🎬 Amazon Prime Movies & TV Shows — Analytics Dashboard

End-to-end exploratory data analysis of Amazon Prime Video content using Python.

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📌 Project Overview

This project performs a comprehensive analysis of the Amazon Prime Video content library — covering Movies and TV Shows. The goal is to uncover trends, patterns, and insights about content types, genres, countries, ratings, and more using Python-based data analysis and visualization.

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📁 Project Structure

amazon-prime-analytics/
│
├── 📁 data/
│   ├── raw/                         ← Original dataset (amazon_prime_titles.csv)
│   └── processed/                   ← Cleaned dataset after preprocessing
│
├── 📁 notebooks/
│   ├── 01_data_cleaning.ipynb       ← Data loading, cleaning & preprocessing
│   ├── 02_eda.ipynb                 ← Exploratory Data Analysis
│   └── 03_visualizations.ipynb     ← Advanced charts & visual insights
│
├── 📁 visualizations/               ← Saved chart images (.png)
├── 📁 reports/                      ← Summary insights (optional PDF)
│
├── requirements.txt
├── .gitignore
└── README.md

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📊 Dataset

Source: Kaggle — Amazon Prime Movies and TV Shows

File: amazon_prime_titles.csv

Column	Description
show_id	Unique ID for each title
type	Movie or TV Show
title	Name of the content
director	Director name(s)
cast	Main cast members
country	Country of production
date_added	Date added to Prime
release_year	Year of original release
rating	Content rating (PG, R, TV-MA, etc.)
duration	Runtime (minutes for movies, seasons for shows)
listed_in	Genre categories
description	Short plot description

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🔍 Analysis Performed

Notebook 1 — Data Cleaning

• Load & inspect dataset
• Handle missing values
• Parse date columns
• Feature engineering (year_added, month_added, duration_int)
• Export cleaned data

Notebook 2 — Exploratory Data Analysis (EDA)

• Movies vs TV Shows distribution
• Content added over the years
• Top 10 countries producing content
• Content rating analysis
• Genre frequency analysis
• Top directors & cast members

Notebook 3 — Advanced Visualizations

• Interactive Plotly charts
• Heatmaps (country × genre, year × type)
• Treemap of genres
• Sunburst chart (type → rating → genre)
• Word cloud of titles/descriptions

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🛠️ Tech Stack

Tool	Purpose
Python 3.x	Core language
Pandas	Data manipulation
Matplotlib	Static charts
Seaborn	Statistical visualizations
Plotly	Interactive charts
WordCloud	Text visualizations
Jupyter Notebook	Development environment

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/yourusername/amazon-prime-analytics.git
cd amazon-prime-analytics

2. Install dependencies

pip install -r requirements.txt

3. Download the dataset

• Go to: https://www.kaggle.com/datasets/shivamb/amazon-prime-movies-and-tv-shows
• Download amazon_prime_titles.csv
• Place it in data/raw/

4. Run the notebooks in order

notebooks/01_data_cleaning.ipynb
notebooks/02_eda.ipynb
notebooks/03_visualizations.ipynb

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📈 Key Insights (Preview)

• Amazon Prime has significantly more Movies than TV Shows
• Content additions peaked around 2019–2021
• USA, India, and UK are the top content-producing countries
• Drama and Comedy are the most common genres
• Most content is rated 13+ or 16+

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👩‍💻 Author

Your Name

• GitHub: @prasanthmax
• LinkedIn: Prasanth Shanmugam

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📄 License

This project is open-source under the MIT License.