Getting Started

Machine learning is the field of study that enables computers to learn patterns from data and make predictions or decisions without being explicitly programmed. In this course, we will explore machine learning in an applied and hands-on manner using the scikit-learn library. It is one of the most widely used and beginner-friendly tools in Python for building, training, and evaluating machine learning models. Through practical examples and experiments, we will develop a solid understanding of how data-driven models work in real applications.

To learn how to build machine learning models with scikit-learn, you should focus on three key activities:

• Read the course textbook: it provides the conceptual understanding you need
• Explore the scikit-learn documentation: it not only explains how scikit-learn works but also showcases powerful examples you can learn from
• Write code yourself: “Knowing is not enough; we must apply.” said someone very smart.

On NumPy, Pandas, and Matplotlib

These are the core libraries of data science. You should already know their basics (see the prerequisites on the course homepage), but their possibilities are nearly endless. Rather than memorizing every method, what truly matters is knowing what’s possible and having the skill to use any method you haven’t seen before by reading its documentation.

We won’t offer a separate “advanced” course, instead, you’ll keep expanding your mastery through real use cases as they arise in the self-study notebooks. Whenever you encounter a new function or method, treat it as an opportunity to strengthen this essential skill: learning directly from documentation. Of course, you can always use LLMs to explain you a method. However, keep in mind that, LLMs are not always up-to-date with the documentations.

The datasets

To build and evaluate machine learning models, we need data. The scikit-learn library provides several well-known datasets that are ideal for learning and experimentation. We will use a selection of these datasets during the self-study. You will apply what you have learned on these datasets to your creative brief dataset.

Scikit-learn provides 4 types of datasets:

1. Toy datasets
2. Real world datasets
3. Generated datasets
4. Loading external datasets

In the self-study notebooks, we will use the following four datasets, from toy, real, and external datasets:

1. Breast cancer Wisconsin (diagnostic) dataset (binary classification)
2. Vehicle dataset (multi-class classification)
   • fetch_openml documentation
   • Use X, y = fetch_openml(name="vehicle", version=1, as_frame=True, return_X_y=True)
3. Diabetes dataset (regression)
4. California Housing dataset (regression)

you can load scikit-learn datasets very easily. Let’s load the first dataset. Go to the documentation of the Breast cancer dataset, scroll down to find the examples sections, which tells you how to load the dataset:

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()

but what is data? Again the documentation:

Returns:
    data: Bunch
        Dictionary-like object, with the following attributes.

        data: {ndarray, dataframe} of shape (569, 30)
            The data matrix. If as_frame=True, data will be a pandas DataFrame.
...

# since it is a dictionary like object
# we can check the keys
data.keys()

dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])

# this is a numpy array
data['data']

array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,
        1.189e-01],
       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,
        8.902e-02],
       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,
        8.758e-02],
       ...,
       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,
        7.820e-02],
       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,
        1.240e-01],
       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,
        7.039e-02]], shape=(569, 30))

# these are the column names
data['feature_names']

array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
       'mean smoothness', 'mean compactness', 'mean concavity',
       'mean concave points', 'mean symmetry', 'mean fractal dimension',
       'radius error', 'texture error', 'perimeter error', 'area error',
       'smoothness error', 'compactness error', 'concavity error',
       'concave points error', 'symmetry error',
       'fractal dimension error', 'worst radius', 'worst texture',
       'worst perimeter', 'worst area', 'worst smoothness',
       'worst compactness', 'worst concavity', 'worst concave points',
       'worst symmetry', 'worst fractal dimension'], dtype='<U23')

We can create a Pandas DataFrame to view each column with its header. However, if you read the documentation, you’ll notice that load_breast_cancer() can directly return the data as X and y (explained below).

This function has two useful parameters: return_X_y and as_frame. Read what they do in the documentation, then check the code below to see their effect.

X, y = load_breast_cancer(as_frame=True, return_X_y=True)

In machine learning, it is a common convention to use X and y to represent the data:

• X (uppercase) refers to the input features — the independent variables used by the model to make predictions. It is typically a table or matrix where each row is a data sample and each column is a feature.
• y (lowercase) refers to the target variable — the value we want to predict. It can be categorical (for classification) or continuous (for regression).

This convention helps keep code consistent and readable across different datasets and models.

# X is a pandas DataFrame containing the feature variables,
# and y is a pandas Series containing the target variable.
type(X), type(y)

(pandas.DataFrame, pandas.Series)

X.head()

	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	...	worst radius	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension
0	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	0.07871	...	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	0.05667	...	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
2	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	0.2069	0.05999	...	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
3	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	0.2597	0.09744	...	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
4	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	0.1809	0.05883	...	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678

5 rows × 30 columns

y.head()

0    0
1    0
2    0
3    0
4    0
Name: target, dtype: int64

This is the Breast Cancer Wisconsin dataset. In this dataset, each row represents one patient, and the data describe characteristics of a breast tumor found in that patient. The input data (X) contain 30 features, which are numerical measurements describing the tumor’s shape, size, texture, and other properties obtained from medical images. The target variable (y) tells us whether the tumor is malignant (0), meaning cancerous, or benign (1), meaning not cancerous.

