Assignment 1

Use the data from heights_weights_genders.csv to create a simple predictor that takes in a person's height and guesses their weight based on a model using all the data, regardless of gender. To do this, find the parameters (lm.params) and use those in your function (i.e. don't generate a model each time)



In [9]:

    
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf



In [20]:

    
df = pd.read_csv("data/heights_weights_genders.csv")



In [21]:

    
lm = smf.ols(formula="Weight~Height",data=df).fit()



In [22]:

    
lm.params









    Out[22]:





Intercept   -350.737192
Height         7.717288
dtype: float64



In [23]:

    
#Getting the parameters:
intercept, height = lm.params



In [37]:

    
#Testing my formula:
#example_Height = 60



In [38]:

    
weight = height*example_Height+intercept



In [39]:

    
weight









    Out[39]:





112.30006663498375



In [54]:

    
users_height = int(input('Please enter your height in inches: '))
weight = height*users_height+intercept
print('You are approximately', round(weight), "pounds, which is", round(weight * 0.453592), 'kg.')









    



Please enter your height in inches: 78
You are approximately 251.0 pounds, which is 114.0 kg.



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