Skip to content

README.md

Latest commit

 

History

History
180 lines (125 loc) · 5.32 KB

README.md

File metadata and controls

180 lines (125 loc) · 5.32 KB

📌 Restaurant Data Analysis

📝 Project Overview

This project focuses on analyzing a dataset of restaurants, examining their distribution, ratings, and geospatial locations. The analysis is structured into two parts:

🔹 Part 1: Data Analysis

  • Task 1: Data Exploration & Preprocessing
  • Task 2: Descriptive Analysis
  • Task 3: Geospatial Analysis

🔹 Part 2: Advanced Insights

  • Task 4: Sentiment Analysis
  • Task 5: Price vs Rating Correlation
  • Task 6: Restaurant Rating Prediction

The dataset is stored in:

Dataset.csv

📂 Dataset Details

The dataset contains the following key columns:

  • Restaurant ID: Unique identifier for each restaurant
  • Name: Restaurant name
  • City: The city where the restaurant is located
  • Country Code: Country identifier
  • Cuisines: Types of cuisine served
  • Aggregate Rating: Overall restaurant rating
  • Latitude & Longitude: Geospatial coordinates
  • Review Text (for sentiment analysis)
  • Price Range (for pricing insights)

🔍 Analysis Breakdown

🔹 Part 1: Data Analysis

🏷️ Task 1: Data Exploration & Preprocessing

📁 File Location: Task1.ipynb

  • Loading Data: Reads Dataset.csv using Pandas.
  • Handling Missing Values:
    • Identifies missing values in the "Cuisines" column.
    • Drops rows where "Cuisines" data is unavailable.
  • Statistical Overview:
    • Uses .head(), .info(), .describe() for a summary.
    • Plots the distribution of "Aggregate Rating" using Matplotlib.

📷 Visualization:

  • Histogram of Aggregate Ratings

image

📊 Task 2: Descriptive Analysis

📁 File Location: Task2.ipynb

  • Loading Preprocessed Data: Reads cleaned data from Dataset.csv.
  • City & Country Analysis:
    • Finds the most common restaurant locations.
    • Groups data by "City" and "Country Code".
  • Visualizing Trends:
    • Bar plots of the top 10 countries and cities using Seaborn.

📷 Visualizations:

  • Top 10 Countries by Restaurant Count

image

  • Top 10 Cities by Restaurant Count

image

  • Top 10 Cuisines

image

🗺️ Task 3: Geospatial Analysis

📁 File Location: Task3.ipynb

  • Loading Data: Reads Dataset.csv.
  • Mapping Restaurants:
    • Extracts latitude and longitude data.
    • Creates an interactive map with restaurant locations using Folium.
  • Visualization:
    • Displays restaurants as clusters on an interactive map.

📷 Visualizations:

  • Restaurant Location Map

image

🔹 Part 2: Advanced Insights

💬 Task 4: Sentiment Analysis

📁 File Location: Task4.ipynb

  • Objective: Analyze customer reviews to determine restaurant sentiment.
  • Approach:
    • Cleans text data (removes stopwords, punctuation, etc.).
    • Applies sentiment analysis using NLP libraries (e.g., VADER, TextBlob).
    • Categorizes reviews into Positive, Neutral, and Negative.
  • Visualization:
    • Pie charts and bar graphs to show sentiment distribution.

📷 Visualizations:

  • Aggregate Rating Of Restaurants With Table Booking And Those Without.

image

  • Availability of Online Delivery Among Restaurants With Different Price Ranges.

image

💰 Task 5: Price vs Rating Correlation

📁 File Location: Task5.ipynb

  • Objective: Examine how price affects restaurant ratings.
  • Approach:
    • Compares price range with average aggregate rating.
    • Uses scatter plots and correlation heatmaps.
  • Findings:
    • Identifies whether higher-priced restaurants have better ratings.

📷 Visualizations:

  • Price vs Rating Scatter Plot

image

  • Correlation Heatmap

image

📈 Task 6: Restaurant Rating Prediction

📁 File Location: Task6.ipynb

  • Objective: Convert categorical data into numerical format for further analysis.

  • Approach:

    • Encodes categorical variables using techniques like One-Hot Encoding or Label Encoding.
    • Ensures the dataset is in a structured numerical format.
    • Exports the processed data as CSV or Excel for machine learning models.

  • Visualization: -Displays summary statistics of transformed data.

⚙️ Setup & Installation

To run this project, install the required libraries:

pip install numpy pandas matplotlib seaborn folium nltk textblob scikit-learn

Run the Jupyter notebooks in sequence:

  1. Task1.ipynb
  2. Task2.ipynb
  3. Task3.ipynb
  4. Task4.ipynb
  5. Task5.ipynb
  6. Task6.ipynb

🤝 Contributing

Feel free to fork this repository and open pull requests with improvements!

📜 License

This project is open-source under the MIT License.