Dataset

This dataset was created by chewytteok

Contents

Dataset for my (German) Python Data Science Tutorial on YouTube.

Playlist: https://www.youtube.com/playlist?list=PLW4WJMmOF9juA1Ebs1vNwTBuF7ck6YCT7

My version of: 'Bike Share Daily Data' (https://www.kaggle.com/contactprad/bike-share-daily-data)

Data used in this competition: https://www.kaggle.com/c/bike-sharing-demand

Use of this dataset in publications must be cited to the following publication:

[1] Fanaee-T, Hadi, and Gama, Joao, "Event labeling combining ensemble detectors and background knowledge", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3.

@article{ year={2013}, issn={2192-6352}, journal={Progress in Artificial Intelligence}, doi={10.1007/s13748-013-0040-3}, title={Event labeling combining ensemble detectors and background knowledge}, url={http://dx.doi.org/10.1007/s13748-013-0040-3}, publisher={Springer Berlin Heidelberg}, keywords={Event labeling; Event detection; Ensemble learning; Background knowledge}, author={Fanaee-T, Hadi and Gama, Joao}, pages={1-15} }

Dataset

This dataset was created by Rahul Nakka

Contents

Dataset

This dataset was created by Şükrü Yusuf Kaya

Released under MIT

Contents

Dataset

This dataset was created by Chao CHEN

Contents

Dataset

This dataset was created by Joe Fitzgerald

Contents

Dataset

This dataset was created by Kristo Radion Purba

Released under Apache 2.0

Contents

Dataset

This dataset was created by Daniil Barysevich

Released under CC0: Public Domain

Contents

Context

This dataset is comprised of several others and is collected specially for the Vowpal Wabbit tutorial, Kernel. The tutorial covers (both theoretically and in practice) two reasons of Vowpal Wabbit's exceptional training speed, namely, online learning and hashing trick. We'll try it out with the Spooky Author Identification dataset as well as with news, letters, movie reviews datasets and gigabytes of StackOverflow questions.

Content

The included datasets are:

• SOCR weights and heights, link (CC-BY licensed)
• UCI bikes sharing demand, link (no license)
• the 20 newsgroups text dataset, link (BSD licensed)
• pickled VW-versions of IMDB movie reviews (no license)
• a sample from the 10mln StackOverflow dataset (preprocessed and shared under the cc-by-sa 3.0 license)

Dataset

This dataset was created by Abhinand

Contents

Dataset

This dataset was created by pistachio_overlord

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Chao CHEN

Contents

Dataset

This dataset was created by annie2509

Released under MIT

Contents

This is the dataset that goes along with the Deep Learning basics with Python, TensorFlow and Keras p.2 Tutorial provided by Sentdex. Link here: https://www.youtube.com/watch?v=j-3vuBynnOE&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN&index=2

Original data source: https://www.youtube.com/redirect?redir_token=wz7434m56UWH1Vs4X1Je76XEoE58MTUzOTA3NDQwMUAxNTM4OTg4MDAx&q=https%3A%2F%2Fwww.microsoft.com%2Fen-us%2Fdownload%2Fconfirmation.aspx%3Fid%3D54765&v=j-3vuBynnOE&event=video_description

Dataset

This dataset was created by Pritam Purohit

Contents

Dataset

This dataset was created by Guangyu Song

Released under CC0: Public Domain

Contents

Predict name from which language

Data for the pytorch example: https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html

Source: https://download.pytorch.org/tutorial/data.zip

빅데이터 분석기사 실기 준비 놀이터

함께 놀아볼까요? 무궁화 꽃이 피었습니다 😜 빅데이터 분석기사 실기 준비를 위한 데이터 셋입니다. 더 좋은 코드를 만든다면 많은 공유 부탁드려요🎉 (Python과 R모두 환영합니다.)

