Spam Email Classifier

Developed a machine learning-based Spam Email Classifier to accurately identify and filter spam emails from legitimate ones. This project involved preprocessing large email datasets, including tokenization, removing stop words, and applying techniques like TF-IDF vectorization for feature extraction. Leveraged algorithms such as Naïve Bayes and Logistic Regression to train the classifier, achieving high accuracy and precision in spam detection.

The model was evaluated using performance metrics like confusion matrix, precision, recall, F1-score, and ROC-AUC curve to ensure robustness. The project highlights expertise in natural language processing (NLP), machine learning, and data preprocessing, delivering a practical solution for email spam detection.

.Tools and technologies used: Python, scikit-learn, pandas, NumPy,and Matplotlib