Visprex

<div> <p align="center"> <img src="https://github.com/visprex/visprex/assets/20113339/03fae63d-6518-45b5-affd-da00e0c746b2" /> </p> <p align="center">Visualise your CSV files <b>in seconds</b> without sending your data anywhere</p> <p align="center"> <a href="https://www.visprex.com">Visprex App</a> | <a href="https://docs.visprex.com">Documentation</a></p> </div>

Getting Started

Loading your dataset

Your data is processed entirely in your browser without sending your data to any backend servers. The only network calls this application makes are for downloading example datasets from public GCS buckets.

Once you load your CSV file, it will automatically parse your data into one of Categorical, Number, or DateTime types.

Reference: Loading your dataset

Visualise your data

Histogram

Histograms are useful for understanding feature distributions, especially to see if they follow certain distributions (e.g. Gaussian, uniform, Poisson etc).

There are also built-in feature transformation methods (square, natural log, log10) available.

Reference: Understand feature distributions

Scatter Plot

You can choose two variables to visualise on a 2D scatter plot. The first variable you choose will appear on the X-axis, and the second will appear on the Y-axis.

Hovering over individual points will show you the details of that instance.

You can optionally add filters to control for certain variable values or look at specific sub-sections. See reference for a list of supported operators.

Reference: Visualise linear relationships

Line Plot

Line plot can visualise the trends and seasonality of your Numerical features. Only DateTime features will be available in the X-axis. Hovering over the line plot will display the values at that point in time.

Reference: Identify trends and seasonality over time

Correlation Matrix

You can find linear correlations for any pair of variables from the correlation map, where purple indicates positive correlation and red indicates negative correlation.

Reference: Check correlatins between features

Modelling

You can run linear regressions on your uploaded dataset, with R-squared, standard errors, 95% confidence interval, Z-scores, and p-values diplayed along with the coefficients for each explanatory variable.

Reference: Quantify your intuition with linear regression

Development

npm ci
npm run dev
open http://localhost:5173/