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Build an NLP Model for Customer Intent Classification

In this project, we will build a natural language processing (NLP) model for customer intent classification. We will use TensorFlow to build an NLP model to categorize the intentions behind customer queries or messages.

Build an NLP Model for Customer Intent Classification

You will learn to:

Explore the dataset using Python packages.

Prepare the text preprocessing pipeline.

Build an NLP model for customer intent classification.

Automate the categorization of severity levels of customer queries.


Natural Language Processing

Data Cleaning

Data Visualization

Data Analysis


Basic understanding of Python

Familiarity with basic machine learning concepts






Project Description

Intent classification is a machine learning process used to identify and classify user intent automatically. It helps in business operations by enabling faster responses to queries and allowing for a more customer-centric support service.

In this project, we will build a natural language processing (NLP) model using TensorFlow for intent classification in a customer support context. The model will be fed with textual data comprising 13,083 customer service queries labeled with 77 intents in a banking domain. The trained NLP model will identify customer intent from questions and automatically categorize them into one of several severity levels (e.g., high, medium, or low). This can help business owners prioritize urgent and high-priority cases, serving as an early warning system to identify potential risks to customer retention. With this information, they can devise timely strategies to win back customers.

Project Tasks



Task 0: Get Started

Task 1: Import Libraries and Modules

Task 2: Load the Datasets


Exploratory Data Analysis

Task 3: Check the Missing Values

Task 4: Check the Target Balance

Task 5: Display the Distribution of Labeled Intents


Text Preprocessing

Task 6: Shuffle the Dataset

Task 7: Transform the Data

Task 8: Tokenize the Words

Task 9: Pad the Training and Test Sequences

Task 10: Encode the Labels


Train Model

Task 11: Prepare a Validation Set

Task 12: Define a Neural Network Architecture

Task 13: Fit the Model

Task 14: Plot Training and Validation Loss Curves

Task 15: Retrain the Model


Evaluate Model

Task 16: Examine the Model’s Performance with the Test Dataset

Task 17: Predict the Outcomes for New Data

Task 18: Match Predictions with Severity Levels