NLP Text Classification with Gradient Boosting

This project focuses on classifying text data using Natural Language Processing (NLP) techniques and machine learning models. It explores the effectiveness of TF-IDF and CountVectorizer for feature extraction and utilizes Gradient Boosting as the primary classifier. Model optimization is conducted using GridSearchCV with k-fold cross-validation.

🧠 Objectives

• Preprocess and vectorize text data
• Compare feature extraction methods (TF-IDF vs CountVectorizer)
• Train and optimize a Gradient Boosting Classifier
• Evaluate model performance using cross-validation

🔍 Features

• Text preprocessing: tokenization, stopword removal, lowercasing
• Feature extraction using:
  • TF-IDF Vectorizer
  • CountVectorizer
• Model training using:
  • GradientBoostingClassifier
• Hyperparameter tuning using:
  • GridSearchCV
• Model evaluation using:
  • k-Fold Cross-Validation

📦 Libraries Used

• Python 3.x
• scikit-learn
• pandas
• numpy
• matplotlib / seaborn (for visualization)
• nltk (optional for preprocessing)

🧪 Model Evaluation

Evaluation metrics used:

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix
• Cross-validation scores

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/nlp-text-classification.git