🎬 IMDb Comprehensive Analytics Dashboard

📖 Project Description

This project provides a Streamlit-powered interactive dashboard for analyzing IMDb movie data.
It combines three datasets:

• imdb_clean.csv → cleaned movie-level dataset
• imdb_cast_exploded.csv → exploded cast dataset (one row per actor)
• imdb_genre_exploded.csv → exploded genre dataset (one row per genre)

The dashboard delivers multi-perspective insights into movies, cast members, and genres, with interactive filters and polished Plotly charts.

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📸 Dashboard Preview

Overview

Genre Analysis

Cast Analysis

Yearly Trends

Scatter Plots

Correlation Heatmap

Data Table

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✨ Features

• Sidebar Filters
  • Filter by genre(s), cast member(s), year range, and metadata score range.
• Overview Tab
  • KPIs: total movies, average duration, average metadata score.
  • Top genres and actors with bar and pie charts.
• Genre Analysis Tab
  • Frequency of genres.
  • Average metadata score per genre.
  • Boxplots of duration and metadata by genre.
  • Genre popularity heatmap over years.
• Cast Analysis Tab
  • Most frequent actors.
  • Average metadata score per actor.
  • Boxplots of metadata by actor.
  • Actor appearances trend over years.
• Yearly Trends Tab
  • Movies per year.
  • Average duration per year.
  • Average metadata score per year.
  • Genre popularity heatmap.
• Scatter Plots Tab
  • Metadata vs Duration.
  • Metadata vs Year.
  • Duration vs Year.
• Correlation Tab
  • Correlation heatmap of numeric features.
• Data Table Tab
  • Interactive view of filtered data.
  • Export filtered dataset as CSV.

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⚙️ Setup Instructions

Step‑by‑Step Guide

# 1. Clone the repository
git clone https://github.com/bhanu2006-24/imdb-analysis.git
cd imdb-analysis

# 2. Create a virtual environment
python -m venv venv

# 3. Activate the environment
# On macOS/Linux
source venv/bin/activate
# On Windows
venv\Scripts\activate

# 4. Install dependencies
pip install -r requirements.txt

# 5. Run the dashboard
streamlit run dashboard.py
# imbd-analysis