Build Your First ANN Model Under 10 Minutes

Author(s): Souradip Pal

Originally published on Towards AI.

A simple model trained on the Customer Churn Dataset

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Ever wondered why customers leave a service or product? This “churn” can be costly for companies, and predicting it is key to retaining customers. In this post, we’ll go through the process of building a Customer Churn Prediction Model using Keras, a powerful library for deep learning. We’ll break down the entire workflow, from data preprocessing to building and optimizing a neural network, all while keeping it simple and easy to follow.

This journey is designed to feel like a story, guiding you step-by-step. Let’s dive into the world of machine learning and build a model that can make sense of churn patterns.

To predict customer churn, we’ll go through the following concepts:

The Machine Learning Flow — Understanding how a model is built from start to finish.Data Preprocessing — Techniques like One-Hot Encoding (OHE) and data scaling.Train-Test Split — Why it’s important to split your data and create validation sets.Improving Model Accuracy — Tips on optimizers, scaling, and validation.Code Walkthrough — Step-by-step code snippets to guide you.

In any machine learning project, there’s a typical flow:

Data Collection and Exploration — Understand the data, clean it, and make it ready for modeling.Data Preprocessing… Read the full blog for free on Medium.

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Published via Towards AI