Netflix Data Analysis Project

• This project involves analyzing Netflix's content dataset to gain insights into content distribution, temporal trends, and other characteristics using Python, Excel, and Power BI.

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Project Structure

• data/
  Contains the original dataset (netflix_titles.csv), the cleaned CSV (netflix_titles_clean.csv), and an Excel version (netflix_titles_clean.xlsx) for easier analysis.

• images/
  Contains background images and screenshots used in the Power BI report, including the final dashboard screenshot.

• reports/
  Includes the Power BI report file (netflix_presentation.pbix) used for the data visualization and presentation.

• scripts/
  Contains Python scripts for data cleaning and preprocessing (netflix_data_cleaning.py).

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Requirements

Before running the scripts, make sure to install the required Python libraries listed in requirements.txt:

pip install -r requirements.txt

Usage Instructions

Data Cleaning

Run the Python script to clean and preprocess the raw Netflix dataset:

python scripts/netflix_data_cleaning.py

• This script handles missing values, converts date columns to proper datetime format, and outputs a cleaned CSV file.

Data Analysis and Visualization

Open the Power BI report to explore and interact with the cleaned data visualizations:

reports/netflix_presentation.pbix

• The report contains various charts and dashboards that illustrate content additions over time, genre distributions, content type breakdowns, and more.

PowerBi Dashboard Screenshot

Future Improvements

The project can be further enhanced with the following improvements:

• Automated Data Updates: Implement scripts or workflows that automatically fetch updated Netflix datasets periodically to keep the analysis current.

• Advanced Feature Engineering: Extract more granular features such as actor/crew popularity, sentiment analysis from descriptions, or user ratings integration if available.

• Deeper Statistical Analysis: Perform trend analysis, forecasting content additions, or clustering content based on genres and other attributes.

• Integration with External Data Sources: Combine Netflix data with social media trends, viewer ratings, or global events to uncover richer insights.

• Documentation and Testing: Improve code documentation and add unit tests for the cleaning scripts to ensure robustness.

Additional Notes

• The cleaned Excel file netflix_titles_clean.xlsx can be used for additional manual analysis or as a data source for Power BI.

• Background images and assets used in the report are stored in the images folder for reference and reproducibility.

• The Python script is designed to be modular and can be extended for additional preprocessing or feature engineering tasks.