Video Description:
In this video, we will walk you through a complete sentiment analysis project using Python. We will cover the following key steps:

Introduction to the Dataset: We’ll begin by loading a large sentiment analysis dataset containing text and sentiment labels.

Text Preprocessing: Learn how to clean the text data by removing punctuation, converting text to lowercase, and filtering out stopwords using the NLTK library.

Feature Extraction: Discover how to convert the cleaned text into numerical features using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization.

Model Training: We’ll split the data into training and testing sets and train a Logistic Regression model on the training data.

Model Evaluation: Evaluate the model’s performance by calculating accuracy and generating a classification report.

Model Saving: Finally, we will save the trained model for future use.