📌 Project Overview

Netflix date report

This project explores the Netflix dataset to gain insights into content availability, trends, and distribution. It was originally analyzed in a Jupyter Notebook and exported as a PDF.

The analysis covers:

Dataset overview and structure

Cleaning and preprocessing steps

Exploratory Data Analysis (EDA)

Key insights into movies and TV shows

📂 Dataset

The dataset contains information about Netflix titles, including:

Show ID

Type (Movie or TV Show)

Title

Director

Cast

Country

Date Added

Release Year

Rating

Duration

Listed In (genre/category)

Description

🔍 Analysis Performed

Data Cleaning

Handling missing values

Standardizing formats (e.g., date fields)

Exploratory Data Analysis (EDA)

Count of movies vs TV shows

Distribution by release year

Top genres/categories

Country-wise distribution of content

Ratings breakdown

Visualizations

Bar charts and pie charts for type and genre

Trend analysis over time

Country distribution plots

📊 Key Insights

Movies dominate the Netflix catalog compared to TV shows.

The majority of content was added in the last decade.

The United States and India contribute the largest share of Netflix titles.

TV-MA and TV-14 are among the most frequent ratings.

Dramas, comedies, and international content are widely represented.

⚙️ How to Use

Clone this repository

git clone https://github.com/your-username/netflix-analysis.git cd netflix-analysis

Open the Jupyter Notebook:

jupyter notebook Netflix_Analysis.ipynb

Run the notebook to reproduce results or extend the analysis.

📌 Requirements

Python 3.x

pandas

matplotlib / seaborn

numpy

jupyter

Install dependencies with:

pip install -r requirements.txt

📖 Future Work

Sentiment analysis of Netflix descriptions

Time series forecasting for content release trends

Comparing Netflix with other streaming platforms