This GitHub repository contains a comprehensive analysis of the Netflix film industry, focusing on viewership trends, genre preferences, and optimal release strategies. The project aims to provide valuable insights for movie and TV show producers to make informed decisions for maximizing viewership and profitability.
-
Objective:
- Determine popular genres on Netflix.
- Analyze viewership preferences between movies and TV shows.
- Identify the optimal month for releases.
-
Data Collection:
- Gathered data from https://www.kaggle.com/datasets/shivamb/netflix-shows/data
- Included viewer ratings, genre information, and release dates.
-
Exploratory Data Analysis (EDA):
- Conducted in-depth EDA to uncover patterns and correlations.
- Explored the distribution of viewership for movies and TV shows.
- Analyzed genre popularity and seasonal trends.
-
Data Visualization:-
- Heatmap:
- Utilized heatmap visualization to showcase the correlation between various factors.
- Heatmap:
- Donut Chart:
- Employed donut chart to represent the distribution of viewership between movies and TV shows, offering a clear visual comparison.
- Bar Chart:
- Implemented bar chart to display TV rating preferences.
- Bi-directional Bar Chart:
- Utilized bi-directional bar chart to showcase the viewer engagement levels for both movies and TV shows across different TV ratings.
- Funnel Chart:
- Applied funnel chart to illustrate the step-by-step process of viewership each month.
- Treemap:
- Implemented treemap to represent hierarchical data structures, allowing for a visually rich exploration of genre relationships.
- Waterfall Chart:
- Integrated waterfall charts to demonstrate the number of movies added every year.
- Line Chart:
- Leveraged line charts to showcase trends in viewership over time, offering insights into audience engagement.
-
Genre Preferences:
- Uncovered the most preferred genres among Netflix viewers
-
Movie vs. TV Show Viewership:
- Compared and contrasted viewership trends for movies and TV shows.
-
Optimal Release Month:
- Determined the best month for releases based on historical data.