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Supervised Machine Learning project with KNN, decision tree, random forest and adaboost algorithms
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.
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.
Analysis of Terry Stops in Seattle
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.
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.
Detect fraudulent transaction.
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
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. I have found out the top 20 positive and negative feedback words from thousands how feedbacks. Also after processing this much I have automated the whole process with one function so that it can be used as generic for many machine learning algorithms. I have also tested another algorithm called DummyClassifier which gives an accuracy of around 84%. After that, I have executed the famous algorithm which is TF-IDF for NLP. I have combined TF-IDF with LogisticRegression which gives almost 93% accuracy but deep insights. Also, while working with data has solved the problem of imbalanced data through RandomOverSampler class from imblearn library.