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🎬 Netflix Data Cleaning & Exploratory Data Analysis (EDA) 🧠 Project Overview

This project focuses on cleaning and exploring a Netflix dataset containing details about movies and TV shows available on the platform. The goal is to understand trends in Netflix’s catalog — such as content types, release patterns, country contributions, and popular directors — while ensuring the data is clean and analysis-ready.

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🎯 Objectives

. Clean and prepare the dataset for analysis. . Identify and handle duplicates and missing values. . Convert data types (e.g., dates) for consistency. . Explore key business questions through EDA.

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🧰 Tools & Libraries

1. Python (Jupyter Notebook)
2. Libraries: pandas, matplotlib, seaborn, numpy

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🧹 Data Cleaning Process

Steps performed to prepare the dataset:

1. Removed duplicate records to maintain data integrity.
2. Identified and summarized missing values.
3. Converted the Release_Date column to standard datetime format.
4. Dropped irrelevant columns and standardized categorical values.
5. Verified data types and ensured overall dataset consistency.

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📊 Exploratory Data Analysis

Key exploratory queries and findings:

. Content Type Distribution: Number of Movies vs TV Shows on Netflix. . Release Trends: Year-wise release frequency — shows Netflix’s expansion. . Regional Analysis: Titles produced or released in 'India' and the 'UK'. . Top Directors: Identified the most prolific Netflix contributors. . Actor Spotlight: Movies featuring 'Tom Cruise'. . Genre & Rating Patterns: Distribution of categories and rating types.

Example visuals from the notebook include:

. Bar charts for 'content distribution and release years'. . Countplots showing 'category breakdowns' using Seaborn.

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📈 Key Insights

. Movies make up the majority of Netflix’s catalog, though TV Shows are growing steadily. . The 'early 2020s' show a spike in global releases. . 'India' and the UK remain strong contributors of Netflix Originals. . 'Top directors' have produced a significant portion of Netflix’s total content. . The dataset reveals diverse 'rating systems' across countries.

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📂 Repository Structure

📁 Netflix-EDA-Project/
│
├── NetflixSC.ipynb              # Main Jupyter Notebook
├── data/
│   └── Netflixd.csv             # Dataset file (optional upload)
├── visuals/
│   └── readme_banner.PNG       # Example chart or screenshot
└── README.md                    # Project documentation

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🧠 Skills Demonstrated

. Data Cleaning (duplicates, missing data, data type conversions) . Exploratory Data Analysis (EDA) . Data Visualization using Matplotlib & Seaborn . Python and Jupyter Notebook workflow . Analytical storytelling and business insight derivation

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📬 Contact

Name: Onagadanalyst Role: Data & Market Analyst | Building Expertise in Augmented Analytics and AI Automation LinkedIn: GitHub: https://github.com/o-danalyst Email: onagatheanalyst@gmail.com

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