Business Intelligence Education Sector Final Year Project with Source Code | Source Code
LIMITED TIME
Get Source Code ₹99
Real project UI Full source included

No live demo yet — message us for access

Our team shares demo access on WhatsApp within minutes

Business Intelligence Education Sector Final Year Project with Source Code

Request Demo Access

Complete final-year project source code with frontend, backend, database, and setup guide. Instant download after secure payment.

  • MACHINE-LEARNING Stack
  • Instant Download
Secure CCAvenue payment Instant download link WhatsApp support

Choose your plan

Source Code Only

Full ZIP with frontend, backend, database & documentation.

₹99 one-time
  • Complete project source files
  • Database script included
  • How-to-run guide
Best value

Code + Setup Support

We install & configure the project on your laptop within 24 hours.

₹248 one-time
  • Everything in Source Code plan
  • Remote setup assistance
  • Run-ready in 24 hours

What's in your download

Review features, setup steps, and credentials before you pay.

Project Overview

Description, tech stack, and what is included

Full source Frontend + backend
Database .sql file
Setup guide README included

Business Intelligence Education Sector is a Python Flask based final year project developed for institutional learning analytics, student performance monitoring, faculty management, course tracking, attendance analysis, marks management, dropout-risk prediction, AI-assisted reporting, and academic decision-making. This major project includes a public landing page, admin command centre, and role-based learner/faculty workspace. Admins can manage users, departments, students, teachers, courses, enrollments, attendance, marks, data uploads, AI reports, ML predictions, recommendations, insights, notifications, exports, feedback, case studies, literature, guidelines, brand settings, and platform configuration. Students can view courses, attendance, marks, charts, reports, notifications, AI coaching, feedback, and risk alerts. Faculty users can manage course rosters, attendance records, marks, gradebook entries, and class analytics. This business intelligence education project source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Python Flask, AI, ML, and education analytics.

Technical snapshot

Project
Business Intelligence Education Sector Final Year Project with Source Code
Stack
MACHINE-LEARNING
Includes
Code, DB, README
License
Academic submission
Secure CCAvenue payment · Instant download · Need help? WhatsApp us

Admin Features

Modules and controls available to administrators

  • Admin login and logout in this final year project
  • Executive dashboard with KPI tiles
  • View users, students, teaching personas, courses, AI reports, and insights
  • View stewardship activity log
  • Real-time BI dashboard
  • View low-attendance learners
  • View weak subject indicators
  • View top students snapshot
  • View recent risk predictions
  • Charts and heatmaps dashboard
  • Course-level attainment average charts
  • Department-wise student count charts
  • Attendance trend charts
  • Institution brand management
  • Update institution name
  • Update address and contact details
  • Update theme colours
  • Upload institution logo
  • User CRUD management
  • Add platform users
  • Edit platform users
  • Delete platform users
  • Manage roles: admin, department head, teacher, student
  • Rotate user passwords
  • Auto-provision linked student or teacher records
  • Department CRUD management
  • Add department
  • Edit department
  • Delete department when not referenced
  • Student CRUD management
  • Add learner dossier
  • Edit learner details
  • Delete learner dossier
  • Attach orphan student user rows
  • Purge student dossier and related enrollment data
  • View student performance drill-down
  • Teacher CRUD management
  • Add teacher/faculty profile
  • Edit teacher details
  • Delete teacher profile
  • Assign teacher to department
  • Free course teacher references on safe removal
  • Course CRUD management
  • Add course or subject
  • Edit course details
  • Delete course with related enrollment cleanup
  • Manage course code, name, credits, description, department, and assigned teacher
  • Enrollment CRUD management
  • Bind student, course, and semester
  • Enforce unique enrollment
  • Add opening attendance row during enrollment
  • Add diagnostic mark during enrollment
  • Delete enrollment with linked attendance and marks cleanup
  • CSV/Excel data ingestion
  • Upload attendance sheets
  • Upload marks sheets
  • Parse CSV files
  • Parse XLSX/XLS files
  • Store DataUpload audit rows
  • Delete upload log metadata
  • Attendance record management
  • Create attendance record
  • Update attendance record
  • Delete attendance record
  • Manage enrollment, date, presence, and topic
  • Marks and exam management
  • Create marks record
  • Update marks record
  • Delete marks record
  • Manage exam, assignment, and internal mark types
  • View portfolio mean KPI
  • AI performance briefing
  • Generate governance council narrative
  • View OpenAI configuration audit card
  • AI report factory
  • Generate archival AI reports
  • Store generated report body and generation dossier
  • Delete archived generated report
  • ML prediction bulk run
  • Recompute StudentRiskPrediction rows for all students
  • Recommendation management
  • Add recommendations
  • Edit recommendations
  • Delete recommendations
  • Generate AI draft recommendations
  • Manage cohort or user-targeted recommendations
  • AI insight library management
  • Add AI insight bulletin
  • Edit AI insight
  • Delete AI insight
  • Notification centre
  • Broadcast notifications to all users
  • Broadcast notifications to learners only
  • Broadcast notifications to faculty and department heads
  • Browse recent notification rows
  • Delete notification audit lines
  • Export management
  • Export students CSV
  • Export attendance template CSV
  • Export marks template CSV
  • Export executive summary PDF
  • Feedback inbox
  • View stakeholder feedback
  • Delete feedback entries
  • Case study management
  • Literature item management
  • Guideline management
  • Platform settings management
  • Update academic year label
  • Manage grading nomenclature toggles
  • Configure notification placeholders
  • Configure backup placeholders
  • Store OpenAI API key setting
  • View effective OpenAI runtime summary

