Project information

  • Name : YouTube Dashboard
  • Aim : To Finding good and Hidden Things About Youtube Channel By Dashboard..
  • Concept : Interactive Dashboard working.
  • Dataset : Kaggle
  • Library : Pandas,Numpy,re
  • Platform : Dash Plotly
  • GitHub : Code

Motivation:

Why This Topic?

  1. Often Time Users Confused How to Choose the Best Channel For My Learning Or Entertainment? So this Dashboard helps those users.

  2. How does This Dashboard help? Dashboard Can Compare Two different channel By their views and likes. (Box Plot)

  3. Sometimes Youtube Analyst needs To Track Some of the best youtube videos and channels by Longitudinal analysis So This Dashboard helps Our Analyst. (Timeline Chart)

  4. Which type of Contain (category e.g comedy,music) released in every day/month/year in proportion.? It's helpful for youtube analysts.(Sunbrust)
`

Description

Here In this Dashboard, I am using a framework(python). It's an amazing experience working with the frontend and backend simultaneously. In this project, I build an interactive graph by using Plotly and updating the graph by using dash library.

  • Video:


    • Below functionality performed by this Dashboard.

    1. Filter Data by :-Data table
    2. Current date with videos category wise proportion by filter data :-Sunbrust
    3. Top 10 trending videos by date likes/views :-Bar Chart
    4. Channel wise descriptive statistics :-Table
    5. Most static likely :-Bar Plot
    6. Most static views :-Bar Plot
    7. Channel wise comparison distribution Views and like :-Boxplot
    8. Channel wise comparison distribution Views and like :-Boxplot
    9. longitudinal analysis of videos views:-Timeline Chart

  • Extra Concept:
    Internal Structure Callback : How this internal callBack works while dropdown And graph changes?
    1. Default Callback by Dash (Slower)
    2. ClientSide Callback by Javascript (Faster)

  • ClientSide Callback:


  • Advantage:
    1. Dash Plotly Is Easy to learn and no needs too much Knowledge About Javascript.
    2. Dash Plotly run on python so we can use Computational Power of python In our dashboard.
    3. Machine learning engineers used dash plolty for visuliazing thier model Machine learning model and nowadays dash plolty became a competitor for Microsoft Bi and Tableau.
    4. Beginner Level Analyst can easliy implement a dashboard.

  • Disadvantage: If we have 10,000+ row then chart loading and updating time increase.