Netflix Data: Cleaning, Analysis, and Visualization

Project Overview

This project involves the comprehensive analysis of Netflix's content dataset to uncover valuable insights regarding viewer preferences, content trends, and international reach. Through meticulous data cleaning and visualization techniques, we aim to transform raw data into actionable information that can support decision-making in the entertainment industry.

Key Objectives

Data Cleaning: Prepare the dataset by handling missing values, correcting data types, and ensuring consistency for accurate analysis.

Exploratory Data Analysis (EDA): Perform in-depth analysis to identify patterns in content releases, popular genres, and audience ratings.

Visualization: Create insightful visualizations to illustrate trends and relationships within the data, making it easier to interpret findings.

Insights Generation: Derive key insights about Netflix's content diversity, ratings distribution, and the influence of top contributors.

Technologies Used

Python

Pandas

Matplotlib

Seaborn

Results

The analysis highlights important trends such as:

The most popular genres and their evolution over time. Viewer preferences regarding content length and ratings. Regional contributions to Netflix's content library.

Future Work

Potential future enhancements include developing machine learning models for content recommendations and predictive analytics based on historical data.