---
pretty_name: NutriRecIndia19M
license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
tags:
- synthetic-data
- recommender-systems
- india
- nutrition
- food
- health
- tabular
size_categories:
- 10M<n<100M
---

# NutriRecIndia19M

**A large-scale synthetic dataset for food recommendation, dietary compliance, and health-outcome modeling grounded in Indian population statistics.**

NutriRecIndia19M is a multi-table synthetic dataset (~19M rows across 10 linked tables) simulating one year of eating behavior, food recommendations, and health outcomes for 5,000 synthetic Indian users. It was built to support research and prototyping in recommender systems, health-behavior modeling, and nutrition-focused ML — in a domain where real user-level eating and health data is sensitive and rarely shareable.

Every distribution used to generate this dataset (demographics, meal timing, cuisine preference, fasting behavior, compliance rates, app-interaction patterns) is grounded in a cited, published source — census data, national health surveys, and public industry benchmarks — rather than arbitrary parameters. Full source list is in [Data Source & Grounding](#data-source--grounding).

## Dataset Summary

| Table | Approx. Rows | Description |
|---|---|---|
| `users` | 5,000 | Demographic, cultural, and health profile per user |
| `meal_logs` | ~4.6M | Every logged meal: timing, dish, nutrition, occasion |
| `interactions` | ~7.3M | Recommendation impressions: skip / click / order events |
| `reorder_events` | ~2.2M | Repeat-order behavior per user–dish pair |
| `social_eating_context` | ~4.6M | Social setting (alone / family / friends / restaurant) per meal |
| `user_weekly_context` | ~41,600 | Weekly nutritional gaps, budget state, compliance rate |
| `fast_days` | ~7,900 | Religious fasting events (Ramadan, Ekadashi, Paryushan, etc.) |
| `skip_events` | ~11,000 | Meal-skip events with reason and compensatory behavior |
| `health_outcomes` | ~4,000 | Quarterly BMI change and dietary-compliance trend per user |
| `life_events` | ~640 | Job change, marriage, pregnancy, diagnosis, relocation, etc. |

**Total: ~19 million rows**, all joinable via a shared `user_id` key.

## Intended Uses

- Training and benchmarking recommendation models (ranking, cold-start, session-based) in a food/health context
- Dietary-compliance and health-outcome prediction research
- Studying position bias, reorder/habit modeling, and social-context effects on eating behavior
- Teaching/coursework on recommender systems where real health data cannot be used
- Prototyping India-specific nutrition or food-delivery ML products before real user data is available

**Out-of-scope uses:** This dataset should **not** be used to make real clinical, medical, or individual health decisions. It is synthetic and does not represent any real person's data.

## Dataset Structure

### `users.csv`
Demographic and health profile: age, gender, religion, region/state, BMI, health conditions (diabetes, hypertension, PCOS, obesity), dietary restrictions (vegetarian, halal, Jain, gluten-free, dairy-free, low-sodium), occupation, income tier, health literacy, cooking skill, observance level, sleep hours, persona type. 1 row per user (5,000 rows, ~40 columns).

### `meal_logs.csv`
One row per logged meal: `user_id`, timestamp, occasion (breakfast/lunch/dinner/snack/late-night), dish name, cuisine, calories, macros (protein/carbs/fat/fiber), glycemic index, portion multiplier, cooked-at-home vs. delivery, social context flag, health-compliance flag.

### `interactions.csv`
Recommendation-impression log: `user_id`, session ID, dish shown, recommendation rank, action (skip/click/order), session duration, `user_health_match`, `price_match_score`, `cuisine_affinity`, final order flag.

### `reorder_events.csv`
Per user–dish repeat-order history: days between orders, cumulative order count, reorder flag, rating proxy.

### `social_eating_context.csv`
Per-meal social setting: solo / family / friends / colleagues / spouse / restaurant, group size, location type, budget multiplier, variety score.

### `user_weekly_context.csv`
Weekly rollup per user: average calories, macro/fiber gaps vs. RDA targets, budget state (month-start vs. month-end), weekly health-compliance rate, stress level.

### `fast_days.csv`
Religious fasting-day records: fast type (Ramadan, Ekadashi, Monday fast, Navratri, Paryushan, Jain monthly), observance level, calorie impact, complete vs. partial fast.

### `skip_events.csv`
Meal-skip records: occasion skipped, reason (not hungry, running late, meeting, fasting, forgot), whether a compensatory meal followed and its calorie delta.

### `health_outcomes.csv`
Quarterly per-user health trend: BMI at quarter start/end, BMI change, compliance-rate change, condition-severity change, overall health trend (improving/stable/declining).

### `life_events.csv`
Life events per user: type (job change, marriage, pregnancy, health diagnosis, city relocation, started gym, financial stress), event date, and (where implemented — see [Known Limitations](#known-limitations-honest-disclosure)) downstream behavioral effects.

