Spam Mail and SMS Prediction using Python and Intel OneAPI
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This project aims to build a spam detection system using machine learning algorithms. The goal is to train a machine learning model that can accurately classify messages as spam or not spam. By doing so, we can save time and avoid the hassle of dealing with unwanted messages. ...learn more
Project status: Concept
oneAPI, Artificial Intelligence, Cloud
Overview / Usage
The project is about building a machine learning model that can predict whether an incoming message (email or SMS) is spam or not. The project uses the SMS Spam Collection Dataset from the UCI Machine Learning Repository. The dataset contains 5,574 messages labeled as spam or ham.
Methodology / Approach
✅ Data Preprocessing: The dataset is preprocessed by removing punctuations, converting all the words to lowercase, and removing stopwords.
✅ Feature Extraction: The text is converted into a numerical representation using the TF-IDF vectorization technique.
✅ Model Building: A Support Vector Machine (SVM) classifier is trained on the preprocessed and feature-extracted dataset.
✅ Model Evaluation: The trained SVM model is evaluated on a test dataset to check its accuracy and performance
Technologies Used
Python, Flask, StreamLit.IO, OneAPI, Scikit
Repository
https://github.com/Ronak2247232/Spam-Mail-and-SMS-Prediction_OneAPIHackathon_2247232