Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

Simplifying Machine Learning with PyCaret_ .png

c.tar.gz

jupyter

RunCode

spa1

PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

# Initializing the PyCaret environment

After we complete the EDA, we need to initialize the PyCaret environment. We can accomplish this easily with the `setup()` function, which prepares the data for model training. This function has numerous parameters and lets us create a complete data preprocessing pipeline. Regardless, we’ll only examine the most important functionality to keep things simple. Please check the [PyCaret Classification module](https://pycaret.readthedocs.io/en/latest/api/classification.html) documentation page for more details.

After we run `setup()`, we get a table of useful information about its settings and parameters. Let’s see it in detail below.

# PyCaret environment setup.Setting different parameters in setup() function
# to prepare model training and deployment data.

classf = setup(data = data, target = 'species', train_size = 0.8,
normalize = True,silent=True, session_id = 3934)

PyCaret environment setup

align-center

# Identifying numeric and categorical features
PyCaret can automatically infer whether a feature is numeric or categorical. In the `setup()` function, we can specify which features are categorical or numeric using the `categorical_features` and `numeric_features` parameters. In this case, all the features are correctly identified as numeric, so there is no need for any changes.

# Train/Test split
Splitting a dataset into a train and test subset is standard practice in machine learning. We set the `train_size` parameter to $0.8$. This means that we’ll train the machine learning model on $80$% of the original data, and use the remaining $20$% for testing purposes.


# Normalization of numeric features
Some classification models require numeric features to be normalized to perform optimally. Normalized features have $\mu = 0$ and $\sigma = 1$. We can accomplish that by replacing each value of the feature with the $z$-score, which is defined as $
z=\frac{x-\mu}{\sigma}
$. We enable normalization by setting the `normalize` parameter to `True`.

# Comparing classification models
The `compare_models()` function simplifies our choice for the best classification model. It trains all available models and prints the results.
 


Get familiarized with the Pycaret environment setup for the classification task.

	Description	Value
0	session_id	3934
1	Target	Species
2	Target Type	Multiclass
3	Label Encoded	Iris-setosa:0,Iris-versicolor:1,Iris-virgininca:2
4	Original Data	(150,5)
5	Missing Values	False
6	Numeric Features	4
7	Categorical Features	0
8	Ordinal Features	False
9	High Cardinality Features	False
10	High Cardinality Method	None
11	Transformed Train Set	(120,4)
12	Transformed Test Set	(30,4)
13	Shuffle Train-Test	True
14	Stratify Train-Test	False
15	Fold Generator	StratifiedKFold
16	Fold Number	10
17	CPU Jobs	-1
18	Use GPU	False

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Getting Familiar with the PyCaret Environment

Initializing the PyCaret environment

PyCaret environment setup

Identifying numeric and categorical features

Train/Test split