/UMD_Data_Challenge

Performing data analysis and prediction for the recycling rate in the New York's metropolitan area using ARIMA and KNN.

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Recycling Diversion

In New York City, Recycling Diversion rate and Capture Rate for paper and MGP(Metal,Glass and Plastic) are collected for each zone and district from 2016 to 2019. Diversion Rate gives us the ratio of total waste recycled while capture rate represents the efficiency of the recyclable waste (waste recycled/ total recyclable waste).

In order to understand what the participation rate or rate at which a district recycles is, we can explore the data for recycling collection. This gives opportunities for targeting education for specific zones and specific types of recycling. We can also use this dataset to predict recycling behavior.

Authors

  1. Aman Virmani: Engineering professional with a Masters of Engineering focused in Robotics from the University of Maryland. Actively looking for internship opportunities in Computer Vision, Robot Planning and Deep Learning.
  2. Naman Gupta: Roboticist actively looking for summer internship opportunity within an automation industry in the field of robotics and autonomous vehicles.

Deliverables

Complete an EDA to explore things, such as

  • which district recycles the most for the different type of recycling wastes?
  • which type of recycling waste is collected at high rates?
  • what month is recycling lower or higher?

Use Machine learning to predict the amount of recycling that will be collected for a specific zone.

Data Considerations

This is a time series data set containing 9 columns and 2,832 rows.