This is a mini project on Estonian traffic accidents between 2010 and 2019

Technologies

• Python
• PostGres, MySql
• Pandas, jupyter
• Matplotlib
• Numpy
• Pandas
• Sklearn
• Math

Dataset	Description
Dataset 1 (62.5 MB)	Misdemeanors for this and last year
Dataset 2 (177 MB)	Misdemeanors for last five years
Dataset 3 (92.9 MB)	Misdemeanors from last five to ten years

Objectives of the work.

Goal 1: Predict the probability of reoccurring misdemeanors using ML Goal 2: Find out and visualise different misdemeanors by cities (what are the most likely places to have serious accidents) Goal 3: Find out if speed limit change and speed cameras have affected the number of misdemeanours

Links:

The metadata can be sourced from data.europa.eu - The official portal for European data [1] https://www.europeandataportal.eu/data/datasets/https-opendata-riik-ee-andmehulgad-liiklusjarelevalve-alased-syyteod-?locale=et

To reproduce this work:

For all of the ipynb files:

• Make sure you have the neccessary files downloaded: liiklusjarelevalve_1.csv, liiklusjarelevalve_2.csv, liiklusjarelevalve_3.csv
• Open the notebook and replace the file locations in the beginning of the code(if needed)
• You can use Google Colab as well to run those ipynb files
• Run all cells

Goals:

• linnad.ipynb file is related to goal 1 which is about finding out the misdemeanours per person in Estonia in 2018
• speedcameras.ipynb is about goal 2 where we analysed the relation of speed cameras and misdemeanours

Links:

[1] https://www.europeandataportal.eu/data/datasets/https-opendata-riik-ee-andmehulgad-liiklusjarelevalve-alased-syyteod-?locale=et

License

MIT