X_pca = pca.fit_transform(X)

plt.figure(figsize=(8,6))

plt.scatter(principal_df.iloc[:,0], principal_df.iloc[:,1], s=40)

principal_df = pd.DataFrame(data = X_pca, columns = ['PC1', 'PC2'])

plt.title('PCA plot in 2D')
plt.xlabel('PC1')
plt.ylabel('PC2')

pca = PCA(n_components=2)

Провести розкладання коваріаційної матриці на власні значення

Знайдіть коваріаційну матрицю за заданими точками даних

Стандартизуйте точки даних

Відсортуйте власні значення та власні вектори

To identify the group to which a data instance belongs. Groups can be obtained by discretizing a

continuous range of values. For example, a DL method that classifies a truck loading capacity according to

whether it is empty, 25%, 50%, 75% or 100% full rather than estimating the loading weight

To determine the location and region of a target, i.e., localising an object or agent within an

environment. An example is to draw a circle around oversized rocks in an image

Deep learning models, particularly autoencoders, can be used for anomaly detection. They learn the

normal patterns in data and can identify anomalies or outliers

A specific task in image or point cloud perception to classify an individual pixel or point

into its respective group

Determining the value of a subject within a range of a continuous function. Estimating is comparable

to regression. An example is to determine the cost of an operation given a set of conditions

Deep learning plays a crucial role in building recommendation systems, as it can capture complex

user-item interactions and provide personalized recommendations

Recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks are commonly used

for time series data mining, including financial predictions, demand forecasting, and more

Deep learning can handle various data types, including text, images, audio, and sequences. This

flexibility is valuable in data mining tasks that involve heterogeneous data sources.

Deep learning models are capable of automatically learning relevant features from raw data. This eliminates

the need for manual feature engineering, making it well-suited for tasks where the underlying patterns are not well-understood