🧠 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