Collection of practical codes for Savitribai Phule Pune University's Machine Learning Laboratory (410246).
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Updated
Oct 20, 2024 - Jupyter Notebook
Collection of practical codes for Savitribai Phule Pune University's Machine Learning Laboratory (410246).
Machine Learning | Deep Learning | Data Science case studies
Email Spam Tool is a powerful application designed for testing and analyzing email systems by generating and sending bulk emails. This tool is meant for security professionals and developers to evaluate email filtering systems and anti-spam measures.
This project is to classify emails as spam or not spam using various machine learning models. Hyperparameter tuning is performed to optimize model performance.
To check email is spam or not spam
email spam classification is to identify and filter out unwanted emails,
Email Spam Detection Using Logistic Regression
A email spam classifier based on Multinomial Naive Bayes model and running on Streamlit.
A simple model based on naive bayes algorithm to classify spam emails from regular (ham) emails.
Classify the message is spam or not using Multinomial Naive Bayes.
Email Spam Detection using Machine Learning
An Email Spam Classifier project, helps you detect your spam email from correct email. Try it out here!
Email Spam detection using Machine Learning
An end-2-end project
One of the primary methods for spam mail detection is email filtering. It involves categorize incoming emails into spam and non-spam. Machine learning algorithms can be trained to filter out spam mails based on their content and metadata.
Email spam classification for Naive Bayes, Gradient Boosting Machine, Support Vector Machine and Random Forest
Linear classifier using Support Vector Machines (SVM) which can determine whether an email is Spam or not with an accuracy of 98.7%. Used regularization to prevent over-fitting of data. Pre-processed the E-mails using Porter Stemmer algorithm. Used a spam vocabulary to create a Feature Vector for each E-mail. Prints the top 15 predictors of spam
Implemented Preprocessing steps, Feature Extraction techniques and Naive Bayes Classifier in C++. Moreover, we have also implemented all the steps using python for comparative analysis.
Identifying and distinguishing spam SMS and Email using the multinomial Naïve Bayes model.
Email Spam Classifier using Naive Bayes algorithm
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