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PROJECT
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.
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.
Skills
Natural Language Processing
Data Cleaning
Data Visualization
Data Analysis
Prerequisites
Basic understanding of Python
Familiarity with basic machine learning concepts
Technologies
Python
Pandas
seaborn
TensorFlow
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
1
Introduction
Task 0: Get Started
Task 1: Import Libraries and Modules
Task 2: Load the Datasets
2
Exploratory Data Analysis
Task 3: Check the Missing Values
Task 4: Check the Target Balance
Task 5: Display the Distribution of Labeled Intents
3
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
4
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
5
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
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.