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PROJECT
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.
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.
Skills
Machine Learning
Data Visualisation
Cloud Deployment
Prerequisites
Intermediate knowledge of Python
Intermediate knowledge of classification
Intermediate knowledge of plotting
Intermediate knowledge of Amazon Web Services
Technologies
Python
FastAPI
PyCaret
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
1
Preprocessing
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
2
Optimize and Tune the Model
Task 5: Initialize the Setup
Task 6: Compare the Models
Task 7: Tune the Model
3
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
4
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
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.