This analysis examined data documentation to identify unclear columns in a dataset. It highlighted the importance of clear documentation to avoid misinterpreting data and potentially reaching skewed conclusions.

This project delves into the Netflix dataset through random sampling and analysis to gain insights into the variability and potential anomalies within the data, focusing on the distribution of IMDb scores across different genres and samples.

This analysis explores Netflix content based on type, age rating, and genre. TV shows generally have higher IMDb scores than movies, while ratings vary across age groups—popular genres like comedy and drama exhibit distinct rating patterns. Analyzing combinations of type and age ratings reveals common and rare pairings, providing insights into Netflix's content distribution. Visualizations highlight these findings, uncovering trends and potential areas for further exploration.

This analysis explores the Netflix dataset, focusing on summary statistics, visualizations, and insights into various attributes of Netflix content. The data dive includes: Numeric summaries for columns such as runtime and IMDb scores, providing insights into the central tendency, distribution, and range of values. Categorical summaries for content type, age certification, and genres, revealing the distribution of different categories in the dataset. Visualizations including histograms, scatter plots, and bar charts to explore runtime distributions, the correlation between IMDb and TMDB ratings, and the relationship between genres and age certification. Novel questions are addressed, such as comparing the average runtime between shows and movies, and visualizing interactions between categorical and continuous variables. The analysis provides an in-depth look at Netflix's content structure, target audience, and rating systems.