Pinned Repositories
Credit-Scoring-using-Support-Vector-Machines-in-R-
Retail Credit Scoring using Support Vector Machines in R
metaheuristics
Implement the-state-of-the-art meta-heuristic algorithms using python (numpy)
pyomo
An object-oriented algebraic modeling language in Python for structured optimization problems.
PyomoGallery
A collection of Pyomo examples
Sentiment-Analysis-of-Text-Data-Tweets-
This project addresses the problem of sentiment analysis on Twitter. The goal of this project was to predict sentiment for the given Twitter post using Python. Sentiment analysis can predict many different emotions attached to the text, but in this report, only 3 major were considered: positive, negative and neutral. The training dataset was small (just over 5900 examples) and the data within it was highly skewed, which greatly impacted on the difficulty of building a good classifier. After creating a lot of custom features, utilizing bag-of-words representations and applying the Extreme Gradient Boosting algorithm, the classification accuracy at the level of 58% was achieved. Analysing the public sentiment as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like the stock exchange.
SVM4SC
SVM for CS
necaati's Repositories
necaati/Credit-Scoring-using-Support-Vector-Machines-in-R-
Retail Credit Scoring using Support Vector Machines in R
necaati/metaheuristics
Implement the-state-of-the-art meta-heuristic algorithms using python (numpy)
necaati/pyomo
An object-oriented algebraic modeling language in Python for structured optimization problems.
necaati/PyomoGallery
A collection of Pyomo examples
necaati/Sentiment-Analysis-of-Text-Data-Tweets-
This project addresses the problem of sentiment analysis on Twitter. The goal of this project was to predict sentiment for the given Twitter post using Python. Sentiment analysis can predict many different emotions attached to the text, but in this report, only 3 major were considered: positive, negative and neutral. The training dataset was small (just over 5900 examples) and the data within it was highly skewed, which greatly impacted on the difficulty of building a good classifier. After creating a lot of custom features, utilizing bag-of-words representations and applying the Extreme Gradient Boosting algorithm, the classification accuracy at the level of 58% was achieved. Analysing the public sentiment as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like the stock exchange.
necaati/SVM4SC
SVM for CS