📄 Document Classifier: Text Mining Project

This repository contains a complete pipeline for classifying Reddit posts into two categories: Data Science and Game of Thrones.
It encompasses data preprocessing, model training, evaluation, and deployment-ready prediction scripts.

📁 Project Structure

document-classifier-textmining/
├── data/
│   ├── reddit_posts.csv
│   ├── GameofThrones.csv
│   ├── reddit_database.csv
│   ├── best_model.pkl
│   ├── tfidf_vectorizer.pkl
│   └── label_encoder.pkl
├── src/
│   └── doc_classifier_tm.ipynb
├── README.md
└── LICENSE

🧠 Features

• Data Preprocessing: Cleaning, emoji removal, and text normalization.
• Model Training: Utilizes multiple algorithms including Naive Bayes, Logistic Regression, and SVM.
• Evaluation: Calculates accuracy, precision, recall, and F1-score to select the best model.
• Model Persistence: Saves the trained model, TF-IDF vectorizer, and label encoder for future use.
• Prediction Script: Loads saved components to predict categories for new text inputs.

🚀 Getting Started

Prerequisites

• Python 3.7 or higher
• Install required packages:

pip install -r requirements.txt

Training the Model

Navigate to the src/ directory and run the .ipynb file

This script will:

• Load and preprocess data from data/reddit_posts.csv.
• Train multiple models and evaluate their performance.
• Save the best-performing model and associated components in the data/ directory.
• let you make predictions (single & batch, both. )

📝 Dataset Details

• reddit_posts.csv: Combined and cleaned dataset containing Reddit posts related to Data Science and Game of Thrones.
• GameofThrones.csv: Raw dataset with Game of Thrones-related Reddit posts.
• reddit_database.csv: Raw dataset with Data Science-related Reddit posts.

📦 Saved Components

• best_model.pkl: Serialized best-performing model.
• tfidf_vectorizer.pkl: Serialized TF-IDF vectorizer used for feature extraction.
• label_encoder.pkl: Serialized label encoder mapping categories to numerical labels.

📄 License

This project is licensed under the GPL-3.0 License.

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

📬 Contact

For any inquiries or feedback, please open an issue in the repository.