Data Mining about

Movie Industry

중어중문학과 2011312723 홍경택

문헌정보학과 2014314637 박지원

Contents

Overview1.

Exploratory Data Analysis

Data Present Condition

Modeling

Analysis Result & Verification

Result

2.3.4.5.6.

Overview1.

연도 2011 2012 2013 2014 2015

극장매출

(억원)

전체 12,358 14,551 15,513 16,641 17,154

증감률 5.8% 17.8% 6.6% 7.3% 3.1%

관객수(만명)

총관객수 15,972 19,489 21,335 21,506 21,729

증감률 7.1% 22.0% 9.5% 0.8% 1.0%

1인당관람횟수 (회) 3.15 3.83 4.17 4.19 4.22

< 2011-2015 Movie Theater Sales, Attendance,the Number of Watching Movie per Person >

Recent 5 years from 2011 to 2015, movie theater sales, total attendance

and the number of watching movie per person all continued to increase.

Overview1.

13,414,009

6,129,681

12,705,700

Hit Movie of 2015 and

its Total Attendance

Overview1.

13,414,009

6,129,681

12,705,700

???

Overview1.

“ A Study on Prediction Model of Movie Success ”

Topic

Purpose

To understand the factors that influence movie success with

various analysis techniques

To predict total attendance by framing prediction model of

movie success

To exploit the result for marketing strategy development

Data Present Condition2.

Annual Accounts ReportNumerical Data

Data Present Condition2.

Annual Accounts ReportNumerical Data

It provides overview of 2015

Korean Film Industry. It

includes box-office value,

production cost with

investment returns and

overseas sales as well.

Data on Excel files including..

Title

Release month

Genre

The number of screen

Nationality

Distributor

The number of screening

Screen share

Total attendance

Data Present Condition2.

Preconditioning Process

Remove sales which are not much different with attendance

Filter it by setting a period 2013 ~ 2016

Remove data less than 10000 attendance

(No outlier because it is achievement record)

Turn release date into release month to assort peak/off season

Data Present Condition2.Data baseline statistics (train data [2013~2015])

Data Present Condition2.

After data cleaning

Train data [2013 – 2015]9663 -> 836

Test data [2016]4120-> 294

Data baseline statistics (train data [2013~2015])

Exploratory Data Analysis 3.

Exploratory Data Analysis 3.

Histogram of numeric data

Total attendance Showing frequency The number of screens

Distribution of Total attendance and Showing frequency is about the same

Exploratory Data Analysis 3.

• The most high ratio of released month is January is high and the rests are similar

• In October, It shows a large number of spectators compared to other months.

Histogram and Bar chart

Exploratory Data Analysis 3.

Plots between Total attendance and

factors show the distribution of data

Plot in factors

Exploratory Data Analysis 3.

Plots between Total attendance and

factors show the distribution of data

Plot in factors

U.S.A

Korea

ActiondramaOver 15

Exploratory Data Analysis 3.

• Too many, various numeric data in Total attendance make analysis more difficult

• So, we categorized the number of Total attendance

Discovering a problem

Exploratory Data Analysis 3.

table(movie$관객수범주) 100만미만 100만 200만 300만 400만 500만 600만 700만 800만 900만 1000만이상

687 59 32 18 10 10 5 2 2 3 8

What is changed?

Categorize and make a new data column

Exploratory Data Analysis 3.

Exploratory Data Analysis 3.

• Finding meaning in the data has become easier.

Exploratory Data Analysis 3.

• With “prop.table”, it is better to see the data directly.

mosaicplot

Modeling4.Model introduction

Linear Regression Decision Tree

• One of the most frequent used techniques in statistics is linear regression

• Multiple regression is an extension of linear regression into relationship between more than two variables. In simple linear relation we have one predictor and one response variable, but in multiple regression we have more than one predictor variable and one response variable.

• Decision tree is a graph to represent choices and their results in form of a tree. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. It is mostly used in Machine Learning and Data Mining applications using R.

• Generally, a model is created with observed data also called training data. Then a set of validation data is used to verify and improve the model.

Modeling4.

1. All data (except attendancecategory) was inserted

2. Released month,The number of screen Showing frequency, Genre(historical drama),show meaning correlation

Multiple regression

Modeling4.

BUT

Too high VIF in distributor, nation and Genre, so except these factors, regression was run again

Released month The number of screenShowing frequency, All age class

show meaning correlation

Multiple regression

Modeling4.

Predict attendance category in test data(2016)

>fit.results2 <- predict(train.dt, newdata = test)

Decision Tree 1

Modeling4.

Predict attendance category in test data(2016)

>fit.results2 <- predict(train.dt, newdata = test)

Decision Tree 2

Analysis Result & Verification5.

Decision tree’s accuracy is 89%

Confusion Matrix 1

Analysis Result & Verification5.

Decision tree’s accuracy is 81%

Confusion Matrix 2

Result6.

• The number of screenings• The number of screens boxoffice

Result6.

Lack of various data Various genre could not analyzed

BetterPrediction and

analysis

Limitations and expectation

Thank You