This dataset is for online qualifiers for ML CHALLENGE by IEEE SB,GEHU
- This is used for educational purpose no ownership is declared
About Dataset -
System Description and Problem Context
Input Features (F01–F47): The dataset consists of 47 numerical features, labeled F01 through F47. These variables represent quantitative measurements captured by an embedded detection or monitoring system. Each feature corresponds to a specific operational parameter recorded during device activity cycles. The values are automatically generated by the detector and reflect the internal state, performance metrics, or environmental interactions of the device at a given time.
Target Variable (Class): The objective is to determine the operational status of each device based on the 47 input features. The classification label is represented by the variable Class, which indicates whether the device is functioning normally or experiencing a fault condition.
Class = 0: Device operating under normal conditions.
Class = 1 : Device exhibiting a faulty condition.
For many analytical purposes, the problem can be framed as a binary fault detection task, where:
0 : Normal
TRAIN.csv should be used in training of model
TEST.csv should be used to generate the predictions and send them for evaluation
NOTE - TEST.csv has ID associated with each test entry the final output for evaluation should be a csv as ID -> Prediction by Model
FINAL Output should be in exact order as in TEST.csv and no of rows should be same.
EXAMPLE of FINAL.csv