This is a Data Mining project, developed at Spring of 2017 by Agapiou Marinos and Kamaras Georgios for the Data Mining Techniques course. The goal of this project is the understanding and the exploration(data exploration) of the input data, as well as the evaluation of their features (feature selection). For the implimentation we use the Python programming language and the tools/libraries: jupyter notebook, pandas, gensim and SciKit Learn.

• Part 1: Data Visualisation, at Visualisation.ipynb
• Part 2: Classification implementation, using Support Vector Machines, Random Forests and Naive Bayes, at classification.ipynb
• Part 3: Feature Selection, at feature_selection.ipynb

For complete documentation (presentation of our code, our methods and our results) please see the files visualization.pdf, classification.pdf and feature_selection.pdf.

• Agapiou Marinos: [email protected]
• Kamaras Georgios: [email protected]