Round 1: 25 days left #classroom

# New Test

Predict incomes from census data

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This is a Classroom Challenge forked from INCPR.

🛠 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

We have found a creative and a very useful application of US Census Bureau Data. In this problem, you have to predict if a person is earning more or less than `\$50,000 per year` based on their Census data.

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

## 💾 Dataset

This data was extracted from the US Census Bureau Database . It conatains various datapoints for each person - such as age, education, working hours(per week) and more!

The last column contains  `1`  if the income of the citizen is more than or equal to `\$50,000` and `0` if it is less. More information about the dataset fields can be found in dataset_info.txt.

You need to predict `1` if the person earns more than 50k/year otherwise `0`.

## 📁 Files

The following files can be found in the resources section:

• `train.csv` - (`32559` samples) This csv file contains the information about the person along with the label as `1/0` i.e. if he earns more than or less that 50k/year.

• `test.csv` - (`16280` samples)This csv file contains the information about the person but not the label as `1/0` i.e. if he earns more than or less that 50k/year. The labels of this samples will be used for evaluation.

## 🚀 Submission

• Prepare a csv containing header as `income` and predicted value as `1/0`.
• Sample submission format available at 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,

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