Abstract

This project, developed during an internship, presents a email spam detection system utilizing various machine learning classification algorithms, including Logistic Regression, Decision Tree Classifier, and Support Vector Machine. The dataset was meticulously examined and cleaned to ensure accuracy before applying the classification techniques. A comprehensive evaluation was conducted using performance metrics such as accuracy, precision, recall, and F1-score, alongside cross-validation, to assess the effectiveness of each algorithm in identifying spam emails. The findings contribute to the ongoing efforts to enhance email filtering systems, highlighting the potential of machine learning in improving user experience and security.

Introduction

Email spam is a pervasive issue in the digital communication landscape, affecting individuals and organizations globally. According to recent studies, a significant percentage of emails sent and received daily are spam, leading to decreased productivity and increased risks of phishing attacks. As the volume of spam continues to rise, effective spam detection becomes crucial to safeguard users and maintain the integrity of email communication.

Despite advancements in email filtering technologies, many existing systems struggle to keep pace with the evolving tactics employed by spammers. This is further compounded by the diverse nature of spam content, which can vary widely in terms of language, structure, and intent. Consequently, users often find themselves overwhelmed by unwanted messages, resulting in missed important communications and a heightened sense of frustration.

The advent of machine learning and natural language processing (NLP) offers promising avenues for enhancing spam detection capabilities. By analyzing the content and characteristics of emails, these technologies can effectively classify messages as spam or legitimate.

In this project, we aim to develop a robust email spam detection system utilizing various machine learning classification algorithms, including Logistic Regression, Decision Tree Classifier, and Support Vector Machine. Through careful data preprocessing and evaluation using performance metrics such as accuracy, precision, recall, and F1-score, we seek to create a model that effectively identifies spam emails while minimizing false positives.

Methodology

This section details the technical approaches and tools employed in the project, ensuring that others can replicate our work effectively.

Tools and Libraries:
Effectively utilized the following tools and libraries:

• Pandas: For efficient data manipulation and analysis.
• NumPy: For performing numerical operations and array manipulations.
• Matplotlib and Seaborn: For data visualization, providing insights into the
  dataset's structure and features.
• Scikit-learn: For implementing machine learning models and evaluating their performance through various metrics.

Data Collection:

• The dataset used in this project is called emails.csv, which contains features representing the frequency of various words in the emails and a target variable.
• Data Structure: The dataset contains 5,172 emails and 3,001 features, including important metadata about word counts.

Data Preparation

• Check the missing values.
• Analyze the dataset structure and data types.
• Dropping Unnecessary Column
• Generate summary statistics to understand the distribution of features.

Exploratory Data Analysis:

• Visualize the distribution of the target variable
• Visualized the data to identify patterns

Model Training

• Data Splitting:
  • Split the dataset into training and testing sets using an 80-20% split.

Models Used:

• Define multiple classification models:
  • Logistic Regression: Good for simple, linear binary classification.
  • Decision Tree Classifier: Handles complex patterns; may overfit.
  • Support Vector Machine (SVC): Great for high-dimensional, non-linear data.
  • Implement a function to fit and score each model on the training set and evaluate on the test set.

Model Evaluation

• Performance Metrics:
  • Evaluate each model using accuracy, precision, recall, and F1 score metrics.
  • Generate and visualize a confusion matrix for each model to assess
    performance in detail.
• Cross-Validation
  • To ensure the robustness of the model, cross-validation is applied to the best-performing model (Logistic Regression).

Results

• Logistic Regression:
  • Accuracy: Approximately 97.20%
  • Show the best overall performance with the least number of classification errors.
• Decision Tree Classifier:
  • Accuracy: Approximately 92.66%
  • It slightly lower scores still performing well.
• Support Vector Machine:
  • Accuracy: 81.74%
  • It has the lowest accuracy

The Logistic Regression model demonstrated the best performance among the evaluated models, achieving an accuracy of 97.20%.

Additionally, the confusion matrices for each model are presented below to provide further insight into their performance:

Confusion Matrices

Conclusion

The project successfully demonstrates the application of machine learning techniques for email spam detection. Through effective data preprocessing and careful selection of classification models, we achieved high accuracy in identifying spam and non-spam emails. The Logistic Regression model, in particular, exhibited superior performance with an accuracy of 97.20%, making it a reliable choice for this task. The confusion matrix and cross-validation results further validate the model's robustness and its ability to generalize well to unseen data.

Acknowledgements

I would like to thank the following:

Markdown Formatting Guide A helpful resource for understanding and using Markdown effectively.

References

• Libraries and Frameworks:

  • Scikit-learn Documentation
  • Pandas Documentation
  • NumPy Documentation
  • Matplotlib Documentation

• Additional Link:
  Linkedin Quratulain
  Github Qurat