June 20, 2024

Best NLP Project Ideas for Beginners

Are you interested in practically mastering NLP? Then you are in the right place.

But there is a huge crowd looking to master this! To stand out among them you need to create a strong portfolio.

You can start creating your unique portfolio by starting with the below-mentioned NLP projects for beginners.

10 Beginner-Friendly NLP Project Ideas – Overview

Here’s an overview of the 10 best NLP projects for beginners:

S.No.	Project Title	Complexity	Estimated Time	Source Code
1	Text Summarization	Easy	10 hours	View Code
2	Fake News Detection	Easy	12 hours	View Code
3	Spam SMS Classification	Easy	10 hours	View Code
4	Toxic Comments Classification	Easy	10 hours	View Code
5	Named Entity Recognition	Easy	10 hours	View Code
6	Text Generation	Medium	15 hours	View Code
7	Spell and Grammar Checking	Medium	15 hours	View Code
8	Sentence Autocomplete	Medium	15 hours	View Code
9	Chatbot Using NLP	Medium	20 hours	View Code
10	Sentiment Analysis	Medium	20 hours	View Code

Top 10 Natural Language Processing Projects for Beginners

Below are the top 10 simple NLP projects for beginners:

1. Text Summarization

This project involves developing a model to summarize long pieces of text into concise summaries.

You will learn about text preprocessing, feature extraction, and implementing algorithms for text summarization.

Duration: 10 hours

Project Complexity: Easy

Learning Outcome: Understanding of text preprocessing and summarization techniques.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of NLP concepts
Familiarity with machine learning libraries

Resources Required:

Code Editor
NLP libraries (e.g., NLTK, spaCy)

Real-World Application:

News article summarization
Document summarization for business reports

Get Started

2. Fake News Detection

This project involves creating a model to detect fake news articles using NLP techniques.

You will learn about text classification, feature extraction, and implementing machine learning algorithms.

Duration: 12 hours

Project Complexity: Easy

Learning Outcome: Understanding of text classification and feature extraction techniques.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of NLP concepts
Familiarity with machine learning libraries

Resources Required:

Code Editor
NLP libraries (e.g., NLTK, scikit-learn)

Real-World Application:

Social media monitoring
News credibility verification

Get Started

3. Spam SMS Classification

This project involves developing a model to classify SMS messages as spam or not spam.

You will learn about text preprocessing, feature extraction, and implementing classification algorithms.

Duration: 10 hours

Project Complexity: Easy

Learning Outcome: Understanding of text classification and preprocessing techniques.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of NLP concepts
Familiarity with machine learning libraries

Resources Required:

Code editor
NLP libraries (e.g., NLTK, scikit-learn)

Real-World Application:

Spam filtering for messaging apps
Email spam detection

Get Started

4. Toxic Comments Classification

This project involves creating a model to classify comments as toxic or non-toxic using NLP techniques.

You will learn about text preprocessing, feature extraction, and implementing classification algorithms.

Duration: 10 hours

Project Complexity: Easy

Learning Outcome: Understanding of text classification and sentiment analysis techniques.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of NLP concepts
Familiarity with machine learning libraries

Resources Required:

Code editor
NLP libraries (e.g., NLTK, TensorFlow)

Real-World Application:

Moderating online comments
Enhancing social media user experience

Get Started

5. Named Entity Recognition (NER)

This project involves developing a model to identify and classify named entities in text, such as names of people, organizations, and locations.

You will learn about tokenization, part-of-speech tagging, and NER algorithms.

Duration: 10 hours

Project Complexity: Easy

Learning Outcome: Understanding of tokenization, POS tagging, and NER.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of NLP concepts
Familiarity with NER libraries

Resources Required:

Code Editor
NLP libraries (e.g., spaCy, NLTK)

Real-World Application:

Information extraction from documents
Automated customer support

Get Started

6. Text Generation

This project involves creating a model that can generate text based on a given input, using techniques like RNNs or Transformers.

You will learn about sequence modeling and text generation algorithms.

Duration: 15 hours

Project Complexity: Medium

Learning Outcome: Understanding of sequence modeling and text generation.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of deep learning concepts
Familiarity with machine learning libraries

Resources Required:

Code Editor
Deep learning libraries (e.g., TensorFlow, PyTorch)

Real-World Application:

Creative writing tools
Automated content creation

Get Started

7. Spell and Grammar Checking

This project involves developing a model to check and correct spelling and grammar in text.

You will learn about text preprocessing, error detection, and correction techniques.

Duration: 15 hours

Project Complexity: Medium

Learning Outcome: Understanding of error detection and correction in text.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of NLP concepts
Familiarity with NLP libraries

Resources Required:

Code Editor
NLP libraries (e.g., spaCy, NLTK)

Real-World Application:

Text editing software
Online writing assistants

Get Started

8. Sentence Autocomplete

This project involves creating a model to autocomplete sentences based on the initial input.

You will learn about sequence prediction and language modeling techniques.

Duration: 15 hours

Project Complexity: Medium

Learning Outcome: Understanding of sequence prediction and language modeling.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of deep learning concepts
Familiarity with NLP libraries

Resources Required:

Code Editor
Deep learning libraries (e.g., TensorFlow, PyTorch)

Real-World Application:

Text input prediction
Enhanced typing tools

Get Started

9. Chatbot using NLP

This project involves developing a chatbot that can interact with users and respond to their queries using NLP techniques.

You will learn about dialogue management, intent recognition, and response generation.

Duration: 20 hours

Project Complexity: Medium

Learning Outcome: Understanding of dialogue systems and chatbot frameworks.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of NLP concepts
Familiarity with chatbot frameworks

Resources Required:

Code Editor
Chatbot framework (e.g., Rasa, Dialogflow)

Real-World Application:

Customer service automation
Virtual assistants

Get Started

10. Sentiment Analysis

This project involves creating a model to analyze text data’s sentiment, determining whether it is positive, negative, or neutral.

You will learn about text preprocessing, feature extraction, and implementing sentiment analysis algorithms.

Duration: 20 hours

Project Complexity: Medium

Learning Outcome: Understanding of sentiment analysis and text classification.

Portfolio Worthiness: Yes

Required Pre-requisites:

Basic Python knowledge
Understanding of NLP concepts
Familiarity with machine learning libraries

Resources Required:

Code Editor
NLP libraries (e.g., NLTK, TextBlob)

Real-World Application:

Social media monitoring
Market research analysis

Get Started

Frequently Asked Questions

1. What are some easy NLP project ideas for beginners?

Some easy NLP projects for beginners are:

Text Summarization
Spam SMS Classification
Text Generation

2. Why are NLP projects important for beginners?

NLP projects are important for beginners as they provide practical experience with text data, helping them understand and apply natural language processing techniques to real-world problems.

3. What skills can beginners learn from NLP projects?

From NLP projects, beginners can learn text preprocessing, tokenization, sentiment analysis, named entity recognition, and machine learning model implementation.

4. Which NLP project is recommended for someone with no prior programming experience?

A simple Text Summarization NLP project is recommended for someone with no prior programming experience.

5. How long does it typically take to complete a beginner-level NLP project?

It typically takes 15 hours to complete a beginner-level NLP project.

Final Words

NLP mini projects for beginners can help you build a strong portfolio to ace technical interviews in data science.

Based on your experience and understanding of these NLP projects for beginners, you can develop them to suit your requirements.

Explore More Project Ideas

Thirumoorthy

Thirumoorthy serves as a teacher and coach. He obtained a 99 percentile on the CAT. He cleared numerous IT jobs and public sector job interviews, but he still decided to pursue a career in education. He desires to elevate the underprivileged sections of society through education