A library that generates culturally accurate, statistically consistent mock Indian demographic profiles backed by Census 2011 data.
This repository provides two native implementations:
- A Node.js / TypeScript package (
@abhay557/indian-fakedata) - A Python package (
indian-fakedata)
Unlike traditional mock generators that produce impossible demographic combinations (such as a Sikh named Mohammed Sharma from Mizoram), this library correctly links variables together so that every generated person makes logical sense based on real-world statistical correlations.
Sample generated by the Python implementation. The TypeScript implementation is independently deterministic: the same seed is reproducible within one implementation, but the two runtimes draw RNG streams differently.
{
"id": "d6e2a61e-e297-4eb4-9866-5fb355fbc2ea",
"synthetic": true,
"generator": "indian-fakedata@2.1.0",
"firstName": "Sarwan",
"lastName": "Das",
"fatherName": "Shetan Das",
"motherName": "Girijarani Kumari",
"spouseName": "Kishan Das",
"gender": "female",
"age": 40,
"dateOfBirth": "1986-01-05",
"bloodGroup": "B+",
"heightCm": 144.0,
"weightKg": 44.1,
"bmi": 21.3,
"appearance": {
"heightCm": 144.0,
"build": "average",
"faceShape": "round",
"skinTone": "deep_brown",
"noseType": "button",
"eyeColor": "dark_brown",
"eyeShape": "almond",
"hairColor": "black",
"hairTexture": "wavy",
"hairLength": "medium",
"facialHair": null
},
"aadhaarNumber": "839128189565",
"panNumber": "FMMPD7406C",
"voterIdNumber": "YSR0818288",
"phoneNumber": "9444448053",
"email": "sarwan.das645@gmail.com",
"nativeScript": {
"script": "Telugu",
"language": "Telugu",
"firstName": "సర్వన",
"lastName": "దస",
"district": "గుంతుర",
"addressLine": "492/గ, జయనగర, గుంతుర"
},
"state": "Andhra Pradesh",
"stateCode": "AP",
"district": "Guntur",
"areaType": "urban",
"addressLine": "492/G, Jayanagar, Guntur",
"locality": "Jayanagar",
"pinCode": "500766",
"geo": {
"latitude": 15.7495,
"longitude": 80.2038
},
"religion": "Hindu",
"caste": "Madiga",
"socialCategory": "SC",
"motherTongue": "Telugu",
"secondLanguage": "Hindi",
"education": "graduate",
"occupation": "other_worker",
"employmentTimeline": [
{
"jobTitle": "Receptionist",
"sector": "private",
"occupation": "other_worker",
"employerType": "private",
"startYear": 2007,
"status": "completed",
"monthlyWageINR": 48000,
"location": "Guntur",
"endYear": 2009
},
{
"jobTitle": "Sales Executive",
"sector": "private",
"occupation": "other_worker",
"employerType": "private",
"startYear": 2009,
"status": "completed",
"monthlyWageINR": 74200,
"location": "Guntur",
"endYear": 2022
},
{
"jobTitle": "IT Support Executive",
"sector": "private",
"occupation": "other_worker",
"employerType": "private",
"startYear": 2022,
"status": "current",
"monthlyWageINR": 102900,
"location": "Guntur"
}
],
"employmentSector": "private",
"maritalStatus": "married",
"annualIncomeINR": 1235000,
"monthlyExpenditureINR": 89100,
"numberOfChildren": 1,
"dietaryPreference": "non_vegetarian",
"disability": "none",
"isMigrant": true,
"migrationOriginState": "Karnataka",
"bankIFSC": "KKBK0682206",
"bankName": "Kotak Mahindra Bank",
"bankAccountNumber": "50991831492",
"rationCardType": "APL",
"healthInsurance": "none",
"landOwnershipAcres": 0,
"vehicleRegistration": "AP 28 SF 4832",
"vehicleType": "two_wheeler",
"hasInternetAccess": true,
"hasSmartphone": true,
"usesSocialMedia": false,
"upiId": "9444448053@apl",
"personality": {
"openness": 59,
"conscientiousness": 36,
"extraversion": 64,
"agreeableness": 49,
"neuroticism": 57
},
"personalityTraits": {
"summary": "An outgoing, people-oriented person who is open-minded, easy-going and assertive. They feel things deeply and care about those around them.",
"strengths": [
"creative and curious",
"adapts to change quickly",
"stands their ground"
],
"weaknesses": [
"worries about small things",
"needs company to feel energised",
"procrastinates under pressure"
],
"traitLabels": [
"open-minded",
"easy-going",
"outgoing",
"assertive",
"sensitive"
],
"communicationStyle": "expressive",
"decisionStyle": "intuitive",
"socialBehavior": "outgoing"
},
"politicalLeaning": "regionalist",
"religiosity": "somewhat_religious",
"cognitiveProfile": {
