📊 Netflix Data Analysis

📝 Project Overview

The Netflix Data Analysis project aims to explore, clean, and visualize Netflix’s dataset to uncover key insights about its movies and TV shows. The analysis helps in understanding content trends, popular genres, distribution across countries, and the evolution of Netflix’s library over time.

Through this project, we perform data cleaning, exploratory data analysis (EDA), and visualization using Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn.

🎯 Objectives

Analyze Netflix’s catalog of movies and TV shows.

Identify trends in genres, ratings, and release years.

Compare the distribution of movies vs. TV shows.

Visualize country-wise and time-based content growth.

Gain hands-on experience in data preprocessing and visualization.

🗂️ Dataset

Source: Netflix Dataset on Kaggle

File: netflix_titles.csv

Key Columns:

show_id

type (Movie/TV Show)

title

director

cast

country

date_added

release_year

rating

duration

listed_in (Genre)

description

🧩 Technologies Used

Programming Language: Python

Libraries:

Pandas – Data manipulation and cleaning

NumPy – Numerical computation

Matplotlib & Seaborn – Data visualization

Jupyter Notebook – Interactive analysis environment **

⚙️ Project Workflow

** Importing Libraries & Dataset

Load the Netflix dataset into a pandas DataFrame.

Data Cleaning

Handle missing values, remove duplicates, and standardize formats.

Exploratory Data Analysis (EDA)

Summary statistics, unique values, and type distributions.

Visualization

Graphical insights using bar plots, pie charts, heatmaps, and count plots.

Insights & Conclusions

Summarize findings and interpret key trends.

📈 Key Insights

Movies make up the majority of Netflix’s content.

The USA and India contribute the most titles to Netflix’s catalog.

Drama and Comedy are the most common genres.

A significant increase in Netflix content can be seen after 2015.

The most common content rating is TV-MA (for mature audiences).

💡 Conclusion

The analysis provides a clear picture of Netflix’s content strategy and growth. It highlights how Netflix has expanded globally, diversified its genres, and increased its focus on original programming in recent years.

🚀 How to Run the Project

Clone this repository:

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

Navigate to the project folder:

cd netflix-data-analysis

Install required libraries:

pip install pandas numpy matplotlib seaborn

Open the Jupyter Notebook:

jupyter notebook netflix_analysis.ipynb

👤 Author

Anand Singh B.Tech – Cloud Computing & Machine Learning 📧 Email: anandsi1726j@gmail.com