📊 Netflix Titles Data Analysis & Data Cleaning

📌 Project Overview

This project focuses on cleaning, exploring, and visualizing the Netflix Titles dataset.
The goal is to demonstrate a data analyst–oriented workflow, starting from raw data and ending with clear, interpretable insights supported by visualizations.

Instead of building predictive models, this project emphasizes:

• data cleaning
• exploratory data analysis (EDA)
• effective data visualization

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📁 Dataset

• Source: Netflix Titles Dataset
• Content: Movies and TV Shows available on Netflix
• Main features:
  • type (Movie / TV Show)
  • release_year
  • duration
  • rating
  • country
  • listed_in (genres)
  • date_added

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🧹 Data Cleaning Process

The raw dataset contains missing values, inconsistent formats, and multi-value categorical columns.
The following cleaning steps were applied:

• Removed duplicate records
• Standardized column names
• Converted date columns to proper datetime format
• Handled missing values using simple and explainable strategies
• Cleaned and separated the duration column
• Identified and handled multi-label categorical features (listed_in)
• Dropped columns that were not useful for analysis

The focus was on clarity, reproducibility, and realistic data cleaning decisions.

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🔍 Exploratory Data Analysis (EDA)

After cleaning, exploratory analysis was performed to understand patterns and trends in the data.

Key questions explored:

• How is Netflix content distributed between Movies and TV Shows?
• How has Netflix content changed over time?
• Which content ratings are most common?
• What are the most frequent genres?
• How are movie durations distributed?

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📊 Key Visual Insights

🎬 Movies vs TV Shows Distribution

Movies dominate the Netflix catalog compared to TV Shows.

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📈 Content Growth Over Time

The number of titles added to Netflix increased significantly after 2015.

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⏱ Movie Duration Distribution

Most movies cluster around 90-120 minutes (standard feature-length).

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🎭 Top 10 Genres

International Movies and Dramas are the most common genres on Netflix.

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📊 Movie vs TV Show Trend

Both content types have grown over time, with movies consistently leading.

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📈 Key Insights

• Netflix has rapidly expanded its content in recent years
• Movies make up the majority of the catalog
• Certain content ratings appear much more frequently than others
• Movie durations show a clear clustering pattern

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🛠 Tools & Libraries

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook

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📌 What I Learned

• Cleaning real-world datasets with missing and inconsistent values
• Handling categorical and multi-label features
• Structuring an EDA-focused data analysis project
• Communicating insights through clear visualizations

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📂 Repository Structure

📦 netflix-data-analysis
┣ 📁 images
┃ ┣ type_distribution.png
┃ ┣ content_over_time.png
┃ ┣ release_year_histogram.png
┃ ┣ top_genres.png
┃ ┗ type_by_year_trend.png
┣ 📄 nb.ipynb
┣ 📄 netflix_titles.csv
┗ 📄 README.md