Netflix Data Analysis Project

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