This project analyzes Zomato's customer ordering data to gain insights into customer behavior, preferences, and restaurant performance. Our analysis focuses on answering key questions that can help Zomato improve its service and make data-driven business decisions.

1. What type of restaurant do the majority of customers order from?
2. How many votes has each type of restaurant received from customers?
3. What are the ratings that the majority of restaurants have received?
4. What is the average spending on each order for couples who order food online?
5. Which mode (online or offline) has received the maximum rating?
6. Which type of restaurant received more offline orders, so that Zomato can provide those customers with some good offers?

The analysis is based on the "Zomato data.csv" file. This dataset contains information about restaurant orders, customer preferences, ratings, and more. [You may want to add more specific details about the dataset here, such as the number of records, time period covered, and key variables.]

We use Python in a Jupyter Notebook environment to analyze the data. Our approach includes:

1. Data cleaning and preprocessing
2. Exploratory Data Analysis (EDA)
3. Statistical analysis
4. Data visualization

Data-Analysis-Project-1/
│
├── Zomato_Analysis_checkpoint.ipynb
├── Zomato data.csv
└── README.md

• Python 3.x
• Jupyter Notebook
• Required Python libraries (list these in your notebook or in a requirements.txt file)

1. Clone the repository:

   git clone https://github.com/Pabitra3/Data-Analysis-Project-1.git
   

2. Navigate to the project directory:

   cd Data-Analysis-Project-1
   

3. Install the required packages:

   pip install -r requirements.txt
   

1. Open the Jupyter notebook:

   jupyter notebook Zomato_Analysis_checkpoint.ipynb
   

2. Run the cells in order to reproduce the analysis.

This section should be updated with key findings as the analysis progresses. Below are placeholders for each research question:

Most popular restaurant type is Dinning catagory

Dinning restaurants have recieved maximum votes

The majority restaurants recieved ratings from 3.5 to 4

The majority of couples prefer restaurants with an approximate cost of 300 rupees

Offline order recieved lower ratings in comparison to online order

Dinning restaurants primarily accept offline orders, where as cafes primarily recieve online orders. This suggests that clints prefer to place orders in person at restaurants,but prefer online ordering at cafes.

We welcome contributions to this project. Please feel free to submit a Pull Request.

For any queries regarding this project, please open an issue in this repository.