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 Find the weights and use those in your function (i.e. don't generate a model each time)
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import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf # package we'll be using for linear regression
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df = pd.read_csv("data/heights_weights_genders.csv")
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lm = smf.ols(formula="Weight~Height",data=df).fit() #notice the formula regresses Y on X (Y~X)
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lm.params #get the parameters from the model fit
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intercept, slope = lm.params #assign those values to variables
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df.plot(kind="scatter",x="Height",y="Weight")
plt.plot(df["Height"],slope*df["Height"]+intercept,"-",color="red") #we create the best fit line from the values in the fit model
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def predicting_weight(height):
return intercept + float(height) * slope
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x = input("What is the height? ")
print("Expected Weight : "+str(predicting_weight(x)))
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