Working with numerical dataΒΆ

In the previous notebook, we trained a k-nearest neighbors model on some data.

However, we oversimplified the procedure by loading a dataset that contained exclusively numerical data. Besides, we used datasets which were already split into train-test sets.

In this notebook, we aim at:

  • identifying numerical data in a heterogeneous dataset;

  • selecting the subset of columns corresponding to numerical data;

  • using a scikit-learn helper to separate data into train-test sets;

  • training and evaluating a more complex scikit-learn model.

We will start by loading the adult census dataset used during the data exploration.

Loading the entire datasetΒΆ

As in the previous notebook, we rely on pandas to open the CSV file into a pandas dataframe.

import pandas as pd

adult_census = pd.read_csv("../datasets/adult-census.csv")
# drop the duplicated column `"education-num"` as stated in the first notebook
adult_census = adult_census.drop(columns="education-num")
adult_census.head()
age workclass education marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country class
0 25 Private 11th Never-married Machine-op-inspct Own-child Black Male 0 0 40 United-States <=50K
1 38 Private HS-grad Married-civ-spouse Farming-fishing Husband White Male 0 0 50 United-States <=50K
2 28 Local-gov Assoc-acdm Married-civ-spouse Protective-serv Husband White Male 0 0 40 United-States >50K
3 44 Private Some-college Married-civ-spouse Machine-op-inspct Husband Black Male 7688 0 40 United-States >50K
4 18 ? Some-college Never-married ? Own-child White Female 0 0 30 United-States <=50K

The next step separates the target from the data. We performed the same procedure in the previous notebook.

data, target = adult_census.drop(columns="class"), adult_census["class"]
data.head()
age workclass education marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country
0 25 Private 11th Never-married Machine-op-inspct Own-child Black Male 0 0 40 United-States
1 38 Private HS-grad Married-civ-spouse Farming-fishing Husband White Male 0 0 50 United-States
2 28 Local-gov Assoc-acdm Married-civ-spouse Protective-serv Husband White Male 0 0 40 United-States
3 44 Private Some-college Married-civ-spouse Machine-op-inspct Husband Black Male 7688 0 40 United-States
4 18 ? Some-college Never-married ? Own-child White Female 0 0 30 United-States
target
0         <=50K
1         <=50K
2          >50K
3          >50K
4         <=50K
          ...  
48837     <=50K
48838      >50K
48839     <=50K
48840     <=50K
48841      >50K
Name: class, Length: 48842, dtype: object

Note

Here and later, we use the name data and target to be explicit. In scikit-learn documentation, data is commonly named X and target is commonly called y.

At this point, we can focus on the data we want to use to train our predictive model.

Identify numerical dataΒΆ

Numerical data are represented with numbers. They are linked to measurable (quantitative) data, such as age or the number of hours a person works a week.

Predictive models are natively designed to work with numerical data. Moreover, numerical data usually requires very little work before getting started with training.

The first task here will be to identify numerical data in our dataset.

Caution

Numerical data are represented with numbers, but numbers are not always representing numerical data. Categories could already be encoded with numbers and you will need to identify these features.

Thus, we can check the data type for each of the column in the dataset.

data.dtypes
age                int64
workclass         object
education         object
marital-status    object
occupation        object
relationship      object
race              object
sex               object
capital-gain       int64
capital-loss       int64
hours-per-week     int64
native-country    object
dtype: object

We seem to have only two data types. We can make sure by checking the unique data types.

data.dtypes.unique()
array([dtype('int64'), dtype('O')], dtype=object)

Indeed, the only two types in the dataset are integer and object. We can look at the first few lines of the dataframe to understand the meaning of the object data type.

data.head()
age workclass education marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country
0 25 Private 11th Never-married Machine-op-inspct Own-child Black Male 0 0 40 United-States
1 38 Private HS-grad Married-civ-spouse Farming-fishing Husband White Male 0 0 50 United-States
2 28 Local-gov Assoc-acdm Married-civ-spouse Protective-serv Husband White Male 0 0 40 United-States
3 44 Private Some-college Married-civ-spouse Machine-op-inspct Husband Black Male 7688 0 40 United-States
4 18 ? Some-college Never-married ? Own-child White Female 0 0 30 United-States

