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

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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

# Create Local Outlier Factor model with an outlier proportion of 10%
# and assign it to model variable

 model = create_model('lof', fraction = 0.10)

# Assign anomaly labels and scores to the data_assigned variable

 data_assigned = assign_model(model)

# Create new dataset of outliers named data_outliers

 data_outliers = data_assigned.query('Anomaly == 1')

# Print first 5 instances of the outlier dataset

 data_outliers.head(5)

# Explanation
* **Line 4**: We create a local outlier factor model with an outlier proportion of 10% by using the `create_model()` function and assign it to the `model` variable.

* **Line 8**: We assign anomaly labels and scores to the `data_assigned` variable by using the `assign_model()` function.
* **Line 12**: We create a new dataset named `data_outliers` containing only the instances that have been labeled as outliers by using the `query()` function.

* **Line 16**: We print the first 5 instances of the outlier dataset using the `head()` function.

This is the in-depth solution review of the coding challenge "Create Local Outlier Factor Model."

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Solution Review: Create Local Outlier Factor Model

Solution

Explanation