"aptitudeScore": 75,
"numeracyScore": 58,
"literacyScore": 88,
"digitalLiteracyScore": 84,
"financialLiteracyScore": 75
},
"interests": {
"primarySport": "hockey",
"petPreference": "cats",
"entertainment": [
"Bollywood",
"TV Serials",
"Cricket Matches",
"Religious Programs"
],
"readingHabit": "rare",
"musicPreference": "Bollywood",
"preferredSocialMedia": "Facebook"
},
"habits": {
"tobaccoUse": "none",
"alcoholUse": "none",
"exerciseFrequency": "daily",
"avgSleepHours": 6.7,
"cooksAtHome": true,
"chronotype": "moderate"
},
"educationDetails": {
"fieldOfStudy": "Computer Science/IT",
"institutionType": "government",
"mediumOfInstruction": "English",
"qualificationYear": 2008,
"competitiveExamPercentile": null
},
"educationTimeline": [
{
"level": "primary",
"stageName": "Primary School",
"institutionName": "Government Primary School, Guntur",
"institutionType": "government",
"boardOrUniversity": "AP State Board",
"startYear": 1991,
"endYear": 1997,
"status": "completed",
"score": "52.8%"
},
{
"level": "middle",
"stageName": "Middle School",
"institutionName": "Government Middle School, Guntur",
"institutionType": "government",
"boardOrUniversity": "AP State Board",
"startYear": 1997,
"endYear": 2000,
"status": "completed",
"score": "56.7%"
},
{
"level": "secondary",
"stageName": "Secondary School",
"institutionName": "Government High School, Guntur",
"institutionType": "government",
"boardOrUniversity": "AP State Board",
"startYear": 2000,
"endYear": 2002,
"status": "completed",
"score": "61.0%"
},
{
"level": "higher_secondary",
"stageName": "Higher Secondary School",
"institutionName": "Government Higher Secondary School, Guntur",
"institutionType": "government",
"boardOrUniversity": "AP State Board",
"startYear": 2002,
"endYear": 2004,
"status": "completed",
"stream": "PCM",
"score": "52.8%"
},
{
"level": "graduate",
"stageName": "Bachelor's Degree",
"institutionName": "Government Post Graduate College, Guntur",
"institutionType": "government",
"boardOrUniversity": "University of Andhra Pradesh",
"startYear": 2004,
"endYear": 2008,
"status": "completed",
"fieldOfStudy": "Computer Science/IT",
"score": "56.5%"
}
],
"moviePreferences": {
"genres": [
"Drama",
"Sports drama/Biopic",
"Family drama"
],
"favoriteLanguages": [
"Telugu",
"Hindi"
],
"anime": false,
"animePreferences": null,
"favoriteAnimeTitles": null,
"primaryPlatform": "television",
"watchFrequency": "occasional"
},
"culturalProfile": {
"entrepreneurialScore": 37,
"academicOrientation": 32,
"artisticInclination": 40,
"militaryTradition": 8,
"agriculturalRootedness": 15,
"artisanTradition": 22,
"bureaucraticOrientation": 5,
"socialActivism": 86,
"communityBonding": 63,
"migrationTendency": 38,
"careerPreference": "teaching",
"familyStructure": "extended_family",
"savingsOrientation": 21,
"riskAppetite": 15
},
"householdSize": 1,
"householdAssets": {
"hasRadioTransistor": false,
"hasTelevision": true,
"hasComputer": true,
"hasPhone": true,
"hasBicycle": false,
"hasScooter": true,
"hasCar": false,
"bankingService": true,
"treatedWaterSource": true,
"latrineFacility": true,
"numberOfRooms": 5,
"roofMaterial": "metal_sheet",
"wallMaterial": "burnt_brick",
"cookingFuel": "lpg",
"lightingSource": "electricity",
"drinkingWaterSource": "handpump"
},
"probabilityMetrics": {
"nationalReligionFreq": 0.803301791826052,
"stateGivenReligionProb": 0.04704225981630054,
"casteGivenContextProb": 0.09917355371900827,
"lastNameGivenCasteProb": 0.2857142857142857,
"socialCategoryProb": 0.19834710743801653,
"educationProb": 0.14598540145985403,
"occupationProb": 0.4087193460490463,
"jointProbability": 6.388948194089085e-05
},
"generatedAt": "2026-09-25T15:55:52.859912",
"seed": 7,
"skills": {
"technical": [
"Commercial Cooking"
],
"soft": [
"Teamwork",
"Time Management"
],
"certifications": [],
"languages": [
{
"language": "Telugu",
"speaking": "native",
"reading": "fluent",
"writing": "fluent"
},
{
"language": "Hindi",
"speaking": "intermediate",
"reading": "intermediate",
"writing": "intermediate"
},
{
"language": "English",
"speaking": "intermediate",
"reading": "intermediate",
"writing": "intermediate"
}
]
},
"lifeEvents": [
{
"year": 1986,
"event": "born",
"detail": "Born in Guntur."