User Features

What end users can do in this application

Student Features in this Final Year Project

  • Student login and logout
  • Student dashboard
  • View learner KPIs
  • View recent notifications
  • View recommendations
  • View risk snapshot tile
  • View profile details
  • Update identity details
  • Update phone number
  • Update programme/year fields
  • Change password with current password check
  • View user-owned generated reports
  • Download PDF reports
  • View notification mailbox
  • Mark notification as read
  • Mark all notifications as read
  • Submit feedback
  • View own feedback entries
  • Delete own feedback tickets
  • Use AI helpdesk for BI or study-plan Q&A
  • View enrolled courses
  • Open enrollment dossier
  • View course sessions
  • View assessment details
  • View attendance rollup
  • View marks ledger
  • View normalized marks vs attendance charts
  • View AI coaching suggestions
  • View performance-context-grounded briefing
  • View risk sentinel alerts
  • View attendance composite flags
  • View attainment index threshold alerts

Faculty / Department Head Features in this Final Year Project

  • Faculty login and logout
  • Faculty dashboard with teaching KPIs
  • View recent notifications and recommendations
  • Manage profile and credentials
  • View assigned courses
  • Open faculty course roster
  • View learners enrolled in course
  • View simple KPI aggregates
  • Quick attendance data entry
  • Quick marks data entry
  • Capture AttendanceRecord rows
  • Capture Mark rows
  • Manage gradebook
  • Edit attendance and marks owned by teacher
  • Delete attendance and marks with confirmation
  • View class analytics
  • View weak learner buckets
  • View strong learner buckets
  • Open learner roster context
  • Submit feedback
  • Access reports and notifications
  • Use AI helpdesk

Public Website Features in this Final Year Project

  • Public marketing landing page
  • Institution-oriented project introduction
  • Login call-to-action
  • Register call-to-action
  • Public navigation
  • Role-based authentication flow
  • Student registration
  • Teacher registration
  • Auto-provision student code
  • Auto-provision teacher code
  • Login with role-aware redirect
  • Admin redirect to /admin/dashboard
  • Non-admin redirect to /app/dashboard
  • Responsive Bootstrap 5 interface

AI / Machine Learning Features in this Final Year Project

  • scikit-learn based student risk scoring
  • StudentRiskPrediction generation
  • Bulk ML prediction for all students
  • Dropout-risk heuristic support
  • Learning analytics indicators
  • Attendance and marks based risk signals
  • Optional OpenAI-powered AI copilot
  • Offline deterministic fallback briefing
  • AI performance briefing
  • AI report generation
  • AI recommendation drafting
  • AI coaching suggestions
  • AI helpdesk for students and faculty
  • Completion audit metadata
  • Effective OpenAI runtime summary
  • Environment variable based OpenAI configuration
  • Admin platform setting for OpenAI key
  • OpenAI fallback when key or SDK fails

Other Features

Additional capabilities included in the project

  • Complete business intelligence education sector source code
  • Suitable for final year project, major project, and minor project
  • Project report content can be prepared from included modules
  • Public Website, Admin Command Centre, and Learner/Faculty Workspace included
  • Role-based access for admin, department head, teacher, and student
  • Education analytics dashboard
  • Student performance monitoring
  • Attendance management
  • Marks and exam management
  • Course and enrollment management
  • Faculty roster management
  • Gradebook management
  • CSV/Excel ingestion
  • PDF and CSV export support
  • ML-based student risk prediction
  • Optional AI copilot support
  • Offline AI fallback support
  • Notifications system
  • Feedback system
  • Research artefact management
  • Institution branding management
  • Flask application factory architecture
  • SQLite default database
  • SQLAlchemy ORM support
  • Flask-Login authentication
  • Flask-WTF form validation
  • Werkzeug password hashing
  • Bootstrap 5 frontend
  • Chart.js learner graphs
  • Seed data included
  • Useful for viva, source code review, project report writing, and project demonstration

