This article discusses the use of machine learning algorithms to classify spam and non-spam tweets in Twitter. It explores supervised and unsupervised models such as decision tree, random forest, Naïve Bayes, and logistic regression. The article emphasizes the importance of test and train data sets, and performance evaluation using confusion matrix, ROC, and AUC.

Data Analytics for Cyber Security Assignment

To apply skills and knowledge acquired throughout the semester in classification algorithms and machine learning process, rationalize the use of machine learning algorithms to effectively and efficiently process data in big size, demonstrate ability to use R to perform email classification tasks, and scientifically conduct and document machine learning experiments for analytics purposes.

Discover how machine learning can classify spam and non-spam tweets on Twitter using various models and evaluation techniques.

Machine Learning in Twitter: Classifying Spam and Non-Spam Tweets

Application of Machine Learning in Twitter

Machine Learning in R

This is as a result of the number of individual that gets to tweet misleading tweets is lower as compared to the number of individual that tweet relevant tweets(Arora et al., 2018). This is as a result of the number of individual that gets to tweet misleading tweets is lower as compared to the number of individual that tweet relevant tweets(Arora et al., 2018).

The University of Queensland

Theoretical Computer Science

Data Science and Big Data

Artificial Intelligence

Materials Science and Engineering

Data Mining on Twitter Data using Machine Learning Algorithms

Assignment

Hartpury University and Hartpury College

Application of Machine Learning Assignment 2022

Harrisburg University

Statistics and Probability

Large Scale Support Vector Machines PDF

The main objective of this report is to analyze two different datasets with variety of machine learning algorithms. Here the datasets used are Wisconsin and mnist dataset. Here L1 and L2 regularization is used to find the accuracy of the model. Various graphs and tables have been included to process the required result. Logistic Regression has been performed to measure the accuracy of different models developed. Further accuracy also depends on hardware specification. The first part concludes the Binary classification of Wisconsin dataset and then performed Multiclass classification of mnist dataset. There have been different graph and tables were shown for different dataset to analyze the datasets. At the end we discussed about the conclusion of the report topic.

Machine Learning Analysis of Wisconsin and Mnist Datasets

Foundations of Data Science

Machine Learning and Artificial Intelligence | Data Classification

Explanation regarding Machine Learning Techniques

Application of Machine Learning in Twitter

Data Mining on Twitter Data using Machine Learning Algorithmslg...

Application of Machine Learning Assignment 2022lg...

Large Scale Support Vector Machines PDFlg...

Machine Learning Analysis of Wisconsin and Mnist Datasetslg...

Machine Learning and Artificial Intelligence | Data Classificationlg...