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



In [4]:

    
df_female.head()



In [5]:

    
lm1 = smf.ols(formula="Weight~Height",data=df_male).fit()



In [9]:

    
intercept_male, slope_male = lm1.params



In [7]:

    
lm2 = smf.ols(formula="Weight~Height",data=df_female).fit()



In [10]:

    
intercept_female, slope_female = lm2.params



In [28]:

    
def predict_weight_gender(): 
    input_gender=input("What's your gender?").upper()
    input_height=int(input("What's your height?"))
    if input_gender == 'MALE': 
        return (slope_male*input_height) + intercept_male
    elif input_gender == 'FEMALE': 
        return (slope_female*input_height) + intercept_female
    else: 
        print('Invalid inputs')



In [29]:

    
predict_weight_gender()









    



What's your gender?Female
What's your height?68






    Out[29]:





161.58190406343221

	Gender	Height	Weight
5000	Female	58.910732	102.088326
5001	Female	65.230013	141.305823
5002	Female	63.369004	131.041403
5003	Female	64.479997	128.171511
5004	Female	61.793096	129.781407