Human weights

Predicting the weights based on the heights

Easy

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8 Submissions

Context and objective

We will be working with a dataset that relates the height and weight of 10,000 individuals. Your task as a data scientist is to build machine learning models to predict the value of weight based on the height of individuals. Can you model this relationship?


Evaluation Criteria

Once you generate and submit the target variable predictions on evaluation dataset, your submissions will be compared with the true values of the target variable. 

The True or Actual values of the target variable are hidden on the DPhi Practice platform so that we can evaluate your model's performance on unseen data. Finally, a Root-Mean-Squared-Error (RMSE) score for your model will be generated and displayed.

About the dataset

This database contains two attributes. The target variable refers to the weights of the sample individuals.

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

import pandas as pd
heights_data  = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/Weights_Heights_Predict_Weights/Training_set_weights.csv" )

Data Description
  • Height(Inches): Height of the individuals in inches
  • Weight(Pounds): Weight of the individuals in pounds

Evaluation Dataset

Load the evaluation data (name it as weights_eval). You can load the data using the below command.

weights_eval = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/Weights_Heights_Predict_Weights/Testing_set_weights.csv')

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


References

This dataset is adapted from:

University of California, Los Angeles; Statistics Online Computational Resource (SOCR). The comlete Human Weight/Height Dataset. 2008. Available at: http://socr.ucla.edu/docs/resources/SOCR_Data/SOCR_Data_Dinov_020108_HeightsWeights.html.

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File Format

Your submission should be in CSV format.

Predictions

This file should have a header row called 'prediction'.
Please see the instructions to save a prediction file under the “Data” tab.

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