Restaurant Data Analysis

Introduction

This project involves the analysis of a dataset containing information about various restaurants around the world. The dataset includes the following columns:

• Restaurant ID
• Restaurant Name
• Country Code
• City
• Address
• Locality
• Longitude
• Latitude
• Cuisines
• Average Cost for two
• Currency
• Has Table booking
• Has Online delivery
• Is delivering now
• Switch to order menu
• Price range
• Aggregate rating
• Rating color
• Rating text
• Votes

Objectives

The main objectives of this project are:

1. To explore and understand the dataset.
2. To perform data cleaning and preprocessing.
3. To analyze the data and extract meaningful insights.
4. To visualize the data and the insights obtained.

Methodology

The project will involve the following steps:

1. Data Acquisition: The dataset will be obtained from a suitable source, such as a public repository or an API.
2. Data Exploration: The dataset will be explored to understand its structure, identify any missing or inconsistent data, and gain a general understanding of the data.
3. Data Cleaning and Preprocessing: The dataset will be cleaned and preprocessed to ensure data quality and consistency. This may include handling missing values, removing duplicates, and converting data types as necessary.
4. Data Analysis: Various analytical techniques will be applied to the dataset, such as descriptive statistics, correlations, and clustering, to extract meaningful insights.
5. Data Visualization: The insights obtained from the data analysis will be visualized using appropriate charts and graphs to effectively communicate the findings.

Results

The project will result in a comprehensive understanding of the restaurant dataset, including insights into factors such as cuisine preferences, pricing, ratings, and geographical distribution. The findings will be presented in the form of a report or a presentation, depending on the requirements of the project.

Usage

To use the code and the analysis, follow these steps:

1. Clone the repository to your local machine.
2. Install the necessary dependencies, such as the required Python libraries.
3. Run the Jupyter notebook or the Python script to execute the data analysis.
4. Explore the results and the visualizations generated.

Dependencies

The project relies on the following dependencies:

• Python 3.x
• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit-learn (optional, if using machine learning techniques)