Work log - Githubissues

JinSungYoon commented 6 years ago

I start to study about volleyball data analysis from today Today is the first day what i upload the python file. I will practice for my graduate-work

JinSungYoon commented 6 years ago

<2018.07.19> Today I studied python basic function

Integer calculate(정수연산)
Datatype transfer(형변환)
List(리스트)
Sequence(수열)
For loop / zip-function / enumerate So on ......

JinSungYoon commented 6 years ago

<2018.07.20> Today I studied python Object oriented programming and descriptive statistic(s)

Object oriented programming(객체지향 프로그래밍)
Class overriding(클래스 오버라이딩)
Class inherit(클래스 상속)
Incoding / decoding(인코딩/디코딩)
Descriptive statistic(s)(기술통계)
Random generate(난수생성)
Data order change(데이터 순서 바꾸기)
Integer data count(정수 데이터 갯수세기)

JinSungYoon commented 6 years ago

<2018.07.24> Today I studied Pandas library

Pandas Series generate(시리즈 생성 및 관련 연산)
Pandas Index (인덱스 생성 및 관련 연산)
Pandas dataframe(데이터 프레임 생성 및 연산)

JinSungYoon commented 6 years ago

<2018.07.25> Today I studied Pandas

Pandas data count(데이터 갯수 세기)
Pandas category count(데이터 카테고리 count)
Pandas data sort(데이터 정렬)
Pandas row/col sum(데이터 행/열 합)
Pandas apply conversion
Pandas data categorization(데이터 카테고리화)
Pandas data merge(데이터프레임 합병)

JinSungYoon commented 6 years ago

<2018.07.26> Today I studied Web crawling

Install selenium( selenium 라이브러리 설치 )
driver load( selenium의 크롬 webdriver 불러오기 )
site access and input the keyword( 사이트 접속 및 검색어 입력 )
button click event run ( 버튼 클릭 이벤트 실행)

JinSungYoon commented 6 years ago

<2018.07.28> Today I studied Web crawling

Meta data Crawling ( 웹 사이트 데이터 긁어오기)

JinSungYoon commented 6 years ago

<2018.07.30> Today I review Pandas and Numpy function and read CSV file

Data read from CSV ( 엑셀파일에서 데이터 읽어오기 )
Make the CSV file using python( 데이터 엑셀파일로 내보내기)

JinSungYoon commented 6 years ago

<2018.07.31> I was in shoveling ha~ha~ha....

< 2018.08.01> Today I success web crawling about volleyball season result data!!!

get the html source (HTML 소스 가져오기)
web crawling (KOVO 사이트에서 배구 데이터 긁어오기)
make the table using pandas ( Pandas로 table 만들기)
write the csv file ( 테이플 csv 파일로 만들기)

JinSungYoon commented 6 years ago

<2018.08.02> Today I succeed crawling the volleyball game data(a day) And upload the code about web crawling

Extract the player name(선수 이름 가져오기)
Extract the player game data(선수별 경기 데이텉 가져오기)

JinSungYoon commented 6 years ago

<2018.08.03> Today I analyzed whether 1 set of games was an important factor in winning the whole game. In conclusion..... I shoveled today...... There was little relevance in one set of games and the game victory. The process of verifying the hypothesis is as follows.

First. Extracts the winnings of each team in the season result. ( 시즌 경기기록에서 각 팀이 이긴 경기를 추출합니다. )
Second. Calculate the ratio of the winning data to the winning combination.( 추출한 데이터에서 1세트를 이겼을때 이긴 경기의 비율을 계산합니다.)
The average percentage of winners of a match against any of the seven teams was 0.66...

JinSungYoon commented 6 years ago

<2018.08.05> Today I applied multiple-index to data-table. It make access easier to in detail table by applied multiple index

JinSungYoon commented 6 years ago

<2018.08.06> Today I analysis match data in detail.

