Customer Review Rating Prediction

Overview

This project aims to predict customer ratings based on review and summary data. The task involves data exploration, data cleaning, feature engineering, and implementing machine learning models. Both XGBoost and LSTM models are utilized to achieve accurate rating predictions.

Problem Statement

The goal is to predict the overall rating of customers using their review and summary information. Accurate prediction of customer ratings can provide valuable insights into customer satisfaction and help enhance business strategies.

Task List

1. Data Exploration, Cleaning, and Feature Engineering:

   • Explore the dataset to understand its structure and content.
   • Clean the data by addressing missing values, outliers, and inconsistencies.
   • Engineer features to improve model performance.

2. Explanatory Analysis:

   • Analyze the data to uncover key insights and trends.
   • Create visualizations to support findings and convey meaningful stories.

3. Modeling/Training:

   • Implement and train XGBoost and LSTM models to predict customer ratings.
   • Evaluate model performance using relevant metrics.

4. Documentation and Reporting:

   • Document the analysis and model implementation in the provided Jupyter notebook.
   • Prepare a detailed report summarizing findings, model performance, and recommendations.

Project Files

• Musical_Instruments_5.json: Dataset used for modeling.
• CustomerReviewsNLP.ipynb: Jupyter notebook containing code for data exploration, cleaning, feature engineering, and model training.
• MusicalInstrumentReviews.h5: Saved model file (HDF5 format) containing trained models.
• README.md: Documentation for the project.

Data Exploration, Cleaning, and Feature Engineering

1. Data Exploration:

   • Conducted exploratory data analysis to understand the dataset’s structure and content.
   • Visualized distributions and relationships between features.

2. Data Cleaning:

   • Handled missing values through imputation or removal.
   • Addressed outliers and corrected inconsistencies.

3. Feature Engineering:

   • Created features such as sentiment scores from review text.
   • Applied NLP techniques to process and extract useful features from text data.

Explanatory Analysis

• Key Insights:

  • Identified patterns and correlations in customer reviews and ratings.
  • Uncovered important factors influencing customer ratings.

• Visualizations:

  • Generated charts and graphs to illustrate key findings.
  • Used visualizations to demonstrate trends and correlations.

Modeling/Training

1. XGBoost Model:

   • Implemented and trained an XGBoost model for rating prediction.
   • Tuned hyperparameters to optimize model performance.

2. LSTM Model:

   • Implemented and trained an LSTM model to capture sequential dependencies in review text.
   • Applied techniques to handle textual data and long sequences.

3. Model Evaluation:

   • Evaluated model performance using metrics such as Mean Squared Error (MSE) and R-squared (R²).
   • Compared XGBoost and LSTM models to determine the best-performing approach.

Documentation

• Jupyter Notebook (CustomerReviewsNLP.ipynb): Contains detailed code, analysis, and model implementation steps.

How to Run the Project

1. Install Dependencies:

   • Ensure all required libraries are installed. Use the requirements.txt file if available or manually install necessary packages.

2. Run the Notebook:

   • Open CustomerReviewsNLP.ipynb in Jupyter Notebook.
   • Execute the cells to perform data exploration, cleaning, feature engineering, and model training.

3. Load the Model:

   • Use MusicalInstrumentReviews.h5 to load the trained models if needed.

4. View Visualizations:

   • Check the visualizations created during analysis in the notebook.

Author

Ramesh S