Assignment 1

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
import statsmodels.formula.api as smf



In [2]:

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



In [4]:

    
df.head()



In [5]:

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









    Out[5]:





Intercept   -350.737192
Height         7.717288
dtype: float64



In [8]:

    
def pre_weight(height):
    i = -350.737192
    h = 7.717288
    weight = i + (h * float(height))
    return weight



In [10]:

    
df['predict_weight'] = df['Height'].apply(pre_weight)



In [12]:

    
df.head()









    Out[12]:






  
    
      
      Gender
      Height
      Weight
      predict_weight
    
  
  
    
      0
      Male
      73.847017
      241.893563
      219.161506
    
    
      1
      Male
      68.781904
      162.310473
      180.072571
    
    
      2
      Male
      74.110105
      212.740856
      221.191835
    
    
      3
      Male
      71.730978
      220.042470
      202.831427
    
    
      4
      Male
      69.881796
      206.349801
      188.560753



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