## Data Source & Grounding

Rather than choosing generator parameters arbitrarily, every major distribution in this dataset was calibrated against a published source, for example:

- **Census of India (2011)** — age, gender, religion, and regional population distributions
- **NFHS-5 (National Family Health Survey, 2021)** — BMI-by-age/gender, chronic condition prevalence, vegetarian rate, dietary compliance rates
- **ICMR "What India Eats" (2021)** — meal-occasion timing, regional meal frequency, dish/cuisine pool
- **MOSPI Household Consumer Expenditure Survey (2022)** and **RBI Household Finance Survey (2022)** — income tiers and monthly budget cycles
- **Pew Research Center (2021)** — religiosity and fasting-frequency correlation
- **Zomato Annual Report (2023)** and **Swiggy Engineering Blog (2022–2023)** — recommendation click-through/order-conversion rates, reorder rates, session sizes
- **ADA Standards of Medical Care in Diabetes (2023)** and **Appetite journal (2020, India study)** — glycemic-index relevance and stress as a dietary-compliance predictor
- Academic recommender-systems literature (Covington et al. 2016; Joachims et al. 2007; Jannach et al. 2015; Lam et al. 2008) — used only to sanity-check that resulting model metrics (NDCG, AUC, cold-start accuracy) fall in realistic, published ranges, not to shape the raw data itself

A full bibliography of the ~20 sources used is available on request / in the accompanying methodology notes.

### Why synthetic

Real, individually identifiable Indian eating and health-condition data cannot be ethically or legally shared at this granularity. Synthetic data calibrated against public aggregate statistics is an established substitute used elsewhere in industry (e.g., synthetic patient records for early-stage medical AI, synthetic listening/purchase sessions for recommender cold-start testing). This dataset follows the same pattern for the Indian food/nutrition domain.

## Validation & Data Quality

The dataset was validated with an automated suite (150+ rule-based checks) covering biological plausibility, cultural/religious correctness, temporal consistency, and statistical alignment with the source distributions above. As of the latest generation pass, **151 checks pass**.

Passing checks include (non-exhaustive):
- Vegetarian %, religion mix, and BMI distributions matching NFHS-5/Census targets within a few percentage points
- BMI↔diabetes and BMI↔hypertension odds ratios in the expected clinical range
- Meal-occasion timing and regional breakfast/lunch/dinner timing offsets matching ICMR patterns
- Fasting-frequency correlation with religious observance (r ≈ 0.73, consistent with Pew Research findings)
- Recommendation position-bias lift, skip/click/order ratios, and reorder rates within published food-delivery benchmarks
- No cross-religious dietary violations (e.g., no pork for Muslim users, no non-veg on Hindu fast days for the vast majority of records)

### Known Limitations

This dataset is shipped with its validation gaps disclosed rather than hidden:

- **Life-event effects are only partially propagated.** Events such as starting a gym routine, financial stress, or a new health diagnosis do not yet consistently shift downstream protein intake, budget, or compliance in `meal_logs` / `user_weekly_context`. Treat `life_events.csv` as largely independent of the meal-behavior tables for now.
- **A small number of dietary-restriction violations remain**, e.g., a minority of vegetarian users have logged meals (~0.2–0.3% of `meal_logs` rows) containing a non-vegetarian dish, and small counts of no-dairy/no-gluten violations. These are being tightened in the restriction-filtering logic.
- **Minor cross-cuisine leakage in the Northeast**, mainly Tripura, where Bengali-cuisine dishes are somewhat over-represented (~37% of meals) versus the intended Assamese/Odia/Northeast cuisine pool. Historically not entirely implausible (large Bengali-origin population in Tripura) but flagged as higher than intended.
- **Some meal-ordering-within-day inconsistencies** exist (a small number of days where dinner precedes lunch in timestamp order).
- **Health-outcome BMI-change values were highly uniform in earlier generator versions** (near-constant per quarter); this has been substantially improved in later versions (std increased from 0.00 to 0.24) but should still be treated as a simplified physiological model, not a clinical-grade trajectory simulator.
- **`compliance_score` and `compliance_improvement` were derived from a simple deterministic formula** in earlier versions; later versions add real variance, but users doing rigorous modeling should inspect these fields before treating them as fully independent signals.
- **A location-assignment bug** causes a subset of non-hostel-living users to be tagged with `location_type = hostel` in `social_eating_context.csv`.
- **`user_health_match` and `cuisine_affinity` in `interactions.csv` are close to random-uniform** rather than fully computed from actual user–dish nutritional/preference matching, which caps ranking-model performance (NDCG@10 ≈ 0.48) below what a fully-featured version would achieve.
- **Family medical history is not yet correlated with a user's own condition prevalence** in `users.csv` — this remains close to independent random sampling.

None of these are hidden — they're listed so downstream users can decide which tables/fields are safe for their use case and which need caution or their own re-derivation.

## Considerations for Using the Data

**Synthetic, not real people.** All 5,000 users, their conditions, meals, and outcomes are generated, not observed. No real individual's health or eating data is present anywhere in this dataset.

**Not for clinical use.** Health-outcome and compliance fields are simplified statistical models, not validated medical predictions. Do not use this dataset to inform real dietary or medical decisions for real individuals.

**Representativeness.** The dataset is grounded in national-level Indian statistics but reflects a health-app-using, largely urban population sampling rather than the full diversity of the Indian population (e.g., rural populations, very low-income groups, and some smaller regional/religious communities are likely under-represented).

**Cuisine/region caveats.** As noted above, some Northeast Indian states currently show cuisine-assignment skew that does not fully reflect real regional cuisine diversity; users doing region-specific cuisine research should treat those subsets with extra caution or filter them out.

## Licensing & Citation

This dataset is released under the **CC BY 4.0** license (confirm/update before publishing if a different license is intended).

If you use this dataset, please cite it as:

```
NutriRecIndia19M: A Synthetic Multi-Table Dataset for Indian Food Recommendation
and Health-Behavior Modeling. [Darsh Vithlani /2026].
```