Projects
Data Analysis.
Problem Statement:
The food aggregator company FoodHub needs to analyze order data to understand customer preferences, optimize delivery operations, and enhance restaurant partnerships to improve overall business performance
Objective:
Perform comprehensive data analysis on FoodHub’s order dataset to derive actionable insights into customer behavior, delivery efficiency, and restaurant performance, enabling data-driven business decisions
Process Pipeline:
- Data Preparation: Clean and preprocess dataset, handle missing values and ensure correct data types
- Exploratory Data Analysis (EDA): Analyze distributions, trends, and correlations across variables
- Multivariate Analysis: Investigate relationships between key variables (delivery time vs. day, cost vs. ratings)
- Business Metrics Calculation: Compute revenue, identify high-performing restaurants, analyze delivery performance
- Insight Generation: Derive conclusions and recommendations to optimize operations and customer experience
Technologies & Models:
Python (Pandas, NumPy), Matplotlib, Seaborn, Statistical Analysis
Key Outcomes:
Operational insights identified peak demand (weekends), revenue analysis ($6,166 net revenue), restaurant performance rankings, customer behavior patterns (29.3% orders >$20), delivery optimization (10.5% orders >60min)
Intelligence • Architecture • Global Scale