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Artificial Intelligence · Mini Project
Next Word Prediction with LSTM
Suggests the next word as you type, like a phone keyboard
1-2 weeks build
Overview
A word-level language model trained on public-domain books from Project Gutenberg. Text is tokenised into input sequences, an embedding layer feeds a stacked LSTM, and a softmax over the vocabulary gives the top suggestions. The kit compares it with a simple n-gram model so you can show what the neural network actually adds.
What makes it stand up
- 01Sequence generation pipeline from raw text to training pairs
- 02Embedding plus stacked LSTM architecture with dropout
- 03N-gram baseline for a fair comparison
- 04Perplexity and top-k accuracy reported for both models
- 05Live typing demo showing the top three suggestions
Module breakdown
- 01
Corpus
Gutenberg text cleaning and vocabulary limits.
- 02
Sequences
Sliding-window input sequences and padding.
- 03
Model
Embedding, LSTM layers and softmax output.
- 04
Evaluation
Perplexity, top-k accuracy and sample completions.
- 05
Demo
Streamlit text box with live next-word chips.
After this, you will be able to
- Explain how an LSTM remembers earlier words
- Define perplexity and interpret your own figures
- Demo suggestions updating as the examiner types
Technology stack
- Python
- TensorFlow
- Keras
- NumPy
- Streamlit
- Level
- Mini Project
- Domain
- Artificial Intelligence
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support