enterprise-user-management-ai-analytics

name	enterprise-user-management-ai-analytics
description	Enterprise user management system with AI-powered analytics for risk detection, burnout analysis, and predictive insights
triggers	["set up enterprise user management with AI analytics","create user management system with machine learning","implement AI-powered task tracking and user administration","build enterprise dashboard with anomaly detection","add AI analytics to user management platform","integrate ML-based risk prediction for users","develop full-stack user management with FastAPI ML service","create kanban board with burnout detection"]

Enterprise User Management System with AI Analytics

Skill by ara.so — Data Skills collection.

Overview

Enterprise User Management System with AI Analytics is a full-stack application that combines user administration, task management, and support ticket handling with AI-powered insights. The system uses machine learning for risk detection, anomaly detection, burnout analysis, and predictive project insights to help organizations automate workflows and improve decision-making.

Architecture:

• Frontend: React.js dashboard for admins and users
• Backend: Node.js REST API with JWT authentication
• ML Service: FastAPI service using scikit-learn and River for online learning
• Database: MongoDB for data persistence

Installation

Prerequisites

Ensure you have installed:

• Node.js (v14+)
• Python (3.8+)
• MongoDB (running locally or cloud instance)

Clone and Setup

git clone https://github.com/Nareshkumar2583/Enterprise-User-Management-System-with-AI-Analytics.git
cd Enterprise-User-Management-System-with-AI-Analytics

Backend Setup

cd backend
npm install

Create .env file in backend/:

PORT=5000
MONGODB_URI=mongodb://localhost:27017/enterprise-user-mgmt
JWT_SECRET=your_jwt_secret_key_here
JWT_EXPIRE=7d
ML_SERVICE_URL=http://localhost:8000
NODE_ENV=development

Start backend:

npm start
# Backend runs at http://localhost:5000

ML Service Setup

cd ml-service
pip install -r requirements.txt

Create .env file in ml-service/:

MONGODB_URI=mongodb://localhost:27017/enterprise-user-mgmt
MODEL_PATH=./models
LOG_LEVEL=INFO

Start ML service:

uvicorn main:app --reload --port 8000
# ML service runs at http://localhost:8000

Frontend Setup

cd frontend
npm install

Create .env file in frontend/:

REACT_APP_API_URL=http://localhost:5000/api
REACT_APP_ML_URL=http://localhost:8000

Start frontend:

npm start
# Frontend runs at http://localhost:3000

Key API Endpoints

Authentication APIs

// POST /api/auth/register
const registerUser = async (userData) => {
  const response = await fetch('http://localhost:5000/api/auth/register', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      name: userData.name,
      email: userData.email,
      password: userData.password,
      role: userData.role // 'admin' or 'user'
    })
  });
  return response.json();
};

// POST /api/auth/login
const loginUser = async (credentials) => {
  const response = await fetch('http://localhost:5000/api/auth/login', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      email: credentials.email,
      password: credentials.password
    })
  });
  const data =  response.();
  
  .(, data.);
   data;
};

User Management APIs

// GET /api/users (Admin only)
const getAllUsers = async () => {
  const token = localStorage.getItem('token');
  const response = await fetch('http://localhost:5000/api/users', {
    headers: {
      'Authorization': `Bearer ${token}`
    }
  });
  return response.json();
};

// PUT /api/users/:id (Admin only)
const updateUser = async (userId, updates) => {
  const token = localStorage.getItem('token');
  const response = await fetch(`http://localhost:5000/api/users/${userId}`, {
    method: 'PUT',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify(updates)
  });
  return response.json();
};

// DELETE /api/users/:id (Admin only)
const deleteUser =  () => {
   token = .();
   response =  (, {
    : ,
    : {
      : 
    }
  });
   response.();
};

Task Management APIs

// POST /api/tasks
const createTask = async (taskData) => {
  const token = localStorage.getItem('token');
  const response = await fetch('http://localhost:5000/api/tasks', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify({
      title: taskData.title,
      description: taskData.description,
      assignedTo: taskData.userId,
      priority: taskData.priority, // 'low', 'medium', 'high'
      dueDate: taskData.dueDate,
      status: 'todo' // 'todo', 'in-progress', 'done'
    })
  });
  return response.json();
};

