Spam Email Detection

📧 Spam Email Detection Project Description This project focuses on building a machine learning system to automatically classify emails as Spam or Ham (Not Spam). The goal is to improve email filtering using Natural Language Processing (NLP) and supervised learning algorithms. The project includes a complete end-to-end pipeline starting from data exploration and preprocessing to model training and evaluation. 🔍 Key Steps: Data Analysis: Understanding the dataset and analyzing the distribution of spam and ham emails. Data Preprocessing: Cleaning text data by removing noise, stopwords, and applying tokenization and normalization techniques. Feature Extraction: Converting text data into numerical features using TF-IDF and CountVectorizer. Model Building: Training multiple machine learning models including: Logistic Regression Linear Support Vector Classifier (Linear SVC) K-Nearest Neighbors (KNN) Hyperparameter Tuning: Improving model performance using techniques like GridSearchCV. Model Evaluation: Assessing performance using metrics such as Accuracy, Precision, Recall, F1-Score, and ROC-AUC. 📊 The models are compared to select the best-performing classifier for spam detection based on test performance. 💡 This project demonstrates practical application of NLP and machine learning in building real-world email filtering systems.