Toxic Comment Classifier

Project Overview

This project focuses on building a machine learning model to identify toxic comments in text. It provides a user-friendly web-based interface that allows users to input text comments, and the model will classify them as either toxic or non-toxic.

Project Components

This project comprises the following components:

Model Training: The machine learning model for comment toxicity detection is trained using labeled data containing both toxic and non-toxic comments.
Web Application: A Flask-based web application (found in app.py) serves as the user interface for entering comments and obtaining real-time predictions from the trained model.

Prerequisites

Before running the application, make sure you have the following prerequisites installed:

Python 3.7 or higher
Flask (Python web framework)
Scikit-learn (machine learning library)
NLTK (Natural Language Toolkit)
NumPy (numerical computing library)

You can install the required Python packages using pip:

pip install Flask scikit-learn nltk numpy

Installation and Usage

Follow these steps to install and use the web application:

Clone this repository to your local machine:

git clone https://github.com/Surajrs812/Toxic-comment-classifier.git

Navigate to the project directory:
```
cd Toxic-comment-classifier
```
Run the Flask application:
```
python app.py
```
Open a web browser and go to http://127.0.0.1:5000 to access the web application.

How to Use

Visit the application URL (http://127.0.0.1:5000).
You'll see a simple web page with an input field for comments.
Enter a text comment and click the "Check Toxicity" button.
The web application will display the classification result as either toxic or non-toxic.

Directory Structure

The project directory is organized as follows:

app.py: The Flask web application.
templates: HTML templates for the web application.
Model Training: Directory to store the trained comment toxicity model.

Credits

This project was created by Suraj R S.