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 [6]:

    
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import statsmodels.formula.api as smf



In [7]:

    
df=pd.read_csv('/home/sean/git/algorithms/class5/data/heights_weights_genders.csv')



In [8]:

    
df.head()



In [11]:

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



In [12]:

    
lm.params









    Out[12]:





Intercept   -350.737192
Height         7.717288
dtype: float64



In [13]:

    
intercept, slope = lm.params



In [41]:

    
plt.scatter(x=df['Height'], y=df['Weight'])
plt.plot(df["Height"],slope*df["Height"]+intercept,"-",color="red")









    Out[41]:





[<matplotlib.lines.Line2D at 0x7fc825885b00>]



In [32]:

    
def predict_wt(str_ht):
    ht=float(str_ht)
    return '%s' % float('%.4g' % (slope*ht+intercept))



In [37]:

    
ht=input('Enter your height (inches):')
print('Predicted weight:', predict_wt(ht)+'lbs')









    



Enter your height (inches):60
Predicted weight: 112.3lbs



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