Full Machine Learning Project — Detecting Outliers in Sensor Data (Part 4)
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/jD1BSJCAPY...
⏱️ 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: • Full Machine Learning Project: Coding...
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