Visual_Data_Analytics

Performing common visual data analytic tasks using Python and D3.js.

Tools and Languages Used

• Languages:

  • Python v3 (for processing server)
  • HTML
  • CSS
  • Javascript

• Frameworks:

  • d3.js (https://d3js.org/) (for client side)
  • Flask (https://pypi.org/project/Flask/)

Data clustering

• Implemented Random Sampling and Stratified Sampling
• Performed K-means Clustering for Stratified Sampling using Elbow Method

Dimension reduction

• Shows the bias introduced using the dimensionality reduction on both original and sampled data.
• Using PCA to find Intrinsic dimensionality of the data

Scree plot visualization

• Raw Data

• Random Sampled Data

• Stratified Sampled Data

• Three attributes with highest PCA loadings

2D Scatter Plot Visualizations

Using top two PCA vectors

• Raw Data

• Random Sampled Data

• Stratified Sampled Data

Using MDS (Euclidian & correlation distance)

Euclidiean Distance

• Raw Data

• Random Sampled Data

• Stratified Sampled Data

Correlation Distance

• Raw Data

• Random Sampled Data

• Stratified Sampled Data

Scatterplot matrix of the three highest PCA loaded attributes

• Raw Data

• Random Sampled Data

• Stratified Sampled Data

Youtube Link:

https://www.youtube.com/watch?v=tTgrkyfKcsM&t=3s