ishikashah2510 Goto Github PK

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Name: Ishika Shah

Type: User

Company: San Francisco State University

Bio: My name is Ishika Shah. I am currently enrolled as a Masters of Science in Computer Science student at San Francisco State University.

Location: San Francisco

Hi there 👋

My name is Ishika Shah. I am currently enrolled as a Masters of Science in Computer Science student at San Francisco State University.

I am actively looking for internships in Machine Learning, Deep Learning, Data Mining. My skills include python, java, django, C, R, Kotlin, HTML, JS, CSS, Bootstrap. My Past Experience involves interning as a Python Developer at CppSecrets Technologies Pvt. Ltd. for 2 months. (May'21-Jul'21) After which I had been converted to a Technical Lead, part time at the same firm. (Aug'21-Dec'21) I have also interned at ZS Associates as a Business Technology Solutions Associate Intern beginnning Jan'22 until Jun'22. Post this, I worked as a full time Business Technology Solutions Associate beginning Jul'22 till Dec'22.

Find me around the Web🌏

Glad to see you here!

Ishika Shah's Projects

adf_lab_socialmedia

class_09_09_21

cleaning-and-getting-data

Data Set Information: The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. 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 was selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. Check the README.txt file for further details about this dataset. A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: [Web Link] An updated version of this dataset can be found at [Web Link]. It includes labels of postural transitions between activities and also the full raw inertial signals instead of the ones pre-processed into windows.