Coursera_Capstone

Predicting traffic accidents severity by Machine Learning

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1. Introduction

Traffic accidents severity can be predicted based on previous accidents. From the given example data set of traffic accidents, we can build a model that predict future accidents' severity. This will help drivers make their decision to reduce accidents.

2. Data

Data source is from Kaggle: https://www.kaggle.com/tbsteal/canadian-car-accidents-19942014?select=drivingLegend.pdf (Open database License).

This dataset describes Canadian Car Accidents 1994-2014 with details. Target information is accidents is either fatal or non-fatal, accompanied by 21 attributes that closely describes the particular cases. Data and meta data are in the repository.

3. Result

Data studied and ML algorithms deployment can be found in notebook (*.ipynb) or directly at IBM Cloud: https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/e97b21ca-4023-4723-a41a-d8f501919ad9/view?access_token=287e5c54a0e44c12f5af15ea14cf2970b6bf27a5ea0d07b6026c19d7aaf54d83

4. Acknowledgement

Data is from Kaggle and project is the Capstone Project of IBM Data Science course, hosted by Coursera. All this repository content is under MIT License.

Danh Nguyen, 2020