Meron-Abate/Ethiopian-Youtube-Podcast-Analysis

Ethiopian YouTube Podcast Analysis This repository contains a Python-based analysis of Ethiopian YouTube podcasts. The goal is to explore trends, audience engagement, and insights from popular Ethiopian podcasts using data-driven methods. Project Overview Objective: Analyze Ethiopian YouTube podcasts to extract actionable insights on popularity, content trends, and viewer engagement. Data Source: YouTube API (or scraped data from YouTube channels) Tools & Libraries: Python, Pandas, Matplotlib, Seaborn, NumPy, Jupyter Notebook Key Steps: Data collection (API or web scraping) Data cleaning and preprocessing Exploratory Data Analysis (EDA) Data visualization of trends, top podcasts, and engagement metrics Insights generation for content strategy and audience engagement Key Insights Top Channels by Subscribers: Identify the most popular channels with the largest subscriber base. Top Videos by View Count: Highlight videos with the highest number of views. Channel Growth Over Time: Number of channels created per year to observe trends in podcast emergence. Features Visualizations of podcast views, likes, and comments Analysis of top-performing podcasts by category Trends in content type and audience engagement over time How to Use Clone the repository: git clone github.com Open YT Analysis.ipynb in Jupyter Notebook or VS Code Run the notebook step by step to reproduce the analysis Future Work Expand dataset to include more channels and podcasts Apply machine learning for predicting podcast performance Build interactive dashboards for better visualization