Human Activity Recognition Using Smartphones Data Set

Human Activity Recognition Using Smartphones Data Set

In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

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What will you learn

Understanding the problem statement
Initializing necessary libraries and understanding its use
Importing Dataset from amazon AWS and performing basic EDA
Why is it necessary for shuffling data
Performing basic encoding and transformation
Applying a supervised neural network using multi-layer perceptron
Using SGD, LBFGS and ADAM optimizer alternatively
Visualization using strip plot
Applying Logistic Regression
Standard Scaling and normalizing the dataset
Applying Random Forest Regressor as model
Applying Neural Networks using Keras and including Dropouts in between the layer
Understanding "Softmax", "Relu",and "Crossentropyloss"
Applying K-nearest neighbors and visualizing KNN distance and accuracy
Plotting graphs using Scatter Matrix
Applying Recursive Feature Elimination along with SVM
Plotting confusion matrix for visualizing the result
Visualizing the best model using matplotlib
Selecting the best model and making predictions using CSV format

Project Description

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

Problem Statement Overview
03m
Data Set Overview
02m
Shuffle the Data
01m
Separate Input/Ouput Labels
00m
Encoding Labels
01m
Classification Models
02m
Neural Network Model
09m
Graphical Representation of Data
06m
Logistic Regression
11m
Scalar VS PCA
02m
Transformation Function - TSNE
02m
Activation Functions
02m
Keras Neural Network
01m
Random Forest Classifier
01m
SGD Model
04m
K-Nearest Neighbour
00m
Support Vector Classification
10m
TensorFlow Model
08m
K-Neighbour Classifier
05m
Confusion Matrix
06m
Multiple Classifiers
01m
Grid Search
05m
Conclusion
01m