Text Classification with spaCy and Logistic Regression

This project demonstrates a complete machine learning pipeline for binary sentiment classification of customer reviews using Python, spaCy, and scikit-learn.

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📂 Overview

We build a classification model that predicts whether a customer recommends a product based on their written review. The project includes:

• Text preprocessing with spaCy
• Feature extraction using TfidfVectorizer
• Model training with LogisticRegression
• Evaluation using accuracy, confusion matrix, and classification report

🧠 Technologies Used

• Python 3.9
• spaCy (en_core_web_sm)
• pandas
• scikit-learn
• matplotlib

📊 Dataset

A toy dataset of 6 reviews is used for demonstration purposes. Each review is labeled as either 1 (positive) or 0 (negative). You can replace this with a real dataset by loading it with pd.read_csv().

🛠️ Pipeline Structure

Text → spaCy Preprocessing → TfidfVectorizer → Logistic Regression → Prediction