https://github.com/arunagirinathan-k/text-classifier-using-nlp_techniques
A Text Classification using NLP Techniques.
https://github.com/arunagirinathan-k/text-classifier-using-nlp_techniques
matplotlib nlp nltk numpy pandas scikit-learn seaborn spacy text-classification
Last synced: 3 months ago
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A Text Classification using NLP Techniques.
- Host: GitHub
- URL: https://github.com/arunagirinathan-k/text-classifier-using-nlp_techniques
- Owner: ARUNAGIRINATHAN-K
- License: mit
- Created: 2025-06-14T10:01:08.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-25T02:40:54.000Z (about 1 year ago)
- Last Synced: 2025-07-26T00:14:11.324Z (12 months ago)
- Topics: matplotlib, nlp, nltk, numpy, pandas, scikit-learn, seaborn, spacy, text-classification
- Language: Python
- Homepage:
- Size: 428 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## 🧠 Project Idea:
To Develop a machine learning-based text classifier that categorizes text data (e.g., news, emails, tweets, reviews)
into appropriate categories using NLP preprocessing and classification models.
## 📌 Problem Statement:**
With the exponential growth of unstructured text data, manually categorizing text is inefficient. This project aims to
automate text classification using Natural Language Processing (NLP) and supervised machine learning models.
## 🛠️ Technologies Used:**
```
Languages: Python
Libraries: NLTK / spaCy, Scikit-learn, pandas, NumPy
ML Models: Logistic Regression, Naive Bayes, SVM, or even deep learning (LSTM, BERT for advanced)
Frontend (optional): HTML, CSS, JavaScript
Deployment (optional): Streamlit / Flask
```
## 🔍 Key Features:
-Text input box or file upload
-Preprocessing (tokenization, stopword removal, stemming/lemmatization)
-Vectorization (TF-IDF or CountVectorizer)
-Model training & prediction
-Accuracy and confusion matrix
Optional: Downloadable classification report
## 🎯 Use Case Examples:
-Spam vs. Ham email classification
-Sentiment analysis (Positive/Negative/Neutral)
-News categorization (Politics, Sports, Tech, etc.)
-Product review classifier
## 📁 Folder Structure:
```
text_classifier_project/
├── data/
│ └── sample_data.csv
├── model/
│ ├── text_model.pkl
│ └── vectorizer.pkl
├── utils/
│ └── preprocessing.py
├── templates/
│ └── index.html
├── app.py
├── train.py
├── predict.py
├── requirements.txt
└── README.md
```