Kachrimanis
Data Scientist with a strong math background and experience in analytics and text mining. Passionate about explaining Data Science to non-technical audiences.
Netherlands
Pinned Repositories
Aspects-of-Text-Mining
This project is aimed to implement the basic algorithms of text mining including the bag-of-words model. The main goal is to combine the usage of this particular technique with NLTK aiming to surpass orthographical mistakes in order to achieve even better text recognition. The implementation has been made using Python.
Mobile-Application-Recommender-System
A mobile application recommender system using ratings provided from an RFD-TP model.
Principal-Component-Analysis
PCA is a valuable data analysis tool for identifying the principal trends and their related variables. It can succesfully identify which principal components provide the largest contribution to variation in the data. Through this implementation we aim to identify the relations and the correlations of our data which are related with the energy consumption of 8 different rooms of a building
Twitter-Sentiment-Analysis
In this project a highly accurate model of sentiment analysis of tweets is proposed with respect to latest reviews of upcoming movies. The model was made using Python and involved: Nature Language Toolkit (NLTK), Text mining techniques, Feature vector extraction, Classifiers (Support Vector Machine and Naïve Bayes)
Kachrimanis's Repositories
Kachrimanis/Principal-Component-Analysis
PCA is a valuable data analysis tool for identifying the principal trends and their related variables. It can succesfully identify which principal components provide the largest contribution to variation in the data. Through this implementation we aim to identify the relations and the correlations of our data which are related with the energy consumption of 8 different rooms of a building
Kachrimanis/Aspects-of-Text-Mining
This project is aimed to implement the basic algorithms of text mining including the bag-of-words model. The main goal is to combine the usage of this particular technique with NLTK aiming to surpass orthographical mistakes in order to achieve even better text recognition. The implementation has been made using Python.
Kachrimanis/Mobile-Application-Recommender-System
A mobile application recommender system using ratings provided from an RFD-TP model.
Kachrimanis/Twitter-Sentiment-Analysis
In this project a highly accurate model of sentiment analysis of tweets is proposed with respect to latest reviews of upcoming movies. The model was made using Python and involved: Nature Language Toolkit (NLTK), Text mining techniques, Feature vector extraction, Classifiers (Support Vector Machine and Naïve Bayes)