A complete Flask-based analytics dashboard that visualizes Netflix content viewership trends by content type, language, release month, and day of the week to uncover audience preferences. Built with Python, Flask, Pandas, and Plotly.
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🔐 Upload & Manage Data
Upload CSV files containing Netflix content viewership data. -
🔎 Viewership Overview
View total viewership hours by content type, language, release month, and release season. -
📈 Trends Visualization
Interactive line and bar charts showing monthly trends, seasonal trends, and weekday performance. -
🧱 Top Content Insights
View top 5 most viewed Netflix titles with metadata. -
📊 Combined Metrics
Overlay number of releases vs viewership hours by month and day of the week.
# Clone and setup
git clone https://github.com/your-username/netflix-viewership-dashboard.git
cd netflix-viewership-dashboard
python -m venv venv
# Linux/Mac:
source venv/bin/activate
# Windows (PowerShell):
# .\\venv\\Scripts\\Activate.ps1
# Install dependencies
pip install -r requirements.txt
# (Optional) Set environment variables
# Linux/Mac:
export FLASK_APP=app.py
export FLASK_ENV=development
# Windows (PowerShell):
# $env:FLASK_APP="app.py"
# $env:FLASK_ENV="development"
# Run the app
flask run
# Open http://127.0.0.1:5000
The dataset includes viewership data of Netflix content (movies & shows) in 2023. Typical Columns ⦁ Content Metadata: Title, Content Type, Language Indicator, Release Date
⦁ Viewership: Hours Viewed
⦁ Release Time Data: Release Month, Release Day of Week, Release Season
Place your CSV inside data/ (e.g., data/ad_users.csv) or upload via the web UI.
⦁ Content Type & Language
⦁ Release Month, Release Season
⦁ Number of Releases
⦁ Hours Viewed (in billions)
⦁ Top Content Titles
⦁ Data Cleaning & Transformation
⦁ Time-based Aggregation (Release Month/Season/Weekday)
⦁ Interactive Plotly Visualizations (Bar & Line Charts)
⦁ User-friendly Dashboard with Netflix Theme
⦁ Content Type Distribution — Movies vs TV Shows total viewership.
⦁ Language Impact — Top languages by viewership hours.
⦁ Release Month Trends — When does viewership peak?
⦁ Seasonal Preferences — Summer vs Winter viewership patterns.
⦁ Release Day Impact — Does release day affect viewership?
⦁ Interactive bar charts showing total viewership by content type, language, and season.
⦁ Line charts for trends by release month and content type.
⦁ Combined plots showing release counts vs total viewership.
⦁ Top 5 most viewed Netflix titles table with details.
⦁ Backend: Python, Google Colab
⦁ Data Processing: Pandas, NumPy
⦁ Visualization: Plotly
⦁ Utilities: joblib (optional for persistence), python-dotenv (env vars management)
💡 Contributions, issues, and feature requests are welcome!