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Predicting Companies Growth using Website Information
Small and medium enterprises (SMEs) play an important role in the economy of many countries. To support SMEs at improving their competitiveness, we aim at building a SME growth prediction model using Web data. Therefore, various Data and Web Mining techniques are studied and applied.
Keywords: Business Analytics, Company Growth Prediction, Data Mining, Machine Learning, Web Mining, Text Mining
You analyse information given in companies’ website using Web Mining and Text mining techniques. The information extracted from websites will be used as an input for Machine Learning algorithms to build a growth prediction model for companies. Different Machine Learning algorithms will be tested and compared, with the goal to optimize the accuracy of the prediction. This thesis offers you the possibility to work with the Swiss insurance company Die Mobiliar. You have the chance to become a co-author of a scientific paper in a top conference or journal and will become part of Die Mobiliar new digital strategy.
You analyse information given in companies’ website using Web Mining and Text mining techniques. The information extracted from websites will be used as an input for Machine Learning algorithms to build a growth prediction model for companies. Different Machine Learning algorithms will be tested and compared, with the goal to optimize the accuracy of the prediction. This thesis offers you the possibility to work with the Swiss insurance company Die Mobiliar. You have the chance to become a co-author of a scientific paper in a top conference or journal and will become part of Die Mobiliar new digital strategy.
The goal of this study is to extract growth-indicating factors from companies’ websites for company growth prediction using Text Mining and Machine Learning techniques.
The goal of this study is to extract growth-indicating factors from companies’ websites for company growth prediction using Text Mining and Machine Learning techniques.
Yiea-Funk Te
ETH Zürich, D-MTEC
Weinbergstrasse 56/58
8092 Zürich
Mail: fte@ethz.ch
Yiea-Funk Te ETH Zürich, D-MTEC Weinbergstrasse 56/58 8092 Zürich Mail: fte@ethz.ch