BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 2
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...
Part 2: In Part 2 of our series, we dive into the practical side of building our machine learning web app. We'll start by downloading data from Kaggle, one of the most popular platforms for datasets. Using Excel, we'll perform our initial data analysis to gain insights into the dataset's structure and content. Additionally, we'll begin our journey into feature engineering, a critical step in preparing our data for machine learning models. Join us as we take the first steps towards developing our web app and harnessing the power of data-driven insights.
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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