Full Machine Learning Project — Low-pass Filter & Principal Component Analysis (Part 5a)

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 apply the Butterworth low-pass filter and principal component analysis (PCA) in Python. 👉🏻 Source material for this week: https://docs.datalumina.io/tjGyJjXxfpChiL ⏱️ Timestamps 00:00 Introduction 01:22 What is feature engineering 02:23 Python files 05:00 Loading data 07:08 Dealing with missing values 12:16 Calculating set duration 19:26 Butterworth low-pass filter 30:35 Principal component analysis (PCA) 39:42 Sum of squares features 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