Gated Recurrent Unit (GRU) is an enhanced version of the standard Recurrent Neural Network (RNN) that addresses the problem of short-term memory and is well-suited for sequential data processing. This project focuses on building a review classification model using GRU to classify app reviews on a scale of 1 to 5, with 1 indicating negative sentiment and 5 indicating positive sentiment.

To build a classifier to classify app reviews on a scale of 1 to 5 using Gated Recurrent Unit (GRU).

The dataset consists of app reviews and their corresponding ratings. The "score" column contains ratings ranging from 1 to 5 based on the "content" column.

• Language: Python
• Libraries: pandas, TensorFlow, Matplotlib, scikit-learn, Pillow, Gunicorn, TextBlob, NLTK, Keras, Flask

1. Converting words to lowercase.
2. Lemmatization of words.
3. Tokenization of words.
4. One-hot encoding of the scores.

• Training a sequential model in TensorFlow.

• Evaluating the model on test data.

1. Input: Contains the data used for analysis, including:

   • review_data.csv
   • test_review_data.csv (for testing)

2. Output: Contains the saved GRU model and pickle file.

3. src: The heart of the project, this folder contains all the modularized code for various steps. It further includes:

   • ML_pipeline: A folder with functions organized into different Python files, each appropriately named for its functionality. These Python functions are called inside the Engine.py file.

4. GRU-Neural-Network.ipynb and Model_Api.ipynb: The original Python notebooks.

5. requirements.txt: Lists all the required libraries and their versions for easy installation using pip.