4회 기출 유형

• 작업형2 유형 컴피티션 : https://www.kaggle.com/competitions/big-data-analytics-certification-kr-2022
• 베이스라인: (준비중)

3회 기출 유형 및 심화 학습자료

• 빅데이터 분석기사 컴피티션🍭
• https://www.kaggle.com/competitions/big-data-analytics-certification

🆕 New 문제 업데이트 2022.6

• 작업형2
• 회귀: https://www.kaggle.com/code/agileteam/t2-2-2-baseline-r2

• 분류(3회 기출 심화 변형) : https://www.kaggle.com/code/agileteam/3rd-type2-3-2-baseline

• 작업형1 (3회 기출 유형)

  1. https://www.kaggle.com/code/agileteam/3rd-type1-1-3-1-1
  2. https://www.kaggle.com/code/agileteam/3rd-type1-2-3-1-2
  3. https://www.kaggle.com/code/agileteam/3rd-type1-3-3-1-3

• 작업형1 모의문제2(심화) https://www.kaggle.com/code/agileteam/mock-exam2-type1-1-2

🎁 빅데이터 분식기사 실기 입문 강의 Open 🎁

• https://class101.page.link/tp9k
• 입문자를 위한 강의 오픈 했어요 👍
• 파이썬-판다스-머신러닝-모의문제(작업형1,2)-꿀팁 등을 실기 준비에 필요한 내용만 친절하게 알려드려요🎉
• 머신러닝을 해보신 분이라면 수강 할 필요 없을 것 같아요, 바로 모의 문제를 풀기 힘든 설명이 필요한 찐 입문자에게 추천드려요!

📌작업형1 예상문제 (P:파이썬, R)

Tasks 탭에서 문제 및 코드 확인

• [2회차 기출 유형] 작업형1 P: https://www.kaggle.com/agileteam/tutorial-t1-2-python R: https://www.kaggle.com/limmyoungjin/tutorial-t1-2-r-2

• 공식 예시문제(작업형1) P: https://www.kaggle.com/agileteam/tutorial-t1-python R: https://www.kaggle.com/limmyoungjin/tutorial-t1-r

• T1-1.Outlier(IQR) / #이상치 #IQR P: https://www.kaggle.com/agileteam/py-t1-1-iqr-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-1-iqr-expected-questions-2

• T1-2.Outlier(age) / #이상치 #소수점나이 P: https://www.kaggle.com/agileteam/py-t1-2-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-2-expected-questions-2

• T1-3. Missing data / #결측치 #삭제 #중앙 #평균 P: https://www.kaggle.com/agileteam/py-t1-3-map-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-3-expected-questions-2

• T1-4. Skewness and Kurtosis (Log Scale) / #왜도 #첨도 #로그스케일 P: https://www.kaggle.com/agileteam/py-t1-4-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-4-expected-questions-2

• T1-5. Standard deviation / #표준편차 P: https://www.kaggle.com/agileteam/py-t1-5-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-5-expected-questions-2

• T1-6. Groupby Sum / #결측치 #조건 P: https://www.kaggle.com/agileteam/py-t1-6-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-6-expected-questions-2

• T1-7. Replace / #값변경 #조건 #최대값 P: https://www.kaggle.com/agileteam/py-t1-7-2-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-7-2-expected-questions-2

• T1-8. Cumulative Sum / #누적합 #결측치 #보간 P: https://www.kaggle.com/agileteam/py-t1-8-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-8-expected-questions-2

• T1-9. Standardization / #표준화 #중앙값 P: https://www.kaggle.com/agileteam/py-t1-9-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-9-expected-questions-2

• T1-10. Yeo-Johnson and Box–Cox / #여존슨 #박스-콕스 #결측치 #최빈값 P: https://www.kaggle.com/agileteam/py-t1-10-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-10-expected-questions-2

• T1-11. min-max scaling / #스케일링 #상하위값 P: https://www.kaggle.com/agileteam/py-t1-11-min-max-5-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-11-min-max-5-expected-questions-2

• T1-12. top10-bottom10 / #그룹핑 #정렬 #상하위값 P: https://www.kaggle.com/agileteam/py-t1-12-10-10-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-12-10-expected-questions-2

