The-alpha-male/Kenya-YouTube-Analysis-Using-YouTube-API

The project involves a detailed analysis of Kenyan YouTube channels using the YouTube API to collect data on various genres. It examines metrics like total views, video uploads, and subscriber counts, culminating in the creation of visualizations and an interactive dashboard for an enhanced understanding of the findings. # YouTube Analysis This project is a comprehensive analysis of Kenyan YouTube channels, leveraging the YouTube API to gather data and draw insights. The analysis categorizes channels into various genres, focusing on metrics such as total views, number of videos uploaded, and subscriber count. Visualizations and a dashboard are created to provide an interactive and detailed understanding of the data. # Project Objectives - Categorize Kenyan YouTube channels into 'Comedy', 'Lifestyle', 'Talk Show', 'Podcast', 'Storytelling', and 'Music'. - Identify and highlight the leading channels in each category based on: - Total views - Number of videos uploaded - Subscriber count - Extract and analyze video-level details such as: - Video title - Total views - Likes - Comments - Create visualizations to compare metrics across channels and categories. - Develop a dashboard for presenting findings interactively. # Tools and Technologies - Python: Primary programming language used for data extraction and analysis. - YouTube API: Used to fetch data related to channels and videos. - Libraries: - ```google-api-python-client```: For accessing YouTube API data. - ```pandas```: For data manipulation and analysis. - ```seaborn``` and ```matplotlib```: For creating visualizations. - ```Infogram```: For building the dashboard. # Project Workflow 1. Data Collection: - Created a virtual environment and installed necessary Python packages. - Used the YouTube API to extract data related to Kenyan YouTube channels. 2. Data Categorization: - Channels were categorized into 'Comedy', 'Lifestyle', 'Talk Show', 'Podcast', 'Storytelling', and 'Music'. 3. Data Analysis: - Loaded the data into Pandas DataFrames. - Extracted metrics at both channel and video levels. - Saved cleaned data into CSV files for reproducibility and further analysis. 4. Visualizations: - Used Seaborn and Matplotlib to create comparisons of metrics such as views, subscriber counts, and video uplo …