// GET /api/tasks/user/:userId
const getUserTasks = async (userId) => {
  const token = localStorage.getItem('token');
   response =  (, {
    : {
      : 
    }
  });
   response.();
};


  =  () => {
   token = .();
   response =  (, {
    : ,
    : {
      : ,
      : 
    },
    : .({ : newStatus })
  });
   response.();
};

Support Ticket APIs

// POST /api/tickets
const createTicket = async (ticketData) => {
  const token = localStorage.getItem('token');
  const response = await fetch('http://localhost:5000/api/tickets', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify({
      subject: ticketData.subject,
      description: ticketData.description,
      priority: ticketData.priority,
      category: ticketData.category
    })
  });
  return response.json();
};

// GET /api/tickets
const getAllTickets = async () => {
  const token = localStorage.getItem('token');
  const response = await fetch('http://localhost:5000/api/tickets', {
    headers: {
      'Authorization': `Bearer `
    }
  });
   response.();
};

AI/ML Service Integration

Risk Prediction

// POST /predict/risk
const predictUserRisk = async (userId) => {
  const response = await fetch('http://localhost:8000/predict/risk', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      user_id: userId,
      features: {
        login_frequency: 45,
        failed_logins: 3,
        task_completion_rate: 0.75,
        avg_response_time: 120,
        ticket_count: 5
      }
    })
  });
  const data = await response.json();
  // Returns: { risk_score: 0.23, risk_level: 'low', factors: [...] }
  return data;
};

Anomaly Detection

// POST /detect/anomaly
const detectAnomaly = async (userActivity) => {
  const response = await fetch('http://localhost:8000/detect/anomaly', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      user_id: userActivity.userId,
      activity_data: {
        login_time: userActivity.loginTime,
        ip_address: userActivity.ipAddress,
        location: userActivity.location,
        device: userActivity.device,
        actions: userActivity.actions
      }
    })
  });
  const data = await response.json();
  // Returns: { is_anomaly: false, anomaly_score: 0.12, details: {...} }
  return data;
};

Burnout Detection

// POST /predict/burnout
const detectBurnout = async (userId) => {
  const response = await fetch('http://localhost:8000/predict/burnout', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      user_id: userId,
      metrics: {
        tasks_assigned: 25,
        tasks_completed: 18,
        avg_task_duration: 180, // minutes
        overtime_hours: 15,
        weekend_work_frequency: 3,
        break_frequency: 2
      }
    })
  });
  const data = await response.json();
  // Returns: { burnout_risk: 'medium', score: 0.65, recommendations: [...] }
  return data;
};

Ticket Classification

// POST /classify/ticket
const classifyTicket = async (ticketContent) => {
  const response = await fetch('http://localhost:8000/classify/ticket', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      subject: ticketContent.subject,
      description: ticketContent.description
    })
  });
  const data = await response.json();
  // Returns: { category: 'technical', priority: 'high', suggested_assignee: 'team-a' }
  return data;
};

Predictive Project Insights

// POST /predict/project-delay
const predictProjectDelay = async (projectData) => {
  const response = await fetch('http://localhost:8000/predict/project-delay', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      project_id: projectData.projectId,
      total_tasks: projectData.totalTasks,
      completed_tasks: projectData.completedTasks,
      days_remaining: projectData.daysRemaining,
      team_size: projectData.teamSize,
      avg_velocity: projectData.avgVelocity
    })
  });
  const data = await response.json();
  // Returns: { delay_probability: 0.45, estimated_delay_days: 3, risk_factors: [...] }
  return data;
};

Common Usage Patterns

Protected Route Component (React)

import React from 'react';
import { Navigate } from 'react-router-dom';

const ProtectedRoute = ({ children, requiredRole }) => {
  const token = localStorage.getItem('token');
  const user = JSON.parse(localStorage.getItem('user') || '{}');

  if (!token) {
    return <Navigate to="/login" />;
  }

  if (requiredRole && user.role !== requiredRole) {
    return <Navigate to="/unauthorized" />;
  }

  return children;
};

export default ProtectedRoute;

