• Identification of the main problem or challenge faced by the company/organization.

• When the campaign is raised, its open count is usually meager because the emails land in the spam.

• Using a spam detection model can predict the probability of the mail to land in spam with a spam-triggering words list.

• Users can change those spam-triggering words and increase their mail probability of landing in spam.

• Data Collection and Preparation: Gather a diverse dataset of emails, preprocess them, and extract relevant features.

• Feature Extraction: Convert text-based features into numerical representations suitable for machine learning algorithms.

• Model Selection and Training: Choose a suitable algorithm in our case we have used a Random forest, and train it on the prepared dataset.

• Model Evaluation: Assess the trained model's performance using validation data, considering metrics like accuracy and precision.

• Iterative Refinement: Fine-tune the model based on evaluation results and experiment with different features or parameters.

• Integration and Deployment: Integrate the trained model into the email system's filtering pipeline for effective spam detection.

• The technology used - is TFIDF, RandomForestClassifier, and NLTK library.

• Performance Metrics used -Based on business requirements Precision(False Positive) is considered as a performance metric.

• Problem statement - Predicting the percentage of mail landing in spam.

• The dataset was Enron Corpus, SMS spam, Job Title Spam Classification, and SpamAssassin which is a publicly available platform such as Kaggle. The technology used - is TFIDF, RandomForestClassifier, and NLTK library.

Based on the Email content model will predict the percentage of mail landing in spam.

In this case study, we explored the development of a spam detection system for email communication. By implementing a comprehensive methodology, including data collection, feature extraction, model training, evaluation, refinement, and integration, we aimed to enhance the security and productivity of email systems.

In conclusion, this case study highlights the importance of developing effective spam detection systems. By employing a robust methodology and leveraging machine learning techniques, organizations can minimize the impact of spam, reduce security risks, and enhance the overall email experience for users. It is essential for organizations to continually invest in spam detection technologies to stay proactive in combating evolving spam threats and ensuring secure and efficient communication channels.