https://github.com/shogunbanik18/spam-defender

Spam Detector is an online Email/SMS Spam Classifier based binary classification model to detect whether a text message is spam or not (i.e Ham).
https://github.com/shogunbanik18/spam-defender

data-science knearest-neighbor-algorithm machine-learning naive-bayes-classifier random-forest supervised-machine-learning

Last synced: 9 months ago
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Spam Detector is an online Email/SMS Spam Classifier based binary classification model to detect whether a text message is spam or not (i.e Ham).

• Host: GitHub
• URL: https://github.com/shogunbanik18/spam-defender
• Owner: shogunbanik18
• Created: 2022-05-06T19:34:44.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-05-06T19:41:52.000Z (over 3 years ago)
• Last Synced: 2025-01-12T19:33:48.182Z (10 months ago)
• Topics: data-science, knearest-neighbor-algorithm, machine-learning, naive-bayes-classifier, random-forest, supervised-machine-learning
• Language: Jupyter Notebook
• Homepage: https://detectospam.herokuapp.com/
• Size: 753 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
 


## Spam-Detector

Spam Detector is an online Email/SMS Spam Classifier based binary classification model to detect whether a text message is spam or not (i.e Ham).

In this project, I have used many algorithms to create a model that can classify SMS messages as spam or not spam. Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam'. Also, this is a supervised learning problem, as we will be feeding a labelled dataset into the model, that it can learn from, to make future predictions.

## Project Preview : 

![2](https://user-images.githubusercontent.com/75260179/158168267-d033726b-03c4-4df8-80cb-376b155fa66a.png)

![1](https://user-images.githubusercontent.com/75260179/158168273-dcf52476-2636-4a9b-b214-2f770431c4c2.png)

## Overview

This project has been broken down in to the following steps:

This project has been broken down in to the following steps:

1. Data cleaning

2. EDA (Exploratory Data Analysis)

3. Text Preprocessing

4. Model building

5. Evaluation

6. Improvement

## TechStack

*  The code is implemented in Google Colaboratory with the help of Python 3.9

*  Libraries used :  Numpy,Pandas, Matplotlib,Seaborn and Sklearn.

*  For Deploying : Streamlit , Herokuapp

## Project Link : 

* https://detectospam.herokuapp.com/