The recommendation system now recommends books based on a list of favorite books. Additionally, I began adding a clustering feature for genres, but this has not been finished yet.
Finished the schematic for the macropad pcb and began laying out the pieces. Changed 3x3 switch matrix to make one of the switches in the matrix for the rotary encoder button.
Worked on macropad from the guides.
For some reason the first 2 hours of recording didn’t go through.
But then I decided to upgrade my macropad and I am working on the PCB Schematic. I want 9 keys, a rotary encoder, LEDs, and an OLED screen. Decided to make a 3x3 switch matrix.
I began work on my Book Recommendation System.
First found a dataset off of Kaggle with a comprehensive collection of information on each book. I wanted to do content based filtering so I made sure the dataset had descriptions and genres for each book.
First, I cleaned up the data, created a “combined features” column that took all the string data and combined it. Any book that didn’t have the string data was disregarded.
Then I used TFIDF Vectors and Cosine Similarity to find relating books.
The similarity matrix was also visualized with Matplotlib.
