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 [1]:

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



In [2]:

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



In [3]:

    
df.head()



In [6]:

    
df_male = df[df['Gender'] == 'Male']



In [7]:

    
df_female = df[df['Gender'] == 'Female']



In [8]:

    
lm_male = smf.ols(formula="Weight~Height",data=df_male).fit()



In [9]:

    
lm_female = smf.ols(formula="Weight~Height",data=df_female).fit()



In [10]:

    
lm_male.params









    Out[10]:





Intercept   -224.498841
Height         5.961774
dtype: float64



In [11]:

    
lm_female.params









    Out[11]:





Intercept   -246.013266
Height         5.994047
dtype: float64



In [12]:

    
def find_user_weight(user_height, user_gender):
    if user_gender == 'male':
        user_weight = 5.961774 * float(user_height) - 224.498841
    if user_gender == 'female':
        user_weight = 5.994047 * float(user_height) - 246.013266
    return user_weight



In [ ]:

    
user_height = input("How tall are you in inches?: ")
user_gender = input("Are you male or female?: ")
find_user_weight(user_height, user_gender)



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