• T1-13. Correlation / #상관관계 P: https://www.kaggle.com/agileteam/py-t1-13-expected-questions R: https://www.kaggle.com/limmyoungjin/r-t1-13-expected-questions-2

• T1-14. Multi Index & Groupby / #멀티인덱스 #정렬 #인덱스리셋 #상위값 P: https://www.kaggle.com/agileteam/py-t1-14-2-expected-question R: https://www.kaggle.com/limmyoungjin/r-t1-14-2-expected-question-2

• T1-15. Slicing & Condition / #슬라이싱 #결측치 #중앙값 #조건 P: https://www.kaggle.com/agileteam/py-t1-15-expected-question R: https://www.kaggle.com/limmyoungjin/r-t1-15-expected-question-2

• T1-16. Variance / #분산 #결측치전후값차이 P: https://www.kaggle.com/agileteam/py-t1-16-expected-question R: https://www.kaggle.com/limmyoungjin/r-t1-16-expected-question-2

• T1-17. Time-Series1 / #시계열데이터 #datetime P: https://www.kaggle.com/agileteam/py-t1-17-1-expected-question R: https://www.kaggle.com/limmyoungjin/r-t1-17-1-expected-question-2

• T1-18. Time-Series2 / #주말 #평일 #비교 #시계열데이터 P: https://www.kaggle.com/agileteam/py-t1-18-2-expected-question R: https://www.kaggle.com/limmyoungjin/r-t1-18-2-expected-question-2

• T1-19. Time-Series3 (monthly total) / #월별 #총계 #비교 #데이터값변경
  P: https://www.kaggle.com/agileteam/py-t1-19-3-expected-question R: https://www.kaggle.com/limmyoungjin/r-t1-19-3-expected-question-2

• T1-20. Combining Data / 데이터 #병합 #결합 / 고객과 궁합이 맞는 타입 매칭
  P: https://www.kaggle.com/agileteam/py-t1-20-expected-question R: https://www.kaggle.com/limmyoungjin/r-t1-20-expected-question-2

• T1-21. Binning Data / #비닝 #구간나누기 P: https://www.kaggle.com/agileteam/py-t1-21-expected-question R: https://www.kaggle.com/limmyoungjin/r-t1-21-expected-question-2

• T1-22. Time-Series4 (Weekly data) / #주간 #합계 P: https://www.kaggle.com/agileteam/t1-22-time-series4-weekly-data R: https://www.kaggle.com/limmyoungjin/r-t1-22-time-series4-weekly-data-2

• T1-23. Drop Duplicates / #중복제거 #결측치 #10번째값으로채움 P: https://www.kaggle.com/agileteam/t1-23-drop-duplicates R: https://www.kaggle.com/limmyoungjin/r-t1-23-drop-duplicates-2

• T1-24. Time-Series5 (Lagged Feature) / #시차데이터 #조건 P: https://www.kaggle.com/agileteam/t1-24-time-series5-lagged-feature R: https://www.kaggle.com/limmyoungjin/r-t1-24-time-series5-2

• [MOCK EXAM1] TYPE1 / 작업형1 모의고사 P: https://www.kaggle.com/agileteam/mock-exam1-type1-1-tutorial R: https://www.kaggle.com/limmyoungjin/mock-exam1-type1-1

• [MOCK EXAM2] TYPE1 / 작업형1 모의고사2 P: https://www.kaggle.com/code/agileteam/mock-exam2-type1-1-2

📌작업형2 예상문제

Tasks 탭에서 문제 및 코드 확인 - [3회차 기출유형 작업형2] : 여행 보험 패키지 상품 (데이터를 조금 어렵게 변경함) P: https://www.kaggle.com/code/agileteam/3rd-type2-3-2-baseline

• [2회차 기출유형 작업형2] : E-Commerce Shipping Data P: https://www.kaggle.com/agileteam/tutorial-t2-2-python R: https://www.kaggle.com/limmyoungjin/tutorial-t2-2-r

