Welcome back! In this video, we are going to code the actual classification models and make predictions on the data. We'll create a train/test split, select feature sets, perform a grid search for hyperparameter tuning, select the model, and finally, evaluate the result. 👉🏻 Source material for this week: https://docs.datalumina.io/GQBbHfi4hJA0FV ⏱️ Timestamps 00:00:00 Introduction 00:03:21 Loading the data 00:03:52 Create a training and test set 00:11:01 Split feature subsets 00:19:46 Perform forward feature selection using simple decision tree 00:28:57 Grid search for best hyperparameters and model selection 00:40:06 Create a grouped bar plot to compare the results 00:43:11 Select best model and evaluate results 00:50:04 Select train and test data based on participant 00:58:02 Try a more complex model with the selected features 01:03:45 Discussion of results 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...   If you find these videos helpful, consider subscribing https://www.youtube.com/channel/UCn8ujwUInbJkBhffxqAPBVQ