This project focuses on analyzing real-world online food delivery data from FoodHub, a food aggregator operating in New York. The goal is to uncover patterns in restaurant demand, order behavior, and delivery performance to help the company optimize its operations, improve customer satisfaction, and make data-driven strategic decisions.
The primary objective was to explore customer order data, identify key business insights, and provide actionable recommendations. By understanding order trends, popular restaurants, peak demand times, and delivery metrics, the analysis aims to support better decision-making for marketing, logistics, and partnership strategies.
- Source: Provided as part of the project coursework
- Size: ~1,000+ customer orders
- Key Features:
- Restaurant names and categories
- Order dates, times, and delivery durations
- Customer location and preferences
- Target: Not applicable (exploratory analysis project)
- Data Cleaning & Preprocessing – Removed duplicates, handled missing values, and standardized categorical fields.
- Exploratory Data Analysis (EDA) – Conducted univariate and bivariate analysis to identify trends in order frequency, restaurant performance, and delivery times.
- Visualization & Insights – Built visual dashboards using Matplotlib and Seaborn to illustrate peak order times, best-performing restaurants, and delivery performance patterns.
- Business Recommendations – Translated analytical findings into actionable strategies for FoodHub’s marketing and operations teams.
- Identified peak order hours and weekdays to optimize delivery workforce allocation.
- Highlighted top-performing restaurants by order volume and revenue potential.
- Discovered key factors influencing delivery delays and proposed operational improvements.
- Provided data-driven recommendations to improve customer satisfaction and reduce delivery time.
- Language: Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn
- Tools: Jupyter Notebook / Google Colab
Sandesh S. Badwaik