Pinned Repositories
BScv1819CAD1-1
BSCV5_RobEng2
Ros Indigo Project using a turtlebot2 to use the navigation stack and Lidar, frontier exploration and Turtlebot arm moving boxes.
BSCVDemoCpp
CoMR_project
Aarhus University MSc Course Project Control of Mobile Robots
connect4
simple connect4 javascript game on flask
fullstack
React/ApolloGraphQL/Node/Mongo demo written in Typescript
MachineLearningProject
This project requires this sklearn library. The project involves taking a training dataset consisting of 30,000 features and extracting at most 15 features which are the most significant (this was done by taking the pearson coefficient) and then running a custom alorigthm. The steps are as follows: i) Read data and labels ii) Overall Feature selection (Reduce from approx 30k to 2k) iii) 5-fold cross validation: a)Pearson coefficients calculated b)Four classifiers used (worth 1 "count" each) c)Accuracy depends on the sum of the value counts by each classifier: ex. svm predicts 0, logistic_regression predicts 0 gaussian_nearest_means predicts 0 and nearest_centroid predicts 1 value = 0 + 0 + 0 + 1 if value <= 1 then classify in 0 if value >= 3 then classify in 1 else (if value = 2 or other) then classify as svm predicted iv) Read test data and perform feature selection (features from train data) [extract 15 columns] v) Output the num of features and the features themselves on console & save test labels as a file named "sh486_testLabels" This was a project I made for course CS675 [Machine Learning] at NJIT Fall Semester 2017.
Mickael_Arsanios_exam_cpp
Second semester exam in cpp 2019
python-migs
Migs payment in python
koalasession's Repositories
koalasession/BSCV5_RobEng2
Ros Indigo Project using a turtlebot2 to use the navigation stack and Lidar, frontier exploration and Turtlebot arm moving boxes.
koalasession/python-migs
Migs payment in python
koalasession/BScv1819CAD1-1
koalasession/BSCVDemoCpp
koalasession/CoMR_project
Aarhus University MSc Course Project Control of Mobile Robots
koalasession/connect4
simple connect4 javascript game on flask
koalasession/fullstack
React/ApolloGraphQL/Node/Mongo demo written in Typescript
koalasession/MachineLearningProject
This project requires this sklearn library. The project involves taking a training dataset consisting of 30,000 features and extracting at most 15 features which are the most significant (this was done by taking the pearson coefficient) and then running a custom alorigthm. The steps are as follows: i) Read data and labels ii) Overall Feature selection (Reduce from approx 30k to 2k) iii) 5-fold cross validation: a)Pearson coefficients calculated b)Four classifiers used (worth 1 "count" each) c)Accuracy depends on the sum of the value counts by each classifier: ex. svm predicts 0, logistic_regression predicts 0 gaussian_nearest_means predicts 0 and nearest_centroid predicts 1 value = 0 + 0 + 0 + 1 if value <= 1 then classify in 0 if value >= 3 then classify in 1 else (if value = 2 or other) then classify as svm predicted iv) Read test data and perform feature selection (features from train data) [extract 15 columns] v) Output the num of features and the features themselves on console & save test labels as a file named "sh486_testLabels" This was a project I made for course CS675 [Machine Learning] at NJIT Fall Semester 2017.
koalasession/Mickael_Arsanios_exam_cpp
Second semester exam in cpp 2019
koalasession/tourcms-py
Python wrapper class for TourCMS.com Rest API