Explored the 311 New York City Request Calls Dataset . Got some useful insights from the data and tried to calculate which factors affect the closing rate of each request call.
NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.
Analysis has been done in python with the help of following libraries :
- Pandas
- Numpy
- Matplotlib
- Scipy
New York City officials aim to provide best services to their citizens and aim to make sure to provide quick effective response to each of the complaints. We analysed the data about past complaints so that we could get an insight into what factors are responsible behind the delays that occured during past . Based on the analysis and other factors, New York City Officials may consider spending resources what satistics done in the best interests of New York Citizens as always.
I always believe in constant improvement ad would highly welcome any feedback on the analysis.Onne of tings we have missed opout is building a model which can predict resoponse time based on the factors I predicted as important. Once you construct a model, you can have more accurate interpretation of improving what factors will improve the service of costumers better .