📺 Netflix Data Analysis Project

This project involves analyzing a Netflix dataset using Python (pandas and SQL), cleaning the data, and visualizing insights with Power BI. The objective is to explore content distribution, types, durations, and trends over time.

📁 Folder Structure

netflix-data-analysis/ ├── data/ │ ├── netflix_titles.xlsx # Raw Netflix dataset │ └── CLEANED_DATA.xlsx # Cleaned data after preprocessing ├── scripts/ │ ├── clean.py # Python script for data cleaning │ └── clean_using_sql.py # Python script to query data using SQLite ├── database/ │ └── cleaned_data.db # SQLite database generated from cleaned data ├── dashboard/ │ └── netflix_dashboard.pbix # Power BI dashboard file ├── README.md # Project documentation

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📊 Key Tasks Performed

✅ Data Cleaning

• Removed or handled null values
• Standardized column formats (e.g., date_added, duration)
• Resolved data type mismatches (e.g., year to integer)

✅ Data Querying

• Used sqlite3 and pandas.read_sql() to run SQL queries on the dataset
• Extracted insights like:
  • Number of shows per year
  • Movies vs TV shows distribution
  • Country-wise production frequency
  • Duration patterns by content type

✅ Dashboard (Power BI)

• Map of content distribution by country 🌍
• Line/bar chart showing release trends over the years 📈
• Comparison of durations across content types ⏱
• Filters by genre, country, and type

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🧰 Tools & Technologies Used

• Python (pandas, sqlite3)
• Power BI (interactive dashboards)
• SQLite (in-memory + persistent queries)
• Excel (for initial and final data handling)

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