Project: Sentiment Analysis of IMDB Reviews using LSTM

This project aims to perform sentiment analysis on IMDB movie reviews by building and training a deep learning model using Long Short-Term Memory (LSTM) networks. The key steps involved are:

Data Retrieval:The notebook accesses movie review datasets from Kaggle, which includes setting up Kaggle credentials within the environment.

Preprocessing: The raw text data undergoes cleaning and preprocessing to make it suitable for training the LSTM model.

Model Building: The project uses TensorFlow and Keras libraries to construct an LSTM model designed to handle sequential text data effectively.

Training and Evaluation: The model is trained on the IMDB dataset to predict the sentiment (positive or negative) of the reviews, followed by evaluating its performance.

Visualization and Insights: The results of the model, including accuracy and loss metrics, are visualized to assess its effectiveness in classifying the reviews. The project showcases the use of LSTM for handling natural language processing tasks like sentiment analysis, a crucial area in machine learning and AI.