// Usage in App.js
import { BrowserRouter, Routes, Route } from 'react-router-dom';

function App() {
  return (
    <>
      
        } />
        
            
          
        } />
        
            
          
        } />
      
    
  );
}

Kanban Board Component

import React, { useState, useEffect } from 'react';

const KanbanBoard = ({ userId }) => {
  const [tasks, setTasks] = useState({ todo: [], inProgress: [], done: [] });

  useEffect(() => {
    fetchTasks();
  }, [userId]);

  const fetchTasks = async () => {
    const token = localStorage.getItem('token');
    const response = await fetch(`http://localhost:5000/api/tasks/user/${userId}`, {
      headers: { 'Authorization': `Bearer ${token}` }
    });
    const data = await response.json();
    
    // Organize tasks by status
    const organized = {
      todo: data.filter(t => t.status === 'todo'),
      inProgress: data.filter(t => t.status === 'in-progress'),
      done: data.filter( t. === )
    };
    (organized);
  };

    =  () => {
     token = .();
     (, {
      : ,
      : {
        : ,
        : 
      },
      : .({ : newStatus })
    });
    (); 
  };

   (
    
  );
};

Time Tracking Hook

import { useState, useEffect } from 'react';

const useTimeTracker = (taskId) => {
  const [isTracking, setIsTracking] = useState(false);
  const [elapsedTime, setElapsedTime] = useState(0);

  useEffect(() => {
    let interval;
    if (isTracking) {
      interval = setInterval(() => {
        setElapsedTime(prev => prev + 1);
      }, 1000);
    }
    return () => clearInterval(interval);
  }, [isTracking]);

  const startTracking = () => {
    setIsTracking(true);
  };

  const stopTracking = async () => {
    setIsTracking(false);
    
    // Log time to backend
    const token = localStorage.getItem('token');
    await fetch(`http://localhost:5000/api/tasks/${taskId}/log-time`, {
      method: 'POST',
      headers: {
        : ,
        : 
      },
      : .({ : elapsedTime })
    });
  };

    = () => {
    ();
    ();
  };

   { isTracking, elapsedTime, startTracking, stopTracking, resetTracking };
};

  useTimeTracker;

AI-Powered User Analytics Dashboard

import React, { useState, useEffect } from 'react';

const UserAnalyticsDashboard = ({ userId }) => {
  const [analytics, setAnalytics] = useState({
    riskScore: null,
    burnoutRisk: null,
    anomalies: []
  });

  useEffect(() => {
    fetchAnalytics();
  }, [userId]);

  const fetchAnalytics = async () => {
    try {
      // Fetch risk prediction
      const riskRes = await fetch('http://localhost:8000/predict/risk', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ user_id: userId })
      });
      const riskData = await riskRes.json();

      // Fetch burnout detection
      const burnoutRes = await fetch('http://localhost:8000/predict/burnout', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        : .({ : userId })
      });
       burnoutData =  burnoutRes.();

      ({
        : riskData,
        : burnoutData,
        : []
      });
    }  (error) {
      .(, error);
    }
  };

   (
    
  );
};

Configuration

Backend Configuration (backend/config.js)

module.exports = {
  port: process.env.PORT || 5000,
  mongoURI: process.env.MONGODB_URI,
  jwtSecret: process.env.JWT_SECRET,
  jwtExpire: process.env.JWT_EXPIRE || '7d',
  mlServiceURL: process.env.ML_SERVICE_URL || 'http://localhost:8000',
  
  // Rate limiting
  rateLimitWindowMs: 15 * 60 * 1000, // 15 minutes
  rateLimitMax: 100,
  
  // File upload
  maxFileSize: 5 * 1024 * 1024, // 5MB
  
  // Email (if configured)
  emailService: process.env.EMAIL_SERVICE,
  emailUser: process.env.EMAIL_USER,
  emailPassword: process.env.EMAIL_PASSWORD
};

