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KSP-IPH-2019-Table21

#Tool and Technologies

Programming language: Python, Javascript
Frameworks and Libs:  Pytorch, OpenCV, TensorFlow, Keras,Pandas, Scikit-learn, Plotly, W3.css, Inceptionv3
Technologies: NLP, Data analytics, State of art ML and DL  like:- UlmFIT fine tune model, VGG16 DL Network, RandomForestRegrassor,Sequence to sequuence model

Dependencies install

pip install -r requirements.txt

Insights from Data:

  1. Accidents in the month of January, May and December are comparatively more than other months.
  2. Accidents on Friday, Saturday and Sunday are relatively more than other weekdays.
  3. Correlation of features tells us:
      a. Accidents on bridges are more on narrow bridges and culverts.
      b. Bottleneck accident spots are arterial roads, asphalted road separation and roads which are crest of hill.
  4. Feature Relevance towards Road Accident
  

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Dashboard:

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Correlation analytics:

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location_images_tensorboard

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accidents_prediction

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Architecture Diagram:

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HOW run service

Analytics and prediction service

python heatmap_service.py

Dashboard service

Run service from:
html/grid.html

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