How to Run

Step-by-step setup on your laptop or PC

  • Open project root folder:

    
 
    cd "Business Intelligence 2.0 AI and ML Based Real-Time Decision-Making System for Education Sector"

  • Create virtual environment on Windows:

    
 
    python -m venv .venv
.\.venv\Scripts\Activate.ps1

  • Create virtual environment on Linux/macOS:

    
 
    python -m venv .venv
source .venv/bin/activate

  • Install dependencies:

    
 
    pip install -r requirements.txt

  • Seed demo database:

    
 
    python seed_db.py

  • Run project:

    
 
    python run.py

  • Open application:

    
 
    http://127.0.0.1:5000

  • To reset demo data, stop server and delete:

    
 
    edu_bi20.db

  • Run again:

    
 
    python seed_db.py
python run.py

Login Credentials

Default demo accounts for testing after setup

Administrator Account

Department Head / Faculty Account

Teacher Accounts

Student Accounts

License

Usage terms for academic and personal projects

Related Tags

Search terms and categories for this source code

Business Intelligence Education Sector Final Year Project with Source Code Source Code Final Year MACHINE-LEARNING Project Ready-to-Run Code With Database File Plagiarism-Free Faculty Approved final year project major project minor project source code project report business intelligence education project education analytics project learning analytics system AI ML education project Flask final year project student performance prediction dropout risk prediction academic dashboard project student attendance system marks management system CSV Excel ingestion project AI report generation project Python Flask source code
Recommended for you

Related & Popular Projects

Same stack · Instant download · Ready for submission & viva

View all MACHINE-LEARNING projects
Instant ZIP download Database File included Setup README All at ₹99

₹99 each · Instant ZIP · Database File included

Most picked

Electronic Health Recognition Summarization Final Year Flask Project

<p>MedSynapse EHR is a <strong>final year project</strong> built with Python Flask for students who want a healthcare-focused web application with document processing and clinical summarization features. This <strong>final year project</strong> allows users to add patients, upload PDF or TXT electronic health records, extract text, generate structured 8-section summaries, compare extracted content with generated sections, edit the final narrative, and download summary reports as PDF. The admin side of this <strong>final year project</strong> includes users, patients, EHR documents, summaries, medical categories, terms, diseases, medicines, reports, and branding settings. The system works with a heuristic summarizer by default and also supports optional Hugging Face Flan-T5 fine-tuning for advanced experimentation. MedSynapse EHR is suitable for a <strong>final year major project</strong> in Flask, healthcare software, NLP, or AI-assisted clinical documentation.</p>

  • Source + DB
  • Viva ready
Most picked

Diabetes Detection Machine Learning Final Year Project with Source Code

<p>Diabetes Detection is a machine learning based <strong>final year project</strong> developed to predict whether a person is likely to have diabetes using diagnostic health measurements such as glucose level, blood pressure, BMI, insulin, age, pregnancies, skin thickness, and diabetes pedigree function. This <strong>major project</strong> uses the Pima Indians Diabetes dataset and applies data preprocessing, feature selection, correlation analysis, outlier detection, outlier treatment, feature transformation, feature scaling, model selection, and hyperparameter tuning. Multiple algorithms are compared, including Logistic Regression, Naive Bayes, K-Nearest Neighbors, Decision Tree Classifier, and Support Vector Classifier. Logistic Regression is selected for the Flask web app because it provides the best confusion matrix accuracy among the compared models. This <strong>Diabetes Detection source code</strong> is suitable for students who need a <strong>final year project</strong>, <strong>major project</strong>, <strong>minor project</strong>, <strong>source code</strong>, and <strong>project report</strong> based on Python, machine learning, healthcare analytics, and Flask.</p>

