Supervised Machine Learning project with KNN, decision tree, random forest and adaboost algorithms
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Updated
Jun 29, 2022 - Jupyter Notebook
Supervised Machine Learning project with KNN, decision tree, random forest and adaboost algorithms
Analysis of Terry Stops in Seattle
Tweet Sentiment Classifier using Classic Machine Learning Algorithms. This repository provides my solution for the 2nd Assignment for the course of Text Analytics for the MSc in Data Science at Athens University of Economics and Business.
Clustering and Classification of Members of the European Parliament's Tweets. This repository provides my solution for the 3rd Assignment for the course of Practical Data Science for the MSc in Data Science at Athens University of Economics and Business.
Used different types of machine learning classifiers such as Passive Aggressive, Extra Trees, Dummy Classifier to detect the DDos attack and compared the accuracies of the classifiers to determine the best out of the three.
Showcasing data science skills for a dataset provided by State Farm for a coding interview.
Detect fraudulent transaction.
Predicting customer sentiments from feedbacks for amazon. While exploring NLP and its fundamentals, I have executed many data preprocessing techniques. In this repository, I have implemented a bag of words using CountVectorizer class from sklearn. I have trained this vector using the LogisticRegression algorithm which gives approx 93% accuracy. …
Tweet Sentiment Classification using Multi Layer Perceptrons. This repository provides my solution for the 3rd Assignment for the course of Text Analytics for the MSc in Data Science at Athens University of Economics and Business.
Tweet Sentiment Classifier using Recurrent Neural Networks. This repository provides my solution for the 4th Assignment for the course of Text Analytics for the MSc in Data Science at Athens University of Economics and Business.
This Jupyter Notebook serves as a comprehensive guide to performing support vector machine (LinearSVC) classification and calculating accuracy scores for machine learning tasks. It provides step-by-step instructions and code examples for building, training, and evaluating a LinearSVC classifier
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