🎬 Netflix Data Analysis & Visualization (Python | Colab | Plotly)

📌 Overview

This project performs data cleaning, exploration, and visualization on the Netflix titles dataset (Netflix.json).
It uses Python 3, Pandas, Matplotlib, Seaborn, and Plotly within Google Colab to uncover insights such as:

• Year-wise growth of movies and TV shows on Netflix
• Country-wise content distribution
• Genre popularity trends over time
• Movie duration patterns and release-year analysis

🎯 Objectives

• Clean and preprocess the raw JSON dataset
• Handle missing values & duplicates
• Normalize duration, date, and genre information
• Perform Exploratory Data Analysis (EDA)
• Generate interactive visualizations for deeper insights

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🧰 Tech Stack

Category	Tools / Libraries
Language	Python 3
Data Handling	Pandas, NumPy
Visualization	Matplotlib, Seaborn, Plotly
Environment	Google Colab
Version Control	Git + GitHub

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🧹 Data Cleaning Steps

1. Loaded the Netflix dataset (Netflix.json) into a Pandas DataFrame
2. Standardized column names and trimmed whitespaces
3. Parsed date fields (e.g., date_added) into datetime objects
4. Normalized duration (minutes / seasons)
5. Split multi-valued columns (cast, country, listed_in) into lists
6. Handled missing values using imputation / placeholder logic
7. Removed duplicates based on show_id and title + release_year
8. Exported a cleaned dataset (netflix_cleaned.csv) for further analysis

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📊 Exploratory Data Analysis (EDA)

Key analyses performed:

• Content type distribution (Movie vs TV Show)
• Rating distribution across types
• Country and genre frequency
• Correlation heatmap between numeric features
• 3D scatter plots using Plotly (Release Year × Duration × Type)
• Genre-over-time heatmap

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🌈 Visualizations Preview

Below are a few examples of generated plots (replace with your screenshots):

Visualization	Preview
Bar Chart
Bar Chart
Histogram
Boxplot
Interactive 3D render
Numeric_Heatmap
Group_BarChart

🖼️ To insert screenshots, save them in a figures/ folder and reference them with relative paths as shown above.

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🚀 How to Run This Project

🔹 Option 1: Open in Google Colab

Click the badge below to launch instantly in Colab:

🔹 Option 2: Run Locally

git clone https://github.com/yourusername/netflix-eda.git
cd netflix-eda
pip install -r requirements.txt
jupyter notebook notebooks/netflix_eda.ipynb



📈 Key Insights

📅 Sharp rise in content added after 2015
🎞️ Movies form ~70% of total titles
🌍 US, India, UK top content producers
🎭 Drama & Comedy most common genres
⏱️ Average movie duration ≈ 100 minutes
🧠 Learnings

Hands-on data wrangling and cleaning in Pandas
Feature engineering (date, duration, multi-value columns)
Interactive EDA with Plotly
GitHub project structuring and documentation
🪪 License
This project is licensed under the MIT License.
You are free to use, modify, and share with attribution.