Leelu1002/Movie_Profitability_Analysis

Movie Profitability Analysis - EDA Summary

Dataset Overview

This project explores the “Movies Metrics, Features and Statistics” dataset from Kaggle.
The dataset contains 6,569 movies and 32 features, including:

• Production Budget
• Worldwide & Domestic Gross
• Running Time
• Genre
• Creative Type
• Production Method
• Ratings and Release Date

The goal is to understand which pre-release factors influence a movie’s ability to generate positive profit.

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Prediction Question

Can we predict whether a movie will be profitable using only pre-release features such as budget, runtime, genre, and production characteristics?

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Target Variable

Binary classification target:

• is_profitable = 1 → if Worldwide Gross > Production Budget
• is_profitable = 0 → otherwise

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Exploratory Data Analysis (EDA)

Below are the research questions and the insights based on the dataset’s visualizations.

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1. How does production budget influence profitability?

Visualization: Profit vs. Production Budget

Profit X Budget

Insight

• A clear positive correlation exists between budget and profit.
• Higher-budget films tend to achieve higher profit, but with larger variance.
• Budget is a strong predictor of financial success.

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2. How does genre affect profitability?

Visualization: Profitability Rate by Genre

Profit X Genre

Insight

The most profitable genres:

• Adventure (highest)
• Horror
• Romantic Comedy
• Action

Less profitable genres include Drama and Documentary.

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3. How does running time correlate with profitability?

Visualization: Running Time Density (Profitable vs. Not)

Profit X Running Time

Insight

• Profitable movies tend to be slightly longer (around 105–120 minutes).
• Extremely long films are less common but can still be profitable.
• Running time has a weak-to-moderate influence on profit.

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4. How does production method influence profitability?

Visualization: Average Profit by Production Method

Profit X Production Method

Insight

Highest profit methods:

• Animation + Live Action
• Digital Animation
• Hand Animation

Lower profitability:

• Stop-Motion, Live Action, Multiple Methods, Rotoscoping

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Key Insights Summary

Strong Predictors of Profitability

• Production Budget
• Genre
• Creative Type
• Production Method

Moderate Predictor

• Running Time

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Final Summary

This analysis shows that pre-release movie characteristics can be used to meaningfully predict profitability.
The strongest indicators are budget, genre, and production method, while running time offers additional but weaker predictive value.

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Project Files

Below is a complete list of all files used throughout this project:

Dataset Files

• movies_dataset.csv — Original dataset downloaded from Kaggle
• movies_cleaned.csv — Cleaned version after handling missing values and removing duplicates

Notebook Files

• Leelu_EDA_&_Dataset.ipynb — Main notebook containing:
  • Data loading
  • Data cleaning
  • Target variable creation (is_profitable)
  • Full Exploratory Data Analysis (EDA)
  • Visualizations and insights

Visualization Outputs

(Images included in the README)

• Profit X Budget.png — Profit vs. Production Budget
• Profit X Running Time.png — Running Time Density (Profitable vs. Not Profitable)
• Profit X Genre.png — Profitability by Genre
• Profit X Production Method.png — Average Profit by Production Method

Documentation

• README.md — Project summary and final results documentation
• Presentation Video

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Author

Leelu Alfi
Reichman University - Data Science Track
2025