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Netflix Data Analysis Project

Netflix cover

Overview

This project performs comprehensive data analysis on Netflix's shows and movies dataset using Python's Pandas and Seaborn. The analysis includes a thorough data cleaning process followed by answering specific business questions to derive meaningful insights about Netflix's content library.

Data Cleaning Process

The dataset underwent a rigorous 8-step cleaning process to ensure data quality:

Click to view Data Cleaning Details

  1. Irrelevant Data Removal

    • Eliminated non-essential data based on business objectives
    • Focused on maintaining relevant features for analysis

  2. Missing Data Handling

    • Addressed NULL values and blank entries
    • Applied appropriate imputation or removal strategies

  3. Duplicate Data Elimination

    • Identified and removed duplicate entries
    • Ensured data uniqueness

  4. Structural Error Correction

    • Fixed inconsistent naming conventions
    • Corrected typos and capitalization issues

  5. Data Type Conversion

    • Standardized data types (String, Integer, DateTime, Timestamp)
    • Ensured proper format for analysis

  6. Data Standardization

    • Normalized units for consistency
    • Standardized scales across variables

  7. Outlier Management

    • Applied Interquartile Rule for outlier detection
    • Analyzed and handled extreme values

  8. Data Validation

    • Performed final checks for data integrity
    • Verified data consistency

Business Questions Analyzed

The analysis answers several key business questions including:

  • Show identification and director information for specific titles
  • Temporal analysis of content release patterns
  • Distribution of Movies vs TV Shows
  • Country-specific content analysis
  • Director contribution analysis
  • Content categorization and filtering
  • Cast member analysis
  • Rating distribution and analysis
  • Duration analysis
  • Content sorting and filtering capabilities

Results Highlights

  • Identified peak years for content release
  • Analyzed content distribution across different countries
  • Discovered popular directors and their contributions
  • Mapped rating distributions across regions
  • Identified content patterns and trends

Technology Stack

  • Python
  • Pandas for data manipulation
  • Seaborn for visualization
  • Jupyter Notebook for development