This repository contains a machine learning project for detecting spam emails using TensorFlow. The goal of the project is to develop a model that can classify emails as either spam or not spam based on certain features.
Implemented using Jupyter Notebook and Python Utilized TensorFlow, Pandas, NumPy, Matplotlib, Seaborn, NLTK, and Wordcloud libraries
Dataset: Labeled emails (spam and non-spam)
Data Preparation:
- Loaded and preprocessed the email dataset using Pandas.
- Cleaned the text data, removed stop words, and transformed it into numerical representations suitable for machine learning.
Feature Engineering:
- Extracted relevant features from the email data, such as email length, keyword presence, and other patterns commonly observed in spam emails.
Model Training:
- Built a machine learning model using TensorFlow (neural network architecture).
- Trained the model on preprocessed email data to learn patterns distinguishing spam from non-spam emails.
Model Evaluation:
- Evaluated the model's performance using metrics like accuracy, precision, recall, and F1 score.
- Analyzed the model's ability to correctly classify spam and non-spam emails.
Results and Visualization :
- Generated distribution plots using Matplotlib and Seaborn to analyze the characteristics of spam and non-spam emails.
- Used Wordcloud to visualize the most frequently occurring words in spam emails.
Resources
- TensorFlow Documentation
- Pandas Documentation
- NumPy Documentation
- Matplotlib Documentation
- Seaborn Documentation
- NLTK Documentation
- Wordcloud Documentation
Acknowledgments
<> This project was inspired by the need to develop an effective spam email detection system to enhance email security and filtering.
<> The dataset used in this project was sourced from Kaggle and Additionally, I have uploaded the dataset for you to download and use in your own use case.
<> The project structure and code implementation were guided by various online resources and tutorials.