Compare two team about competition element in match (경기에서 두 팀의 각 항목 성공률 비교)
Describe the graph about success rate for each element ( 각 항목의 성공률 그래프로 표현)
I solve the problem about Hangul font encoding in matplotlib (matplotlib에서 한글 폰트 문제를 해결)
Match_on_air data convert to text complete ( 경기 생중계 데이터 텍스트와 완료)

< Tomorrow's work >

Organized the Match_on_air_data

JinSungYoon commented 6 years ago

<2018.08.07> Today I analysis On_air_message_data

Collected game data for each set from the kovo-site( 세트별 경기 데이터를 크롤링)
Pre-processed game data for each set ( 세트별 경기 데이터 전처리)
Create the table about success count and success rate for each game element ( 경기 항목별 성공 횟수 및 성공률을 테이블로 생성)
Draw a graph about success count and success rate for each game element ( 경기 항목별 성공 횟수 및 성공률 그래프로 표현)

JinSungYoon commented 6 years ago

<2018.08.09> Today i am back to collecting 10 years of season data. That's because I thought I had ignored the analysis procedure. I tried to analyze the factor of entering playoff through 10 years data. However, we did not consider the factor of entering the playoffs by concentrating on analyzing the victory factor during the last period. So I will analyze the data for 10 years and analyze it again from the factors of the teams that are within the 3rd place.

JinSungYoon commented 6 years ago

<2018.08.10> I succeeded collecting volleyball Season data for 10 years. But, it was not possible to analyze the factor that entered the play-off So, I need to find a way to handle the click event to collect the mach_part_ranking data.

<2018.08.13> Today it seemed like it was going backwards........ but i researched web crawling through Selenium to scratch seasonal sector data.

Collect season's match data through selenium (셀레니움을 통한 경기 데이터 크롤링)
Collect season's attack,receive,miss_rate part of match data through selenium (셀레니움을 통한 경기 부문별 데이터 크롤링)

JinSungYoon commented 6 years ago

<2018.08.14> Today i studied basic statistic.

Mean and median(평균값 그리고 중위값)
Quantile(4분위값)
de_mean(평균값과의 거리)
variance(분산)
standard_deviation(표본분산)
covariance(공분산)
correlation(상관관계

JinSungYoon commented 6 years ago

<2018.08.16> Today i analyzed five years of professional volleyball men and women season result data today. I have not found a clear element to advance into the playoffs, so i will have to analyze it again tomorrow.

JinSungYoon commented 6 years ago

<2018.08.17~2018.08.22> I try to draw Decision Tree using my data But it is not easy to use Decision Tree library I have import problem, utf-8 encoding problem and run_time error. So i search for the error solution for google. Today I success draw Decision tree!!!

JinSungYoon commented 6 years ago

<2018.08.30 ~ 2018.09.03> I studied linear algebra.

Scalar/vector/matrix
Transpose operation
square matrix
linear combination
linear regression model

JinSungYoon commented 5 years ago

<2018.09.03 ~ 2018.09.21> I was busy due to enrollment and school adjustment. I will study hard again today.

matrix norm( 행렬 놈)
trace sum( 대각합 )
determinant( 행렬식 )
inverse matrix( 역행렬 )
least square problem( 최소자승법 )
Identity matrix( 단위행렬 )
orthogonal( 직교 )
linearly independent and linearly dependent( 선형독립&선형종속 )
Rank( 랭크 )
vector_space and vasis_vector( 벡터공간&기저벡터)
eigenvalue and eigenvector( 고윳값&고유벡터 )
characteristic equation( 특성방정식 )
diagonalization( 대각화 )

JinSungYoon commented 5 years ago

<2018.10.12> Data columns change the simple name Since the value between the data is large, so normalization is performed.

Data nomalized(데이터 정규화)

JinSungYoon commented 5 years ago

<2019.01.16>

Code review
Selection of feature_variable
Increase prediction accuracy through random forest technique
Identify variable importance through random forest

JinSungYoon commented 5 years ago

<2019.01.17>

Logistic_regression Analysis
Add a dataset extracted from raw data

JinSungYoon commented 5 years ago

<2019.01.18>

Support-vector-machine Analysis
Modify the result

JinSungYoon commented 5 years ago

<2019.01.19>

Total Analysis
Attack_efficiency feature add

JinSungYoon commented 5 years ago

<2019.01.21>

Gradient_boosting
Draw the feature weight graph

JinSungYoon commented 5 years ago

<2019.01.25>

Eliminate unnecessary data

JinSungYoon commented 5 years ago

<2019.01.29>

Write the graduate work abstract

JinSungYoon commented 5 years ago

<2019.01.30>

Neural network

JinSungYoon commented 5 years ago

<2019.02.06>

Match feature modified
Model order change

JinSungYoon commented 5 years ago

<2019.05.02>

check the male data var and outlier and female data var and outlier
check the variable combination

JinSungYoon commented 5 years ago

<2019.05.03>

add the t-test and statistic

JinSungYoon / sports_analysis_graduate_work

Work log #1