Project: CareerFoundry Data Immersion Program – Achievement 4
Objective: Analyze Instacart customer behavior to inform targeted marketing and optimize sales.

• When do customers order and spend the most?
• Which product departments are most popular?
• How do behaviors differ by customer profile?

• Instacart Online Grocery Shopping Dataset 2017
• CareerFoundry Customer Dataset

Python, Jupyter Notebook, Pandas, NumPy, Matplotlib, Seaborn, SciPy

• Data Cleaning & Preparation: handled duplicates, missing values, and data types
• Feature Engineering: price ranges, loyalty, demographics, vegan profile, order timing
• Aggregation & Segmentation: order frequency, spending, product preferences
• Visualization: histograms, bar charts, heatmaps, line plots

• Order activity peaks on specific days and times
• Product preferences vary by demographics and region
• Family size, income and eating habits affect purchasing patterns

• Target marketing by day, time, and customer profile
• Promote high-margin products during peak spending hours
• Tailor campaigns for vegan vs non-vegan and demographic groups
• Optimize product mix regionally

Author: Gabriela Cascione