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

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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

We will now perform EDA on the Iris dataset. EDA is a fundamental part of every machine learning project because it helps us understand the fundamental statistical properties of a dataset by using visualizations.

# Pie charts
**Pie charts** let us easily visualize the proportions of categorical variables. 

# Plotting pie chart

data['species'].value_counts().plot(kind='pie')
plt.ylabel('')
plt.show()

As we can see in the output, the Iris classes are evenly distributed. Each one is $33.3$%  of the dataset. This is beneficial because most classification models perform optimally with balanced classes. If you’re working with imbalanced data, you can refer to the [PyCaret documentation](https://pycaret.org/fix-imbalance/). It supports various ways to fix them, such as the SMOTE technique.

# Box plot
A **box plot** is a type of graph that visualizes the distribution of numeric variables to enable comparisons between categories.

Learn how to perform exploratory data analysis for classification on our dataset and plot data on pie charts, box plots, color-encoded matrices, and scatter plots.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Exploratory Data Analysis

Pie charts