},
{
"year": 2004,
"event": "migrated",
"detail": "Migrated from Karnataka to Andhra Pradesh."
},
{
"year": 2007,
"event": "job_started",
"detail": "Started working as Receptionist."
},
{
"year": 2009,
"event": "job_changed",
"detail": "Changed job to Sales Executive."
},
{
"year": 2009,
"event": "married",
"detail": "Married Kishan Das."
},
{
"year": 2010,
"event": "child_born",
"detail": "Birth of child 1."
},
{
"year": 2022,
"event": "job_changed",
"detail": "Changed job to IT Support Executive."
}
],
"householdEconomy": {
"monthlyBudget": {
"food": 46324,
"housing": 19007,
"transport": 8691,
"education": 6372,
"health": 8706,
"other": 0
},
"loans": [],
"creditHistory": {
"score": 782,
"activeLoans": 0,
"missedPayments12m": 0,
"oldestAccountYears": 0
}
}
}npm install @abhay557/indian-fakedataRequires Node.js >= 18.
pip install indian-fakedataRequires Python 3.8+.
Both packages ship with the indian-fakedata CLI binary. The arguments are identical across both versions!
# Node.js
npx @abhay557/indian-fakedata [options]
# Python (or globally installed Node package)
indian-fakedata [options]Run with no arguments to display the full help menu.
| Flag | Alias | Description | Default |
|---|---|---|---|
--count <n> |
-c |
Number of profiles to generate | 100 |
--output <path> |
-o |
File path to save output | stdout |
--format <fmt> |
-f |
Output format: json, jsonl, csv |
json |
--seed <value> |
-s |
Reproducibility seed (number or string, e.g. 011) |
random |
--no-metrics |
Exclude probability metrics from output | included | |
--family |
Generate a full family (head + spouse + parents + children + siblings) from one seed | off | |
--help |
-h |
Show help screen |
Filter generated profiles to specific demographic slices:
| Flag | Values |
|---|---|
--religion <string> |
Hindu, Muslim, Christian, Sikh, Buddhist, Jain |
--state <string> |
e.g. Maharashtra, Tamil Nadu, Punjab |
--gender <gender> |
male, female, other |
--caste <string> |
e.g. Brahmin, Maratha, Jat |
--socialCategory <cat> |
SC, ST, OBC, General |
--areaType <type> |
urban, rural |
--minAge <n> |
Minimum age (0–100) |
--maxAge <n> |
Maximum age (0–100) |
--education <level> |
illiterate, primary, secondary, graduate, etc. |
--occupation <sector> |
cultivator, other_worker, non_worker, etc. |
--maritalStatus <status> |
never_married, married, widowed, etc. |
| Flag | Description |
|---|---|
--enrich |
Enable ALL enrichment layers (outcomes + narrative:all + persona) |
--outcomes |
[Layer 2] Add credit score, health risk, employment outcome, education attainment |
--bias <0-1> |
Bias dial for outcome simulation. 0.0 = pure meritocracy, 1.0 = max historical discrimination. Default: 0.3 |
--narrative <type> |
[Layer 3] Generate realistic Indian text documents. Repeat for multiple types: loan_application, medical_consultation, school_enrollment, ration_card_application, hinglish_conversation, all |
--persona |
[Layer 4] Generate LLM-ready agent persona (system prompt + full roleplay prompt, beliefs, memory seeds) |
# 1000 profiles as CSV
indian-fakedata -c 1000 -f csv -o profiles.csv
# 50K Tamil Nadu Hindus as JSONL
indian-fakedata -c 50000 -f jsonl -o tn_data.jsonl --state "Tamil Nadu" --religion Hindu
# All enrichment layers with moderate bias
indian-fakedata -c 100 --enrich --bias 0.3 -f jsonl -o enriched.jsonl
# SC community fairness audit
indian-fakedata -c 5000 --outcomes --bias 0.5 --socialCategory SC -f jsonl -o sc_bias.jsonlBoth runtimes expose ergonomic entry points built on top of generate:
import { generateUser, generateUsers, generateFamily, generatePersona } from '@abhay557/indian-fakedata';
// One user — same shape as the "Output Sample" above. Seed may be a
// number (7) or string ('011'); string seeds are hashed deterministically.
