📬 Logistic Regression Spam Classifier

This project implements a Spam Email Classifier using Logistic Regression, trained on a dataset of SMS messages. The model distinguishes between ham (non-spam) and spam messages. This project demonstrates how to process text data, apply machine learning, and evaluate model performance.

🚀 Features:

• Text Preprocessing: The text data is cleaned and transformed using TF-IDF Vectorization, which converts the raw text into numerical feature vectors.
• Model Training: A Logistic Regression model is trained to classify SMS messages as either "ham" or "spam".
• Model Evaluation: Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the model's effectiveness.

📊 Steps:

1. Data Preprocessing:

   • The dataset is cleaned by removing stop words and converting all text to lowercase.
   • The text is transformed into numerical features using the TF-IDF vectorizer.

2. Training:

   • The Logistic Regression model is trained on the processed data.

3. Evaluation:

   • The model is evaluated on both training and test datasets using multiple performance metrics (accuracy, precision, recall, F1-score).

📋 Dependencies:

• pandas: For data manipulation and handling.
• numpy: For numerical operations.
• scikit-learn: For machine learning models, including logistic regression and vectorization.