Features

  • Text Classification: The app uses a pre-trained Naive Bayes model to classify input text into predefined categories.
  • Web Interface: Users can interact with the app through a straightforward web interface.
  • Bootstrap CSS: The frontend is designed using Bootstrap for a clean and responsive layout.

Prerequisites

Make sure you have the following installed:

  • Python 3.x
  • Flask
  • Scikit-learn (for the Naive Bayes model)
  • Pandas (for data handling)
  • HTML and Bootstrap (for frontend)

Installation

Clone this repository:

git clone https://github.com/anupamavm/text-classifier.git
cd text-classifier

Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate

Install the required packages:

pip install flask scikit-learn pandas

Usage

Run the Flask app:

python app.py

Open your web browser and navigate to http://localhost:5000.

Enter some text in the input field and click the “Classify” button to see the predicted category.

Dataset

The dataset used for training the Naive Bayes model was acquired from Kaggle. It contains labeled examples of text data along with their corresponding categories.

Model Training

The Naive Bayes model was trained using the following steps:

  1. Data Preprocessing:

    • Cleaned and tokenized the text data.
    • Removed any noise, special characters, or irrelevant information.
    • Ensured consistent formatting.

  2. Feature Extraction:

    • Used techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or bag-of-words to convert text into numerical features.
    • TF-IDF assigns weights to words based on their importance in a document relative to the entire corpus.
    • Bag-of-words represents each document as a vector of word frequencies.

  3. Model Training:

    • Trained the Naive Bayes classifier on the processed data.
    • Naive Bayes is a probabilistic algorithm based on Bayes' theorem.
    • It assumes that features are conditionally independent given the class label.

  4. Model Evaluation:

    • Assessed the model’s performance using techniques such as cross-validation or a holdout test set.
    • Metrics like accuracy, precision, recall, and F1-score were used to evaluate the model.

Here is the Colab Notebook

Acknowledgments

  • Kaggle for providing the dataset.
  • The Flask and Bootstrap communities for their excellent documentation.