🔐 Encrypted Text Classifier – 20 Newsgroups Cipher Challenge

This project is built for the Kaggle Ciphertext Challenge, where the goal is to classify encrypted text documents into 20 different newsgroup categories.

🎯 Even without decrypting the text, we trained a character-level machine learning model that achieves over 63% accuracy.

📂 Project Structure

cipher-classifier/ ├── app.py # Streamlit app ├── cipher_classifier.pkl # Pickled model + vectorizer ├── train.csv # Kaggle training data ├── requirements.txt # Libraries for deployment └── README.md

🧠 Model Overview

Input: Ciphertext strings (unreadable encrypted text)
Vectorization: CountVectorizer with char-level n-grams (1 to 3)
Model: Logistic Regression (sklearn)
Accuracy: ~63% (without decryption)

Example Output Input (Ciphertext) Predicted Label ['W')(7x1zay7Hb3... 15 Tx4a8M\HNsyp;HM... 8

📦 Deployment This app is designed to run on:

🟢 Hugging Face Spaces

🟢 Streamlit Cloud

🔵 GitHub

📌 Kaggle Link You can download the dataset from the official competition: 👉 Kaggle – 20 Newsgroups Ciphertext Challenge