https://github.com/devanshsahu47/prime-content-analytics

Prime Data Explorer analyzes Amazon Prime's content and credits data to uncover trends in release years, genres, and ratings. It cleans, merges, and visualizes the data to provide actionable insights for optimizing content strategy and boosting audience engagement.
https://github.com/devanshsahu47/prime-content-analytics

data-analysis data-visualization exploratory-data-analysis jupyter-notebook python3

Last synced: about 1 month ago
JSON representation

Prime Data Explorer analyzes Amazon Prime's content and credits data to uncover trends in release years, genres, and ratings. It cleans, merges, and visualizes the data to provide actionable insights for optimizing content strategy and boosting audience engagement.

• Host: GitHub
• URL: https://github.com/devanshsahu47/prime-content-analytics
• Owner: devanshsahu47
• Created: 2025-03-02T13:26:41.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-02T13:49:09.000Z (over 1 year ago)
• Last Synced: 2025-03-02T14:29:16.907Z (over 1 year ago)
• Topics: data-analysis, data-visualization, exploratory-data-analysis, jupyter-notebook, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 6.12 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Prime Data Explorer: Data-Driven Insights on Amazon Prime Content

## Problem Statement & Business Objective

Companies and streaming platforms often struggle to understand the dynamics of content performance and audience engagement. With an ever-growing library of movies and TV shows, identifying trends, assessing content quality, and optimizing production strategies can be challenging. This project addresses these challenges by leveraging a comprehensive dataset comprising Amazon Prime Titles and Credits to extract actionable insights. Our objectives are to:

-  **Assess Content Quality:** Evaluate ratings and runtime distributions to gauge audience satisfaction.

-  **Identify Trends:** Analyze release years, genre popularity, and production countries to understand market evolution.

-  **Enhance Strategic Decision-Making:** Provide data-driven insights that help optimize content acquisition, production, and marketing strategies.

-  **Improve Audience Engagement:** Uncover patterns that lead to higher engagement and identify potential areas of negative performance.

## Approach & Methodology

The project follows an end-to-end data analysis process:

1. **Data Exploration:**

    We begin by loading and exploring the datasets using commands like df.head(), df.info(), and df.describe(include='all') to assess structure, duplicates, and missing values.

2. **Data Wrangling & Preprocessing:**

   - Cleaning: Duplicate records are removed and missing values are addressed (e.g., filling missing age_certification with 'NR' and imputing missing runtime values using the median).

   - Data Type Conversion: Key fields such as release_year are converted to appropriate data types for accurate analysis.

   - List Processing: String representations of list-like columns (e.g., genres and production_countries) are converted into actual Python lists.

   - Feature Engineering: A new column num_genres is created to capture the number of genres per title.

   - Merging Data: The Titles and Credits datasets are merged using the id column, creating a unified view that links content with its cast and crew details.

3. **Visualization & Analysis:**

   Following the Univariate, Bivariate, and Multivariate (UBM) framework, we develop over 20 visualizations that include:

     - Univariate Analysis: Bar charts, histograms, and word clouds to examine individual variable distributions.

     - Bivariate Analysis: Scatter plots, line charts, and box plots to uncover relationships between variables such as ratings and runtime.

     - Multivariate Analysis: Correlation heatmaps, pair plots, and bubble charts to explore deeper interactions and clusters within the data.

   These visualizations provide insights such as:

     - Trends over release years.

     - Popularity of genres and production regions.

     - Relationships between IMDb and TMDB ratings.

     - Identification of potential areas of underperformance or negative growth.

## Dependencies

This project requires the following Python packages:

- Python 3.x

- pandas

- numpy

- matplotlib

- seaborn

- wordcloud

- jupyter

Install all dependencies using:

```bash

pip install -r requirements.txt