const user = generateUser({ seed: 7 });
// Many users driven by a single seed
const users = generateUsers({ count: 5, seed: '011' });
// A highly educated female IT professional from Karnataka
const dev = generateUser({ highlyEducated: true, gender: 'female', constraints: { state: 'Karnataka' } });
// Full relational household from one seed — spouse, parents, children,
// siblings all share the head's state/religion/caste/surname and keep
// age-consistent relationships. Fully reproducible for the same seed.
const family = generateFamily({ seed: '011' });
family.spouse?.lastName; // === family.head.lastName
family.children.map(c => c.age); // younger than head
// User + LLM-ready agent persona (system prompt, beliefs, memory seeds)
const { user: u, persona } = generatePersona({ seed: '011' });
persona.systemPrompt; // ready to inject into any LLM system role
persona.fullPrompt; // complete self-contained roleplay prompt: identity,
// education timeline, personality traits, movie/anime
// preferences, habits, beliefs, behaviour rulesfrom indian_fakedata import generate_user, generate_users, generate_family, generate_persona
user = generate_user(seed=7)
users = generate_users(count=5, seed="011")
dev = generate_user(highly_educated=True, gender="female", constraints={"state": "Karnataka"})
family = generate_family(seed="011")
out = generate_persona(seed="011") # {"user": ..., "persona": ...}
out["persona"]["fullPrompt"] # complete roleplay prompt (see above)import { generate, generateEnriched } from '@abhay557/indian-fakedata';
const profiles = generate({ count: 10 });
const enriched = generateEnriched({ count: 5, includeOutcomes: true });from indian_fakedata import generate, generate_enriched
profiles = generate(count=10)
enriched = generate_enriched(count=5, include_outcomes=True)See TUTORIAL.md for comprehensive, side-by-side code snippets including data exporting, streams, and narratives. Section 6 covers everything new in 2.0.9 with copy-paste examples.
The generator is calibrated against publicly available survey data. The
bundled distributions are approximations derived from published reports,
not raw census tables — actual census microdata (team/data/*.xlsx) is
provided for reference but is not compiled into the package at build time.
- Census of India 2011 (D-Series & C-Series Tables): Reference material for religion shares, state populations, and mother tongue frequencies; distributions are hand-calibrated approximations.
- National Family Health Survey (NFHS-5): Dietary preferences, BMI, blood groups, height/weight-by-age published statistics.
- MSME Census: Community-level occupational sectors, vocational rates, industry divisions.
- UIDAI & RTO Records: Structural syntax for Aadhaar, Voter ID, PAN, IFSC, and RTO registrations (Aadhaar uses a true Verhoeff checksum; PAN's 10th character is self-consistent but not the official check digit).
- CSDS/Lokniti Election Studies: Political leanings and religiosity index biases.
Note: All data is synthetic mock data. Names, IDs, and numbers are randomly generated and do not correspond to any real individuals.
- 760 districts across all 36 states/UTs (UP has all 75, Tamil Nadu all 38) — was 369
- 471 surnames keyed to 48 communities (Jain, Buddhist/navayana fully covered) — was 211
- +566 first names for Jain (previously empty), Buddhist, Muslim and Christian pools
- 130+ anime titles, 21 anime genres, 25 movie genres, 34 state cinema languages
- 120 urban / 60 rural locality patterns for addresses
Because pool sizes changed, a given seed may resolve to a different person than in <= 2.0.3. Reproducibility within one version is guaranteed.
generateEnriched/generateEnrichedStream(Python:generate_enriched/generate_enriched_stream) crashed with aTypeErrorwhen given string seeds such as"011"— string seeds now work everywhere, as documented.
