email-spam-detection-using-machine-learning

Spam Detector CLI Welcome to the Spam Detector CLI! This command-line tool helps you determine if an email is spam or not. Project Structure data manipulation/ : Data manipulation and visualization notebooks model/ : Trained model notebook Clone the repository: 2.Install Python dependencies: Usage Run the CLI with the email content: Example output: Data Visualization To better understand the dataset, I visualized the key aspects of the data, such as class distribution and common words in spam and ham emails. Class Distribution The dataset was imbalanced, with significantly more "Ham" emails than "Spam." This imbalance can bias the model toward predicting "Ham." To address this, I applied SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset. The graph above shows the proportion of "Ham" and "Spam" emails before applying SMOTE. Word Frequency Analysis I created a word cloud to visualize the most frequent words in spam and ham emails. This helped identify distinct patterns in text, such as spam emails containing words like "Congratulations," "Free," and "Prize." Spam Word Cloud Ham Word Cloud