ABSTRACT: –

E-mail communication has grown in popularity in this internet age because it is inexpensive and simple to use for sending messages and sharing important information with others; however, spam messages frequently generate a large number of unwanted messages in users’ inboxes, wasting resources as well as valuable user time; therefore, it is efficient and accurate to identify the message as spam or a technique is required. In this article, we propose a new model for detecting spam messages based on sentiment analysis of body data from an email body. We integrate word embeddings and a bidirectional Lstm network to analyze the sentimental and sequential properties of texts. In addition, we speed up training time and extract higher-level content text properties for the bidirectional Lstm network using the convolution neural network. It includes two datasets, namely the ling spam dataset and spam text message classification dataset, and we use retrieval accuracy and f-score to compare and rank the performance of our models. suggested approach, our model achieves an improved accuracy performance of about 98–99. Furthermore, we demonstrate that our model outperforms not only some popular and some unpopular machine learning methods, but also some more advanced approaches to spam message detection, demonstrating its superiority on its own.

SYSTEM:-

System description:

The email spam detection system is a python project that uses machine learning algorithms to detect whether an email is spam or not the system analyzes the content of the email and classifies it as either spam or not spam based on the probability score assigned to it the system allows users to upload an email file or copy and paste the content of an email to be analyzed

• functional requirements :

• The system must be accessible from a user interface that allows users to upload an email file or copy and paste the content of an email to be analyzed
• The system must use machine learning algorithms to analyze the content of the email and classify it as spam or not
• The system must display the probability score assigned to the email indicating the likelihood that it is spam
• The system must display the result of the classification as either spam or not spam
• The system must provide an option for users to train the machine learning algorithms using their own data to improve the accuracy of the classification

• Non-functional requirements

• The system must be fast and responsive providing results within seconds of the users input
• The system must be accurate with a high rate of correctly classifying emails as either spam or not spam
• The system must be secure protecting the privacy and confidentiality of the emails being analyzed the system must be easy to use and provide clear and concise instructions to the user

• implementation details:

The system will use python libraries such as scikit-learn numpy and pandas to implement machine learning algorithms for email classification the system will use natural language processing nlp techniques to preprocess the text of the emails and extract relevant features for classification the system will use a trained model to assign a probability score to each email indicating the likelihood that it is spam the system will use a threshold value to classify emails as spam or not based on the probability score assigned to them the system will provide an option for users to train the machine learning algorithms using their own data to improve the accuracy of the classification the system will use appropriate error handling and validation to prevent crashes and ensure the system functions as intended the system will be tested and validated to ensure it meets the functional and non-functional requirements overall the email spam detection system will provide users with a reliable and efficient way to detect and filter out spam emails improving the efficiency and security of email communication

PROPOSED SYSTEM:-

CNN is divided into a convolutional layer and a pooling layer. We use the convolution layer as a feature extractor from the context and then use the pooling layer to compress the data and feature count to improve the model’s fault tolerance. The convolutional layer learns the local features through the receptive field of its neurons and fuses the local features of the text to form a more general global feature. It is converted to nonlinear features using the tanh activation function, and then to the nonlinear features in the input to the pooling layer. We use the maximal pooling method to extract the most important features and remove relatively less obvious features, simplifying the network complexity and reducing the computational effort. When the CNN processes the text features appropriately, we pass the output to the LSTM model. In the LSTM model, each neural cell has three mechanisms: the forget gate, the update gate, and the exit gate. The flow of data input into the model begins at the embedding layer, where the data is tokenized in the form of text, consumed as streams, and then vectorized using Gauntlet word embedding. In this case, the number of words was limited to 70. In addition, the glove word embedding model generated a vector of 300 values for each of these words. The separated data was then trained on up to 100 LSTMs, then parsed into two dense neural networks with a measurement of 1024, and finally passed to a dense size 2 with a softmax activation function since they only spanned two classes (spam and non-spam). The value after softmax was then compared to the label value of the data in question to generate an error or loss. The loss function used in this model was categorical cross-entropy with Adam’s optimizer.

