Predicting pedestrian traffic and visualizing on a map

Photo by Tommaso Scalera on Unsplash

Introduction to the Objective

The skill to train a model on the batch/static data is quite important to have but so is the ability to apply that model on streaming data. In today’s fast world people/organizations want the responses/predictions to their queries in real-time as everyone is quite busy in their own worlds and frankly we would all agree there is a lot to do. So this post is dedicated to the group who is trying to imbibe the skill of real-time prediction. This post is in continuation to my last post where we trained the model using Apache Spark.

Machine Learning

Predicting next hour’s pedestrian traffic using Spark

Photo credits Apache Spark Foundation

Introduction to the Objective

These days in high-tech or smart cities the pedestrian counts can be monitored by deploying sensors at certain locations which can count the number of pedestrians every hour(as per the data used for this blog) or as required. From the title of the post itself one can understand that here we will try to predict the count of pedestrians or pedestrian traffic at certain locations for the next hour from the data of previous hour(s). This technique is also called a one-step time-series prediction, where we are predicting the next value with the previous values. Therefore this is a time-series…

Akash Goyal

Working as a Senior Data Engineer

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