End-to-end data analytics case study applying the 6-phase process (Plan → Prepare → Process → Analyze → Share → Act) to meal planning and cooking scenario data.
This project demonstrates how the standard 6-step data analytics process applies to everyday life decisions — specifically meal planning, grocery shopping, and cooking. Created as part of Data Analytics Fundamentals coursework, it grounds abstract analytical concepts in an accessible, relatable context.
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Plan — Define the question: "How can I plan meals for the week to minimize cost, reduce waste, and meet nutritional goals?" Identify required data (recipes, ingredient prices, nutritional values).
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Prepare — Collect data: compile a recipe database, weekly grocery prices, and household preferences. Assess data quality and document sources.
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Process — Clean and transform the data: standardize ingredient units, handle missing nutritional values, and calculate cost-per-serving for each recipe.
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Analyze — Apply statistical reasoning: identify lowest-cost meals meeting nutritional targets, find patterns in ingredient reuse across recipes to minimize unique item purchases.
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Share — Communicate findings: present a weekly meal plan with cost breakdown, nutritional summary, and shopping list in a clear visual format accessible to all household members.
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Act — Implement and iterate: execute the meal plan, track actual vs. estimated cost and waste, and refine the model for the following week based on outcomes.
├── Presentation.pdf # Complete case study with detailed examples and visual process flow
└── README.md
- Python — Pandas, Matplotlib for data processing and visualization
- Excel — Tabular organization and cost calculations
Data Analytics Fundamentals project, PDEU — AY 2024–2025.
Author: Sakhi Patel · sakhipatel20@gmail.com