• T2. Exercise / 예시문제 : 백화점고객의 1년간 데이터 (dataq 공식 예제) P: https://www.kaggle.com/agileteam/t2-exercise-tutorial-baseline

• T2-1. Titanic (Classification) / 타이타닉 P: https://www.kaggle.com/agileteam/t2-1-titanic-simple-baseline R: https://www.kaggle.com/limmyoungjin/r-t2-1-titanic

• T2-2. Pima Indians Diabetes (Classification) / 당뇨병 P: https://www.kaggle.com/agileteam/t2-2-pima-indians-diabetes R: https://www.kaggle.com/limmyoungjin/r-t2-2-pima-indians-diabetes

• T2-3. Adult Census Income (Classification) / 성인 인구소득 예측 P: https://www.kaggle.com/agileteam/t2-3-adult-census-income-tutorial R: https://www.kaggle.com/limmyoungjin/r-t2-3-adult-census-income

• T2-4. House Prices (Regression) / 집값 예측 / RMSE P: https://www.kaggle.com/code/blighpark/t2-4-house-prices-regression R: https://www.kaggle.com/limmyoungjin/r-t2-4-house-prices

• T2-5. Insurance Forecast (Regression) / P: https://www.kaggle.com/agileteam/insurance-starter-tutorial R: https://www.kaggle.com/limmyoungjin/r-t2-5-insurance-prediction

• T2-6. Bike-sharing-demand (Regression) / 자전거 수요 예측 / RMSLE P: R: https://www.kaggle.com/limmyoungjin/r-t2-6-bike-sharing-demand

• [MOCK EXAM1] TYPE2. HR-DATA / 작업형2 모의고사 P: https://www.kaggle.com/agileteam/mock-exam-t2-exam-template(템플릿만 제공) https://www.kaggle.com/agileteam/mock-exam-t2-starter-tutorial (찐입문자용) https://www.kaggle.com/agileteam/mock-exam-t2-baseline-tutorial (베이스라인)

📌6 주 완성 코스 (아래 표 참고)

📌입문자를 위한 머신러닝 튜토리얼 (공유해주신 노트북 중 선정하였음👍)

- https://www.kaggle.com/ohseokkim/t2-2-pima-indians-diabetes 작성자: @ohseokkim 😆

Use this dataset with Misra's Pandas tutorial: How to use the Pandas GroupBy function | Pandas tutorial

The original dataset came from this site: https://data.cityofnewyork.us/City-Government/NYC-Jobs/kpav-sd4t/data

I used Google Colab to filter the columns with the following Pandas commands. Here's a Colab Notebook you can use with the commands listed below: https://colab.research.google.com/drive/17Jpgeytc075CpqDnbQvVMfh9j-f4jM5l?usp=sharing

Once the csv file is uploaded to Google Colab, use these commands to process the file.

import pandas as pd # load the file and create a pandas dataframe df = pd.read_csv('/content/NYC_Jobs.csv') # keep only these columns df = df[['Job ID', 'Civil Service Title', 'Agency', 'Posting Type', 'Job Category', 'Salary Range From', 'Salary Range To' ]] # save the csv file without the index column df.to_csv('/content/NYC_Jobs_filtered_cols.csv', index=False)

Context

The competition is over 2 yrs ago. I just wanna play around the dataset.

Content

The labeled data set consists of 50,000 IMDB movie reviews, specially selected for sentiment analysis. The sentiment of reviews is binary, meaning the IMDB rating < 5 results in a sentiment score of 0, and rating >=7 have a sentiment score of 1. No individual movie has more than 30 reviews. The 25,000 review labeled training set does not include any of the same movies as the 25,000 review test set. In addition, there are another 50,000 IMDB reviews provided without any rating labels.

• id - Unique ID of each review
• sentiment - Sentiment of the review; 1 for positive reviews and 0 for negative reviews
• review - Text of the review

Acknowledgements

The origin place is here. Awesome tutorial is here, we can play with it.

Inspiration

Just for study and learning