

In [1]:

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

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 [2]:

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



In [3]:

    
df.head()



In [4]:

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



In [5]:

    
lm.params









    Out[5]:





Intercept   -350.737192
Height         7.717288
dtype: float64



In [6]:

    
intercept, slope = lm.params



In [10]:

    
df1 = df[df['Gender'] == 'Female']
df2 = df[df['Gender'] == 'Male']



In [25]:

    
df1.head(2)



In [26]:

    
df2.head(2)



In [15]:

    
lm.female = smf.ols(formula="Weight~Height",data=df1).fit()
lm.male = smf.ols(formula="Weight~Height",data=df2).fit()



In [16]:

    
lm.female.params









    Out[16]:





Intercept   -246.013266
Height         5.994047
dtype: float64



In [17]:

    
lm.male.params









    Out[17]:





Intercept   -224.498841
Height         5.961774
dtype: float64



In [21]:

    
F_intercept, F_slope = lm.female.params



In [20]:

    
M_intercept, M_slope = lm.male.params



In [22]:

    
def weight_predictor(height, Gender):
    if Gender == 'Female':
        return F_slope*height + F_intercept
    elif Gender == 'Male':
        return M_slope*height + M_intercept



In [27]:

    
#Example 1

Gender = 'Male'
height = 73.847017

weight_predictor(height, Gender)









    Out[27]:





215.76037134150519



In [29]:

    
#Example 2

Gender = 'Female'
height = 58.910732

weight_predictor(height, Gender)









    Out[29]:





107.10040797652709



In [ ]:



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