ML Service Configuration

Create ml-service/config.py:

import os
from dotenv import load_dotenv

load_dotenv()

class Config:
    MONGODB_URI = os.getenv('MONGODB_URI', 'mongodb://localhost:27017/enterprise-user-mgmt')
    MODEL_PATH = os.getenv('MODEL_PATH', './models')
    LOG_LEVEL = os.getenv('LOG_LEVEL', 'INFO')
    
    # ML Model Parameters
    RISK_THRESHOLD = 0.7
    ANOMALY_THRESHOLD = 0.5
    BURNOUT_THRESHOLD = 0.6
    
    # Online learning
    RETRAIN_INTERVAL = 86400  # 24 hours in seconds
    MIN_SAMPLES_RETRAIN = 100

config = Config()

Troubleshooting

Issue: JWT Token Expired

// Add token refresh logic
const refreshToken = async () => {
  const refreshToken = localStorage.getItem('refreshToken');
  const response = await fetch('http://localhost:5000/api/auth/refresh', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ refreshToken })
  });
  const data = await response.json();
  localStorage.setItem('token', data.token);
  return data.token;
};

// Axios interceptor for auto-refresh
import axios from 'axios';

axios.interceptors.response.use(
  response => response,
  async error => {
    if (error.response?.status === 401) {
      try {
        const newToken = await refreshToken();
        error.config.headers. = ;
         (error.);
      }  (refreshError) {
        
        .. = ;
      }
    }
     .(error);
  }
);

Issue: ML Service Connection Failed

// Add fallback when ML service is unavailable
const getPredictionWithFallback = async (endpoint, data) => {
  try {
    const response = await fetch(`http://localhost:8000${endpoint}`, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(data),
      timeout: 5000
    });
    
    if (!response.ok) throw new Error('ML service error');
    return await response.json();
  } catch (error) {
    console.warn('ML service unavailable, using fallback:', error);
    // Return default/cached predictions
    return { 
      available: false, 
      message: 'AI analytics temporarily unavailable' 
    };
  }
};

Issue: MongoDB Connection Error

// backend/db.js - Add retry logic
const mongoose = require('mongoose');

const connectDB = async (retries = 5) => {
  try {
    await mongoose.connect(process.env.MONGODB_URI, {
      useNewUrlParser: true,
      useUnifiedTopology: true,
      serverSelectionTimeoutMS: 5000
    });
    console.log('MongoDB Connected');
  } catch (error) {
    console.error('MongoDB connection error:', error);
    
    if (retries > 0) {
      console.log(`Retrying connection... (${retries} attempts left)`);
      setTimeout(() => connectDB(retries - 1), 5000);
    } else {
      process.exit(1);
    }
  }
};

module.exports = connectDB;

Issue: CORS Errors

// backend/server.js - Configure CORS properly
const express = require('express');
const cors = require('cors');

const app = express();

app.use(cors({
  origin: process.env.FRONTEND_URL || 'http://localhost:3000',
  credentials: true,
  methods: ['GET', 'POST', 'PUT', 'DELETE', 'PATCH'],
  allowedHeaders: ['Content-Type', 'Authorization']
}));

Issue: Model Performance Degradation

# ml-service/main.py - Add model monitoring
from datetime import datetime
import logging

class ModelMonitor:
    def __init__(self):
        self.predictions = []
        self.performance_log = []
    
    def log_prediction(self, input_data, prediction, actual=None):
        entry = {
            'timestamp': datetime.now(),
            'input': input_data,
            'prediction': prediction,
            'actual': actual
        }
        self.predictions.append(entry)
        
        # Check if model needs retraining
        if len(self.predictions) >= 100:
            self.evaluate_performance()
    
    def evaluate_performance(self):
        # Calculate accuracy, drift, etc.
        if self.should_retrain():
            logging.warning('Model performance degraded, triggering retrain')
            # Trigger retraining pipeline

monitor = ModelMonitor()

This skill provides comprehensive coverage for developers to use the Enterprise User Management System with AI Analytics, including API integration, ML service usage, and common implementation patterns.