Every generated profile carries self-labeling fields ("synthetic": true,
"generator": "indian-fakedata@...") so data stays identifiable as fake
wherever it travels. Aligned with the Acceptable Use policy above.
- Python RNG bias fixed. The JS→Python port of the mulberry32 PRNG used
signed shifts;
rng.next()never returned values >= 0.5, so every weighted choice in Python was skewed toward options listed early in the tables. All Python distributions are now statistically correct. All seeds produce different output than <= 2.0.6 in Python. - DOB/age drift fixed (both runtimes). ~1/3 of profiles previously had a
dateOfBirthwhose real calendar age was off by one fromage.
Every profile now carries a nested appearance object describing physical
traits: faceShape, skinTone, noseType, eyeColor, eyeShape,
hairColor, hairTexture, hairLength, facialHair and build.
- Regional variation. Adult height is shifted by broad geographic region
(North-West tallest, South and North-East shorter), so a seeded profile's
heightCmnow reflects where they live. Existing seeds resolve to slightly different heights than <= 2.0.7. - Skin tone buckets use named, descriptive values:
fair,wheatish,brown,deep_brownanddark. - Agent personas automatically describe each person's appearance in the generated system prompt.
- The
appearanceblock is appended at the end of generation, so every other field for a given seed stays stable.
See CHANGELOG.md for the full 2.0.9 list.
- Employment timeline. Every profile now carries
employmentTimeline: a chronological list of job spells (jobTitle,sector,occupation,employerType,startYear,endYear,status,monthlyWageINR,location). Wages progress towards the current income; students, the unemployed and children get an empty timeline, retirees get completed-only history. Attached after profile assembly, soidand every <= 2.0.8 field for a given seed stay byte-identical. - Skills and languages. Every profile now carries
skills:technicalandsoftskill lists,certifications, and per-language speaking/reading/writing levels (basic/intermediate/fluent/native). Pools follow education and occupation; children get languages only. Same isolated-stream guarantee as the employment timeline. - New narrative documents. Layer 3 gains
resume(CV grounded in the education timeline, work history and skills) andcustomer_support_chat(Hinglish bank helpline dialogue, phone masked). Both work viagenerateNarrative,--narrativeandgenerateAllNarratives, which appends them at the end so existing document order is unchanged. - Hindi/Hinglish personas. Layer 4 personas accept a
languageoption (english/hindi/hinglish):generateAgentPersona(profile, { language: 'hindi' }),generateEnriched({ ..., agentPersonaLanguage: 'hinglish' }), or CLI--persona --persona-lang hindi. Hindi renders the system prompt in Devanagari with Hindi section headers; Hinglish uses roman script. Defaultenglishoutput is unchanged. - CLI field selection and stats.
--fields firstName,state, appearance.skinToneoutputs only those fields (dot paths allowed, repeatable, works for json/jsonl/csv).--statsprints a distribution summary (religion/state/gender/area/education/occupation) to stderr. - Schema and validation.
getProfileSchema()exports a versioned JSON Schema for the profile shape;validateProfile(profile)returns{ valid, errors }checking required fields, enums and thesynthetic/generatorprovenance markers. Zero dependencies, works on plain JSON. The canonical schema is also committed asschema/profile-2.0.9.json. - PII stripping.
stripPII(profile)returns a share-safe copy with Aadhaar, PAN, voter ID, phone, email, bank account, UPI ID and the street address emptied (same shape,piiStripped: truemarker, provenance kept).maskNames: truereduces names to initials. Validate before stripping. - CLI privacy.
--strip-piiempties identifiers in CLI output (profile fields only, not narrative/persona text);--mask-namesreduces names to initials. - CLI validation.
--validatechecks every full profile and exits 1 with errors on stderr for the first invalid record. Runs before any shaping, so it composes with--strip-piiand--fields.
- Occupation now follows education (breaking). Occupation used to be
sampled independently of schooling, so graduates routinely rolled farm
jobs. Weights are now conditioned on education: graduates skew strongly
white-collar, the unschooled toward farm work. Same draw count, so the
stream layout is intact, but occupation-driven fields resolve differently
than 2.0.9 for the same seed. Explicit
occupationconstraints still win. - Employment timeline fix. Stages used to pick titles from the
employment sector, so a cultivator could show up as "Kirana Shop Owner".
