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

Видео Full Machine Learning Project — Detecting Outliers in Sensor Data (Part 4) канала Dave Ebbelaar