Email Spam Detection

➲ Project description

Email spam detection system is used to detect email spam using Machine Learning technique called Natural Language Processing and Python, where we have a dataset contain a lot of emails by extract important words and then use naive classifier we can detect if this email is spam or not.

➲ Prerequisites

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

• Python3
• Pandas
• Numpy
• Scikit-learn
• NLTK

Install all required packages :

 pip install -r requirements.txt

➲ The Dataset

Human activites dataset contain about 5728 record which is a sample of an email
and a target column "type" which describe the state of an email spam or not.

Dataset features and target :

➲ Coding Sections

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

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

  • Load dataset
  • Check for duplicates and remove them
  • Check for missing data for each column
  • Cleaning data from punctuation and stopwords and then tokenizing it into words (tokens)
  • Convert the text into a matrix of token counts
  • Split the data into training and testing sets
    
    

• Section 2 | Model Creation :
  The dataset is ready for training, so we create a K-nearest Neighbors "KNN" model using scikit-learn and thin fit it to the data.
  

• Section 3 | Model Evaluation :
  Finally we evaluate the model by getting accuracy, classification report and confusion matrix.

➲ Installation

1. Clone the repo

   git clone https://github.com/theritik01/Suspicious-Email-Detection.git

2. Run the code from cmd

   python email_spam_detection.py

➲ Output

Now let's see the project output after running the code :

Dataset head :

Dataset missing data count :

Dataset after cleaning puncituations and tokenizing text :

Classification report, confusion matrix and accuracy :