enterprise-user-management-ai-system

# Enterprise User Management AI System > Skill by [ara.so](https://ara.so) — Data Skills collection. A full-stack enterprise user management system featuring AI-powered analytics, task management with Kanban boards, support ticket handling, and intelligent insights including risk detection, anomaly detection, and burnout analysis. ## What It Does This system provides: - **User Management**: Role-based access control, authentication with JWT - **Task Management**: Kanban boards (To Do → In Progress → Done) with time tracking - **Support System**: Ticket creation, tracking, and AI-based classification - **AI Analytics**: Risk prediction, anomaly detection, burnout analysis, project delay prediction - **Admin Controls**: User CRUD operations, audit logs, organization analytics - **Real-time Insights**: Performance metrics, workload analysis, suspicious activity alerts ## Installation ### Prerequisites ```bash # Node.js 14+ for backend/frontend # Python 3.8+ for ML service # MongoDB running locally or remote connection ``` ### Clone and Setup ```bash git clone https://github.com/Nareshkumar2583/Enterprise-User-Management-System-with-AI-Analytics.git cd Enterprise-User-Management-System-with-AI-Analytics ``` ### Backend Setup ```bash cd backend npm install # Create .env file cat > .env << EOF PORT=5000 MONGODB_URI=mongodb://localhost:27017/enterprise-user-mgmt JWT_SECRET=${JWT_SECRET} JWT_EXPIRE=7d ML_SERVICE_URL=http://localhost:8000 EOF npm start # Backend runs at http://localhost:5000 ``` ### ML Service Setup ```bash cd ml-service pip install -r requirements.txt # Create .env file for ML service cat > .env << EOF MODEL_PATH=./models LOG_LEVEL=INFO EOF uvicorn main:app --reload --host 0.0.0.0 --port 8000 # ML service runs at http://localhost:8000 ``` ### Frontend Setup ```bash cd frontend npm install # Create .env file cat > .env << EOF REACT_APP_API_URL=http://localhost:5000/api REACT_APP_ML_URL=http://localhost:8000 EOF npm start # Frontend runs at http://localhost:3000 ``` ## Key API Endpoints ### Authentication ```javascript // Register new user POST /api/auth/register { "name": "John Doe", "email": "john@example.com", "password": "securepass123", "role": "user" // or "admin" } // Login POST /api/auth/login { "email": "john@example.com", "password": "securepass123" } // Returns: { token, user: { id, name, email, role } } ``` ### User Management (Admin) ```javascript // Get all users GET /api/users Headers: { Authorization: "Bearer ${JWT_TOKEN}" } // Update user PUT /api/users/:userId { "name": "Updated Name", "role": "admin", "status": "active" } // Delete user DELETE /api/users/:userId ``` ### Task Management ```javascript // Create task POST /api/tasks { "title": "Implement new feature", "description": "Build user profile page", "assignedTo": "userId", "status": "todo", // todo, inprogress, done "priority": "high", "dueDate": "2026-05-01" } // Update task status PATCH /api/tasks/:taskId/status { "status": "inprogress", "timeSpent": 3600 // seconds } // Get user tasks GET /api/tasks/user/:userId ``` ### Support Tickets ```javascript // Create ticket POST /api/tickets { "subject": "Login issue", "description": "Cannot access dashboard", "priority": "high", "category": "technical" } // Get tickets (admin) GET /api/tickets?status=open&priority=high // Update ticket PATCH /api/tickets/:ticketId { "status": "resolved", "resolution": "Password reset sent" } ``` ### AI Analytics Endpoints ```javascript // Risk prediction POST /api/ai/risk-prediction { "userId": "user123", "taskLoad": 15, "overdueCount": 3, "avgCompletionTime": 72 } // Returns: { riskLevel: "high", probability: 0.78, factors: [...] } // Anomaly detection POST /api/ai/anomaly-detection { "userId": "user123", "loginTime": "2026-04-15T03:30:00Z", "location": "unusual-ip", "activityPattern": [...] } // Returns: { isAnomaly: true, score: 0.85, reason: "..." } // Burnout analysis POST /api/ai/burnout-analysis { "userId": "user123", "weeklyHours": 65, "taskCount": 25, "overtimeFrequency": 0.8 } // Returns: { burnoutRisk: "high", recommendation: "..." } // Project delay prediction POST /api/ai/project-prediction { "projectId": "proj123", "tasksCompleted": 40, "tasksRemaining": 60, "averageVelocity": 8, "deadline": "2026-06-01" } // Returns: { delayProbability: 0.65, estimatedCompletion: "2026-06-15" } ``` ## Frontend Integration Examples ### Authentication Hook ```javascript // hooks/useAuth.js import { useState, useEffect } from 'react'; import axios from 'axios'; const API_URL = process.env.REACT_APP_API_URL; export const useAuth = () => { const [user, setUser] = useState(null); const [loading, setLoading] = useState(true); useEffect(() => { const token = localStorage.getItem('token'); if (token) { axios.defaults.headers.common['Authorization'] = `Bearer ${token}`; fetchUser(); } else { setLoading(false); } }, []); const fetchUser = async () => { try { const res = await axios.get(`${API_URL}/auth/me`); setUser(res.data.user); } catch (error) { localStorage.removeItem('token'); } finally { setLoading(false); } }; const login = async (email, password) => { const res = await axios.post(`${API_URL}/auth/login`, { email, password }); localStorage.setItem('token', res.data.token); axios.defaults.headers.common['Authorization'] = `Bearer ${res.data.token}`; setUser(res.data.user); return res.data; }; const logout = () => { localStorage.removeItem('token'); delete axios.defaults.headers.common['Authorization']; setUser(null); }; return { user, loading, login, logout, isAdmin: user?.role === 'admin' }; }; ``` ### Kanban Board Component ```javascript // components/KanbanBoard.jsx import React, { useState, useEffect } from 'react'; import axios from 'axios'; import './KanbanBoard.css'; const API_URL = process.env.REACT_APP_API_URL; const KanbanBoard = ({ userId }) => { const [tasks, setTasks] = useState({ todo: [], inprogress: [], done: [] }); const [loading, setLoading] = useState(true); useEffect(() => { fetchTasks(); }, [userId]); const fetchTasks = async () => { try { const res = await axios.get(`${API_URL}/tasks/user/${userId}`); const grouped = res.data.reduce((acc, task) => { acc[task.status].push(task); return acc; }, { todo: [], inprogress: [], done: [] }); setTasks(grouped); } catch (error) { console.error('Error fetching tasks:', error); } finally { setLoading(false); } }; const updateTaskStatus = async (taskId, newStatus) => { try { await axios.patch(`${API_URL}/tasks/${taskId}/status`, { status: newStatus }); fetchTasks(); } catch (error) { console.error('Error updating task:', error); } }; const TaskCard = ({ task, status }) => ( <div className="task-card" draggable> <h4>{task.title}</h4> <p>{task.description}</p> <div className="task-meta"> <span className={`priority ${task.priority}`}>{task.priority}</span> <span className="due-date">{new Date(task.dueDate).toLocaleDateString()}</span> </div> <select value={status} onChange={(e) => updateTaskStatus(task._id, e.target.value)} > <option value="todo">To Do</option> <option value="inprogress">In Progress</option> <option value="done">Done</option> </select> </div> ); if (loading) return <div>Loading tasks...</div>; return ( <div className="kanban-board"> {['todo', 'inprogress', 'done'].map(status => ( <div key={status} className="kanban-column"> <h3>{status.replace(/([A-Z])/g, ' $1').toUpperCase()}</h3> <div className="task-list"> {tasks[status].map(task => ( <TaskCard key={task._id} task={task} status={status} /> ))} </div> </div> ))} </div> ); }; export default KanbanBoard; ``` ### AI Risk Dashboard Component ```javascript // components/AIRiskDashboard.jsx import React, { useState, useEffect } from 'react'; import axios from 'axios'; const API_URL = process.env.REACT_APP_API_URL; const AIRiskDashboard = ({ userId }) => { const [riskData, setRiskData] = useState(null); const [burnoutData, setBurnoutData] = useState(null); const [loading, setLoading] = useState(true); useEffect(() => { fetchAIAnalytics(); }, [userId]); const fetchAIAnalytics = async () => { try { const [riskRes, burnoutRes] = await Promise.all([ axios.post(`${API_URL}/ai/risk-prediction`, { userId }), axios.post(`${API_URL}/ai/burnout-analysis`, { userId }) ]); setRiskData(riskRes.data); setBurnoutData(burnoutRes.data); } catch (error) { console.error('Error fetching AI analytics:', error); } finally { setLoading(false); } }; if (loading) return <div>Analyzing data...</div>; return ( <div className="ai-dashboard"> <div className="risk-card"> <h3>Risk Level</h3> <div className={`risk-indicator ${riskData.riskLevel}`}>