Ray

Added error message when locations are not in Ottawa

Added error message when song and artist are incorrect

Created frontend using streamlit

Added readme

Added front-end to the project using streamlit, and added spotify web player for each similar song.

Finally set up, integrated, and combined contact me form.

Added working links, hrefs, and icons

Updated front-end to be more user-friendly, such as updating results menu, added a origin and destination on results page (also required backend to retrieve the origin and destination)

Created backend using python and google maps api. It finds most optimal park and ride location. Then I used fastapi to connect it with html, css and javascript frontend. Spent a lot of time debugging, adding front end features, and getting fastapi to connect. Hosted on render

Used Google Maps API to complete calculations, including driving time to parking lot, transit time from parking lot to destination. Starting time, starting point, and destination are all customizable. Currently project is not fully finished, and is still stuck in the terminal

Added readme and minor text changes

Generated most of the website with AI, changed some of it up

Transfered code from chromebook to windows. Added exploratory data analysis step, displaying data with matplotlib. Added breakpoint results after each line with imaginary input for learning. Construcuted and debugged interpreting results for cosine_similarity matrix.

Cosine similarity, attempting to interpret the results, learning structure etc of pandas data structures, exploring np argmax

I learned the process behind TF-IDF vectorization

Imported data, analyzed data using .describe panada and visualized using matplotlib

Load 'housing.csv'
Split into training and test sets
Train a 'LinearRegression' model
Compute MSE and R² value
Plot data points (train and test) and line of best fit