Use the data from heights_weights_genders.csv to create a simple predictor that takes in a person's height and guesses their weight based on a model using all the data, regardless of gender. To do this, find the parameters (lm.params) 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 [13]:

    
df.head()



In [4]:

    
df.plot(kind="scatter",x="Height",y="Weight")









    Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x1157a3b38>



In [5]:

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



In [6]:

    
lm.params









    Out[6]:





Intercept   -350.737192
Height         7.717288
dtype: float64



In [7]:

    
def find_user_weight(user_height):
    user_weight = 7.7172876407853712 * float(user_height) - 350.737192
    return user_weight



In [ ]:

    
user_height = input("How tall are you in inches?: ")
find_user_weight(user_height)



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