Email Spam Detection using Machine Learning Algorithms

Description

This project can predict whether an email is spam or ham with higher accuracy based on the content of the email body using various Machine learning algorithms.

1. Basic classifiers:

    1.1 Multinomial NaïveBayes
    
    1.2 Support VectorMachine
    
    1.3 K-NearestNeighbours
    
    1.4 DecisionTree  
   

2. Ensemble classifiers:

    2.1 RandomForest
   
    2.2 Bagging
   
    2.3 Boosting AND AdaBoosting
   
    2.4 Voting
   

Languages used: Python

Modules used: sklearn, numpy, pandas, Tkinter, seaborn, pickle

IDE used: VScdoe

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How to run the project

Running the .py files

1. Download the repository on your local machine.
2. Create a virtual environment, command: **_"python -m venv <name_of_the_env>"_**.
3. Activate the virtual environment.
3. Install the above mentioned modules, command **_"pip install sklearn numpy pandas tkinter searborn pickle"_**.
4. Execute the **_"gui_windows.py"_**.

Running the jupyter notebook

1. Run your jupyter notebook.
2. Change the file name for the different datasets.

Note:

Datasets for this project is in **_"datasets"_** folder.

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Running project images:

GUI of the Classifier software

Data in the sample file emails.csv

Result of all the classifiers

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Developers

This repository contains the code for our BTech Major Project "Spam Detection using Machine Learning Algorithms".
Code contirbution:

1. Sanket Sonowal
2. Nikhil Kumar
3. Nishant