training dataset
• Smarter smoothing techniques
• Multiple word predictions instead of one