https://github.com/lamiaaali/depi-graduation-project
SkinCare Sentiment Analysis Reviews
https://github.com/lamiaaali/depi-graduation-project
analytics azure azure-data-factory azure-data-lake azure-databricks azure-synapse-analytics data data-analytics data-engineering machine-learning pyspark python sql ssms unsupervised-learning
Last synced: 6 months ago
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SkinCare Sentiment Analysis Reviews
- Host: GitHub
- URL: https://github.com/lamiaaali/depi-graduation-project
- Owner: lamiaaali
- Created: 2024-10-17T08:39:38.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-10-20T07:56:17.000Z (12 months ago)
- Last Synced: 2025-04-13T06:15:54.128Z (6 months ago)
- Topics: analytics, azure, azure-data-factory, azure-data-lake, azure-databricks, azure-synapse-analytics, data, data-analytics, data-engineering, machine-learning, pyspark, python, sql, ssms, unsupervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 7.72 MB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DEPI-Graduation-Project
# Skincare Product Sentiment Analysis System 🧴✨
# Data Engineering ALX1_AIS4_S1e
# Team Members:
- Nada Hamdy Fatehy Abedelsalam
- Toqa Mohsen
- Shahd Ammar
- Omar Salah
- Yousef Magdy
[](https://github.com/nadahamdy217/DEPI-Graduation-Project/tree/main)
[](https://www.python.org/downloads/release/python-3120/)
## Project Overview
This project is a collaborative effort aimed at analyzing customer feedback for skincare products and predicting the sentiment (positive or negative) for each review using an **unsupervised model**. The system includes a **web application** where users can input reviews and get real-time sentiment predictions based on a **pre-trained unsupervised model** for sentiment analysis.
### Key Features
- 📊 **Sentiment analysis** for skincare product reviews using an unsupervised model.
- 🌐 **Web application** for real-time sentiment predictions.
- ⚡ **ETL pipeline** built using **Azure Data Factory**, **Azure Databricks**, and **Azure Synapse Analytics**.
- 🚀 **Optimized for large-scale data processing** with Azure services.
## Tech Stack
- **Azure Data Factory** for ETL orchestration
- **Azure Databricks** for data processing
- **Azure Synapse Analytics** for data storage and analysis
- **Unsupervised model** for sentiment analysis
- **Flask** for the web application
## Table of Contents
1. [Setup](#setup)
2. [ETL Pipeline](#etl-pipeline)
3. [Model Details](#model-details)
4. [Website](#website)
5. [Power BI Dashboard](#power-bi-dashboard)
6. [Contributing](#contributing)
7. [License](#license)
## Setup
### Prerequisites
- Python 3.12 or higher
- Azure Subscription (can be student subscription)
- Access to Azure Data Factory, Databricks, and Synapse Analytics
- Flask for running the web app
## Repository Access
- Ensure all team members have access to the shared repository.
- Collaborate using branches for feature development.
## ETL Pipeline
The ETL pipeline is designed to handle large volumes of customer feedback data:
- **Azure Data Factory**: Ingests raw review data from various sources.
- **Azure Databricks**: Processes and cleans the data using predefined transformations.
- **Azure Synapse Analytics**: Stores processed data for analysis and visualization in **Power BI**.
## Model Details
This project leverages a **pre-trained unsupervised sentiment analysis model** to classify product reviews as **positive** or **negative**. The model is used in the web app for internal real-time predictions but is not designed for API usage or external requests.
## Website
The web application allows users to submit product reviews and instantly receive sentiment predictions based on the unsupervised model.
### Key Features:
- Simple and intuitive interface for entering product reviews.
- Displays the predicted sentiment of the review.
- Deployed using **Flask**.
## Power BI Dashboard
A **Power BI dashboard** is used for advanced data visualization and analysis, allowing users to explore trends in review data:
- **Sentiment distribution** for skincare products.
- **Top-rated products** based on customer feedback.
- **Time-based analysis** of reviews.
- ### Dashboard Preview
## Documentation
- All the details are recorded here [Google Docs](https://docs.google.com/document/d/1le43WPQ_EMTB1sGkLuMi0PLx4spRrAn3YTNdpiX-V2o/edit?usp=sharing)
## Technologies Used
- *Python*: Backend logic and machine learning model.
- *Flask*: Web framework for building the backend API.
- *HTML/CSS/JavaScript*: Frontend interface.
- *Azure*: Azure Blob Storage and Azure App Service for deployment.
## Contributing
This is a collaborative project. To contribute:
1. Work on your feature or bug fix in a separate branch.
2. Ensure your changes are tested and reviewed by another team member.
3. Submit a pull request when your work is ready for review.