Food Menu Nutrition Dataset

By shrijeetverma13 · May 21, 2026

A comprehensive, cross-brand, cross-country dataset capturing nutrition facts, pricing, and popularity signals for food menu items across 18 international…

The analysis calculated a revenue potential score for each country by combining average price, bestseller rate, and number of items. A bar chart has been generated showing how countries compare, with color indicating average price levels. You can hover over each bar to see the bestseller rate and item count for each country.

The analysis successfully produced charts and data tables showing average customer ratings broken down by item category and brand. Two bar charts were generated: one showing ratings across all item categories, and another highlighting the top 10 brands by average rating. Additional data tables provide the full rankings for deeper exploration.

The analysis successfully mapped brands across two key dimensions: price per gram and average rating. Two visualizations were generated to help identify the best-value brands. The scatter plot (Brand Value Map) shows where each brand sits in terms of cost efficiency vs. customer satisfaction — brands in the upper-left quadrant offer the best combination of low price per gram and high ratings. The horizontal bar chart ranks the Top 10 Best Value Brands using a composite score that rewards high ratings and penalizes high per-gram costs. Brands with larger bubbles in the scatter plot also have more products, giving more confidence in their scores.

Three visualizations and summary tables were generated to compare price and ratings across brand tiers (budget, premium) and city tiers (metro, tier-2). The heatmaps and grouped bar chart clearly show that price varies significantly by brand tier, while ratings remain surprisingly consistent regardless of tier combination.

The analysis reveals which item categories and price ranges achieve the highest bestseller rates. Three charts and multiple data tables were generated to visualize these patterns. By category, Rice/Bowl items have the highest bestseller rate at 17.99% (93 out of 517 items), while Burger has the lowest at 13.85%. The heatmap chart shows how bestseller rates vary across both category and price range combinations simultaneously, making it easy to spot the sweet spots.

None of the three nutritional factors — calories, sugar, or sodium — have any meaningful relationship with customer ratings. All three correlations are extremely close to zero, meaning customer ratings are essentially independent of these nutritional values. Two bar charts and supporting data tables were generated to visualize these findings.

The nutritional breakdown between Veg and Non-Veg items reveals clear differences across macronutrients. Two data tables were generated comparing the full nutritional profiles. Non-Veg items are significantly higher in protein (18.91g vs 8.10g) and fat (19.53g vs 11.17g), while Veg items contain notably more sugars (17.46g vs 9.03g). Total carbohydrates are nearly identical between both categories. Non-Veg items also cost slightly more ($9.43 vs $8.03) but have a marginally higher average rating (3.82 vs 3.80).

Using both the IQR method and Z-score method, 6 items were flagged as calorie outliers — representing just 0.08% of the 7,500 total items. The IQR method (bounds: -130 to 847.5 calories) caught 14 outliers, while the stricter Z-score method (threshold 3) identified 6. All 6 items flagged by both methods fall under the 'Burger' category, spanning 5 different brands. Two histograms show the calorie distribution before and after IQR outlier removal, and a detailed table lists the top 10 most extreme outliers with full nutritional info.

The analysis successfully calculated sodium statistics across item categories and generated two summary tables showing the distribution of sodium content. The tables display mean, median, standard deviation, and item counts per category, sorted from highest to lowest average sodium. A danger threshold of 1500mg was applied to flag high-sodium categories, and individual items exceeding this threshold were also identified.

The analysis produced three data tables comparing brand ratings against normalized prices. The tables show brand-level aggregations including average ratings, average prices, item counts, and value category classifications. Brands were segmented into four quadrants: Best Value (high rating, low price), Worst Value (low rating, high price), Premium (high rating, high price), and Budget (low rating, low price) — all relative to the median rating and median price thresholds.