Human Activity Recognition Using Multiclass Classification in Python

Human Activity Recognition Using Multiclass Classification in Python

In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.
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Camille St. Omer linkedin profile url

Artificial Intelligence Researcher, Quora 'Most Viewed Writer in 'Data Mining'

I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More

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What will you learn

Understanding the problem statement
Understanding the Data science life cycle
Initializing necessary libraries and understanding its use
Importing Dataset from amazon AWS and performing basic EDA
Univariate and Bi variate analysis to understand the Data
Data visualizations using various charts
Cleaning and preparing the data for modelling
Standard Scaling and normalizing the dataset
Performing PCA to reduce the number of features
Applying Logistic Regression
Applying SVM, Random Forest Regressor, XGBoost and KNN
Applying Deep Neural Networks
Hyper Parameter tuning for ANN and SVM
Plotting confusion matrix for visualizing the result
Selecting the best model and making predictions
Develop the Flask API for the selected model

Project Description

In this machine learning project you will build a classification system to classify human activities.

The Human Activity Recognition dataset was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data.

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Curriculum For This Mini Project

Business Problem
Data Science Lifecycle
Data Import And Understanding
EDA Univariate Analysis
EDA Bivariate Analysis
EDA Visualization
Data Preparation
Principal Component Analysis
Model Building
Hyper Parameter Tuning
Model Evaluation
Deployment with Flask Api