386 skills found · Page 9 of 13
UlusoyRobotic / Python PyQt5 QtDesigner Pyqtgraph Plot A GraphPython Plot a Grap with QtDesigner,pyqtgraph
Timmeh42 / Bokeh Market Depth GraphUsing Python and Bokeh to live plot a cryptocurrency market depth graph
AlexB67 / CairoPlotPlot 2D graphs with cairomm in gtkmm applications
tragisch / PlotGraphviz.jlUse Graphviz to render graphs in Julia
daniel-huang-1230 / Python Financial AnalysisExtract data directly from online sources such as Yahoo Finance using Pandas DataReader. Also the plotted graph is then embedded into a Flask web app
KuhakuPixel / CsvPlotterAndGrapherGUI Software to create plot and graph from CSV powered by matplotlib
ankitkanojia / React Fusion BarchartDescription A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column chart.
nodelab-org / Dash React Force Graphreact-force-graph components ported to Plotly Dash
ChoudharyRamesh / Force Directed Graph LayoutA graph plotter application based on force directed algorithm.
PrincetonUniversity / Globus StatsPull transfer logs from Globus.org and plot various graphs.
nikhilkalige / Plotly FullscreenJupyter notebook extension that allows you make the plotly graphs fullscreen.
anishsingh20 / Network Analysis Of Game Of ThronesNetwork Analysis is the study of relationships and dependencies between objects . I will use Directed Acyclic Graphs to plot the relationships in R.The project document including complete steps is included here--
inLeague / Chartjs CfcCFC wrappers for ChartJS - easily draw bar charts, pie charts, scatter plots, and line graphs
seanli9604 / Subformula GraphA loose collection of modules which can read mass spectral data (EI-MS or MS/MS), produce a ranked list of formula annotations using the parent subformula graph (PSG) method, and then for every (possible) mass peak in the mass spectrum, visualise the annotated mass spectrum as a 2 dimensional fragment plot.
mikeczabator / Graph FORScan Datagraph ODBII vehicle data from FORScan using pandas. takes in raw files from FORScan, plots all of the data in relation to time and RPM.
SofianeOuaari / MNIST DIGITS KMEANS ClusteringThis is a notebook where I used KMEANS on the mnist digits dataset imported from sklearn. And I also applied t-SNE (Stochastic Neighbors Embedding) to reduce the dimensions of the dataset in order to graph the resullting clusters in a 2D scatter plot using matplotlib and plotly
GreenAITorch / GATorchGATorch is a tool seamlessly integrated with PyTorch that enables ML developers to generate an energy consumption report. By attaching your model, the tool automatically tracks the energy consumption of your model's training and generates graphs and plots to gain in-depth insights into the energy consumption of your model.
Tirth8038 / Multiclass Image Classification The main aim of the project is to scan the X-rays of human lungs and classify them into 3 given categories like healthy patients, patients with pre-existing conditions, and serious patients who need immediate attention using Convolutional Neural Network. The provided dataset of Grayscale Human Lungs X-ray is in the form of a numpy array and has dimensions of (13260, 64, 64, 1). Similarly, the corresponding labels of X-ray images are of size (13260, 2) with classes (0) if the patient is healthy, (1) if patient has pre-existing conditions or (2) if patient has Effusion/Mass in the lungs. During data exploration, I found that the class labels are highly imbalanced. Thus, for handling such imbalanced class labels, I used Data augmentation techniques such as horizontal & vertical flips, rotation, altering brightness and height & width shift to increase the number of training images to prevent overfitting problem. After preprocessing the data, the dimension of the dataset is (31574, 64, 64, 1). For Model Selection, I built 4 architectures of CNN Model similar to the architecture of LeNet-5, VGGNet, AlexNet with various Conv2D layers followed by MaxPooling2D layers and fitted them with different epochs, batch size and different optimizer learning rate. Moreover, I also built a custom architecture with comparatively less complex structure than previous models. Further to avoid Overfitting, I also tried regularizing Kernel layer and Dense layer using Absolute Weight Regularizer(L1) and to restrict the bias in classification, I used Bias Regularizer in the Dense layer. In addition to this, I also tried applying Dropout with a 20% dropout rate during training and Early Stopping method for preventing overfitting and evaluated that Early Stopping gave better results than Dropout. For evaluation of models, I split the dataset into training,testing and validation split with (60,20,20) ratio and calculated Macro F1 Score , AUC Score on test data and using the Confusion Matrix, I calculated the accuracy by dividing the sum of diagonal elements by sum of all elements. In addition to this, I plotted training vs. validation loss and accuracy graphs to visualize the performance of models. Interestingly, the CNN model similar to VGGNet with 5 Conv2D and 3 MaxPooling layers and 2 Dense layers performed better than other architecture with Macro F1 score of 0.773 , AUC score of 0.911 and accuracy of 0.777.
abhshkdz / Graph PlotterA graph plotting application written in C++
michaelAlvarino / React PlotlyReact components of the Plotly graphing library
