Association Rule Mining (ARM) Analysis

Methodology and Data Preparation

Association Rule Mining was implemented to discover meaningful relationships between weather conditions and food delivery patterns. The analysis transformed raw delivery and weather data into transaction-based format, where each row represented a set of conditions that occurred together. Weather conditions were categorized into meaningful segments such as 'Cold_Weather' (temperature < 50°F), 'Hot_Weather' (temperature > 80°F), 'Rainy_Day' (precipitation > 0), and 'High_Humidity' (humidity > 70%). These were combined with delivery features, cuisine types, and city information to create comprehensive transaction records suitable for ARM analysis.

Key Findings and Rule Metrics

The analysis revealed strong associations between weather conditions and delivery patterns across different cities. The top rules by support show a particularly strong connection between windy conditions and cold weather (support: 0.818, confidence: 0.848), indicating these conditions frequently occur together in the dataset. City-specific patterns emerged, with New York showing strong associations with both cold weather and windy conditions (support: 0.597, confidence: 1.0). The highest lift values were observed in Los Angeles, where windy conditions strongly predicted high humidity (lift: 3.252), suggesting unique regional weather-delivery relationships.

Support, Confidence, and Lift Analysis

The ARM analysis identified impressive confidence values for several city-specific rules. Chicago consistently showed a perfect confidence (1.0) for associations with cold weather, indicating that whenever Chicago appears in the data, cold weather is guaranteed to be present. Similarly, New York paired with various conditions (rainy days, cuisine types, or delivery availability) showed perfect confidence for both cold weather and windy conditions. The lift values, which measure how much more likely specific conditions occur together compared to by chance, were particularly high for Los Angeles and high humidity (3.252), demonstrating the system's ability to identify non-obvious but statistically significant associations.

Rule Visualization and Network Analysis

The network visualization of association rules revealed complex interconnections between weather conditions, city locations, and delivery attributes. The network diagram showed clear clustering of rules by city, with each city exhibiting its own characteristic pattern of weather-delivery associations. New York dominated many of the strongest rules, appearing as both an antecedent and consequent in numerous high-confidence associations. This visualization effectively communicated the multi-dimensional nature of the relationships discovered, showing how delivery availability interacts with both location and weather patterns in complex ways that might not be apparent through traditional analysis methods.

Practical Implications of Rules

The discovered association rules provide actionable insights for food delivery operations. The strong association between windy conditions and cold weather across cities suggests the need for delivery services to prepare for these combined challenges during winter months. City-specific patterns, like the perfect association between Chicago and cold weather, can guide regional resource allocation and policy development. The high lift values for associations between delivery availability and specific cities under certain weather conditions could inform expansion strategies for delivery services. These patterns enable data-driven decision-making for restaurants, delivery platforms, and urban planners seeking to optimize food delivery systems under varying weather conditions.