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I am looking to predict whether someone is a smoker from several columns of demographic data stored in a csv, as well as their smoker status.

The columns used are: Gender, Age,Race, ServedInMilitary, CountryofBirth, EducationLevel MaritalStatus, HouseholdIncome, FamilyIncome, ChildrenInHouse, QuantitiyofAlcohol, PerUnitTime, ShortnessOfBreath, Asthma, Exercise, Smoker, SmokedBefore, AgeStartedSmoking.

All columns have numeric, but not necessarily binary values. Could someone help me correct my code to take these factors into account when determining smoker status and then help me measure the accuracy of my classifier?

I have the following code from a similar question: how to Load CSV Data in scikit and using it for Naive Bayes Classification

target_names = np.array(['Positives','Negatives'])

# add columns to your data frame
data['is_train'] = np.random.uniform(0, 1, len(df)) <= 0.75
data['Type'] = pd.Factor(targets, target_names)
data['Targets'] = targets

# define training and test sets
train = data[data['is_train']==True]
test = data[data['is_train']==False]

trainTargets = np.array(train['Targets']).astype(int)
testTargets = np.array(test['Targets']).astype(int)

# columns you want to model
features = data.columns[0:7]

# call Gaussian Naive Bayesian class with default parameters
gnb = GaussianNB()

# train model
y_gnb = gnb.fit(train[features], trainTargets).predict(train[features])

#Predict Output
DMop
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    Just use `gnb.predict(test[features])` to get the predicte labels. And then compare them to your `testTargets` – Vivek Kumar Dec 08 '17 at 01:41

1 Answers1

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There seems to be a missing line here for the dataframe, but I will assume you have it. If you don't, then read your data using pandas.read_csv.

Also, your columns seem to have data that is both categorical and numerical. For example, the "SmokedBefore" column is likely 1/0 whereas your "Age" column is likely numbers such as 20 or 30.

This makes a difference, because in "SmokedBefore" the intent is not to say that 1>0. The intent is to say Yes/No. If your model assumes that higher (or lower) is better, then this will cause an issue. Therefore it is categorical and should not be treated like a numerical value. It is simply a tag to indicate whether someone has smoked before.

However, in "Age" the intent is to say that 30 is different than 20 by 10. Therefore, it is numerical and should be treated as such.

To treat this, you will need to transform your categorical features into another set of binary features that will balance out this effect and handle it for you. This is called One Hot Encoding. Instead of 1/0 on your "SmokedBefore", it will become "is_1" and "is_0" with corresponding data. Like that, each column will have a 1 and a 0.

You can simply use the onehotencoder function provided in sklearn. Use the categorical_features argument to specify which columns have categorical features

Aiham Hatem
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