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 [24]:

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



In [25]:

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



In [26]:

    
df.columns









    Out[26]:





Index(['Gender', 'Height', 'Weight'], dtype='object')



In [27]:

    
df[df['Gender']=='Male'].plot(kind="scatter",x="Height",y="Weight")









    Out[27]:





<matplotlib.axes._subplots.AxesSubplot at 0x10afbb710>



In [28]:

    
df[df['Gender']=='Female'].plot(kind="scatter",x="Height",y="Weight")









    Out[28]:





<matplotlib.axes._subplots.AxesSubplot at 0x10b2cb3c8>



In [29]:

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



In [30]:

    
lm.params









    Out[30]:





Intercept   -350.737192
Height         7.717288
dtype: float64



In [31]:

    
intercept, slope = lm.params



In [32]:

    
df.plot(kind="scatter",x="Height",y="Weight")
plt.plot(df["Height"],slope*df["Height"]+intercept,"-",color="red")









    Out[32]:





[<matplotlib.lines.Line2D at 0x10b43b5c0>]



In [33]:

    
def predict_weight(input_height): 
    return (slope*input_height)+intercept



In [34]:

    
predict_weight(68)









    Out[34]:





174.03836776126673