Loading
Feedback
Monsoon 2020: Completed #classroom Weight: 35.0

DA Project LIGHT

Predict the class of the LED bulb

1272
183
83
2423

🕵️ Introduction

The way energy is being wasted, soon light bills might be the scariest thing for a lot of us. Not an extreme but a simple temporary solution can be switching to better lights. So, this challenge brings you attributes and asks you to predict the class of the LED bulb.

 

💾 Dataset

The database contains various attributes about LED lights which are used almost everywhere. The database classifies the LED's into 10 different types of classes from 0 to 9. All the attributes are nominal types and all have 2 unique values 0 or 1. There are in total 25 attributes out of which 24 are the nominal 0 or 1 types and the last one is the class of the lED light.

For simplification, all the attributes have been stored in the CSV file which has 24 columns, the last column is the class and the rest 23 contain the information about the LED.

📁 Files

Following files are available in the resources section:

  • train.csv - (7500 samples) File that should be used for training. It contains the feature representation and the respective classes.
  • test.csv - (2500 samples) File that will be used for testing. Unlike the training file it contains only the feature representation of hands and not the classses.

🚀 Submission

  • Prepare a python file which should produce a csv file with name "submission.csv" containing header as "class" and predicted value as digit between [0…9] representing one of the 10 possible classes to which the LED belongs.
  • Your submission should read the train and test data (available as environment variables) and should write 'submission.csv' containing the predictions for the test set.     
  • Sample submission format available in resources section.                                                                                                               Make your first submission here 🚀 !!

🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

📱 Contact

  • DA TAs

📚 References

Leaderboard

01
  K7S3
0.7488
01
0.7488
03
0.7484
04
0.7468
05
0.7464

Latest Submissions

2019201040_amit graded
2019201040_amit graded
2019201040_amit graded
2019201040_amit graded
2019201040_amit graded