Visualization is one way to gain insights from data. It is an effective technique for Data Mining which is the process of looking at large sets of information in a different way so that new information can be derived from that which already exists. Visualizations can be used to answer questions related to a specific problem. For this visualization, we are going to create a dashboard in python using plotly with Dash.
Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
Built on top of the Plotly JavaScript library (plotly.js), plotly.py enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash.
Thanks to deep integration with the orca image export utility, plotly.py also provides great support for non-web contexts including desktop editors (e.g. QtConsole, Spyder, PyCharm) and static document publishing (e.g. exporting notebooks to PDF with high-quality vector images).
This dashboard answers specific questions that give some insight to the data we have. Some questions are general questions about the users and some are specific to the users. After installing all the necessary requirements in the requirements.txt file, the notebook should run with no errors. Make sure your notebook is in the same folder as the data you want to visualize. Dash runs on local host at http://127.0.0.1:8000 in your browser