🍽️ FoodHub Order Analysis
📌 Overview
The number of restaurants in New York is growing rapidly, and busy professionals and students increasingly rely on online food delivery. FoodHub connects users to multiple restaurants through a single app, making food ordering seamless and efficient.
In this project, I explored and analyzed real-world order data from FoodHub to uncover key trends, customer preferences, and operational patterns. The goal is to help the platform enhance its customer experience and improve restaurant and delivery performance through data-driven insights.
🧠 Project Objective
Understand demand patterns across restaurants.
Identify customer ordering behavior and preferences.
Analyze restaurant performance and delivery efficiency.
Support business decisions with actionable insights.
📊 Dataset Description
The dataset contains information about orders placed through the FoodHub platform.
Data Dictionary:
order_id — Unique ID of the order
customer_id — ID of the customer
restaurant_name — Name of the restaurant
cuisine_type — Cuisine ordered by the customer
cost_of_the_order — Total cost of the order
day_of_the_week — Weekday or weekend order indicator
rating — Customer rating out of 5
food_preparation_time — Time (in minutes) taken by the restaurant to prepare the order
delivery_time — Time (in minutes) taken by the delivery person to deliver the order
🧰 Tools & Technologies
Programming Language: Python
Libraries: Pandas, NumPy, Matplotlib, Seaborn
Techniques: Exploratory Data Analysis (EDA), Data Cleaning, Statistical Analysis, Data Visualization
📈 Key Insights
Identified top-performing restaurants and popular cuisines.
Observed ordering behavior variations between weekdays and weekends.
Analyzed preparation and delivery times to highlight operational bottlenecks.
Explored the relationship between order cost, delivery speed, and customer ratings.
🧭 Project Highlights
Developed strong data visualization skills by creating clear and impactful charts.
Applied statistical methods to validate hypotheses about business performance.
Enhanced Python proficiency and data handling efficiency.
Translated analytical findings into actionable strategies for improving service quality.
🚀 How to Run the Project
Clone this repository:
git clone https://github.com/rishovm/FoodHub-Data-Analysis.git
Navigate to the project directory:
cd foodhub-order-analysis
Install the required dependencies:
pip install -r requirements.txt
Run the Jupyter Notebook or Python script to explore the analysis.
📚 Future Enhancements
Predictive modeling for order volume forecasting.
Real-time delivery optimization suggestions.
Interactive dashboards for restaurant performance tracking.
📝 License
This project is for educational and portfolio purposes.
✨ Acknowledgements
Inspired by real-world online food delivery systems.
Special thanks to the data science community for open-source tools and resources.