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Scalable Machine Learning Model for Accurate Predictions on AWS

In this project, we’ll learn about the PyCaret module to create a machine learning model for predicting diabetes. We’ll deploy the model on AWS S3, load and test the model from the cloud, and create a FastAPI web application to interact with the model for predicting diabetes.

Scalable Machine Learning Model for Accurate Predictions on AWS

You will learn to:

Compare the multiple machine learning models and choose the best.

Classify with PyCaret and create, tune, plot, and save machine learning models.

Deploy models to the Amazon Web Services and use them to make predictions.

Create a FastAPI of the machine learning model.


Machine Learning

Data Visualisation

Cloud Deployment


Intermediate knowledge of Python

Intermediate knowledge of classification

Intermediate knowledge of plotting

Intermediate knowledge of Amazon Web Services



fastapi logo



Amazon S3

Project Description

In this project, we’ll use PyCaret, a Python library for machine learning, to create a predictive model for diabetes.

Once the model is built and finalized, we’ll store it on AWS S3, a scalable storage service. This will allow us to store the model and make it available for other applications.

After deploying the model, we’ll create a Python script to load it and use it to make predictions on new data. This will allow us to test the model and ensure it works as expected.

Finally, we’ll use FastAPI, a modern, high-performance web framework for building APIs, to create a web application allowing users to interact with the model. This application will take input from the user and use the deployed model to predict whether an individual has diabetes.

Project Tasks



Task 0: Get Started

Task 1: Import Modules

Task 2: Load Dataset

Task 3: Plot the Dataset

Task 4: Split the Training and Testing Data


Optimize and Tune the Model

Task 5: Initialize the Setup

Task 6: Compare the Models

Task 7: Tune the Model


Visualize the Model

Task 8: Plot the Learning Curve of the Model

Task 9: Plot the Classification Report of the Model

Task 10: Create the Morris Sensitivity Analysis

Task 11: Finalize the Model


Deploying the Model

Task 12: Configure AWS

Task 13: Create the S3 Bucket

Task 14: Deploy the Model

Task 15: Load the Model from Cloud

Task 16: Predict Using the Deployed Model

Task 17: Create a Fast API