LSTM has the special property of remembering. The main idea of the LSTM model is simply the state of the cell. The cell state flows directly through the current almost unchanged, with only a small linear interaction. Another important thing about LSTM is the gate. gate governs the data that is safely added to or removed from the cell. LSTM has limitations as it only considers previous contexts in the current one. Therefore, both LSTM and RNN can only get information from earlier time steps. Therefore, the benefit of using LSTM is in the form of storage in cell state and an RNN with access to previous and subsequent context information. LSTM has the important benefit of remembering long-term dependencies. The output is based on the call state, and the output is considered a feature vector. Finally, the weighted sum of the outputs from the dense layer is used as input to the Softmax activation function, where we predict the probability that the email content is spam, or ham. We integrated all three blocks, namely word embedding, a convolutional network, and an LSTM network, to separate email messages from text based on sentiment and sequential properties.

MODULES:-

• preprocessing module: this module is responsible for preprocessing the text of the emails to extract relevant features for classification it will use natural language processing nlp techniques such as tokenization stemming and stop-word removal to preprocess the text the module will also remove any special characters and convert the text to lowercase to standardize the data.

• feature extraction module: this module is responsible for extracting features from the preprocessed text of the emails it will use techniques such as bag of words term frequency inverse document frequency tf-idf and word embeddings to convert the text into a numerical representation the module will also handle any feature scaling or normalization required for the machine learning algorithms.
• machine learning module :this module is responsible for implementing the machine learning algorithms for email classification it will use supervised learning algorithms such as naive bayes support vector machines svm and random forests to classify emails as spam or not the module will also handle model training and testing using a labeled dataset

• Evaluation module: this module is responsible for evaluating the performance of the machine learning algorithms it will use metrics such as accuracy precision recall and f1-score to evaluate the classification results the module will also handle any model tuning or hyperparameter optimization required to improve the performance of the algorithms .

• User interface module: this module is responsible for providing a user interface for the system it will use libraries such as tkinter pyqt or flask to create a user-friendly interface for users to upload an email file or copy and paste the content of an email to be analyzed the module will also display the probability score and classification result to the user.

• Data handling module :this module is responsible for handling the data used by the system it will handle loading and preprocessing the labeled dataset used for model training and testing the module will also provide an option for users to upload their own data to train the machine learning algorithms and improve the accuracy of the classification overall these modules will work together to create a reliable and efficient email spam detection system that accurately classifies emails as spam or not using machine learning algorithms

APPLICATION:-

• Email Client Integration: The Email Spam Detection system can be integrated into email clients such as Microsoft Outlook, Gmail, or Thunderbird. The system will analyze incoming emails and flag any emails that are classified as spam, helping users to filter out unwanted and potentially harmful emails.
• Web-based Email Service Integration: The Email Spam Detection system can be integrated into web-based email services such as Yahoo Mail, AOL Mail, or ProtonMail. The system will analyze incoming emails in real-time and flag any emails that are classified as spam, providing users with a safer and more secure email experience.
• Enterprise Email Security: The Email Spam Detection system can be used by enterprises to improve their email security. The system can be integrated into the company’s email server to analyze incoming and outgoing emails for spam, reducing the risk of phishing attacks, malware infections, and other security threats.
• Personal Email Security: The Email Spam Detection system can be used by individuals to improve their personal email security. The system can be installed on a personal computer or smartphone and used to analyze incoming emails for spam, reducing the risk of identity theft, financial fraud, and other security threats.
• Research and Development: The Email Spam Detection system can be used by researchers and developers to improve the accuracy and efficiency of email spam detection algorithms. The system can be used to test and evaluate new machine learning algorithms, feature extraction techniques, and evaluation metrics for email spam detection.

HARDWARE AND SOFTWARE REQUIREMENTS:-

HARDWARE:-

1. Processor: Intel i3 or above.
2. RAM: 6GB or more
3. Hard disk: 160 GB or more

SOFTWARE:-

1. Operating System : Windows 7/8/10
2. python
3. Anaconda
4. Jupyter notebook
5. flask framework