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Analyzing the data and generate insights that could help Netflix in deciding which type of shows/movies to produce and how they can grow the business in different countries.
- Python
- Numpy, Pandas, Matplotlib, Seaborn
-Data Cleaning, Visualization, Data Analysis
| Column Name | Description |
|---|---|
| Show ID | The ID of the show |
| Type | Identifier – A Movie or TV Show |
| Title | Title of the Movie / TV Show |
| Director | Director of the Movie |
| Cast | Actors involved in the movie/show |
| Country | Country where the movie/show was produced |
| Date_added | Date it was added on the platform |
| Release_year | Actual release year of the movie/show |
| Rating | TV rating of the movie/show |
| Duration | Total duration – in minutes or number of seasons |
| Listed_in | Genre |
| Description | Short summary description |
- Data Collection - Source and format of the dataset.
- Data Cleaning & Preprocessing - Handling missing values, feature engineering.
- Exploratory Data Analysis (EDA) - Visualizations and insights.
- Insights .
- Focus on producing more International Movies, Dramas, and Comedies, as these genres have shown popularity. For TV Shows, prioritize International TV Shows and TV Dramas
- Movies of duration close to 2 hrs and TV Shows with 1-4 seasons suggested.
- USA is leading the consumer market for Netflix, create content thet should resound with bigger market audiences like USA.
- For Movies consider releasing in Week 1 and July to maximize viewership
- For TV shows Week 27 and December seems to be popular, aligning releases with these times may attract more audience
- For future projects include casts & directors from the top 10 members for both Movie and TV Shows categories.
- Address the unknown data gap
- As there is a higher number of movies added each year compared to TV shows, consider a balanced approach based on the observed trend.