Predictive Models Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Predictive Models projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Predictive Models Final Year Projects

A Distracted Driving Detection Model Based On Driving Performance

This project studies how a driver behaves when fully focused and when mentally distracted. Researchers collected driving data from many people using a simulator. They trained a deep learning model to recognize whether a driver is distracted just from the way they drive. The model works very well and can help detect unsafe driving in real time.
A Machine Learning Approach Using Statistical Models for Early Detection of Cardiac Arrest in Newborn Babies in the Cardiac Intensive Care Unit

This project focuses on detecting cardiac arrest in newborn babies early. It uses a machine learning model to analyze babies’ vital signs in the cardiac ICU. The model can predict cardiac arrest before it happens, allowing doctors to act quickly. This approach aims to reduce deaths and complications in newborns.
A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

This project focuses on detecting Autism Spectrum Disorder (ASD) early using machine learning. It compares different ways of preparing data and several simple machine learning methods to see which works best. The study tests these methods on datasets for toddlers, children, adolescents, and adults. The results show high accuracy and identify the most important factors for predicting ASD, helping doctors make better decisions.
A Stock Price Prediction Method Based on BiLSTM and Improved Transformer

This project develops a new method to predict stock prices more accurately and reliably. It combines three advanced models—BiLSTM, Transformer, and TCN—to use their strengths in analyzing stock data. The method was tested on multiple stocks and consistently outperformed existing prediction approaches. It provides stable, precise forecasts without timing issues.
Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine

This project focuses on predicting air quality using a smart computer method. The researchers improved a type of machine learning model by combining it with a genetic algorithm, which helps the model learn better and make more accurate forecasts. They tested it on real air quality data from a city in China and found that it predicts pollutants and the Air Quality Index faster and more accurately than other common methods. This can help in planning and managing air pollution more effectively.
Application of X-Ray Imaging and Convolutional Neural Networks in the Prediction of Tomato Seed Viability

This project focuses on predicting whether tomato seeds will grow successfully without damaging them. The researchers used X-ray images of seeds to check their internal structure. They created two prediction models: one based on image analysis and another using a type of artificial intelligence called a convolutional neural network (CNN). The CNN model was more accurate, achieving 86% accuracy, showing that this method can help farmers and scientists test seed quality quickly and safely.
Automated Stroke Prediction Using Machine Learning An Explainable and Exploratory Study With a Web Application for Early Intervention

This project focuses on predicting strokes using machine learning. The researchers developed a system that can identify people at risk early, which may help save lives. They tested several models and found that more advanced ones achieved up to 91% accuracy. They also used techniques to explain how these models make decisions, making the predictions more understandable for medical professionals.
BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations

This project develops a computer system that can automatically detect breast cancer from ultrasound images. It uses artificial intelligence to tell apart dangerous tumors from harmless ones. The system also explains its decisions using medical features that doctors rely on. Tests show it improves diagnosis accuracy and helps doctors understand its reasoning.
Agent-Based Modeling of Malaria Transmission

This project studies how malaria spreads using a computer simulation called an agent-based model. Unlike traditional mathematical models, it can consider differences in people and mosquitoes, such as how often they interact and how mosquitoes develop. The model was tested using real climate and population data from three cities and showed accurate predictions of malaria outbreaks. These predictions can help health authorities plan interventions in advance to reduce malaria cases.
Brain Age Prediction Based on Resting-State Functional MRI Using Similarity Metric Convolutional Neural Network

This project focuses on predicting a person’s brain age using MRI scans. It uses a special type of neural network to compare brain images from different people and measure their similarity. The model learns important features from the images and predicts brain age with good accuracy. Tests show it works well on a dataset of brain scans over time.
Explainable Artificial Intelligence EXAI Models for Early Prediction of Parkinsons Disease Based on Spiral and Wave Drawings

This project aims to detect Parkinson’s disease early using advanced deep learning models. It combines two powerful neural networks to accurately distinguish patients from healthy individuals. The model is designed to be transparent, showing which parts of patient drawings influence its predictions. This approach helps doctors understand and trust the results, potentially improving early treatment and patient care.
Leveraging Regression Analysis to Predict Overlapping Symptoms of Cardiovascular Diseases

This project uses deep learning to predict heart diseases early from patient medical records. The researchers trained a model on over 2,600 records, including age, symptoms, and disease details. They used a special neural network called LSTM to improve prediction by analyzing combinations of symptoms. Their method achieved up to 91% accuracy in predicting cardiovascular problems.
Toward Design of Internet of Things and Machine Learning-Enabled Frameworks for Analysis and Prediction of Water Quality

This project focuses on monitoring and predicting water quality using smart sensors and machine learning. Sensors collect data on temperature, pH, turbidity, and dissolved solids from a canal in Pakistan. Machine learning models then analyze this data to predict water quality levels and classify water conditions. The study shows that some models, like MLP for prediction and Random Forest for classification, give very accurate results.
A Cloud Fog Edge Closed Loop Feedback Security Risk Prediction Method

This project focuses on predicting security risks in smart power systems. It uses a combination of cloud, fog, and edge computing to detect complex attacks. The system can work with different devices and other safety equipment. Experiments show it performs better than traditional methods.
A Deep Learning-Based Brain Age Prediction Model for Preterm Infants via Neonatal MRI

This project develops a deep learning model called BAPNET to predict the brain age of premature infants using MRI scans. It helps estimate brain maturity quickly and accurately, reducing reliance on doctors’ manual assessments. The model learns from a large dataset of infant brain images and highlights important brain regions involved in development. This can support doctors in understanding brain growth and planning early interventions for preterm infants.
A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas

This project focuses on improving the management of airport terminals by predicting how busy or congested they will be. It uses a new AI model called ConvTrans-TCN that can understand patterns over time and combine different pieces of information effectively. The model analyzes past traffic data to predict the terminal’s operational status. Tests show it works better than older models and can help air traffic managers make smarter decisions.
A Novel Student Achievement Prediction Method Based on Deep Learning and Attention Mechanism

This project focuses on predicting student grades using a smart computer model. It studies how different factors affect student performance and highlights the most important ones. The model uses advanced learning techniques to understand patterns in student data over time. Experiments show it can predict grades with high accuracy, helping teachers give better guidance to students.
A Ranking Model for Evaluation of Conversation Partners Based on Rapport Levels

This project builds a system to rank conversation partners based on how well people get along. It uses data from both speech and text during interactions. Instead of predicting exact scores, it learns which partner is preferred over another. The model helps match people, like students and teachers, in online one-to-one sessions.
Adjacency Matrix Deep Learning Prediction Model for Prognosis of the Next Event in a Process

This project focuses on predicting the next event in a process to help organizations work more efficiently. Current methods either change the order of events or ignore it completely, which can reduce prediction accuracy. The project proposes a new method called AXDP that keeps the order of events intact while using deep learning to predict the next step. Tests show AXDP performs better than existing models on most datasets.
An Enhanced Recommendation Model Based on Review Text Graph and Interaction Graph

This project improves how online recommendation systems understand users. It uses both the text of user reviews and user ratings to learn what people like. The model studies the full structure of review sentences, not just nearby words. It then combines this with rating patterns to give more accurate recommendations.

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Predictive Models Final Year Projects with Source Code