Welcome to the Spam Email Classification project! This project focuses on developing a deep learning model to classify emails as spam or non-spam. The main components of this repository include:
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spam_email_classification.ipynb: This Jupyter Notebook contains the code for the deep learning model. You can explore the code, run cells, and see how the model is trained and evaluated.
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requirements.txt: This file lists all the Python libraries and dependencies required to run the code in the Jupyter Notebook. You can install these dependencies using the following command:
pip install -r requirements.txt -
emails.csv: This CSV file contains the dataset used for training and testing the machine learning model. Make sure to review the dataset and understand its structure before running the notebook.
To get started with the Spam Email Classification project, follow these steps:
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Clone the repository to your local machine:
git clone https://github.com/Rc17git/spam_email_classifier.git cd Spam Email Classifier -
Install the required dependencies:
pip install -r requirements.txt -
Open the Jupyter Notebook:
jupyter notebook spam-email.ipynb -
Run the cells in the notebook to train and evaluate the machine learning model.
The dataset (emails.csv) contains labeled examples of emails, indicating whether they are spam or non-spam. It is essential to understand the dataset structure to interpret the model's results accurately.
The machine learning model is evaluated using standard metrics such as accuracy, precision, recall, and F1 score. The evaluation results are presented in the notebook, providing insights into the model's performance.
Happy coding!