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Movie Review Sentiment Analysis Project Part-3 | Data Preprocessing on IMDB Dataset using NLP

Hello and welcome back!

In this video, we’re moving a step ahead in our NLP journey! Building on the text preprocessing techniques covered in the previous session, we’ll now apply them directly to the IMDB Movie Reviews Dataset.

🎯 What you’ll learn in this video:

📦 Installing all the necessary libraries to set up your NLP environment
🗂️ Loading and exploring the IMDB dataset
🛠️ Applying full text preprocessing pipeline:

- Lowercasing
- Removing punctuation
- Tokenization with nltk
- Removing stopwords
- Stemming & Lemmatization

✅ Preparing the X (features) and Y (labels) for our model
🧩 Splitting data into Training and Test sets for model development

This video is crucial for anyone looking to understand not just the concepts of text cleaning, but also how to implement them practically on real-world datasets. By the end, you'll have a clean, structured dataset ready for building machine learning models!

🔔 Don’t forget to like, comment, and subscribe for more hands-on NLP and Machine Learning tutorials!
------------Content of Video-----------------

00:00 - Recap
00:21 - Intro
00:31 - Libraries Installation Guide
02:14 - Starting Project
03:53 - Loading Dataset
05:11 - Data Preprocessing
16:27 - Training and Test Set Preparation
18:50 - Summary

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👉 Previous Video (Text Preprocessing in NLP Explained):
https://youtu.be/gY-b9p4roZw?si=ajdeymnxmhe1n6SE

👉 IMDB Dataset:
https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews

👉 Project Resources:
https://drive.google.com/drive/folders/1IfCc20vyoqF6w3xvR6TUZQabltw21hN1?usp=sharing

👉 Full Playlist:
https://www.youtube.com/playlist?list=PLvz5lCwTgdXBaSnHOb805tWouratTk3ix

#SentimentAnalysis #NLPProject #TextPreprocessing #IMDBDataset #DataCleaning #MachineLearningProject #TextClassification #DeepLearningNLP #PythonNLP #NLPTutorial #SentimentClassifier #NLPWithPython #DataScienceProject #Stemming #Lemmatization #TrainTestSplit #TextProcessingPython

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