Full Machine Learning Project — Detecting Outliers in Sensor Data (Part 4)

Want to get started with freelancing? Let me help: https://www.datalumina.com/data-freelancer Need help with a project? Work with me: https://www.datalumina.com/solutions In this video, we will learn how to identify and handle outliers in sensor data using three different methods in Python: the interquartile range (IQR) method, Chauvenet's criterion, and the local outlier factor (LOF). 👉🏻 Source material for this week: https://docs.datalumina.io/jD1BSJCAPYKSwh ⏱️ Timestamps 00:00 Introduction 01:38 Loading the data 02:38 What are outliers 05:03 Boxplots and interquartile range (IQR) 24:04 Chauvenet's criterion 30:55 Local outlier factor (LOF) 42:30 Choose a method and deal with outliers 55:02 Export data 55:37 Conclusion Project overview (what you will learn) Part 1 — Introduction, goal, quantified self, MetaMotion sensor, dataset Part 2 — Converting raw data, reading CSV files, splitting data, cleaning Part 3 — Visualizing data, plotting time series data Part 4 — Outlier detection, Chauvenet’s criterion, local outlier factor Part 5 — Feature engineering, frequency, low pass filter, PCA, clustering Part 6 — Predictive modelling, Naive Bayes, SVMs, random forest, neural network Part 7 — Counting repetitions, creating a custom algorithm Link to playlist: https://youtube.com/playlist?list=PL-Y17yukoyy0sT2hoSQxn1TdV0J7-MX4K If you find these videos helpful, consider subscribing @daveebbelaar