"The Most Important Things Seem Invisible to Eyes." - Suisei Hoshimachi

Keep Grinding !!

Data-Analytics

This repository is used to storing and showcasing on How i would do Data Analytical 😄

data_fork is used to download or get the dataset specifically from kaggle. Keep in mind, if we wanted to fork datasets from kaggle, we need a token (usually named kaggle.json)

Summary

1. For SQL Approach you can look up on World University Rankings 2023
2. For Analytical Approach by Pandas you can look up on 100-most-popular-english-movies-2023
3. Thus for Publicity Dashboard, for example you can look up on automobile-dataset

What inside each folder

• Each folder contains a dataset and a python notebook on how i basically do the analytics (Not all cases i apply the same approach)
• Followed by documentation or dashboard for example
  1. On 100-most-popular-english-movies-2023 i've added whole readme. inside that documentation are explained on what, how approach i use, insights and some to dos
  2. On automobile-dataset i've added dashboard as substitute for insight i've got
• On World University Rankings 2023, i peformed some SQL queries to Clean and Transform data

Therefore i RECOMEND to look out on those each cases as mentioned before since every case is likely handled differently.

Checkmark

• English Movies
• World University Rankings 2023
• Sales
• Automobiles
• Salary Dataset
• Groceries
• Shopping Cart
• Walmart
• Netflix

To do

• Need to add Dashboard or conclusion of analysis that has been done (Cardio,Salary,Walmart)