New Test

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,

🔗 Links

• 💪 Challenge Page : https://www.aicrowd.com/challenges/incpr
• 🗣️ Discussion Forum : https://www.aicrowd.com/challenges/incpr/discussion
• 🏆 leaderboard : https://www.aicrowd.com/challenges/incpr/leaderboards

📱 Contact

• Ayush Shivani

📚 Refrences

• Ronny Kohavi and Barry Becker ,Data Mining and Visualization ,Silicon Graphics.

• Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.