We see that the object data type corresponds to columns containing strings. As we saw in the exploration section, these columns contain categories and we will see later how to handle those. We can select the columns containing integers and check their content.

numerical_columns = ["age", "capital-gain", "capital-loss", "hours-per-week"]
data[numerical_columns].head()
age capital-gain capital-loss hours-per-week
0 25 0 0 40
1 38 0 0 50
2 28 0 0 40
3 44 7688 0 40
4 18 0 0 30

Now that we limited the dataset to numerical columns only, we can analyse these numbers to figure out what they represent. We can identify two types of usage.

The first column, "age", is self-explanatory. We can note that the values are continuous, meaning they can take up any number in a given range. Let’s find out what this range is:

data["age"].describe()
count    48842.000000
mean        38.643585
std         13.710510
min         17.000000
25%         28.000000
50%         37.000000
75%         48.000000
max         90.000000
Name: age, dtype: float64

We can see the age varies between 17 and 90 years.

We could extend our analysis and we will find that "capital-gain", "capital-loss", and "hours-per-week" are also representing quantitative data.

Now, we store the subset of numerical columns in a new dataframe.

data_numeric = data[numerical_columns]

Train-test split the datasetΒΆ

In the previous notebook, we loaded two separate datasets: a training one and a testing one. However, having separate datasets in two distincts files is unusual: most of the time, we have a single file containing all the data that we need to split once loaded in the memory.

Scikit-learn provides the helper function sklearn.model_selection.train_test_split which is used to automatically split the dataset into two subsets.

from sklearn.model_selection import train_test_split

data_train, data_test, target_train, target_test = train_test_split(
    data_numeric, target, random_state=42, test_size=0.25)

Tip

In scikit-learn setting the random_state parameter allows to get deterministic results when we use a random number generator. In the train_test_split case the randomness comes from shuffling the data, which decides how the dataset is split into a train and a test set).

When calling the function train_test_split, we specified that we would like to have 25% of samples in the testing set while the remaining samples (75%) will be available in the training set. We can check quickly if we got what we expected.

print(f"Number of samples in testing: {data_test.shape[0]} => "
      f"{data_test.shape[0] / data_numeric.shape[0] * 100:.1f}% of the"
      f" original set")
Number of samples in testing: 12211 => 25.0% of the original set
print(f"Number of samples in training: {data_train.shape[0]} => "
      f"{data_train.shape[0] / data_numeric.shape[0] * 100:.1f}% of the"
      f" original set")
Number of samples in training: 36631 => 75.0% of the original set

In the previous notebook, we used a k-nearest neighbors model. While this model is intuitive to understand, it is not widely used in practice. Now, we will use a more useful model, called a logistic regression, which belongs to the linear models family.

Note

In short, linear models find a set of weights to combine features linearly and predict the target. For instance, the model can come up with a rule such as:

  • if 0.1 * age + 3.3 * hours-per-week - 15.1 > 0, predict high-income

  • otherwise predict low-income

Linear models, and in particular the logistic regression, will be covered in more details in the β€œLinear models” module later in this course. For now the focus is to use this logistic regression model in scikit-learn rather than understand how it works in details.

To create a logistic regression model in scikit-learn you can do:

# to display nice model diagram
from sklearn import set_config
set_config(display='diagram')
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()

Now that the model has been created, you can use it exactly the same way as we used the k-nearest neighbors model in the previous notebook. In particular, we can use the fit method to train the model using the training data and labels:

model.fit(data_train, target_train)
LogisticRegression()

We can also use the score method to check the model statistical performance on the test set.

accuracy = model.score(data_test, target_test)
print(f"Accuracy of logistic regression: {accuracy:.3f}")
Accuracy of logistic regression: 0.807

Now the real question is: is this statistical performance relevant of a good predictive model? Find out by solving the next exercise!

In this notebook, we learned to:

  • identify numerical data in a heterogeneous dataset;

  • select the subset of columns corresponding to numerical data;

  • use the scikit-learn train_test_split function to separate data into a train and a test set;

  • train and evaluate a logistic regression model.