YouTubeTrendingVideosAnalysis

In this project , I played with the YouTube data API and extracted trending videos in Nigeria on a particular day.

This project was inspired by a dataset I saw on kaggle and the aim was to get the data into an unstructured format, clean it, model it to a point that can be used for analysis and then understanding which videos did well and why, In this project I was able to showcase my:

1. Ability to source for data ( from a public API)
2. Data cleaning skills using Pandas and Numpy
3. Data modeling using Pandas
4. Data visualisation using Matplotlib and Seaborn

For the Data Cleaning/Wrangling, I was able to use Regular Expressions for certain columns i.e the Video Durations

For the Exploratory Data analysis, I asked certain questions before starting the analysis. They were:

1. How many channels belong to the entertainment,sport or comedy categories
2. How many views per video categories
3. Which category had the most accolades
4. Which category generated the most interactions
5. Which channels had the most views
6. Which channels appeared the most on the trending video list.