Abstract: Activity recognition data set built from the recordings of 30 subjects performing basic activities and postural transitions while carrying a waist-mounted smartphone with embedded inertial sensors.

Link to Dataset

• Loaded and Processed TXT-format Train and Test data into DataFrame
• Applied 6 Classification Algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Naive Bayes, Decision Tree, Random Forest) and 10-fold cross validation procedure is used to evaluate each algorithm
• F1-Score: Logistic Regression - 93.86%, Support Vector Machines - 93.60%, Random Forest - 90.32%, K-Nearest Neighbors - 88%, Decision Tree - 81.31%, Naive Bayes - 74.27%

Link to GitHub Repository

• Comparison