First project is in the first folder is on Venice boat classification
Fake news—news articles that are intentionally and verifiably false designed to manipulate people’s perceptions of reality—has been used to influence
politics and promote advertising. But it has also become a method to stir up and intensify social conflict.This project has combined two datasets to classify if a news is fake or true. In this folder, we have the code in jupter notebook, a report and powerpoint. This project was awarded the highest mark among 50 presentations.
Second project is in the Second folder is on Malware Analysis
This project aims to propose a befitting function to classify android malicious software to protect sensitive data against malicious threats using data mining and machine learning classification techniques. In this work, I employed a robust and efficient approach for malware classification by analyzing the data while exploring techniques to combat the imbalanced datasets. Simply put, a binary classification to determine if an application is malware or not.
Third project is in the Third folder is on Naive Bayes
A simple implementation of Naive bayes on spam emails classification
Fourth project is in the Fourth folder is on Venice boat classification
This project is focused on the detection and classification of Venice boats using Deep learning. Past related project used the following classification methods: K-Nearest Neighbor (KNN), a Decision Tree Learning Algorithm, and Random Forest (RF) from the WEKA environment. In this project, I built a convolution neural network(CNN) that can detect Venice boats and identify its category while explaining the techniques used throughout the process. Datasets can be found here