About the Data

The dataset contains information about the match rate of the individuals from different universities, and whether the app (i.e. Tinder) has helped them find a relationship.

Data Description

ID : User id
Segment type : Medium of Usage
Segment Description : Name of Universities
Answer : Do you use tinder ?
Count : Number of Matches
Percentage : % of matches (the value ranges from 0 to 1 as it is not multiplied with 100)
It became a relationship : Success of relationship (Target)

To load the training data in your jupyter notebook, use the below command:

import pandas as pd

train_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/Tinder_Millennial_Match/train_set_label.csv")

Saving Prediction File & Sample Submission

You can find more details on how to save a prediction file here: https://discuss.dphi.tech/t/how-to-submit-predictions/548

Sample submission: You should submit a CSV file with a header row and the sample submission can be found below

prediction

1

1

1

0

1

0

.

.

Etc.

Note that the header name should be ‘prediction’ else it will throw an evaluation error. A sample submission file can be found here

Test Dataset

Load the test data (name it as test_data). You can load the data using the below command.

test_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/Tinder_Millennial_Match/test_set_label.csv')

Here the target column is deliberately not there as you need to predict it

Acknowledgement

Dataset by Adam Halper, Founder / CEO of Whatsgoodly, a millennial social polling company. Adam is an entrepreneur / app developer who studied HCI at Stanford. Technology has made people more lonely, and Adam wants to change that.

License: CC-BY-SA

Data Sprint #24: Tinder Millennial Match

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