Assignment 2

Create two models for the relationship between height and weight based on gender
Modify the code in Assignment 1 to ask for a person's gender as well as their height to produce an estimate of a person's weight using the models you created
Find the weights and use those in your function (i.e. don't generate a model each time)



In [3]:

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









    



/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py:1035: UserWarning: Duplicate key in file "/Users/zhizhou/.matplotlib/matplotlibrc", line #2
  (fname, cnt))
/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py:1035: UserWarning: Duplicate key in file "/Users/zhizhou/.matplotlib/matplotlibrc", line #3
  (fname, cnt))



In [4]:

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



In [5]:

    
df.head(5)



In [12]:

    
women = df[df['Gender']=='Female']
men = df[df['Gender']=='Male']



In [14]:

    
lm_f = smf.ols(formula="Weight~Height",data=women).fit()
lm_m = smf.ols(formula="Weight~Height",data=men).fit()



In [15]:

    
lm_f.params









    Out[15]:





Intercept   -246.013266
Height         5.994047
dtype: float64



In [16]:

    
lm_m.params









    Out[16]:





Intercept   -224.498841
Height         5.961774
dtype: float64



In [18]:

    
intercept_f, slope_f = lm_f.params
intercept_m, slope_m = lm_m.params



In [21]:

    
women.plot(kind="scatter",x="Height",y="Weight",color='lightgray',alpha=0.2,linewidth=0,title ="women")
plt.plot(women["Height"],slope_f*women["Height"]+intercept_f,"-",color="pink",alpha=1)









    Out[21]:





[<matplotlib.lines.Line2D at 0x10d409588>]



In [22]:

    
men.plot(kind="scatter",x="Height",y="Weight",color='lightgray',alpha=0.2,linewidth=0,title ="women")
plt.plot(men["Height"],slope_m*men["Height"]+intercept_m,"-",color="steelblue",alpha=1)









    Out[22]:





[<matplotlib.lines.Line2D at 0x10d539080>]



In [23]:

    
def get_weight(height,gender):
    if gender == "female":
        print("Your height is", height,"and your estimate weight will be", slope_f*height+intercept_f)
    elif gender == "male":
        print("Your height is", height,"and your estimate weight will be", slope_m*height+intercept_m)
    else:
        print("Gender must be male or female, try again!")



In [25]:

    
get_weight(60,"female")









    



Your height is 60 and your estimate weight will be 113.629531145



In [ ]:

	Gender	Height	Weight
0	Male	73.847017	241.893563
1	Male	68.781904	162.310473
2	Male	74.110105	212.740856
3	Male	71.730978	220.042470
4	Male	69.881796	206.349801