387 skills found · Page 12 of 13
Kairos-T / QRNGAn implementation of a quantum random number generator using the Qiskit integrated into a Flask web application. Allows users to generate random numbers and visualise the distribution through a graph plot using Matplotlib.
Jakkarin-Promsee / CTerminalPlotLibA lightweight C library for plotting graphs directly in the terminal. Designed for quick, real-time data visualization with a resolution of up to 30x90 pixels, using UTF-8 symbols and color to represent data lines.
TylerAdamMartinez / Body Signals FilteringBMEN 3311 (Biomedical Signal Analysis): Reads in biomedical data from a patient’s files into a script and plots the data. Then take the discrete Fourier transforms of each signal then plot the frequency Spectrums. In addition, create a digital notch filter to remove the noise coming from mains power in the ECG signal without creating a phase shift due to filtering (zero-phase filtering), determine if the patient was tired during the recording of the EMG signal based on the frequency spectrum of the EMG signal, and determine the mental state of the patient during the recording of the EEG signal based on the frequency spectrum of the EEG signal. Finally, break down how much of the EEG signal is comprised of the four EEG wave components (Delta, Theta, Alpha and Beta), and display it in a bar graph.
aliriahi90 / Machine Learning Classification Regression Twitter BuzzBuzz Prediction on Twitter: Buzz Prediction on Twitter Project Description: There are two different datasets for Regression and Classification tasks. Right-most column in both the datasets is a dependent variable i.e. buzz. Data description files are also provided for both the datasets. Deciding which dataset is for which task is part of the project. Read data into Jupyter notebook, use pandas to import data into a data frame. Preprocess data: Explore data, check for missing data and apply data scaling. Justify the type of scaling used. Regression Task: Apply all the regression models you've learned so far. If your model has a scaling parameter(s) use Grid Search to find the best scaling parameter. Use plots and graphs to help you get a better glimpse of the results. Then use cross-validation to find average training and testing score. Your submission should have at least the following regression models: KNN regressor, linear regression, Ridge, Lasso, polynomial regression, SVM both simple and with kernels. Finally, find the best regressor for this dataset and train your model on the entire dataset using the best parameters and predict buzz for the test_set. Classification Task: Decide about a good evaluation strategy and justify your choice. Find best parameters for the following classification models: KNN classification, Logistic Regression, Linear Support Vector Machine, Kernelized Support Vector Machine, Decision Tree. Which model gives the best results? Buzz Prediction on Twitter Project Description: Use same datasets as Project 2. Run all the models only on 10% data. Use code given in Project 2 for sampling. Preprocess data: Explore data and apply data scaling. Regression Task: Apply any two models with bagging and any two models with pasting. Apply any two models with adaboost boosting Apply one model with gradient boosting Apply PCA on data and then apply all the models in project 2 again on data you get from PCA. Compare your results with results in project 2. You don't need to apply all the models twice. Just copy the result table from project 2, prepare similar table for all the models after PCA and compare both tables. Does PCA help in getting better results? Apply deep learning models covered in class Classification Task: Apply four voting classifiers - two with hard voting and two with soft voting Apply any two models with bagging and any two models with pasting. Apply any two models with adaboost boosting Apply one model with gradient boosting Apply PCA on data and then apply all the models in project 2 again on data you get from PCA. Compare your results with results in project 2. You don't need to apply all the models twice. Just copy the result table from project 2, prepare similar table for all the models after PCA and compare both tables. Does PCA help in getting better results? Apply deep learning models covered in class
DancingOnWater / GraphicsScenePlotSimple drawing plot in Qt Graphics View Framework
alandefreitas / Citation GraphPlots a graph with the relationship between bibtex citations
droidchef / Audio ProcessorAn android based sound processing app to plot graph based on sound signals
rombrew / GpGraph Plotter is a tool to analyse numerical data
striezel / Plotly Node Export ServerNode.js server for offline export of Plotly graphs
bartman / Blot📈 console graphing / plotting library written in C
coderick14 / GraphPlotterWeb Application to plot undirected and/or directed graphs, along with MST
dollodart / Graph BuilderA python implementation of the SAS JMP Graph Builder using the Dash and Plotly Llibraries
yeswecan / OfxPlotterGraph plotting addon for openFrameworks.
ajayarunachalam / RegressorMetricGraphPlotPython package to simplify plotting of common evaluation metrics for regression models. Metrics included are pearson correlation coefficient (r), coefficient of determination (r-squared), mean squared error (mse), root mean squared error(rmse), root mean squared relative error (rmsre), mean absolute error (mae), mean absolute percentage error (mape), etc.
q2apro / Graph PadowanGraph is an GPL-open source plotting software for mathematical functions by Ivan Johansen. This repo is to help developers to fix bugs and suggest features.
unleashed-coding / Plotter BotA Discord bot for plotting graphs
Linyuechuan / AIMDThis is a set of Matlab scripts that can plot MSD graph and calculate the diffusivity using XDATCAR files from VASP
teamquandrum / DataVisualizationToolOrdell Ugo Data Visualization Tool is a tool that helps visualize R ready data sets without knowing how to code in R. The GUI is written in Shiny and allows for reactive input from the user, and any dataset of his choice can be visualized. Currently supports StackOverflow, Tab Plots, Histograms, Line Graphs, Scatter Plots, Box Plots, Pareto Charts, QQ Plots, PP Plots and Time Series visualization.
nirnejak / Csv PlotQuickly plot CSV data into graphs(bar, pie, area, map, etc.)
donald-pinckney / DPGraphViewA reusable graphing view for iOS to easily plot continuous functions.
