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#Table of Contents



titanic.csv

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Source: http://lib.stat.cmu.edu/S/Harrell/data/descriptions/titanic.html

  • sample size: 1313
  • features: 10
Name Levels Storage NAs
pclass 3 integer 0
survived double 0
name character 0
age double 680
embarked 3 integer 492
home.dest 371 integer 559
room character 0
ticket character 0
boat 99 integer 966
sex 2 integer 0

These data were obtained from Robert Dawson, Saint Mary's University, E-mail. The variables are pclass, age, sex, survived. These data frames are useful for demonstrating many of the functions in Hmisc as well as demonstrating binary logistic regression analysis using the Design library. For more details and references see Simonoff, Jeffrey S (1997): The "unusual episode" and a second statistics course. J Statistics Education, Vol. 5 No. 1.



wine.csv

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Source: https://archive.ics.uci.edu/ml/datasets/Wine

Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

wine.csv.csv
Samples 178
Features 13
Classes 3
Data Set Characteristics: Multivariate
Attribute Characteristics: Integer, Real
Associated Tasks: Classification
Missing Values None
column attribute
1) Class Label
2) Alcohol
3) Malic acid
4) Ash
5) Alcalinity of ash
6) Magnesium
7) Total phenols
8) Flavanoids
9) Nonflavanoid phenols
10) Proanthocyanins
11) intensity
12) Hue
13) OD280/OD315 of diluted wines
14) Proline
class samples
1 59
2 71
3 48

Original Owners:

Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy.



iris.csv

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Source:https://archive.ics.uci.edu/ml/datasets/Iris

Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

iris.csv
Samples 150
Features 4
Classes 3
Data Set Characteristics: Multivariate
Attribute Characteristics: Real
Associated Tasks: Classification
Missing Values None
column attribute
1) sepal length in cm
2) sepal width in cm
3) petal length in cm
4) petal width in cm
5) class label
class samples
Iris-setosa 50
Iris-versicolor 50
Iris-virginica 50

Creator: R.A. Fisher (1936)

iris_test40.csv and iris_train60.csv

The datasets iris_test40.csv and iris_train60.csv are randomly sampled from the original iris.csv dataset where iris_test40.csv contains 40% of the original samples and the remaining 60% are contained in iris_train60.csv. The class labels in the 5th column where replaced by integers 1-4, where

  • 1 = Iris-setosa
  • 2 = Iris-versicolor
  • 3 = Iris-virginica

| class | # of samples in test set | # of samples in training set | total | |-------|------|----------| | Iris-setosa | 21 | 29 | 50 | | Iris-versicolor | 22 | 28 | 50 | | Iris-virginica | 17 | 33 | 50 | | total | 60 | 90 | 150|



sms_spam_collection.csv

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A public dataset of 5572 SMS messages that are labeled as either "spam" or "ham" (not spam).

0 1
... ... ...
5567 spam This is the 2nd time we have tried 2 contact u...
5568 ham Will ü b going to esplanade fr home?
5569 ham Pity, * was in mood for that. So...any other s...
5570 ham The guy did some bitching but I acted like i'd...
5571 ham Rofl. Its true to its name

Source: http://www.dt.fee.unicamp.br/%7Etiago/smsspamcollection/

Almeida, Tiago A., José María G. Hidalgo, and Akebo Yamakami. 2011. “Contributions to the Study of SMS Spam Filtering: New Collection and Results.” In Proceedings of the 11th ACM Symposium on Document Engineering, 259–62. DocEng ’11. New York, NY, USA: ACM. doi:10.1145/2034691.2034742.



50k_imdb_movie_reviews.csv

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A CSV file assembled from the 50k IMDb movie review dataset. The dataset consists of 50,000 movie reviews from the original "train" and "test" subdirectories. The class labels are binary (1=positive and 0=negative) and contain 25,000 positive and 25,000 negative movie reviews, respectively.

review sentiment set
... ... ...
I saw 'Descent' last night at the Stockholm Fi... 0 train
Some films that you pick up for a pound turn o... 0 train
This is one of the dumbest films, I've ever se... 0 train

Source: http://ai.stanford.edu/~amaas/data/sentiment/

AL Maas, RE Daly, PT Pham, D Huang, AY Ng, and C Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Lin- guistics: Human Language Technologies, pages 142–150, Portland, Oregon, USA, June 2011. Association for Computational Linguistics.