Model Testing with Stemming, Lemmatization & Stopword Removal | Machine Learning Project (Part 5 of 9)

Welcome to Part 5 of our 9-part Machine Learning Project Series, where we test the Book Genre Classification Model by applying advanced text processing techniques like stemming, lemmatization, and stopword removal—all at once. These Natural Language Processing (NLP) techniques help refine the text data to improve model accuracy and performance.

In this video, you'll learn how to apply these key text processing steps during model testing to ensure the most accurate classification of book genres. Stay tuned as we progress toward deploying the app on Amazon Web Services (AWS) in the upcoming parts.

🔑 What You’ll Learn in Part 5:
➡️ How to apply stemming, lemmatization, and stopword removal simultaneously
➡️ Testing the machine learning model with refined text data for better performance
➡️ Understanding how advanced text processing improves model accuracy
➡️ Evaluating the model’s response to processed text during testing

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💡 Key Features Covered in Part 5:

Simultaneous application of stemming, lemmatization, and stopword removal
Testing and evaluating the machine learning model with processed data 📊
Real-world application of NLP techniques for refining text classification models
Enhancing the Book Genre Classification App by improving text preprocessing
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📢 Don’t forget to: 👍 Like this video if it helped you enhance your model testing!
💬 Comment below with any questions or suggestions!
🔔 Subscribe to stay updated for Part 6 and the rest of the 9-part series as we integrate and deploy the app on AWS!

#ModelTesting #MachineLearningProject #Stemming
#Lemmatization #StopwordRemoval #TextProcessing #NLP