Data
Run_or_walk_information

Run_or_walk_information

active ARFF Publicly available Visibility: public Uploaded 26-09-2017 by Carlos Giraldo
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Author: Viktor Malyi Source: [Kaggle](https://www.kaggle.com/vmalyi/run-or-walk) Please cite: Run or walk This dataset is gather to detect whether a person is running or walking based on deep neural networks and sensor data collected from iOS devices. The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_y acceleration_z gyro_x gyro_y gyro_z There is an activity type represented by "activity" column which acts as label and reflects following activities: "0": walking "1": running The original data also contains a "wrist" column which represents the wrist where the device was placed, and "date", "time" and "username" columns which provide information about the exact date, time and user which collected these measurements.

7 features

activity (target)nominal2 unique values
0 missing
acceleration_xnumeric30307 unique values
0 missing
acceleration_ynumeric23957 unique values
0 missing
acceleration_znumeric19698 unique values
0 missing
gyro_xnumeric40988 unique values
0 missing
gyro_ynumeric38957 unique values
0 missing
gyro_znumeric51296 unique values
0 missing

62 properties

88588
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
6
Number of numeric attributes.
1
Number of nominal attributes.
0.28
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
85.71
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-3.2
Minimum skewness among attributes of the numeric type.
14.29
Percentage of nominal attributes.
0.05
Third quartile of skewness among attributes of the numeric type.
0.09
Maximum skewness among attributes of the numeric type.
4.63
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
13.54
Third quartile of standard deviation of attributes of the numeric type.
18.82
Maximum standard deviation of attributes of the numeric type.
49.92
Percentage of instances belonging to the least frequent class.
-0.05
First quartile of kurtosis among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
44223
Number of instances belonging to the least frequent class.
-2.05
First quartile of means among attributes of the numeric type.
2.99
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.86
Mean of means among attributes of the numeric type.
-1.41
First quartile of skewness among attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
5.38
First quartile of standard deviation of attributes of the numeric type.
1
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
1.62
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-0.76
Mean skewness among attributes of the numeric type.
-0.36
Second quartile (Median) of means among attributes of the numeric type.
50.08
Percentage of instances belonging to the most frequent class.
10.25
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
44365
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.34
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.19
Minimum kurtosis among attributes of the numeric type.
14.29
Percentage of binary attributes.
10.31
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
9.49
Maximum kurtosis among attributes of the numeric type.
-3.43
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
6.42
Third quartile of kurtosis among attributes of the numeric type.
0.29
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

6 tasks

1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: activity
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: activity
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: activity
0 runs - estimation_procedure: 33% Holdout set - target_feature: activity
0 runs - estimation_procedure: 50 times Clustering
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