#Youtube-channel-Analysis-using-Python
This project presents a comprehensive analysis of several YouTube channels using Python and Seaborn. The analysis aims to provide insights into the content trends and performance metrics of five to six YouTubers' channels.The primary questions addressed in this analysis include:
Video Count: How many videos are available on each channel? Upload Trends: In which months do YouTubers upload the most videos? Popular Videos: Which videos have the highest view counts? Engagement Metrics: How many likes, comments, and views do the videos receive? The project seeks to uncover patterns and trends in the YouTube channels' content and audience engagement.
Data Sources The data used for this analysis was collected from the YouTube API through web scraping techniques implemented in Python. The dataset comprises various attributes of the videos, including video titles, view counts, likes, comments, upload dates, and more.
Analysis Steps The analysis process can be summarized in the following steps:
Data Collection: We obtained data from the YouTube API by scraping each selected YouTuber's channel to gather information about their videos.
Data Cleaning: The collected data underwent a cleaning process to address missing values, data type conversions, and handling outliers.
Exploratory Data Analysis (EDA): Python and Seaborn were used to conduct exploratory data analysis. This involved creating visualizations to gain insights into the data and answer key questions.
Key Findings Our analysis led to several noteworthy findings:
YouTuber with Most Videos: BeerBiceps has the highest number of videos among the selected channels. Peak Upload Month:August sees the highest number of video uploads by BeerBiceps Top Video by Views: The video titled 'Why Is America The Most Corrupt Country?Robert Kiyosaki Reveals #shorts' has the highest number of views. Engagement Metrics: MadFit has the highest average number of views in video
To explore the detailed analysis and visualizations, follow these steps: Clone this repository to your local machine. Run the provided Python scripts to reproduce the analysis and visualizations. Please ensure that you have the necessary Python libraries and dependencies installed as listed in the requirements file.
Dependencies The following Python libraries and packages were used in this analysis:
[List of dependencies] 1.pip install --upgrade google-api-python-client 2.import pandas as pd 3.from googleapiclient.discovery import build 4.import seaborn as sns