Add mock data generation for users and request

database/README.MD

@@ -1 +1,69 @@
+# Mock Data Generation
+
+## Purpose
+
+This module generates realistic synthetic mock data for Saayam database tables.
+
+The generated data is useful for:
+
+- Testing
+- Development
+- ETL validation
+- Dashboard demos
+- Non-production environments
+
+No real user data is used.
+
+---
+
+## Input Files
+
+Schema reference files:
+
+- `database/Saayam_Table.column.names_data.xlsx`
+- `database/mock-data-generation/db_info.json`
+
+Lookup/reference tables:
+
+- `database/lookup_tables/`
+
+---
+
+## Generated Tables
+
+### users
+
+References:
+
+- state_id → state
+- country_id → country
+- user_status_id → user_status
+- user_category_id → user_category
+
+### request
+
+References:
+
+- req_user_id → users
+- req_for_id → request_for
+- req_islead_id → request_isleadvol
+- req_cat_id → help_categories
+- req_type_id → request_type
+- req_priority_id → request_priority
+- req_status_id → request_status
+
+---
+
+## Technologies Used
+
+- Python
+- Pandas
+- Faker
+
+---
+
+## Install Dependencies
+
+```bash
+pip install pandas faker openpyxl
 

database/mock-data-generation/common_utils.py

@@ -0,0 +1,49 @@
+import os
+import random
+from datetime import datetime, timedelta
+
+import pandas as pd
+from faker import Faker
+
+fake = Faker()
+
+random.seed(42)
+Faker.seed(42)
+
+
+def load_lookup(lookup_dir, file_name):
+    path = os.path.join(lookup_dir, file_name)
+
+    if not os.path.exists(path):
+        print(f"Missing lookup file: {file_name}")
+        return pd.DataFrame()
+
+    return pd.read_csv(path)
+
+
+def get_id_values(df):
+    if df.empty:
+        return [1]
+
+    id_cols = [
+        col for col in df.columns
+        if col.lower() == "id"
+        or col.lower().endswith("_id")
+    ]
+
+    if id_cols:
+        return df[id_cols[0]].dropna().tolist()
+
+    return df.iloc[:, 0].dropna().tolist()
+
+
+def random_datetime(start_year=2022, end_year=2026):
+    start_date = datetime(start_year, 1, 1)
+    end_date = datetime(end_year, 12, 31)
+
+    diff = end_date - start_date
+
+    return start_date + timedelta(
+        days=random.randint(0, diff.days),
+        seconds=random.randint(0, 86400)
+    )