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

## Histogram of numeric features
We begin the EDA section of the project by visualizing the distribution of numeric features. We’ll use the `hist()` pandas function to accomplish this task.

# Plotting histogram of numeric features

data[numeric].hist(bins=30, figsize=(9,8), grid=False)
plt.show()

All the features are extremely right-skewed. This indicates that some instances have significantly higher values than the rest and are outliers.

## Numeric and categorical features
Visualizing the relationship between a categorical and numeric variable can be useful in many cases. We can do this by using the hue mapping feature of the `histplot()` Seaborn function.

# Relationship b/w categorical&numeric features

fig, axes = plt.subplots(1, 2, figsize = (9,4))
for ax, col in zip(axes.flatten(), categorical): 
  sns.histplot(data=data, x ='Grocery', hue=col,
                 multiple='stack', ax=ax)
plt.show()

In this case, we plotted the `'Grocery'` variable histogram with a different color for every category of the `Channel` and `Region` variables.  Retail customers tend to spend more on grocery products than `Horeca` customers as the histogram bins with large values belong to the `Retail` category. Furthermore, customers with high annual spendings from `Lisbon` and `Other` regions, like those from `Oporto`, tend to spend less on grocery products with a few exceptions.


## Box plot
We use the `boxplot()` Seaborn function to visualize the quartiles of every numeric variable. Each box plot includes the min, Q1, median, Q3, and max values of the respective variable as well as the outliers that are visualized as diamond symbols.

# Histogram of numeric features
We begin the EDA section of the project by visualizing the distribution of numeric features. We’ll use the `hist()` pandas function to accomplish this task.

All the features are extremely right-skewed. This indicates that some instances have significantly higher values than the rest and are outliers.

# Numeric and categorical features
Visualizing the relationship between a categorical and numeric variable can be useful in many cases. We can do this by using the hue mapping feature of the `histplot()` Seaborn function.

In this case, we plotted the `'Grocery'` variable histogram with a different color for every category of the `Channel` and `Region` variables.  Retail customers tend to spend more on grocery products than `Horeca` customers as the histogram bins with large values belong to the `Retail` category. Furthermore, customers with high annual spendings from `Lisbon` and `Other` regions, like those from `Oporto`, tend to spend less on grocery products with a few exceptions.


# Box plot
We use the `boxplot()` Seaborn function to visualize the quartiles of every numeric variable. Each box plot includes the min, Q1, median, Q3, and max values of the respective variable as well as the outliers that are visualized as diamond symbols.

Learn how to perform exploratory data analysis for anomaly detection on our dataset and plot data on histogram, box plot, bar plot, heatmap plot, and scatter plots.

Introduction to Machine Learning

Regression

Classification

Clustering

Customer Segmentation with K-Means Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Exploratory Data Analysis

Histogram of numeric features

Numeric and categorical features