  • Source + DB
  • Viva ready
Most picked

Fake Currency Detection System Final Year Project with Source Code

<p>Fake Currency Detection System is a Python and Streamlit based <strong>final year project</strong> developed for detecting whether an uploaded Indian currency note image is Real Currency or Fake Currency using deep learning. This <strong>major project</strong> uses a trained Convolutional Neural Network model, image preprocessing, file validation, image preview, TensorFlow/Keras model loading, and browser-based prediction output. Users can upload a currency note image, view the uploaded image, run prediction, and receive a classification result as Real Currency or Fake Currency. The project includes recommended dataset structure, model training workflow, CNN architecture explanation, supported input formats, common error handling, limitations, and security recommendations. This <strong>fake currency detection source code</strong> is suitable for students who need a <strong>final year project</strong>, <strong>major project</strong>, <strong>minor project</strong>, <strong>source code</strong>, and <strong>project report</strong> based on Python, Streamlit, CNN, deep learning, and computer vision.<br /> <br /> <br /> &nbsp;</p> <h2>Important Disclaimer</h2> <p>This project provides an AI-based prediction from a currency image. It must not be treated as an official or legally valid currency-authentication tool. Final verification should be performed using authorized banking equipment, official currency security features, or trained professionals.</p>

  • Source + DB
  • Viva ready
Live demo

Deepfake Detection Final Year Project with Source Code

<p>Deepfake Detection Final Year Project is a Python Flask based <strong>final year project</strong> developed for detecting fake videos and images using frequency-domain analysis and transformer-based machine learning. This <strong>major project</strong> includes a public landing page, user panel, and admin panel. Users can register, log in, upload videos, upload images, submit media for deepfake detection, view real/fake classification results, check confidence scores, review frame-level analysis, analyze frequency patterns, download PDF detection reports, manage profile, change password, submit feedback, and contact support. Admins can manage users, uploaded videos, detection requests, detection results, video categories, dataset records, ML settings, model training, system logs, login history, upload history, detection history, feedback, contact queries, daily reports, and monthly reports. This <strong>deepfake detection source code</strong> is suitable for students who need a <strong>final year project</strong>, <strong>major project</strong>, <strong>minor project</strong>, <strong>source code</strong>, and <strong>project report</strong> based on Python, Flask, PyTorch, computer vision, and machine learning.</p>

  • Source + DB
  • Viva ready

Concrete Crack Detection Using Machine Learning

<p>This project leverages computer vision and machine learning techniques to automate the process of detecting cracks in concrete structures. The primary goal is to provide an efficient and accurate method for damage surveillance in buildings, which is crucial for maintaining structural integrity and safety. The project was developed as an entry for the &quot;PS-1, Concrete Crack Detection&quot;. The model has achieved an impressive F1 score of 1, indicating its high accuracy in distinguishing between cracked and non-cracked surfaces.</p>

  • Source + DB
  • Viva ready

Online Food Ordering Data Analysis Final Year Project with Source Code

<p>Online Food Ordering Data Analysis &amp; Machine Learning is a Python-based <strong>final year project</strong> developed for analyzing online food delivery order data using exploratory data analysis, visualization, and machine learning. This <strong>major project</strong> uses a real-world Kaggle dataset containing restaurant orders, subzones, order status, delivery distance, order items, bill subtotal, packaging charges, discounts, total amount, ratings, kitchen preparation time, rider wait time, and customer details. The notebook cleans the data, engineers useful features, creates 12 different visualization charts, and trains a Random Forest Regressor to predict kitchen preparation time in minutes. This <strong>online food ordering data analysis source code</strong> is suitable for students who need a <strong>final year project</strong>, <strong>major project</strong>, <strong>minor project</strong>, <strong>source code</strong>, and <strong>project report</strong> based on Python, data analytics, food delivery analytics, visualization, and machine learning.</p>

  • Source + DB
  • Viva ready

Superstore Sales and Profit Prediction Final Year Project with Source Code

<p>Superstore Sales and Profit Prediction is a full-stack Python Flask based <strong>final year project</strong> developed for predicting sales and profit using the Superstore dataset. This <strong>major project</strong> includes user management, dataset management, CSV upload, dataset preview, data cleaning, filters, machine learning model training, prediction forms, bulk CSV prediction, business insights, training history, prediction history, and admin management. Users can sign up, log in, manage profile, recover passwords through email and security question, upload datasets, select dataset versions, preview data with search/sort, clean missing values and duplicates, apply filters, train Random Forest models, predict sales and profit, and view prediction history. Admin users can manage users and clean datasets. This <strong>Superstore Sales and Profit Prediction source code</strong> is suitable for students who need a <strong>final year project</strong>, <strong>major project</strong>, <strong>minor project</strong>, <strong>source code</strong>, and <strong>project report</strong> based on Flask, pandas, scikit-learn, SQLite, and machine learning.</p>

