Full Machine Learning Project — Data Visualization with Matplotlib (Part 3)

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 are going to create data visualizations to better understand the accelerometer and gyroscope data for the different exercises. 👉🏻 Source material for this week: https://docs.datalumina.io/ccQSzikoeMmqZE ⏱️ Timestamps 00:00 Introduction 01:57 Fix bug from part 2 03:34 Download Python file 04:28 Loading data 05:35 Plot single columns 11:32 Plot all exercises 19:41 Adjust plot settings 24:03 Compare medium vs. heavy sets 31:11 Compare participants 34:45 Plot multiple axis 40:39 Create a loop to plot all combinations per sensor 46:05 Combine plots in one figure 53:58 Loop over all combinations and export figures for both sensor 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 ​