This repository contains code scripts for my submission for the Senior Professional Officer: Data Science role in the Organisational Performance Management Department. Code has primarily been written in Python, jupyter notebooks are also provided. The purpose of this challenge is to evaluate the skills of prospective Data Scientists, Engineers and Analysts for positions in the City of Cape Town's Data Science unit.

Joining the file city-hex-polygons-8.geojson to the service request dataset, such that each service request is assigned to a single H3 hexagon. For any requests where the Latitude and Longitude fields are empty, set the index value to 0.

2.1 Time series challenge: Predicting the weekly number of expected service requests per hex for the next 4 weeks.
2.2 Introspection challenge: Predicting the number of requests per Notification per hex in the last 12 months and identifying the key drivers of these servce requests.

run '1_Timeseries_Challenge.ipynb'
Output:
'sr_hex_Failed_Merges.csv' contains logging of which records the records failed to join
'sr_hex_Shaun_Moloi.csv' contains the final joined city-hex-polygons-8.geojson to the service requests dataset

2.1 Time series challenge: Time_Series_Challenge-Copy1.ipynb
2.2 Analysis: run 'Introspection_Challenfge_Analysis.ipynb'
Output Report: Time_Series_Analysis.html. Navigate to Correlations tab and Pearson's r for a visual display of the correllations.
Time series forecasting can be viewed in 'Introspection_Challenge.ipynb'