eeshangpt Goto Github PK

2015

Public material for CS109

air-traffic-data

android_device_motorola_falcon

android_kernel_motorola_msm8226

Moto G kernel http://sourceforge.net/projects/motog.motorola/files/14.10.0Q3.X-84-14/kernel.tar.gz

ant

Mirror of Apache Ant

ant-ivy

Mirror of Apache Ant Ivy

automatic-ticket-classification

basiccalculator

Basic Java Calculator

beer-data-analysis

A technical challenge.

behealthy

bike-renting

The objective of this case study is to predict number of bikes rented out to customers daily. The number of bikes rented is predicted on the basis of environmental ans seasonal settings of the day. The prediction is based on machine learning algorithms which can predict a number based on the type of day.

bike_sharing_analysis

An analysis of Bike sharing data

churn-reduction

The objective of this case study is to predict customer behaviour. The main study involves around the given usage pattern and the whether the customer has left the business or not. The solution is presented as a machine learning algorithm which predict the churn score based on usage pattern.

complaint-registration

cornell-chatbot

Chatbot using cornell data.

course

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

cross-domain-recommender-system

data-engineering-on-gcp-cheatsheet

datasciencecoursera

datasciencecoursera-1

datasharing

The Leek group guide to data sharing

decompile-dump

Partial stuxnet source decompiled with hexrays, if anyone has better decompile tools feel free to contribute better versions.

docker-curriculum

:dolphin: A comprehensive tutorial on getting started with Docker!

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

employee-absenteeism

The human capital plays an important role in collection, transportation and delivery for a company. The issue absenteeism is an important concern for the company. The aim of the project is to find the changes in the company human resource policy to reduce the number of absenteeism. The company also wants to calculate the losses if the current trend continues. The company want a model built which suggests the strategy that company should follow to reduce the absenteeism numbers.

extractspeechutterances

fraud_detection

ML application for detecting fraud

gabil-ga

Implementation of Generic Algorithm (GABIL) for classification

gcp-data-engineer-exam

Study materials for the Google Cloud Professional Data Engineering Exam