The purpose of this project is to analyze YouTube statistics and draw significant insights using Python. We utilized the dataset titled "Global YouTube Statistics.csv" from Kaggle to examine global popularity trends on YouTube and determine which categories are most successful for content creators. The project involves data cleaning, filling missing values, and generating reports based on the analysis.
The main functions of the project include:
Data Cleaning: Cleaning the dataset to make it suitable for analysis by addressing missing and erroneous information.
Filling Missing Values: Using ChatGPT to fill in missing country and category information.
Generating Reports: Creating detailed reports and visualizations based on the analysis results.
Programming Language: Python
sklearn: For machine learning algorithms
msno: For visualizing and analyzing missing data
pandas: For data processing and analysis
numpy: For numerical computations
seaborn: For advanced data visualization
matplotlib: For basic data visualization
ycimpute: For filling missing data
scipy: For scientific and technical calculations
Platform: ChatGPT was used for collecting and completing categorical data.
During the data analysis, various visualization and machine learning techniques were employed to identify the most popular YouTube categories. The results indicate that Music, Entertainment, and People & Blogs are the most popular categories. These categories attract a broad audience and achieve high view counts.
Our analysis revealed that the majority of successful YouTubers come from countries such as the US, India, Russia, and Japan. Additionally, most YouTubers established their channels in 2006. This suggests that being active on YouTube in its early stages has contributed to long-term success.
The data indicates that even in countries with poor economic conditions and educational levels, YouTube success can be achieved through consistency and choosing the right categories. Categories like Entertainment, Film & Animation, and Music enable content creators to reach a wide audience. These success stories highlight that, despite economic and educational challenges, success on YouTube is possible with the right strategies and passion.
Challenges Encountered
Several key challenges were faced during the project:
Data Cleaning: Addressing missing and incorrect data to ensure the accuracy of the analysis.
Filling Missing Values: The process of filling missing values using ChatGPT improved the completeness of the dataset.
Validating Results: Various validation and testing procedures were applied to ensure the reliability of the results.
More Data: Adding more data to the project to expand analyses and obtain more comprehensive insights.
New Analyses: Conducting different and deeper analyses to generate new and valuable insights.