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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

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SkinCare Sentiment Analysis Reviews

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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



[![Build Status](https://img.shields.io/badge/build-passing-brightgreen)](https://github.com/nadahamdy217/DEPI-Graduation-Project/tree/main) 

[![Python Version](https://img.shields.io/badge/python-3.12%2B-blue)](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 Data Factory](https://github.com/user-attachments/assets/6e6c7f1d-ce0f-4f19-9f39-666fc11f2c92)



- **Azure Databricks**: Processes and cleans the data using predefined transformations.



![Databricks](https://github.com/user-attachments/assets/270773ab-4691-48cf-a456-a7fdab72cfe1)



- **Azure Synapse Analytics**: Stores processed data for analysis and visualization in **Power BI**.



![Synapse](https://github.com/user-attachments/assets/7d0568b5-3bc2-4e7a-8b35-597a84e697db)



## 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.



![image](https://github.com/user-attachments/assets/3c64e3d7-794f-44ea-bd72-844f6de2af3d)



### 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



![Dashboard](https://github.com/user-attachments/assets/85689f57-60cc-4cfd-8fd6-d44d09f32805)



## 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.