Using historical KSI (Killed or Seriously Injured) data from Toronto Police Open Data, we aim to predict the fatality of an accident given a certain input (time/weather condition/etc.).
This information could be used to possibly prevent accidents by encouraging drivers to drive more carefully in a particular area.
Our app is deployed using Heroku and is available at: Accident Fatality Predictor
- Python and Jupyter Notebook
- Libraries: pandas, numpy, sklearn, pickle, keras, tensorflow
- Flask
- Tableau
- HTML/CSS
- Javascript
- Bootstrap
- D3.js
Our Flask app hosts our API endpoints as follows:
-
/: The root endpoint directs the user to the landing page (index.html) where they are able to provide user input. -
/historicaldata: This endpoint shows the data used in our machine learning models as an HTML table that can be filtered. -
/dashboard: Our Tableau story and dashboards are embedded in the HTML page hosted at this endpoint. -
/models: We link our Jupyter notebooks to all the machine learning models that were tested during the project. -
/predict: This endpoint gets user input from the HTML forms and encodes the values into binary value arrays using a predefined function. These arrays are then merged together to form a 2D array with 66 features as required by the ML model.model.predictgives us our prediction of "Fatal" or "Non-Fatal Injury" which is then passed to theprediction.htmlpage usingrender_template.