Netflix_Dataset-Analysis

🎬 Netflix Dataset Analysis, Cleaning & Visualization

A beginner-friendly Data Analytics project that demonstrates the complete workflow of data cleaning, exploratory data analysis (EDA), and data visualization using the Netflix Movies and TV Shows Dataset. The project is built with Python, Pandas, NumPy, Matplotlib, and Seaborn.

This project is ideal for students and beginners who want to learn practical data analysis techniques on a real-world dataset.

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📌 Project Overview

The goal of this project is to analyze Netflix's catalog of Movies and TV Shows and gain meaningful insights through data preprocessing and visualization.

The project covers:

• Data Loading
• Data Cleaning
• Handling Missing Values
• Removing Duplicate Records
• Data Transformation
• Exploratory Data Analysis (EDA)
• Statistical Summary
• Data Visualization
• Insight Generation

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🚀 Features

• Clean and preprocess real-world data
• Handle missing values efficiently
• Remove duplicate entries
• Perform exploratory data analysis
• Create meaningful visualizations
• Discover trends in Netflix content
• Beginner-friendly and easy-to-understand code

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🛠️ Technologies Used

• Python 3
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook

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📂 Dataset

The dataset contains information about Netflix Movies and TV Shows, including:

• Show ID
• Content Type
• Title
• Director
• Cast
• Country
• Date Added
• Release Year
• Rating
• Duration
• Genre
• Description

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📁 Project Structure

Netflix-Data-Analysis/
│__ cleaned_netflix_titles.csv
├── netflix_data_analysis.py
|__data_visual.ipynb
├── netflix_titles.csv
├── README.md
└── requirements.txt

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📊 Project Workflow

1. Data Loading

• Import the dataset
• Understand the dataset structure
• Display rows and columns
• Check data types

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2. Data Cleaning

• Identify missing values
• Handle null values
• Remove duplicate records
• Convert data types
• Format date columns
• Clean categorical values

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3. Exploratory Data Analysis (EDA)

The following analyses were performed:

• Movies vs TV Shows
• Distribution of Content Ratings
• Content Added Over the Years
• Top Countries Producing Content
• Most Popular Genres
• Release Year Distribution
• Movie Duration Analysis
• TV Show Seasons Analysis

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4. Data Visualization

Visualizations created during the analysis include:

• 📊 Bar Charts
• 📈 Line Charts
• 🥧 Pie Charts
• 📦 Box Plots
• 📉 Histograms
• 🔥 Heatmaps
• Count Plots

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📈 Key Insights

Some important insights obtained from the analysis include:

• Netflix contains more Movies than TV Shows.
• The number of titles added to Netflix increased significantly after 2015.
• The United States contributes the largest number of titles.
• Drama and International Movies are among the most popular genres.
• TV-MA is one of the most frequently assigned content ratings.

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💻 Installation

Clone the repository:

git clone https://github.com/your-username/Netflix-Data-Analysis.git

Move into the project folder:

cd Netflix-Data-Analysis

Install the required libraries:

pip install -r requirements.txt

Launch Jupyter Notebook:

jupyter notebook

Open:

Netflix_Data_Analysis.ipynb

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📦 Requirements

Create a file named requirements.txt containing:

pandas
numpy
matplotlib
seaborn
jupyter

Or install manually:

pip install pandas numpy matplotlib seaborn jupyter

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📷 Sample Visualizations

This project includes visualizations such as:

• Movies vs TV Shows Comparison
• Top Countries by Content
• Genre Distribution
• Content Ratings
• Release Year Trend
• Duration Distribution

(You can add screenshots of your graphs here later.)

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🎯 Learning Outcomes

By completing this project, you will learn:

• Importing datasets using Pandas
• Data cleaning techniques
• Handling missing values
• Data transformation
• Exploratory Data Analysis (EDA)
• Data visualization using Matplotlib and Seaborn
• Drawing insights from real-world datasets

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📂 Dataset Source

The dataset used in this project was downloaded from the following GitHub repository:

NETFLIX-DATASET by Rohit Gillela

Repository: https://github.com/rohitgillela/NETFLIX-DATASET

All credit for the dataset goes to the original repository owner for making it publicly available.

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🤝 Contributing

Contributions, suggestions, and improvements are always welcome.

If you'd like to contribute:

1. Fork this repository.
2. Create a new branch.
3. Make your changes.
4. Commit your changes.
5. Open a Pull Request.

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⭐ Support

If you found this project useful, please consider giving it a ⭐ on GitHub. It helps others discover the project and motivates further improvements.

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📜 License

This project is licensed under the MIT License.

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👨‍💻 Author

Subhankit Ranjan

GitHub: https://github.com/Subhankit-R