ETL & Movie Analytics

Project Workflow

1. Data Extraction (Web Scraping + CSV + APIs)

IMDB Web Scraping → BeautifulSoup to collect Title, Revenue, Rating, Director, Year

Kaggle Datasets → CSV datasets for extended insights

Data Integration → Merged everything into one consolidated dataset with Pandas

2. Data Cleaning & Transformation ️

Handled missing values, removed duplicates, standardized formats

Normalized revenue columns for accurate comparisons

Built an automated ETL pipeline for consistency

3. Storage & Processing

Lightweight ETL architecture for structured & semi-structured data

Python + Pandas for processing raw data

Optimized pipeline for scalability and performance

4. Visualization & Insights (Power BI Dashboard)

Top-Grossing Movies

Highest-Rated Movies

Top Directors

Revenue Trends

5. Tech Stack Used

- Python (Pandas, BeautifulSoup, NumPy)

- ETL Pipeline (Extraction, Cleaning, Transformation)

- Power BI (Dashboards & Insights)

- Jupyter Notebook (Development & Analysis)