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 [1]:

    
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf # package we'll be using for linear regression



In [3]:

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



In [4]:

    
df.plot(kind="scatter",x="Height",y="Weight")









    Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x10cec65f8>



In [5]:

    
lm = smf.ols(formula="Weight~Height",data=df).fit() #notice the formula regresses Y on X (Y~X)



In [6]:

    
lm.params









    Out[6]:





Intercept   -350.737192
Height         7.717288
dtype: float64



In [7]:

    
intercept, slope = lm.params



In [8]:

    
df.plot(kind="scatter",x="Height",y="Weight")
#give it df['Height'] as X, and "slope*df["Height"]+intercept" as Y, this is y = mx + b. magical!
plt.plot(df["Height"],slope*df["Height"]+intercept,"-",color="orange") #we create the best fit line from the values in the fit model









    Out[8]:





[<matplotlib.lines.Line2D at 0x10ce3a6d8>]



In [9]:

    
def get_weight(height):
    return slope*height+intercept



In [17]:

    
get_weight(6)









    Out[17]:





174.03836776126673



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