🎬 Movie Review Sentiment Classifier (Beginner ML Project)

This is a simple machine learning project that classifies movie reviews as positive or negative using natural language processing (NLP). It’s built for beginners to understand the complete ML workflow — from data loading and preprocessing to model training, evaluation, and prediction — all in one Python script.

🔍 Key Features

• Uses the NLTK Movie Reviews dataset (no external downloads needed)
• Converts text to numerical features using TF-IDF vectorization
• Trains a Logistic Regression classifier to predict sentiment
• Displays accuracy and classification report
• Allows custom review predictions directly from the terminal

🧠 Tech Stack

• Python 3
• NLTK for dataset & tokenization
• Scikit-learn for ML model & evaluation
• NumPy / Pandas / Matplotlib (optional for future visualization)

⚙️ How It Works

1. Load and shuffle labeled movie reviews from NLTK
2. Convert raw text into TF-IDF vectors
3. Train a Logistic Regression model on 80% of data
4. Evaluate accuracy on the remaining 20%
5. Predict sentiment for new review text inputs

🚀 Usage

py main.py

📈 Accuracy

~80–85% on the NLTK Movie Reviews dataset — great for a baseline sentiment classifier.