Step 1: Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a crucial first step in any machine learning project. Skipping it can lead to mistakes and poor model performance. In this article, you’ll learn why EDA is important and how it helps uncover patterns in data, choose the right features, and improve your model’s accuracy. Whether you’re predicting customer churn or building recommendation systems, this guide will show you the benefits of doing EDA before jumping into modeling.

Want to see how EDA can make your models smarter?

Step 2: Model Building and Evaluation

Evaluating how well your recommendation system works is crucial for improving user experience. In this article, we explore Precision@K, a powerful metric that helps you measure the accuracy of your recommendations. You’ll learn what Precision@K is, why it matters, and how to apply it in your projects. Whether you’re building a movie recommendation system or any other type of recommendation engine, this guide will provide you with practical insights to enhance your algorithm’s performance.

Ready to take your recommendation system to the next level?

Step 3: Creating a Web Application

This article explores how to transition from a notebook-based data scientist to creating full-fledged machine learning web applications. It outlines an 8-step process for deploying a model from a notebook to a live app, covering key tools like Flask, Nginx, and Amazon EC2. By guiding you through tasks like setting up a server, testing locally, and using cloud environments, it empowers you to build functional, scalable applications. Perfect for those eager to move beyond notebooks and deliver real-world value.

Ready to deploy your ML Web App?