🚀 Website Classification App

This is a Machine Learning Website Classification application. It classifies websites into categories based on their content. The project includes data preprocessing, model training, testing, and deployment using Streamlit for a web interface.

✨ Features

✅ End-to-end ML pipeline: EDA → Preprocessing → Training → Testing → Deployment

🌐 Interactive web app with Streamlit

⚡ Real-time website category prediction

📦 Supports .pkl model and label encoder files

🧠 How the Model Works- The model uses TfidfVectorizer to convert website content into numerical features, then a Support Vector Machine (SVM) classifier predicts the website category. User input is preprocessed before prediction.

🛠 How to Run

Clone the repository:

git clone https://github.com/Sananda-Dutta/website-category-classifier cd website-classification

Install dependencies:

pip install -r requirements.txt

Run the Streamlit app:

streamlit run app.py

Enter the website input in the app and see the predicted category.

📂 Project Files

File Description

app.py Streamlit application

website_classifier.pkl Trained ML model

label_encoder.pkl Label encoder for categories

requirements.txt Python dependencies

🛠 Technologies Used

Python

scikit-learn

pandas, numpy

Streamlit

✍️ Author

Sananda Dutta