/Spam-Mail-Detection-using-Machine-Learning

Classified the datasets of emails as spams or non-spams using Machine Learning Algorithms, i.e. SVM and K Nearest Neighouring Algorithms.

Primary LanguageJupyter NotebookMIT LicenseMIT


Machine Learning

CS 253A Course Assignment in Machine Learning


Table of Contents
  1. About The Project
  2. Getting Started

About The Project

This is a course project for CS 253A under Profesor Mr. Indranil Saha related to machine learning algorithm. In this project I implemented an spam Detector Model using SVM(Support Vector Machine) and KNN(K-Nearest Neighbors) Algorithm in python.

Built With

Getting Started

  • The datasets has been uploaded using Kaggle Datasets.
  • The code outputs the plot for the frequency of different vocabularies used as well as the accuracy of the machine learning model created.

Prerequisites

  • To run the script files one needs to install Jupyter Notebook along with Anaconda3 in their computer.
  • Now simply open the Jupyter Notebook and run each code block of the programe.

Assumptions

  • Note that the spam_or_not_spam.csv file should have data in correct format present in it, i.e. each row must contain only 2 column namely email and label with label being either 0 or 1. Thus no empty column should present.

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