{"id":26396280,"url":"https://github.com/vipulbunny/restaurant-insight-analysis","last_synced_at":"2026-05-03T12:38:04.045Z","repository":{"id":280161189,"uuid":"941147191","full_name":"VIPULbunny/Restaurant-Insight-Analysis","owner":"VIPULbunny","description":"A comprehensive data analysis project exploring restaurant ratings, locations, and customer sentiments. 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The analysis is structured into two parts:\n\n### **🔹 Part 1: Data Analysis**\n- **Task 1**: Data Exploration \u0026 Preprocessing\n- **Task 2**: Descriptive Analysis\n- **Task 3**: Geospatial Analysis\n\n### **🔹 Part 2: Advanced Insights**\n- **Task 4**: Sentiment Analysis\n- **Task 5**: Price vs Rating Correlation\n- **Task 6**: Restaurant Rating Prediction\n\nThe dataset is stored in:\n```\nDataset.csv\n```\n\n---\n\n## 📂 Dataset Details\nThe dataset contains the following key columns:\n- **Restaurant ID**: Unique identifier for each restaurant\n- **Name**: Restaurant name\n- **City**: The city where the restaurant is located\n- **Country Code**: Country identifier\n- **Cuisines**: Types of cuisine served\n- **Aggregate Rating**: Overall restaurant rating\n- **Latitude \u0026 Longitude**: Geospatial coordinates\n- **Review Text** (for sentiment analysis)\n- **Price Range** (for pricing insights)\n\n---\n\n## 🔍 Analysis Breakdown\n\n### **🔹 Part 1: Data Analysis**\n\n#### 🏷️ Task 1: Data Exploration \u0026 Preprocessing\n📁 **File Location**: `Task1.ipynb`\n\n- **Loading Data**: Reads `Dataset.csv` using Pandas.\n- **Handling Missing Values**:\n  - Identifies missing values in the \"Cuisines\" column.\n  - Drops rows where \"Cuisines\" data is unavailable.\n- **Statistical Overview**:\n  - Uses `.head()`, `.info()`, `.describe()` for a summary.\n  - Plots the distribution of \"Aggregate Rating\" using Matplotlib.\n\n📷 **Visualization**:\n- Histogram of Aggregate Ratings\n  \n![image](https://github.com/user-attachments/assets/051f1de3-6780-4bbf-b175-cadd36560216)\n\n\n\n#### 📊 Task 2: Descriptive Analysis\n📁 **File Location**: `Task2.ipynb`\n\n- **Loading Preprocessed Data**: Reads cleaned data from `Dataset.csv`.\n- **City \u0026 Country Analysis**:\n  - Finds the most common restaurant locations.\n  - Groups data by \"City\" and \"Country Code\".\n- **Visualizing Trends**:\n  - Bar plots of the top 10 countries and cities using Seaborn.\n\n📷 **Visualizations**:\n- Top 10 Countries by Restaurant Count\n\n![image](https://github.com/user-attachments/assets/6b681317-241b-462f-8688-141b4de3c009)\n\n- Top 10 Cities by Restaurant Count\n\n![image](https://github.com/user-attachments/assets/b3049969-c49b-4c79-9b4b-0dcb765c1de7)\n\n- Top 10 Cuisines\n\n![image](https://github.com/user-attachments/assets/915152f4-1ddc-434a-8aa8-a4b72a60ed2b)\n\n\n#### 🗺️ Task 3: Geospatial Analysis\n📁 **File Location**: `Task3.ipynb`\n\n- **Loading Data**: Reads `Dataset.csv`.\n- **Mapping Restaurants**:\n  - Extracts latitude and longitude data.\n  - Creates an interactive map with restaurant locations using Folium.\n- **Visualization**:\n  - Displays restaurants as clusters on an interactive map.\n\n📷 **Visualizations**:\n- Restaurant Location Map\n\n![image](https://github.com/user-attachments/assets/6132622e-a0bd-441a-b1ab-fb75fce19861)\n\n\n---\n\n### **🔹 Part 2: Advanced Insights**\n\n#### 💬 Task 4: Sentiment Analysis\n📁 **File Location**: `Task4.ipynb`\n\n- **Objective**: Analyze customer reviews to determine restaurant sentiment.\n- **Approach**:\n  - Cleans text data (removes stopwords, punctuation, etc.).\n  - Applies sentiment analysis using NLP libraries (e.g., VADER, TextBlob).\n  - Categorizes reviews into Positive, Neutral, and Negative.\n- **Visualization**:\n  - Pie charts and bar graphs to show sentiment distribution.\n\n📷 **Visualizations**:\n- Aggregate Rating Of Restaurants With Table Booking And Those Without.\n\n![image](https://github.com/user-attachments/assets/34e441b3-0995-4baf-b626-46feb1112afa)\n\n- Availability of Online Delivery Among Restaurants With Different Price Ranges.\n\n![image](https://github.com/user-attachments/assets/589807c8-b9ed-47a6-bf0b-a84a9d46d720)\n\n\n#### 💰 Task 5: Price vs Rating Correlation\n📁 **File Location**: `Task5.ipynb`\n\n- **Objective**: Examine how price affects restaurant ratings.\n- **Approach**:\n  - Compares price range with average aggregate rating.\n  - Uses scatter plots and correlation heatmaps.\n- **Findings**:\n  - Identifies whether higher-priced restaurants have better ratings.\n\n📷 **Visualizations**:\n- Price vs Rating Scatter Plot\n\n![image](https://github.com/user-attachments/assets/142c55b5-031e-4881-b775-405ad05f4475)\n\n- Correlation Heatmap\n\n![image](https://github.com/user-attachments/assets/1539b88f-1e46-4e7d-b337-63f8c1501cb7)\n\n\n#### 📈 Task 6: Restaurant Rating Prediction\n📁 **File Location**: `Task6.ipynb`\n\n- **Objective**: Convert categorical data into numerical format for further analysis.\n- **Approach**:\n  - Encodes categorical variables using techniques like One-Hot Encoding or Label Encoding.\n  - Ensures the dataset is in a structured numerical format.\n  - Exports the processed data as CSV or Excel for machine learning models.\n  \n- **Visualization**:\n  -Displays summary statistics of transformed data.\n\n---\n\n## ⚙️ Setup \u0026 Installation\nTo run this project, install the required libraries:\n```bash\npip install numpy pandas matplotlib seaborn folium nltk textblob scikit-learn\n```\nRun the Jupyter notebooks in sequence:\n1. `Task1.ipynb`\n2. `Task2.ipynb`\n3. `Task3.ipynb`\n4. `Task4.ipynb`\n5. `Task5.ipynb`\n6. `Task6.ipynb`\n\n---\n\n## 🤝 Contributing\nFeel free to fork this repository and open pull requests with improvements!\n\n---\n\n## 📜 License\nThis project is open-source under the MIT License.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvipulbunny%2Frestaurant-insight-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvipulbunny%2Frestaurant-insight-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvipulbunny%2Frestaurant-insight-analysis/lists"}