BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 3
Welcome to our series! We're going to build a machine learning web app from scratch. Whether you're a pro or a beginner, this series is for you. We'll use tools like Amazon SageMaker, AWS Amplify, and more to make our app shine. Our goal? To teach you the basics of ML focused AWS tools integration and web development, one step at a time. Ready to dive in? Let's get started! Here are few key links:
https://aws.amazon.com
https://www.anaconda.com/download
https://www.kaggle.com/datasets/bsugi...
Support us:
https://paypal.com/donate/?hosted_but...
In Part 3 of our series, we delve deeper into the data preprocessing phase by focusing on data cleaning and feature engineering. Using Jupyter Notebook, a powerful interactive computing environment, we'll implement various techniques to clean our dataset, including handling missing values, removing duplicates, and addressing outliers. Additionally, we'll explore advanced feature engineering methods to extract valuable insights from our data and enhance the predictive power of our machine learning models. Join us as we refine our dataset and lay the foundation for building a robust and accurate machine learning web app.
To follow along, you’ll need:
*A text editor (such as Notepad or Notepad++)
*An AWS account and access to the Console. Note: You'll need administrator permissions.
*Some basic knowledge of AWS is preferable, but you can still follow along if you’re an absolute newbie.
*Additionally, Microsoft Excel and access to an Anaconda environment for python editor are required.
Below is the complete code and raw data, necessary for constructing the application:
Loan Data file:
https://drive.google.com/drive/folder...
Python program for model evaluation in local machine:
https://drive.google.com/drive/folder...
SageMaker notebook:
https://drive.google.com/drive/folder...
Lambda function file including JSON text file:
https://drive.google.com/drive/folder...
Amplify HTML file:
https://drive.google.com/drive/folder...
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