Zomato-Data-Analysis-Using-Python

This project focuses on analyzing Zomato’s restaurant dataset to uncover insights related to customer preferences, cuisine trends, cost-effectiveness, and overall restaurant performance.

It utilizes Python with libraries such as Pandas, Matplotlib, and Seaborn in a Jupyter Notebook environment.

---

📁 Project Structure

.
├── performance.ipynb         # 📘 Main Jupyter Notebook with analysis
├── ZomatoData.csv   # 📊 Dataset containing student performance data
├── README.md                 # 📄 Project documentation (this file)

---

✅ Requirements

Ensure the following packages are installed:

• Python 3.7+
• Jupyter Notebook or JupyterLab
• pandas
• matplotlib
• seaborn

📦 Install via pip:

pip install pandas matplotlib seaborn notebook

---

🚀 How to Run the Project

1. 📥 Clone or download the repository.
2. 🖥️ Navigate to the project directory in your terminal:

cd path_to_project_folder

3. 📂 Launch Jupyter Notebook:

jupyter notebook

4. 📑 Open the performance.ipynb file in the Jupyter interface.

5. ▶️ Run the cells one by one to explore the analysis and visualizations.

---

📊 Quick Insight

Most common restaurant: Cafe Coffee Day (83 occurrences)

Top city by count: New Delhi (5,473 entries)

Most popular cuisine: North Indian (936 entries)

Currency types: 12 (e.g., INR, Dollar, Pound, etc.)

Ratings range: 0.0 to 4.9

Votes range: 0 to 10,934

Missing data: Only Cuisines has a few missing values (9 missing)

---

✨ Features

• 📈 Exploratory Data Analysis (EDA)
• 🔍 Insights on correlation between demographics and scores
• 🎨 Data visualizations using Seaborn & Matplotlib

---

📌 Notes

• 🛠️ Ensure Jupyter Notebook is properly installed to view and run the notebook.
• 🧼 The dataset is clean and ready for basic analysis without preprocessing.

Sales Data Visualization Project

This project provides interactive visualizations of company sales data to help understand performance across regions, product categories, and time periods.

Visualizations Included

1. Sales by Region: Bar chart showing revenue distribution across different regions
2. Monthly Sales Trend: Line chart displaying sales patterns throughout the year
3. Product Performance: Interactive bar chart that can toggle between revenue, units sold, and profit margin
4. Sales vs. Profit: Scatter plot showing the relationship between revenue and profit margin by product category

Features

• Interactive tooltips showing detailed information on hover
• Responsive design that works on different screen sizes
• Clean, professional aesthetics with consistent color scheme
• Clear annotations and insights for each visualization
• Interactive filtering for the product performance chart

How to Use

1. Clone this repository
2. Open index.html in a web browser
3. Interact with the visualizations:
   • Hover over chart elements to see detailed values
   • Click the buttons in the Product Performance chart to switch between metrics

Data Source

The visualizations use synthetic sales data for a retail company, including:

• Monthly sales figures
• Regional breakdown
• Product category performance
• Revenue, profit, and units sold metrics

Technologies Used

• D3.js for data visualization
• HTML/CSS for layout and styling
• CSV for data storage

Future Enhancements

• Add date range filtering
• Include more detailed drill-down capabilities
• Add export functionality for charts
• Implement regional comparison views