Machine Learning Final Year Project Source Code Download
Download Machine Learning final year project source code with frontend, backend, and database included. Easy to set up, fully functional, and ideal for students looking for PHP projects with source code in topics like ERP, Real Estate, Vehicle Rental, and Expense Tracker.
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Credit Card Fraud Detection Using Machine Learning — Source Code
Fraud Detection model based on anonymized credit card transaction. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The datasets contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
Concrete Crack Detection Using Machine Learning — Source Code
<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 "PS-1, Concrete Crack Detection". The model has achieved an impressive F1 score of 1, indicating its high accuracy in distinguishing between cracked and non-cracked surfaces.</p>
Brain Tumor Detection System Using Machine Learning — Source Code
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
Dynamic Event Scheduling and Conflict Resolution System — Source Code
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.
Fake News Detection System Using Django and Machine Learning — Source Code
<p>Fake News Detection System Using Django and Machine Learning is a web-based <strong>final year project</strong> developed for classifying news as Real or Fake using a hybrid machine-learning pipeline. This <strong>major project</strong> allows users to register, log in, paste news text, submit article URLs, run fake news detection, view prediction labels, check confidence scores, review hybrid and per-model probabilities, access dashboard analytics, search detection history, delete own records, and generate visual reports. The system uses TF-IDF vectorization, Logistic Regression, Random Forest, NLTK preprocessing, and hybrid ensemble probability averaging for prediction. Staff users can view cross-user data, manage detection records, filter reports, and access extra staff-level analytics. Django admin provides complete management of prediction results. This <strong>fake news 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 Django, Python, NLP, and machine learning.</p>
Agricultural Monitoring and Crop Prediction System with Machine Learning — Source Code
<p><strong>AgriMonitor Pro</strong> is a Flask web application for <strong>smart agricultural monitoring</strong>, <strong>crop recommendation</strong>, <strong>yield prediction</strong>, and <strong>risk classification</strong> using <strong>machine learning</strong>. The system is designed for <strong>farmers</strong> and <strong>administrators</strong> to manage farms, record crop and soil data, train ML models locally, generate predictions, and download reports in <strong>CSV</strong> and <strong>PDF</strong> formats.</p> <p>This agriculture management system uses <strong>Python 3</strong>, <strong>Flask 3</strong>, <strong>SQLAlchemy</strong>, <strong>SQLite</strong>, <strong>pandas</strong>, and <strong>scikit-learn</strong>. It supports <strong>Random Forest classification and regression</strong>, dataset management, user management, farm monitoring, soil health tracking, analytics dashboards, and report generation with <strong>Matplotlib</strong> and <strong>ReportLab</strong>.</p> <p>The platform provides a guided farmer portal for adding farms, entering NPK and weather values, checking crop health, estimating yield, and reviewing prediction history. It also includes a powerful admin panel for managing users, datasets, model training, notifications, feedback, and data exports.</p> <p>This project is suitable for <strong>agriculture technology</strong>, <strong>farm management software</strong>, <strong>smart farming solutions</strong>, <strong>precision agriculture systems</strong>, and <strong>machine learning based crop advisory platforms</strong></p>
Mental Health Chatbot Using Machine Learning and Flask — Source Code
<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>
Electronic Health Recognition Summarization Final Year Flask Project — Source Code
<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>
Skin Disease Detection Final Year using Machine Learning — Source Code
<p>DermaSense is a <strong>final year project</strong> built with Python Flask, TensorFlow/Keras, and SQLite for students who want a machine learning based healthcare web application. This <strong>final year project</strong> allows users to upload skin images, run CNN inference, view predicted disease labels with confidence scores, and maintain private prediction history. The system also supports disease information, precautions, medicine suggestions, optional Grad-CAM overlays, printable reports, and user profile management. The admin console of this <strong>final year project</strong> includes user management, prediction logs, disease catalog management, precautions, medicines, model upload, model activation, monitoring, and maintenance tools. With support for <code>.keras</code> and <code>.h5</code> models, DermNet-style class labels, and a demo model generator, DermaSense is suitable for a <strong>final year major project</strong> in machine learning, Flask, TensorFlow, image processing, or healthcare AI.</p>
Data Sanitization and Restoring Using Python and ML — Source Code
<p>DataSecure Pro is a <strong>final year project</strong> built with Python Flask and machine learning for students who want a practical data privacy and sanitization web application. This <strong>final year project</strong> allows users to upload CSV or XLSX datasets, preview data, run sanitization modes such as masking, anonymization, cleaning, encoding, and sensitive-column detection, then download sanitized outputs. Users can also request controlled restoration of sanitized files, while admins approve, reject, or run restoration workflows using stored mappings where possible. The admin side of this <strong>final year project</strong> includes users, categories, sensitive data types, sanitize/restore rules, datasets, sanitized files, restoration tickets, ML models, value mappings, reports, and audit logs. With ML-assisted column detection, KPI exports, and governance workflows, DataSecure Pro is suitable for a <strong>final year major project</strong> in data privacy, Flask, ML, and cybersecurity.</p>
Deepfake Detection Final Year Project with Source Code — 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>
Deepfake Detection Using Machine Learning Final Year Project with Source Code — Source Code
<p>Deepfake Detection Using Machine Learning is a Python Flask based <strong>final year project</strong> developed for detecting deepfake images and videos using machine learning. This <strong>major project</strong> includes a public landing page, user dashboard, and admin dashboard. Users can register, log in, upload images, upload videos, run deepfake detection, view real/fake predictions, check confidence scores, view frame-wise video analysis, download PDF or TXT reports, manage detection history, update profile details, change password, and securely log out. Admins can monitor total users, total detections, real media count, deepfake count, failed detections, recent users, all detection records, and user accounts. The detection pipeline uses OpenCV face detection, feature extraction, Xception + LSTM machine learning model prediction, and local processing without third-party APIs. 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, TensorFlow, Keras, OpenCV, and machine learning.</p>
Business Intelligence Education Sector Final Year Project with Source Code — Source Code
<p>Business Intelligence Education Sector is a Python Flask based <strong>final year project</strong> 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 <strong>major project</strong> 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 <strong>business intelligence education project 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, AI, ML, and education analytics.</p>
Indian Legal System Reference App Final Year Project with Source Code — 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>
Drone-Based Crop Image Acquisition and Targeted Pesticide Spraying Final Year Project with Source Code — Source Code
<p>Drone-Based Crop Image Acquisition and Targeted Pesticide Spraying is a Python Flask based <strong>final year project</strong> developed for smart agriculture, crop image processing, crop-type identification, and pesticide spray target documentation. This <strong>major project</strong> includes an operator user portal and administrator portal. Operators can sign up, log in, manage profile, register fields and parcels, upload drone crop images, assign images to parcels, run image processing pipeline, view classification results, draw spray target rectangles, download PDF reports, train a Random Forest model using labelled crop samples, test models, and view activity logs. Admins can manage users, field records, drone images, master datasets, machine learning models, classification results, spray records, system reports, backups, restore operations, and admin password settings. This <strong>drone-based crop image acquisition 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, OpenCV, machine learning, and smart farming.</p>
Email Spam Detection System Final Year Project with Source Code — 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>
Online Food Ordering Data Analysis Final Year Project with Source Code — Source Code
<p>Online Food Ordering Data Analysis & 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>
Fake Currency Detection System Final Year Project with Source Code — 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 /> </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>
Superstore Sales and Profit Prediction Final Year Project with Source Code — 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>
Diabetes Detection Machine Learning Final Year Project with Source Code — 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>
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