Round 1: Completed #educational Weight: 10.0

# Labor

Predict Labor Class

4386
379
34
976

🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

The working class is an important support system for any nation. But very often they are exploited and neglected. Let us reverse this negligence. Let us take a big step through this small new challenge.

You will be given information describing various conditions surrounding a given labor `group`, predict whether the `conditions` are `good` or not.

Understand with code! Here is `getting started code` for you.`😄`

## 💾 Dataset

This database contains information about working class or labor class.The databse has information which describes the factors that affect the living conditions of a worker. The attrbutes include the wage increase after first year, second and third year, working hours , pension plan e.t.c. They all have been explained in detail here.

For simplification, attributes have been stored in the CSV file which has `17` columns, the last column is the `class` of the labor.

## 📁 Files

Following files are available in the `resources` section:

• `train.csv` - (`31997` samples) This csv file contains the the feature representation of the factors that affect the living conditions of the workers along with the binary value denoting the class the worker belongs to.

• `test.csv` - (`8000` samples) File that will be used for testing. Unlike the training file it contains only the feature representation and not the class. This csv will be used for actual evaluation for the leaderboard score but does not have the binary value denoting the class the worker belongs to.

## 🚀 Submission

• Prepare a CSV containing header as `class`label and predicted value as digit between [0-1] representing one of the possible calsses.
• The file to be submitted should be names as `submission.csv`.
• Sample submission format is available in the resources section of the challenge page as sample_submission.csv.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

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

$F1 = 2 * \frac{precision*recall}{precision+recall}$