Title: Data Analysis and Visualization of YouTube Video Metrics
Overview: The GitHub project aims to analyze and visualize metrics of YouTube videos using Python programming language and popular data science libraries such as Pandas, Matplotlib, and Seaborn. The project provides insights into various aspects of YouTube videos, including views, likes, dislikes, comments, and publishing trends.
Features:
Data Loading and Cleaning:
The project provides scripts to load YouTube video data from CSV files into Pandas DataFrames. Data cleaning techniques are applied to handle missing values, data type conversions, and other preprocessing steps. Exploratory Data Analysis (EDA):
Exploratory Data Analysis techniques are employed to understand the distribution and characteristics of video metrics. Statistical summaries, distribution plots, and correlation analyses are performed to derive insights. Visualization:
Matplotlib and Seaborn libraries are utilized to create various visualizations such as histograms, scatter plots, and bar charts to represent video metrics. Visualization aids in understanding trends, patterns, and relationships within the data. Time Series Analysis:
Time series analysis techniques are implemented to analyze trends in video metrics over time, including views, likes, and comments. Seasonal decomposition and rolling statistics are used to identify patterns and seasonality in the data. Top and Bottom Performers:
The project identifies top-performing and bottom-performing videos/channels based on metrics such as views, likes, and comments. Rankings and summaries are provided to highlight key insights. Correlation Analysis:
Correlation analysis is conducted to determine relationships between different video metrics. Correlation coefficients are calculated and visualized to understand the strength and direction of relationships. Publishing Trends:
Publishing trends analysis reveals insights into the distribution of video uploads over time, including monthly and yearly trends. Insights into peak publishing periods and seasonality are provided.