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 [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 [40]:

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



In [29]:

    
def female_lm(your_file):
    df = pd.read_csv(your_file)
    female = df[df['Gender'] == 'Female']
    female_lm = smf.ols(formula="Weight~Height",data=female).fit()
    return female_lm.params



In [30]:

    
female_lm("data/heights_weights_genders.csv").Intercept









    Out[30]:





-246.01326574667272



In [26]:

    
def male_lm(your_file):
    df = pd.read_csv(your_file)
    male = df[df['Gender'] == 'Male']
    male_lm = smf.ols(formula="Weight~Height",data=male).fit()
    return male_lm.params



In [27]:

    
male_lm("data/heights_weights_genders.csv").Intercept









    Out[27]:





-224.49884070545866



In [6]:

    
male_lm = smf.ols(formula="Weight~Height",data=male).fit()
male_lm.params









    Out[6]:





Intercept   -224.498841
Height         5.961774
dtype: float64



In [28]:

    
female_lm = smf.ols(formula="Weight~Height",data=female).fit()
female_lm.params









    Out[28]:





Intercept   -246.013266
Height         5.994047
dtype: float64



In [36]:

    
def weight_predictor(gender,your_height):
    if gender == "Male":
        m_weight = your_height * male_lm("data/heights_weights_genders.csv").Height + male_lm("data/heights_weights_genders.csv").Intercept
        return m_weight
    else:
        f_weight = your_height * female_lm("data/heights_weights_genders.csv").Height + female_lm("data/heights_weights_genders.csv").Intercept
        return f_weight



In [41]:

    
weight_predictor("Male",73.847017)









    Out[41]:





215.76037134150502



In [42]:

    
weight_predictor("Female",73.847017)









    Out[42]:





196.62919651927902



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