With fierce competition in the streaming industry, data-driven decisions are crucial. Our team set out to uncover how content strategies differ between Netflix and Amazon Prime Video, focusing on content maturity, duration trends, and genre diversity.

Hypotheses:

• Amazon tends to push genre boundaries with more mature-rated content, while Netflix plays it safer with family-friendly options.
• Over the years, movies tend to be longer, and TV shows tend to be shorter.

We used data wrangling, EDA, and visual analysis to test and support these hypotheses.

• Source: Kaggle – Netflix and Amazon Prime Video datasets
• Records: Thousands of movies and TV shows
• Features: Title, platform, release year, type (movie/TV show), duration, rating, genres, and more.

Performed in Jupyter notebooks using Python (pandas):

• Removed duplicate records across both datasets.
• Fixed and standardized release_year inconsistencies.
• Unified genre and rating labels across platforms via custom dictionaries.
• Converted duration fields to numeric types for analysis.
• Designed a relational schema to handle many-to-many relationships between shows and genres.

Implemented with relational integrity across combined Netflix and Prime datasets.

Tables:

• shows (show_id, title, release_year, rating, duration, type, platform)
• genres (genre_id, genre_name)
• show_genres (show_id, genre_id)

ERD available in: charts/erd_diagram.png

Relational integrity ensured via foreign key constraints.

• Python (pandas, seaborn, matplotlib)
• Jupyter Notebook
• Git + GitHub
• Google Slides (presentation)

EDA was conducted using bar charts, line plots, and percentage comparisons.

• TV shows are getting shorter over time, while movie durations remain stable.
• Netflix dominates the TV-MA category, whereas Prime has more family-friendly content.
• Drama is the leading genre for movies; Netflix offers more genre diversity in TV shows (documentary, sci-fi/fantasy, international).

We produced 5 high-quality visualizations that revealed key content strategies between platforms.

Analysis performed via aggregations and filtering:

• Average movie duration over time by platform.
• Average number of TV show seasons over time by platform.
• Percentage of mature-rated content (TV-MA, R).
• Ratings distribution comparison between platforms.
• Top genres for movies and TV shows.

project-root/
│
├── data/
│   ├── raw/
│   └── clean/
├── charts/                           # Graphs and visualizations, including ERD
├── notebooks/
│   └── netflix_cleaning.ipynb
│   └── prime_cleaning.ipynb
│   └── combined_analysis.ipynb
├── scripts/                          # Functions for cleaning and analysis
├── presentation_link.txt             # Google Slides link
├── README.md                         # This file
└── Trello_link.txt                   # Kanban board link

This structure ensures reproducibility and separation of concerns between cleaning, analysis, and presentation.

• ✔️ Clear business problem + 2 hypotheses
• ✔️ Full data cleaning and justification
• ✔️ Functional relational structure with many-to-many handling
• ✔️ 5+ business questions answered with visual insights
• ✔️ 5+ visualizations with interpretation
• ✔️ Well-structured Git repo with daily commits
• ✔️ Comprehensive README for reproducibility
• ✔️ Google Slides presentation included

📽️ Google Slides – Final Presentation