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Toronto Police Service - Crime Rate Predictive Analysis

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Objective

To create a model that accurately predicts crime while investigating the possible correlation between the prediction, demographics and social services. The purpose for our model is to assist in crime reduction via effective resource reallocation.

Approach

With CRISP-DM as guideline for approach: using 2014-2019 crime data from Toronto Police Service, supplemented with social services and demographics data, perform unsupervised and supervised machine learning.

The Toronto Police Service through their Public Safety Data Portal is "intended to improve the understanding of policing, improve transparency and enhance confidence through the creation and use of open data for public safety in Toronto". https://data.torontopolice.on.ca/

Unsupervised learning includes K-Mean and Hierarchical clustering.

Supervised learning includes linear regression, logistic regression, graident boosting decision tree, K nearest neighbour classifier, Support Vector classification, decision tree classifier, Naïve bayes classifier, Random forest classifier, and neural networks.

In addition, made attempts for time series analysis and sentiment analysis (using Twitter API).

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Documents

File Note
Crime Rate Predictive Analysis - FINAL - Team 2 - Janurary 2021.pdf Final report
York project - Toronto Police - 2021.pptx Powerpoint summary
Final Project - Clustering - Team 2.ipynb Unsupervised learning - Jupyter notebook
Final project - Exploration and Supervised - Team 2.ipynb Supervised learning - Jupyter notebook
Mapping of CRC to TPS.csv source file for mapping rec centers to Toronto neighbourhoods
Masterdf_crime.csv source file for crime data combining social services and demographics
PopdenNeig.csv source file for population density by neighbourhood

File dependencies

'Final project - Exploration and Supervised - Team 2.ipynb' references files 'PopdenNeig.csv' and 'Mapping of CRC to TPS.csv' to be in same folder as the Jupyter notebook

Team members

Suzanne Douglas
Rachna Kumari
Herby Robinson
Pushpendra Sharma
Don Sohn

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