Email Spam Detection

➲ Project description

An email spam detection system identifies spam emails using a machine learning technique known as natural language processing, implemented in Python. We have a dataset containing numerous emails, and by extracting key words, we can utilize a naive classifier to determine whether an email is spam or not.

➲ Prerequisites

This is list of required packages and modules for the project to be installed :

• Python 3.x
• Pandas
• Numpy
• Scikit-learn
• NLTK

Install all required packages :

 pip install -r requirements.txt

➲ The Dataset

The human activities dataset contains about 5728 records, a sample of an email, and a target column "spam" which describes the state of email spam.

➲ Coding Sections

In this part we will see the project code divided into sections as follows:

• Section 1 | The Data :
  In this section we aim to do some operations on the dataset before training the model on it, processes like :

  1. Data Loading: Load the dataset
  2. Data Visualization: Visualize dataset features
  3. Data Cleaning: Remove stopwords and duplicates values
  4. Data Splitting: Split the dataset into training and testing sets
     
     

• Section 2 | The Model :
  The dataset is ready for training, so we create a naive classifier using sci-kit-learn and thin-fit it to the data, and finally, we evaluate the model by getting accuracy, classification report and confusion matrix
  

➲ Installation

1. Clone the repo

   git clone https://github.com/omaarelsherif/Email-Spam-Detection-Using-Machine-Learning.git

2. Open 'main. ipynb' in Google Colab or VScode and enjoy

➲ References

These links may help you to a better understanding of the project idea and techniques used :

1. Spam detection in machine learning: https://bit.ly/3nwiKtA
2. Naive-Bayes algorithm: https://bit.ly/3zc9SLH
3. Model evaluation: https://bit.ly/3B12VOO