The exponential growth of email communication has led to an increase in the number of spam emails. Spam emails are unsolicited messages sent to a large number of recipients with the intention of advertising or promoting a product or service. The presence of spam emails in our inbox can be frustrating, time-consuming, and pose a security risk by exposing us to phishing attacks and malware.
To address this issue, machine learning algorithms can be used to classify emails as spam or not spam. In this project, we have developed an email spam classifier using multiple algorithms. The aim of this project is to compare the performance of different algorithms and identify the most effective algorithm for classifying emails.
The exponential growth of email communication has led to an increase in the number of spam emails, which can be frustrating and time-consuming for users. Additionally, spam emails can pose a security risk by exposing users to phishing attacks and malware.
To address this problem, machine learning algorithms can be used to classify emails as spam or not spam. The aim of this project is to develop an email spam classifier that uses multiple algorithms to classify emails and compare their performance. The project will evaluate the effectiveness of different algorithms and identify the most effective algorithm for classifying emails. The results of this project can be used to develop more effective spam filters for email clients, which can help users to save time and improve their productivity.
For our email spam classification project, we collected a dataset of words extracted from email messages that were labeled as either spam or non-spam (also known as ham) to train and evaluate our machine learning model. We collected the dataset from the Kaggle platform, which is a well-known online community of data scientists and machine learning enthusiasts. Once the dataset was collected, the next step was to preprocess the data. This involved normalizing and scaling the data to ensure consistency and accuracy in the modeling process. Since there were no missing values in the dataset, we did not have to handle them. The data was already cleaned and preprocessed and was free of email headers and HTML tags. After the data preprocessing step, the next step was to select a suitable model for the task. We considered several machine learning models for email spam classification, including logistic regression, decision trees, random forests, Naive Bayes algorithm, and support vector machines (SVMs). After evaluating the performance of each model, we selected Naive Bayes as the best performing model. Once the models had been chosen, the following step was to train them using the preprocessed dataset. This process required partitioning the dataset into training and testing sets, where the training set was utilized to train the model, and the testing set was utilized to assess the performance of the trained model. This methodology allowed us to gauge the precision and efficiency of each model in classifying emails as spam or ham.
After training the models using the preprocessed dataset, the subsequent step was to assess their performance using the testing set. In this regard, the evaluation metrics utilized included accuracy, precision, recall, and F1-score, which aided in evaluating the models' ability to categorize emails as spam or ham based on the testing set. Through the utilization of various evaluation metrics, we could obtain a more holistic comprehension of the performance of each model. Hyperparameters refer to those parameters that are established before training the model and are not learned from the data. Tuning these hyperparameters can enhance the model's performance. In the email classification project, this step involved experimenting with various values for the hyperparameters and choosing those that yielded the best results. Through optimizing the hyperparameters for models such as Random Forest, SVM Classifier, and Naive Bayes, we were able to increase their accuracy and efficacy in classifying emails. This stage is critical in attaining the best performance possible from the models.
Once the optimal model was selected based on the optimized hyperparameters, the subsequent step was to deploy it in a practical setting for email classification. To accomplish this, we used the Streamlit Python library to construct a web application that could incorporate the model. By employing Streamlit, we were able to design a user-friendly interface that facilitated the easy input of the email message and produced precise email classification outcomes as either spam or ham. This phase was vital in enabling the model to be utilized effectively in real-world scenarios by users with limited technical knowledge.