https://github.com/girish119628/restaurant_review_analysis
The Project analyzes restaurant ratings and customer reviews to uncover key factors influencing customer satisfaction and restaurant success
https://github.com/girish119628/restaurant_review_analysis
Last synced: about 1 month ago
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The Project analyzes restaurant ratings and customer reviews to uncover key factors influencing customer satisfaction and restaurant success
- Host: GitHub
- URL: https://github.com/girish119628/restaurant_review_analysis
- Owner: girish119628
- Created: 2025-04-19T14:03:03.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-19T14:26:22.000Z (2 months ago)
- Last Synced: 2025-04-19T18:26:33.699Z (2 months ago)
- Language: Jupyter Notebook
- Size: 4.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Restaurant_Review_Analysis
This project analyzes restaurant ratings and customer reviews to uncover key factors influencing **customer satisfaction** and **restaurant success**.
It includes end-to-end data cleaning, sentiment analysis, statistical testing, and visualization, powered by Python, MySQL, and Power BI.
---
## Problem Statement
**Objective:**
To analyze customer reviews and ratings to:
- Determine what drives customer satisfaction.
- Explore temporal patterns and user engagement metrics.
- Perform sentiment analysis using NLP.
- Identify statistically significant patterns using ANOVA.
- Build dynamic Power BI dashboards using real-time data from MySQL.
---
## 🧰 Tech Stack & Tools
- **Python** – Data cleaning, NLP, statistical analysis
- **MySQL** – Data storage using Python-MySQL connector
- **Power BI** – Dashboard connected directly to MySQL (no manual file import)
- **Excel** – Initial data exploration and profiling
- **Libraries Used:** Pandas, Numpy, NLTK/TextBlob/VADER, Matplotlib/Seaborn, Scikit-learn, Scipy/Statsmodels
---
## 📈 Project Workflow
### 1. **Data Understanding & Exploration**
- Inspected raw dataset: restaurant names, user reviews, followers, ratings, date-time, etc.
- Performed exploratory data analysis (EDA) in **Excel**.
### 2. **Data Cleaning in Python**
- Removed null values and handled incorrect data types.
- Standardized date formats and text fields.
- Saved cleaned data directly to a **MySQL Server** using Python's connector module.
### 3. **Sentiment Analysis (NLP)**
- Applied **TextBlob/VADER** to classify reviews into **Positive**, **Neutral**, and **Negative** sentiments.
- Created sentiment scores to analyze customer emotions.
### 4. **Statistical Analysis**
- Performed **ANOVA** to test whether customer ratings significantly differ based on various features (like review length, sentiment, or time periods).
### 5. **Power BI Dashboard**
- Connected **Power BI directly to MySQL server** (no need for importing CSV/Excel).
- Built dynamic visuals for:
- Ratings vs Time
- Sentiment distribution
- User engagement (followers, number of reviews)
- Comparative trends and key insights
---
## 🎥 Project Explanation Video
*(Coming Soon)* – A video walkthrough is being created to explain each project step and insights derived from the dashboard.
---
## 💡 Key Insights
- Sentiment polarity had a strong correlation with ratings.
- Review volume and follower count positively impact restaurant visibility.
- Significant variation found in rating patterns across time slots and user segments.
---
## 📂 Folder Structure
```bash
📁 restaurant-review-analysis/
│
├── 📄 README.md
├── 📄 requirements.txt
├── 📊 Dashboard.pbix (optional)
├── 📁 data_cleaning/
│ └── data_cleaning_script.py
├── 📁 sentiment_analysis/
│ └── nlp_sentiment_model.py
├── 📁 mysql_connector/
│ └── upload_cleaned_data.py
└── 📁 visuals/
└── screenshots/