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
import statsmodels.formula.api as smf



In [2]:

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



In [3]:

    
df.head()



In [5]:

    
male = df[df['Gender']=='Male']
female = df[df['Gender']=='Female']



In [6]:

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









    Out[6]:





Intercept   -224.498841
Height         5.961774
dtype: float64



In [7]:

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









    Out[7]:





Intercept   -246.013266
Height         5.994047
dtype: float64



In [28]:

    
gender = input("Enter your gender(F/M):")









    



Enter your gender(F/M):m



In [30]:

    
height = input("Enter your height(inches):")









    



Enter your height(inches):99



In [33]:

    
def pre_weight(input_gender,input_height):
    if input_gender.upper() == 'F':
        weight = -224.498841 + (5.961774*float(input_height))
        return weight
    elif input_gender.upper() == 'M':
        weight = -246.013266 + (5.994047*float(input_height))
        return weight
    else: 
        print("Please enter your gender again")



In [34]:

    
pre_weight(gender,height)









    Out[34]:





347.39738700000004



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