Titles and occupation labels now follow the profile's own
occupation; onlynon_workerhistories fall back to a sampled past sector.sectorstill mirrorsemploymentSector, so the two always agree. TheemploymentTimelinekey now sits right belowoccupationinstead of at the end of the profile. - Jobs match the degree. The current job title now follows the profile's field of study (a BTech graduate works as an engineer, a B.Ed graduate teaches; doctor titles need a professional degree), and every sector pool grew with more titles. Education and employment timelines finally agree.
- Native script output. Every profile carries
nativeScriptwith names, district and address transliterated into the mother-tongue script (Devanagari, Bengali, Gujarati, Gurmukhi, Kannada, Malayalam, Tamil, Telugu, Odia; Latin passthrough otherwise).transliterate()andscriptForLanguage()are exported for prompts and free text. - Life events timeline. Every profile carries
lifeEventswith dated birth, marriage, children, migration, job-switch and retirement events, cross-checked against age, marital status, child count, migration flag and both existing timelines. - Household economy kit. Every profile carries
householdEconomywith a monthly budget split summing exactly to expenditure, 0-2 affordable loans with real EMI math (total EMI capped at 60% of income), and a credit history whose score bands track missed payments. - Festival calendar. Personas, chats and QA derive dated observances on demand from religion and state instead of storing them on the profile: pan-Indian festivals from the family's religion plus regional ones that stay in their states (Pongal, Bihu, Onam, Durga Puja, Chhath, Teej, Baisakhi, Ganesh Chaturthi). Everyone shares one stream per profile, so memories and chats always agree. Lunisolar dates are typical, not exact.
- SFT pair builder.
buildSFTPairs()turns a profile (plus optional narratives) into grounded instruction/response pairs, exported as JSONL withsftPairsToJsonl(). - Grounded QA pairs.
buildQAPairs()turns a profile into question/answer pairs for retrieval and comprehension evaluation. Every answer is templated from profile fields and carriescitations, the exact field paths it was built from. - Eval harness.
evaluateDataset()scores any batch with one quality number built from census drift, schema validity and internal consistency, plus anindian-fakedata --eval file.jsonlcommand. - Geospatial points. Every profile carries
geowith an approximate latitude/longitude around the state capital, tighter for urban profiles and clamped inside the state bounding box. District-approximate, not rooftop-accurate.
| Layer | Name | Description |
|---|---|---|
| 1 | Core Demographics | State, gender, religion, caste, names, languages, biological markers, address |
| 2 | Socio-Economic Outcomes | CIBIL credit score, health risk, literacy, employment vulnerability (configurable bias) |
| 3 | Narrative Documents | Loan applications, OPD records, Hinglish WhatsApp chats, school admissions |
| 4 | Agent Persona Prompts | LLM-ready system prompts + full roleplay prompts (education timeline, personality traits, movie/anime preferences), worldview beliefs, stress responses, memory seeds |
| Command | Description |
|---|---|
npm run build |
Compile TypeScript to dist/ |
npm run dev |
Run src/index.ts via tsx |
npm run cli |
Run src/cli.ts via tsx |
npm test |
Run vitest test suite |
npm run demo |
Run demo script |
npm run lint |
Type-check without emitting |
SKILL.md teaches AI coding agents how to correctly use this
library: install commands, the full API matrix (TS/Python), CLI reference, seed
semantics, output shape, verification assertions, and common mistakes.
This library generates synthetic mock data intended for software testing, development, ML/AI research, education, and simulation. By using it you agree not to use it, or data derived from it, for:
- Creating fake identity documents, or bypassing KYC / identity / age verification systems
- Operating fake accounts, bots, or personas that interact with real people — including social-media manipulation, astroturfing, and fake reviews
- Disinformation, impersonation, harassment, spam, or scam content of any kind
- Presenting generated profiles or statistics as real data about real individuals, or publishing datasets derived from this library without clearly labeling them synthetic
- Any purpose that is illegal under applicable law
All identifiers (Aadhaar, PAN, voter ID, phone, email) are fabricated and exist in no government or commercial database. Every profile is fictional; any resemblance to a real person is coincidental. You are responsible for how you deploy the output of this library. If you are unsure whether your use case is acceptable, it probably isn't.
MIT © Abhay Mourya (abhay557)