The project is a combination of various machine learning models that are applied to a personalised list of books a person has read and liked.
A dataset of classical books is analysed and then, based on the analysis of the person's preferences, a classical book can be recommended.
Some of the models used for the analysis of the datasets include LDA (Latent Dirichlet Allocation) which is a type of topic modelling, also SOM (self-organising maps), SVD (Singular Value Decomposition) and K-means.
The models are used for finding similarities within different texts/books, while the system makes use of content-based filtering. Through that, the modern and classical books that are the most similar in terms of topics can be determined.
The results of the analysis can be clearly seen through various graphs/visuals produced in the process.
Final year data analytics student, on track to graduate with 2.1 honours.
Practical experience developing android apps and analyzing various datasets by using different models, mostly using R language.
Other skills include HTML, CSS, Java, Python, MS office. Keeping up to date with developments in data analytics by researching and learning in my free time.
Motivated, organised and keen to secure a graduate role in data analytics, IT, web development.