IMDB Movie Data Analysis for RSVP Movies

Overview

This project is a comprehensive SQL-based analysis of an IMDB dataset. The primary objective is to analyze movie data to derive actionable insights for a fictional movie studio, RSVP Movies, helping them make data-driven decisions for their next film project.

The analysis focuses on identifying trends related to:

• Top-performing genres
• Successful directors and actors
• Movie performance metrics (ratings, duration, etc.)
• Specific insights into the Indian film industry

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Problem Statement

RSVP Movies wants to start a new project and needs to understand the current movie landscape. This analysis answers key business questions to provide a strategic recommendation on what kind of film they should produce.

Key questions explored:

1. What are the most produced and highest-rated genres?
2. Which production companies have the most successful track record?
3. Who are the top-rated actors and directors that audiences love?
4. What is the ideal movie duration?
5. What are the specific trends within the Indian film market (e.g., top actors, actresses)?

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Tools & Technologies

• Database: MySQL
• Language: SQL
• Data Source: IMDB data (originally provided as an Excel file)

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Project Workflow

This project was executed in two main phases, with all scripts included in this repository.

1. Database Creation & Data Loading

The file Database Creation.sql handles the complete setup of the imdb database.

• Schema Design: A relational database schema was designed with 6 tables to hold the data:
  • movie
  • genre
  • ratings
  • names (for actors, directors, etc.)
  • director_mapping
  • role_mapping
• Data Insertion: The script contains all INSERT statements to populate the tables with the provided dataset.

2. Data Analysis & Insight Generation

The file IMDB-question.sql contains all SQL queries used for the analysis. The queries are structured to build a comprehensive picture of the movie industry.

The analysis includes:

• Segment 1 (Data Exploration): Finding the number of rows in each table and identifying columns with NULL values.
• Segment 2 (Movie Analysis):
  • Ranking genres by movie count and average rating.
  • Analyzing movie counts by month of release.
  • Calculating average movie durations for different genres.
• Segment 3 (Business Insights):
  • Identifying top production companies.
  • Segmenting movies into "Hit" or "Superhit" categories based on ratings.
  • Ranking top directors and actors (both male and female) based on average movie ratings.
• Segment 4 (Indian Market Focus):
  • Drilling down into the Indian market to find the top actors, actresses, and genres.

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Key Insights & Recommendations

The SQL analysis (found in IMDB-question.sql) provides several key recommendations for RSVP Movies:

• Top Genres: Drama, Action and Thriller
• Talent:
• Top Directors - James Mangold, Joe Russo, Anthony Russo
• Top actors - Vijay Sethupathi and Fahadh Faasil
• Top actresses in Hindi - Taapsee Pannu, Kriti Sanon, and Shraddha Kapoor
• Production Company - Star Cinema and Twentieth Century Fox led among multilingual, highrated films

Recommendations:

• Producing a multilingual, high-quality film that blends action/drama can attract both domestic and international audiences.
• Collaborate with established directors and actors with a strong past performance in globally released movies.
• Plan the movie release between October and December 2022 to maximize global attention.

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How to Use This Repository

To replicate this analysis:

1. Clone the repository:

   git clone https://github.com/Ishant2104/SQL-Project-IMBD-Movie-Ratings.git

2. Set up the Database:
   • Open your SQL database management tool (e.g., MySQL Workbench, DBeaver).
   • Run the entire Database Creation.sql script. This will create the imdb database and populate it with data.
3. Run the Analysis:
   • Open a new query tab.
   • Run the queries in IMDB-question.sql one by one (or all at once) against the imdb database to see the results.