There are 569 patients in total, with 357 benign and 212 malignant cases. The goal of a machine learning model is to learn from these examples so that, given the measurements of a new tumor, it can predict whether it is likely to be benign or malignant. Note that in machine learning, the term “positive class” simply means the class labelled 1 (here, benign = 1), not that it is “positive” in a good or bad sense.

# 569 patients (i.e. tumors), 30 features
X.shape

(569, 30)

# 212 tumors are labeled as malignant (0)
# 357 tumors are labeled as benign (1)
y.value_counts() # very useful to know this method, instead of writing a loop to count

target
1    357
0    212
Name: count, dtype: int64

I’ve demonstrated how to read the documentation and adjust your code accordingly. From this point on, I won’t show these steps for every example, part of your learning is to practice finding, reading, and applying documentation on your own.

On the documentation page:

https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer

you’ll also see a line that says “Read more in the User Guide.” Click that link to explore detailed information about the dataset, which should take you here:

https://scikit-learn.org/stable/datasets/toy_dataset.html#breast-cancer-dataset

# Gives summary statistics for all features in X, such as mean, standard deviation, min, max, and quartiles.
X.describe()

	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	...	worst radius	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension
count	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	...	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000
mean	14.127292	19.289649	91.969033	654.889104	0.096360	0.104341	0.088799	0.048919	0.181162	0.062798	...	16.269190	25.677223	107.261213	880.583128	0.132369	0.254265	0.272188	0.114606	0.290076	0.083946
std	3.524049	4.301036	24.298981	351.914129	0.014064	0.052813	0.079720	0.038803	0.027414	0.007060	...	4.833242	6.146258	33.602542	569.356993	0.022832	0.157336	0.208624	0.065732	0.061867	0.018061
min	6.981000	9.710000	43.790000	143.500000	0.052630	0.019380	0.000000	0.000000	0.106000	0.049960	...	7.930000	12.020000	50.410000	185.200000	0.071170	0.027290	0.000000	0.000000	0.156500	0.055040
25%	11.700000	16.170000	75.170000	420.300000	0.086370	0.064920	0.029560	0.020310	0.161900	0.057700	...	13.010000	21.080000	84.110000	515.300000	0.116600	0.147200	0.114500	0.064930	0.250400	0.071460
50%	13.370000	18.840000	86.240000	551.100000	0.095870	0.092630	0.061540	0.033500	0.179200	0.061540	...	14.970000	25.410000	97.660000	686.500000	0.131300	0.211900	0.226700	0.099930	0.282200	0.080040
75%	15.780000	21.800000	104.100000	782.700000	0.105300	0.130400	0.130700	0.074000	0.195700	0.066120	...	18.790000	29.720000	125.400000	1084.000000	0.146000	0.339100	0.382900	0.161400	0.317900	0.092080
max	28.110000	39.280000	188.500000	2501.000000	0.163400	0.345400	0.426800	0.201200	0.304000	0.097440	...	36.040000	49.540000	251.200000	4254.000000	0.222600	1.058000	1.252000	0.291000	0.663800	0.207500

8 rows × 30 columns

YOUR TURN

Real datasets often contain missing values. Check if X contains any missing values.

# YOUR CODE HERE

Before building a machine learning model on a dataset, it’s important to start with a clear understanding of the data and the problem you are trying to solve. Ask yourself the following key questions:

• What is the problem? What do I need to predict, and based on which input features?
• Type of problem: Is it a classification (predicting categories) or a regression (predicting continuous values) task?
• If classification: How many classes are there, and what are their value counts?
• If regression: Min-max value of the target? (going forward you need to check the distribution)
• Data size: How many rows (samples) and columns (features) does the dataset contain?
• Feature types: Which features are categorical and which are numerical?
• Data quality: Are there any missing values that need to be handled?

Answering these questions helps you understand your dataset and choose appropriate preprocessing steps and models.

Based on our code above, we can answer these questions for the Breast Cancer Wisconsin dataset.

• What is the problem? The goal is to predict whether a breast tumor is benign (not cancerous) or malignant (cancerous) based on measurements of the tumor.
• Type of problem: This is a classification problem because we are predicting one of two categories.
• If classification: There are 2 classes — malignant (0) and benign (1).
• Value counts per class: There are 212 malignant cases and 357 benign cases.
• Data size: The dataset has 569 rows (patients) and 30 columns (numerical features).
• Feature types: All 30 features are numerical, describing properties such as radius, texture, smoothness, and symmetry of the tumor.
• Missing values: The dataset does not contain any missing values.

YOUR TURN

Repeat the above process for the remaining 3 datasets.

2. Vehicle dataset (multi-class classification)
   • fetch_openml documentation
   • Use X, y = fetch_openml(name="vehicle", version=1, as_frame=True, return_X_y=True)
3. Diabetes dataset (regression)
4. California Housing dataset (regression)

Answer the following questions for each:

• What is the problem? What do I need to predict, and based on which input features?
• Type of problem: Is it a classification (predicting categories) or a regression (predicting continuous values) task?
• If classification: How many classes are there, and what are their value counts?
• If regression: Min-max value of the target?
• Data size: How many rows (samples) and columns (features) does the dataset contain?
• Feature types: Which features are categorical and which are numerical?
• Data quality: Are there any missing values that need to be handled?

# YOUR CODE HERE