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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

## Problem statement
In this challenge, we’ll train a random forest regression model on the automobile dataset. First, we need to load the dataset to a variable named `data`. Second, we have to initialize the PyCaret environment based on the aforementioned dataset, and set the `target` variable as `price`. Furthermore, the initialized environment will be assigned to a variable named `reg1`. We should also set the `silent` parameter to `True` to prevent any prompts from stopping code execution. Finally, we need to create a random forest model and assign it to a variable named `model`.

## Expected output

# Problem statement
In this challenge, we’ll train a random forest regression model on the automobile dataset. First, we need to load the dataset to a variable named `data`. Second, we have to initialize the PyCaret environment based on the aforementioned dataset, and set the `target` variable as `price`. Furthermore, the initialized environment will be assigned to a variable named `reg1`. We should also set the `silent` parameter to `True` to prevent any prompts from stopping code execution. Finally, we need to create a random forest model and assign it to a variable named `model`.

# Expected output

Train a random forest regression model on the automobile dataset.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Coding Challenge: Train Random Forest Model

Problem statement

Expected output