Spam_Detection.ipynb: Jupyter Notebook containing the data exploration, preprocessing, model training, and evaluation. spam.csv: The dataset containing the spam and ham messages. Dataset from Kaggle. requirements.txt: A list of Python libraries required to run the notebook. Technologies Used Python...
This study investigates the performance of classical machine learning and modern deep learning models for email spam detection using a publicly available Kaggle dataset consisting of over 5,000 emails. Among machine learning classifiers, the Support Vector Machine (SVM) demonstr...
📌 Dataset Source: Kaggle SMS Spam Collection 📌 5,500+ labeled SMS messages 📨 📌 Preprocessing Steps: Handling missing & duplicate values 🧹 Label encoding the "type" column 🏷️ Tokenizing text ✂️ Removing special characters, stopwords & punctuation 🔍 Converting text to lowerc...
SMS Spam Collection Dataset.https://www.kaggle.com/uciml/sms-spam-collection-dataset, Accessed 28 Feb 2020 Download references Author information Authors and Affiliations Department of Computer Science and Engineering, Premier University, Chattogram, 4000, Bangladesh ...
Experimental analysis with dataset available in Kaggle we found that hybrid features is more effective for accurate classification as compared to individual features. Additionally, we found for classification the SVM and ANN are more accurate as compared to the Bayes classifier.Sharma, ...
The dataset "Spam email classification" extracted from the Kaggle website is used in this study to detect and categorize email spam. It analyzes the text of the email using natural language processing and applies machine learning techniques to original unbalanced and resampled balanced datasets. The...
Welcome to spam-email-detection repository About This is a Mini-Project for SC1015 (Introduction to Data Science and Artificial Intelligence) which focuses on analysing a dataset containing Spam vs Non-spam (Ham) Emails found here. For a detailed walkthrough, please view the source code in orde...
Email Spam Detection This project aims to classify emails as spam or ham (not spam) using a dataset named spam.csv containing email content and labels. The model achieved an accuracy of 98%. Dataset The dataset used for this project, spam.csv, consists of two columns: label: Indicates whet...
To prevent this we need to come up with an efficient system for email spam detection. In this paper, we try to implement machine learning algorithms using Scikit-learn in Colaboratory and we aim to find the best algorithm. From Kaggle, a publicly accessible email dataset is taken. In order...
This paper interpreted a spam detection model based on self mechanism using BERT on kaggle dataset. Our proposed model outperforms than the machine learning algorithms and deep learning with accuracy 98.80%.Hossain, Syed Md. MinhazSen, Anik