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Cracking the Code: Optimizing Food Delivery Times with Data
- Optimizing food delivery times through GoFood must be a challenge, especially when late deliveries are involved.
- To understand the influencing factors around food delivery times in GoFood, the author begins by identifying the key variables that correlate with delivery time.
- EDA (Exploratory data analysis) is essential because it helps understand the relationships between features and the target variable.
- To implement the analysis, python libraries such as pandas, matplotlib, seaborn, and numpy will be used.
- Imputing missing values using the mean is a practical approach for numerical variables.
- Box plots are a simple and effective way to visualize outliers for continuous variables such as distance.
- The correlation matrix is a quick way to assess how numerical variables relate to each other and to the target variable.
- The significant difference in average delivery speed between high and low traffic levels highlights the importance of traffic conditions in delivery operations.
- The author proposes developing a smart routing system that dynamically adjusts delivery routes based on live traffic conditions, ensuring optimal delivery paths and faster completion times.
- To mitigate adverse weather conditions, the author proposes equipping couriers and operations teams with tools and strategies to maintain efficiency during extreme weather.
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