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https://github.com/mainak-97/imdb-movie-analysis-using-ms-excel
A data-driven project exploring the factors that influence movie success on IMDB, focusing on genre, duration, language, directors, and budgets. The analysis, conducted using MS Excel, provides actionable insights for filmmakers and industry stakeholders to optimize production strategies and maximize audience appeal.
https://github.com/mainak-97/imdb-movie-analysis-using-ms-excel
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A data-driven project exploring the factors that influence movie success on IMDB, focusing on genre, duration, language, directors, and budgets. The analysis, conducted using MS Excel, provides actionable insights for filmmakers and industry stakeholders to optimize production strategies and maximize audience appeal.
- Host: GitHub
- URL: https://github.com/mainak-97/imdb-movie-analysis-using-ms-excel
- Owner: Mainak-97
- Created: 2024-08-27T08:00:51.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-28T13:56:10.000Z (5 months ago)
- Last Synced: 2024-08-28T15:19:34.951Z (5 months ago)
- Homepage:
- Size: 3.95 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# IMDB Movie Analysis
# Overview
This project aims to investigate the factors that influence the success of movies on IMDB. Success is primarily defined by high IMDB ratings. The analysis focuses on several critical variables, including genres, movie duration, language, directors, and budgets. By exploring these factors, we aim to provide actionable insights for movie producers, directors, and investors to help them make informed decisions in future projects.
# Project Structure
- **Data Preparation:** Cleaning and preprocessing the dataset to ensure it is suitable for analysis.
- **Data Analysis:** Using MS Excel to explore relationships between various factors and IMDB ratings.
- **Report Generation:** Summarizing the findings with insights and visualizations.
# Analysis Breakdown
1. **Movie Genre Analysis**
- Objective:
To determine the most common movie genres and their impact on IMDB ratings.
- Methodology:
Counted the number of movies per genre using Excel's COUNTIF function.
Calculated descriptive statistics (mean, median, mode, range, variance, standard deviation) for IMDB ratings across genres.
- Findings:
The most common genres are Action, Adventure, Comedy, Crime, and Drama.
No significant genre showed a clear advantage in ratings.
2. **Movie Duration Analysis**
- Objective:
Analyze the distribution of movie durations and their impact on IMDB ratings.
- Methodology:
Calculated the mean, median, and standard deviation of movie durations.
Created a scatter plot to visualize the relationship between movie duration and IMDB ratings, adding a trendline to assess correlation.
- Findings:
A weak correlation between movie duration and IMDB ratings, suggesting length alone does not significantly affect ratings.
Slight trend indicating movies over 250 minutes might score between 6 and 8.5 on IMDB.
3. **Language Analysis**
- Objective:
Examine the distribution of movies based on language and its impact on IMDB ratings.
- Methodology:
Counted the number of movies for each language.
Calculated descriptive statistics (mean, median, standard deviation) for IMDB ratings across languages.
- Findings:
English is the dominant language, followed by French, Spanish, and others.
Movies in non-English languages generally have slightly lower IMDB scores, indicating potential audience biases.
4. **Director Analysis**
- Objective:
To identify top directors based on their average IMDB scores and their contribution to movie success.
- Methodology:
Calculated the average IMDB score for each director.
Used Excel's PERCENTILE function to identify top-performing directors.
- Findings:
Top directors include John Blanchard, Sadyk Sher-Niyaz, and Christopher Nolan.
These directors consistently produced high-rated movies, indicating a strong influence on audience perception.
5. **Budget Analysis**
- Objective:
To explore the relationship between movie budgets and their financial success.
- Methodology:
Calculated the correlation coefficient between movie budgets and gross earnings using Excel's CORREL function.
Identified movies with the highest profit margin (gross earnings - budget).
- Findings:
A weak positive correlation (0.1021795) between movie budgets and gross earnings.
Movies with budgets over $200 million have a higher likelihood of achieving significant profit margins.
## Screenshots
* All Genres and their movie counts
* Top 10 Movie genres and their movie counts.
* Statistical impact of genre on movie ratings
* Distribution of movie durations and its impact on the IMDB score.
* Distribution of top 10 movies based on their language.
* Influence of directors on movie ratings.
* Top 20 movies with highest profit margin and budget stats
## Project Conclusion
This analysis provides valuable insights into the factors influencing movie ratings and financial performance.
By examining genres, durations, languages, directors, and budgets, we identified key trends and patterns that reveal audience preferences and industry dynamics.
These findings offer actionable recommendations for filmmakers, producers, and studios to enhance their production strategies and maximize audience appeal.
## Tech Stack
- MS Excel 2021: Used for data cleaning, analysis, and visualization.
- MS PowerPoint 2021: Utilized to create a presentation summarizing the project insights.
- Loom Video: Video presentation.
### Links
- Original dataset:- https://drive.google.com/file/d/1JAwSmX1JLDumdMh7jVdi3Szpuuz4HI_g/view?usp=sharing
- Project execution file:- https://docs.google.com/spreadsheets/d/1vdqu33Ck9n_Efg_IBH0eKAGVpoZDkrsB/edit?usp=sharing&ouid=103027981944924775198&rtpof=true&sd=true
- Loom Video Presentation:- https://www.loom.com/share/9dc12a9cf889447688abea7b5b011232?sid=4185c93c-27ac-4d7f-a355-a1dd670408c5
### Author
- **Mainak Mukherjee**
- Email: [email protected]
- Linkedin: www.linkedin.com/in/mainak8
### Concepts Used
* Advanced Excel Technicality
* Data Visualization
* Statistical Knowledge
## Project Impact and Learning Experience
This project had a significant impact by providing deep insights into the factors that influence movie ratings and financial success on IMDB.
Through comprehensive analysis, it highlighted how variables like genre, duration, language, director influence, and budget correlate with movie success.
These insights are crucial for industry stakeholders such as filmmakers, producers, and investors, enabling them to make data-driven decisions that could optimize movie production strategies and enhance audience engagement.
From a learning perspective, the project offered hands-on experience in data cleaning, statistical analysis, and data visualization using MS Excel.
It reinforced the importance of data-driven decision-making and showcased the practical application of statistical methods in real-world scenarios.
Additionally, it provided an opportunity to practice storytelling with data, ensuring that complex findings are communicated effectively to non-technical stakeholders.