📊 Netflix Data: Cleaning, Analysis, and Visualization

Analyzing and visualizing Netflix content data (2008-2021) to explore trends in movies, TV shows, and original content using SQL, Python, and Tableau.

📂 Dataset

This dataset contains details about Netflix's vast catalog of movies and TV shows, including release years, genres, ratings, and addition dates. It has been cleaned and structured for analysis.

📥 Download Dataset

Dataset Features

• Show ID: Unique identifier for each show
  
• Type: Movie or TV Show
  
• Title: Name of the content
  
• Director: Director of the movie or show
  
• Cast: Lead actors in the movie or show
  
• Country: Country where the content was produced
  
• Date Added: When the content was added to Netflix
  
• Release Year: Year the content was released
  
• Rating: Audience rating (e.g., PG-13, TV-MA)
  
• Duration: Length of the content (minutes for movies, seasons for TV shows)
  
• Genres: Categories or genres of the content
  
• Description: Short summary of the movie or show
  

Use Cases

• 📊 Data Analysis: Identify trends in Netflix content additions
  
• 🎥 Genre Trends: Explore most popular content categories
  
• 🕒 Release Year Patterns: Find how Netflix's content library evolved
  
• 🌍 Country-Based Insights: Discover content distribution across countries
  
• 🔍 Rating Analysis: Analyze viewer ratings and audience preferences
  

🛠️ Tools & Technologies

• Python, SQL, Excel: Data cleaning and analysis
  
• PostgreSQL: Data storage and processing
  
• Tableau: Data visualization and dashboard creation
  
• Jupyter Notebook / VS Code: Development environment
  
• Pandas & NumPy: Data manipulation
  
• Matplotlib & Seaborn: Data visualization
  

🔍 Data Cleaning Process

To ensure high-quality analysis, the dataset is cleaned using PostgreSQL with the following steps:

1. 🛑 Handling Null Values

   • Fill missing values in key columns (e.g., Director, Cast)
     
   • Remove rows with excessive missing data
     

2. 📌 Removing Duplicates

   • Identify and remove duplicate records
     

3. 📊 Populating Missing Rows

   • Infer missing values using available data
     

4. 🗑️ Dropping Unneeded Columns

   • Remove irrelevant features to improve dataset efficiency
     

5. 🔍 Splitting Columns

   • Extract key details from columns like Duration, Genres, etc.
     

🔹 Detailed cleaning steps and justifications are included in the code comments.

📈 Data Visualization

After cleaning, the dataset is visualized using Tableau to generate insights.

📌 Steps & Implementation

1. Dataset Exploration

   • Load and inspect the raw data
     
   • Identify missing values and duplicates
     

2. Data Cleaning with SQL & Pandas

   • Perform data transformation, cleaning, and formatting
     

3. Exploratory Data Analysis (EDA)

   • Find trends in content additions, ratings, and genre popularity
     

4. Data Visualization with Tableau

   • Build interactive dashboards showcasing insights
     

📊 Project Difficulty Level

Intermediate

🚀 Getting Started

1. Clone the repository:
   sh git clone https://github.com/AgnideepPoddar/Netflix-Data-Cleaning-Analysis-and-Visualization.git
   
2. Run the analysis scripts to explore insights.
   

🤝 Contributions

Contributions are welcome! Feel free to open issues or submit pull requests.

📜 License

This project is for educational purposes and follows the MIT License.