The following project rerpesents my first Machine Learning project - a classifying task. It was done using the Python programming language. The algorithm used is MLP - multilayer perceptron.
The data base and the problem that this project is solving:
- The data base is called "Glass Identification Data Set" and it is proived by the USA Forensic Science Service
- In this data base the stored information is about 7 types of glass, diferentiated by their content of oxide
- The ideea is that, the glass found at a crime spot can be used as a clue in solving a case, if correctly identified
The structure of the data base:
- A matrix composed of 214 rows and 11 columns
- First column deals with the ordering of the data
- The rest of the columns: informations about the glass, such as refraction index, or the percentage of the oxide
Data splitting:
- Training set: 159 samples - 75% of the data
- Testing set: 54 samples - 25% of the data
Libraries used:
- Pandas: reading data from ".txt" and ".csv" type files
- Scikit-learn: for the ML Algorithm
Conclusion:
- The algorithm is able to make a correct classification based on the provided parameters
- The accuray obtained is 60% - due to the fact that there was no cleansing done on the data