  • Source + DB
  • Viva ready

Indian Legal System Reference App Final Year Project with Source Code

<p>Indian Legal System Reference App is a Python Flask based <strong>final year project</strong> developed for legal awareness, Indian law reference, law section search, citizen legal tools, and admin-managed legal content. This <strong>major project</strong> includes a public landing page, citizen workspace, and admin console. Citizens can register, log in, search laws, browse legal topics, view Acts and law sections, access IPC/CrPC/Constitution reference snippets, read legal FAQs, use a chatbot-style assistant, get case guidance, save laws, download document templates, read awareness articles, view legal notices, check helpline details, submit feedback, contact admin, generate complaint drafts, manage profile, and change password. Admins can manage citizens, desk inquiries, feedback, legal categories, Acts, law sections, IPC records, CrPC records, Constitution records, ML training dataset, chatbot corpus, FAQs, document templates, awareness articles, legal notices, helplines, global lookup, analytics, and credentials. This <strong>Indian legal system source code</strong> is suitable for students who need a <strong>final year project</strong>, <strong>major project</strong>, <strong>minor project</strong>, <strong>source code</strong>, and <strong>project report</strong> based on Python Flask, SQLite, and machine learning.</p>

  • Source + DB
  • Viva ready

Dynamic Event Scheduling and Conflict Resolution System

FestivalOS is a smart and scalable festival management web application developed using Python, Flask, SQLAlchemy, SQLite, Bootstrap 5, and scikit-learn. The system is designed for large-scale festival operations and helps manage events, venues, resources, participants, and schedule conflicts through an intelligent web platform. This project includes a public landing page, a secure admin console, and a dedicated user portal for attendees, volunteers, performers, coordinators, and administrators. The core highlight of the system is its constraint-aware event scheduling engine, conflict detection module, and machine-learning–assisted priority scoring for better slot recommendations. FestivalOS is an ideal final year college project for students looking to build a real-world event management and scheduling system using Flask and Python.

  • Source + DB
  • Viva ready

Email Spam Detection System Final Year Project with Source Code

<p>Email Spam Detection System is a Python Flask based <strong>final year project</strong> developed for classifying email-like text as Spam or Ham using machine learning. This <strong>major project</strong> includes user authentication, user dashboard, spam prediction, prediction history, statistics, profile management, export options, admin dashboard, dataset upload, model training, metrics, confusion matrix, and global prediction management. Users can sign up, log in, analyze email subject and body, view spam/ham prediction result, check confidence score, save prediction history, search and filter records, delete history rows, view dataset information, update profile, change password, and export history as CSV or text. Admin users can manage users, view all predictions, upload datasets, train models, evaluate accuracy, precision, recall, F1 score, and view training history. This <strong>email spam detection source code</strong> is suitable for students who need a <strong>final year project</strong>, <strong>major project</strong>, <strong>minor project</strong>, <strong>source code</strong>, and <strong>project report</strong> based on Flask, SQLite, NLP, and machine learning.</p>

  • Source + DB
  • Viva ready
Live demo

Brain Tumor Detection System Using Machine Learning

NeuroScan is a brain tumor detection web application developed using Python, Flask, Machine Learning, OpenCV, scikit-learn, and SQLite. This project is designed to classify brain MRI images and predict whether the scan indicates a tumor or no tumor, with support for multiclass classification as well. The system provides a complete workflow from MRI image upload and prediction to result history, report generation, and admin-based model training. It is an ideal project for students and developers looking for a medical image classification project in Python or a Flask machine learning project for final year students. This application runs completely on a local environment without using any third-party AI APIs, making it a practical and secure solution for learning medical image processing, Flask web development, and machine learning model deployment

  • Source + DB
  • Viva ready
Live demo

Mental Health Chatbot Using Machine Learning and Flask

<p><strong>MindCare</strong> is a <strong>mental health chatbot web application</strong> built with <strong>Python Flask and Machine Learning</strong>. It helps users with <strong>mental wellness support, mood tracking, self-assessments, chat history, and emotional analysis</strong>. The system uses <strong>NLP, TF-IDF, Logistic Regression, and rule-based response selection</strong> to generate chatbot replies from local training data. It also includes a powerful <strong>admin panel</strong> to manage users, chatbot training data, emotion labels, assessments, reports, and wellness content.</p> <p>This <strong>Flask mental health project</strong> is designed for <strong>academic projects, final year projects, portfolio websites, and machine learning demos</strong>. OpenAI integration is optional and can be enabled in code for AI-generated responses.</p>

  • Source + DB
  • Viva ready

No live demo projects in this list right now.

Not sure which project? Message us on WhatsApp — we'll suggest the best match for your topic & stack.

Get free suggestion