The project is submitted for a challenge for the Digital Product School (DPS). In the project a dataframe consisting of historical values of the accidents from different categories in the city of Munich, Germany.

This challenge for Artificial Intelligence Engineer Consists of 3 tasks.

• Mission 1: Create a AI Model Cleaning the data, visualizing the historical accidents from the dataframe,Training a model to forecast future values.
• Mission 2: Publish source code & Deploy Deploying the model with an endpoint that accepts POST requests in JSON body
• Mission 3: Sending the URL of the task

• Accident_Prediction_Model.ipynb: A jupyter notebook contains a step-by-step of importing the data, cleaning, and then visualizing the result after that loads the preprocessed data, then estimating the parameters passed for the model: Random Forest regressor. The model is trained and tested on the year 2020 data, the model is then evaluated and exported for deployment.
• main.py: thisx server file forecasting and returns the result. The latter is the endpoint, which is deployed through Fastapi.

The endpoint accepts a POST request with a JSON body like this:

{
"year" : 2021,
"month" : 1
}

It return prediction in the following format:

{
"prediction" : value
}

• pandas
• numpy
• matplotlib
• sklearn
• pickle
• Fastapi
• pydantic

visualization historically the number of accidents per category

Result Accuracy

MAE(Mean Absolute Error ) : 54.5

R2 Score :0.98