This Dashboard was created using Python and Plotly.py, an open-source, interactive browser-based graphing library. 

The first step was data cleaning and formatting, which ensured that missing values were handled appropriately. For example, data entries with missing bike models and makes are included in the annual number of bike thefts, but are excluded for the top 10 makes and models.

Next, by using Plotly, I was able to create interactive bar, line, and pie charts. Users can hover over elements of interest to see data values and filter by year to specify their timeframe of interest. 

This investigation was my introduction to data visualization and analysis, and it proved to be a valuable learning experience. Handling real-life datasets provides additional challenges of dealing with human error and external factors. Moreover, this provides a fundamental starting point for investigating further the topic of bike theft and crime in the city of Toronto.  

After logs of debugging and experimentation, I present to you my Dashboard on Bike Thefts in Toronto!

(note: the heatmap requires zooming via the buttons on the top right)