Time series is referred to as sequence of data points measured over successive intervals of time. Time series analysis is performed to extract meaningful statistics and characteristics of the data. For example, this analysis can be used to predict future based on the past events. Such use cases of this analysis are stock market prediction, weather prediction and web traffic prediction.
In this talk, we’ll be understanding the basics and how to perform time series analysis and machine learning for a given dataset. For this talk, we’ll be using the UCI Human Activity Recognition (HAR) Data Set to classify a given activity performed by the human as one of the following activities: Walking, Sitting, Standing or Laying.
The talk would be intermediate level and basics of Python and Pandas are required. Understanding of Machine Learning algorithms and signal processing would be helpful.
Feedback form: https://python.it/feedback-1576
in __on Saturday 4 May at 18:00 **See schedule**