Crime Prediction
-crimepred.ipynb can be opened on jupyter notebook -crimepred.py can be executed directly a executable file -Since data can be fetched from website using API (which is implemented in the provided source code). For that user needs to install sodapy library using following command: pip install sodapy -Also, it can be downloaded from the following link: https://data.sfgov.org/Public-Safety/-Change-Notice-Police-Department-Incidents/tmnf-yvry
Libraries Used: -Python2.7 -pandas -Numpy -sklearn -sodapy
- Program can be executed in sequence in jupyter
- Following steps to execute program:
-Execute everything till you reach to "Method for Classification"
-From here there are methods developed for the ease of user to train classification model and perfirm prediction on testing data
-predictCrimeCategory() can be executed to perform classification for specific district
Parameters:
-district name
-Classifier
-classifyAllDistricts() can be executed to perform classification for all the districts in San Francisco
Parameters: -Classifier -path where output needs to be stored -ensamble_classifiers() can be executed for city as well as district and it generates all 3 classifier predictions and from that performs ensembling in order to generate better prediction Parameters: -district name -city - if true classification will be performed on city level else on district level