Adding analysis of string matching APIs using Urban dictionary

[czgdp1807]

Description of the problem

As in the tutorial description here, we say, "we plan to add some more examples showing usage of pydatastructs on real world data sets such as Stanford Large Network Dataset Collection and Urban Dictionary Words And Definitions". So, this issue is for completing the task of adding a jupyter notebook showing usage of string matching APIs in pydatastructs Urban Dictionary Words And Definitions. This is open ended. For example, you can just take the whole urban dictionary in a string and then query it on a million random inputs and show how different string matching algorithms work.

For reference you can see our current analysis of different shortest path algorithms here.

The urban dictionary dataset is available here. You can use Google colab and your Gdrive to do the analysis.

Example of the problem

References/Other comments

[soma2000-lang]

@czgdp1807 Can I work on this issue??

[czgdp1807]

Sure go ahead.

[onnhyd]

I want to work on this under GSSOC2022. Here is my discord profile DHANANJOY DEY | Contributor#7903

[czgdp1807]

@sceary-expert Feel free to start working on this issue.

[nishtha981]

Can I work on this issue as a GSSOC'23 contributor?

[het-dave]

Is this issue still open? I want to start working on this issue

[czgdp1807]

sure.

[patelalay231]

@czgdp1807 can I start working on this?

[RohitP2005]

@czgdp1807 as i go through the comments, i think nobody hasnt solved it yet, if so i would like to take on this issue