This project analyzes a restaurant dataset to uncover meaningful business insights through data cleaning, exploratory data analysis (EDA), and data visualization. The analysis focuses on understanding customer preferences, restaurant performance, pricing patterns, cuisine popularity, online delivery services, and geographic distribution.
The objective of this project is to demonstrate how Python can be used to transform raw data into actionable insights that support data-driven business decisions.
- Clean and preprocess restaurant data.
- Perform exploratory data analysis (EDA).
- Identify trends and patterns within the dataset.
- Visualize key business metrics.
- Generate actionable insights for business decision-making.
- Which cuisines are the most popular across restaurants?
- Which cities have the highest number of restaurants?
- Which cities have the highest average restaurant ratings?
- How are restaurants distributed across different price ranges?
- What percentage of restaurants offer online delivery?
- Does online delivery have an impact on customer ratings?
- What is the overall distribution of restaurant ratings?
- Which cuisine combinations are the most common?
- Do certain cuisine combinations receive higher ratings than others?
- How are restaurants geographically distributed based on their locations?
- Imported the restaurant dataset into Jupyter Notebook.
- Inspected dataset structure.
- Checked and handled missing values.
- Removed unnecessary columns.
- Corrected data types.
- Prepared the dataset for analysis.
Performed detailed analysis to identify trends and answer key business questions related to:
- Restaurant distribution
- Customer ratings
- Cuisine popularity
- Price ranges
- Online delivery services
- Geographic locations
Created visualizations to effectively communicate insights using Python visualization libraries.
- Identified the most popular cuisines among restaurants.
- Determined cities with the highest restaurant concentration.
- Compared average restaurant ratings across different cities.
- Evaluated restaurant distribution across various price ranges.
- Analyzed the adoption of online delivery services.
- Examined customer rating patterns.
- Identified popular cuisine combinations.
- Explored the geographic distribution of restaurants.
This analysis provides valuable insights that can help businesses:
- Understand customer dining preferences.
- Identify high-performing restaurant markets.
- Improve pricing strategies.
- Evaluate online delivery adoption.
- Support expansion decisions using geographic insights.
- Make informed business and marketing decisions based on data.
- Python
- Pandas
- NumPy
- Matplotlib
- Folium
- Jupyter Notebook
- Data Cleaning
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Data Visualization
- GroupBy Operations
- Aggregation Functions
- Filtering & Sorting
- Statistical Analysis
- Geographic Data Visualization
Restaurant-Data-Analysis/
│
├── Restaurant_Analysis.ipynb
├── README.md
├── requirements.txt
└── Dataset.csv
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Business Analytics
- Data Visualization
- Geographic Analysis
- Insight Generation
- Python Programming
- Analytical Thinking
- Problem Solving
- Build an interactive dashboard using Power BI or Tableau.
- Perform predictive analysis using Machine Learning.
- Develop an interactive web dashboard using Streamlit.
- Conduct sentiment analysis using restaurant reviews.
Ayesha Ijaz
Computer Science Graduate | Data Analyst
- Python
- SQL
- Power BI
- Microsoft Excel
- Data Analysis
- Data Visualization
- Business Intelligence
- Dashboard Development
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Feedback and suggestions are always welcome!