Final Year Project Topics for Data Science

Latest Final Year Project Topics for Data Science Students in 2026

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Introduction

Selecting the right final year project topic for data science students is one of the most critical decisions you’ll make during your academic journey. A well-chosen project can showcase your technical expertise, demonstrate your problem-solving abilities, and position you competitively in the job market. However, with the rapid evolution of data science technologies and the emergence of new applications across industries, finding a topic that’s both relevant and achievable can feel overwhelming.

The field of data science continues to expand at an unprecedented pace in 2026. From predictive modeling that drives business intelligence to machine learning pipelines transforming healthcare diagnostics, the demand for data science professionals who can tackle real-world challenges has never been higher. Your final year project is your opportunity to prove that you possess these capabilities while contributing meaningful insights to your chosen domain.

This comprehensive guide presents 30 carefully curated final year project topics for data science students that reflect current industry trends, emerging technologies, and pressing real-world problems. Whether you’re interested in predictive modeling, data mining, machine learning pipelines, data visualization, or big data analytics, you’ll find topics that align with your interests and career aspirations. Each topic has been designed to be specific, actionable, and achievable within the scope of a final year project, while maintaining relevance to 2026’s technological landscape.

How to Choose the Right Final Year Project Topic

Before diving into our comprehensive list, consider these practical guidelines for selecting your ideal data science project topic:

• Align with Your Interests: Choose a topic that genuinely excites you—you’ll spend months working on this project, and passion drives better outcomes and innovation.
• Assess Data Availability: Verify that you can access sufficient, quality data for your chosen topic, as this is fundamental to any data science project’s success.
• Consider Scope and Timeline: Ensure your topic is achievable within your academic timeframe while remaining substantial enough to demonstrate advanced data science competencies.
• Evaluate Real-World Impact: Prioritize topics that address actual industry challenges or organizational problems, making your research practically valuable beyond the classroom.
• Match Your Technical Skills: Select a topic that pushes your boundaries but remains within reach, allowing you to develop new competencies while building on existing strengths.

Predictive Modeling and Forecasting Topics

1. Predictive Modeling of Customer Churn Rates Using Machine Learning Algorithms and Behavioral Analytics in Nigerian Telecommunication Companies

This research develops machine learning models to predict customer churn by analyzing usage patterns, pricing sensitivity, and service quality metrics in telecom sectors. The project involves data collection from telecommunications providers, feature engineering based on customer behavior, and implementation of classification algorithms such as logistic regression, random forests, and gradient boosting machines. Success metrics include model accuracy, precision, recall, and the actionability of insights for customer retention strategies.

2. Time Series Forecasting of Stock Market Prices Using ARIMA, Prophet, and Deep Learning Neural Networks for African Financial Markets

This project implements and compares multiple time series forecasting models to predict stock price movements, evaluating accuracy across different market conditions and volatility scenarios. You’ll work with historical stock data, implement classical statistical methods like ARIMA, modern approaches using Facebook’s Prophet, and deep learning architectures including LSTM and GRU networks. The research encompasses data preprocessing, stationarity testing, parameter optimization, and backtesting on real market data.

3. Predictive Analysis of Student Academic Performance Using Machine Learning Ensemble Methods in Nigerian University Systems

This research builds predictive models to identify at-risk students early by analyzing academic history, attendance, socioeconomic factors, and engagement metrics to enable intervention. The project collects educational data from university databases, engineers features from multiple data sources, and develops ensemble models combining various algorithms for robust predictions. Implementation focuses on early intervention systems that allow academic advisors to provide targeted support to struggling students before performance deteriorates.

4. Development of a Machine Learning Model for Predicting Housing Prices Using Regression Techniques and Feature Engineering Methods

This project creates a robust pricing model by applying multiple regression algorithms, feature selection techniques, and hyperparameter optimization to real estate datasets. Work with property features including location, size, amenities, and historical sale prices to develop predictive models. The research explores various regression techniques from linear regression to advanced methods like XGBoost and neural networks, comparing their performance and interpretability for real estate valuation applications.

5. Predictive Modeling of Disease Outbreak Trajectories Using Machine Learning and Epidemiological Data Analysis in Sub-Saharan Africa

This research applies machine learning to predict disease spread patterns, incorporating demographic data, mobility patterns, and historical outbreak information for public health planning. The project integrates epidemiological models with machine learning approaches, using data from health organizations to forecast disease trajectories. Implementation includes SIR/SEIR models combined with predictive analytics, enabling public health officials to allocate resources effectively and implement preventive measures proactively.

Data Mining and Pattern Discovery Topics

6. Customer Segmentation and Market Basket Analysis Using Clustering Algorithms and Association Rule Mining in E-Commerce Platforms

This project applies k-means, hierarchical clustering, and association rule mining to identify customer segments and discover purchasing patterns for targeted marketing strategies. Work with e-commerce transaction data to segment customers based on behavior, demographics, and purchase history. The research discovers frequently co-purchased items and patterns that inform product recommendations, cross-selling opportunities, and personalized marketing campaigns that increase customer lifetime value.

7. Sentiment Analysis and Opinion Mining of Social Media Data Using Natural Language Processing and Deep Learning Techniques

This research develops NLP models to extract sentiments from Twitter, Facebook, and Instagram posts, classifying opinions about brands, products, or social issues for business intelligence. The project involves text preprocessing, feature extraction using techniques like TF-IDF and word embeddings, and classification using deep learning models including CNNs and RNNs. Applications include brand monitoring, customer feedback analysis, and understanding public sentiment toward corporate initiatives or social campaigns.

8. Anomaly Detection in Network Traffic Data Using Unsupervised Machine Learning for Cybersecurity Threat Identification

This project implements isolation forests, autoencoders, and one-class SVM models to detect unusual network patterns indicating potential security breaches or intrusion attempts. The research works with network traffic datasets, extracting features like packet sizes, protocol types, and temporal patterns. Implementation develops real-time anomaly detection systems that identify suspicious activities such as DDoS attacks, data exfiltration attempts, and unauthorized access patterns for enhanced cybersecurity.

9. Text Mining and Topic Modeling of Academic Publications Using Latent Dirichlet Allocation for Research Trend Analysis

This research applies LDA and other topic modeling techniques to scholarly articles, identifying emerging research directions, influential topics, and collaboration patterns in data science. The project collects academic papers from repositories like arXiv or Google Scholar, applies text preprocessing and topic modeling, and analyzes temporal trends to understand how research directions evolve. Results inform students and researchers about emerging opportunities and established research clusters within the data science field.

10. Fraud Detection in Financial Transactions Using Machine Learning Classification Models and Behavioral Pattern Analysis

This project develops classification models combining transaction history, merchant data, and behavioral biometrics to identify fraudulent activities in real-time payment systems. Work with financial transaction datasets, engineering features from payment patterns, merchant categories, and user behavior. Implementation includes handling class imbalance through techniques like SMOTE, developing models that balance fraud detection accuracy with minimizing false positives that disrupt legitimate customer transactions.

Machine Learning Pipelines and Model Development Topics

11. Development and Optimization of End-to-End Machine Learning Pipeline for Automated Image Classification Using Convolutional Neural Networks

This research designs a complete ML pipeline incorporating data preprocessing, model training, validation, hyperparameter tuning, and deployment for image recognition applications. The project encompasses image data collection and augmentation, CNN architecture design, transfer learning implementation using pre-trained models, and optimization for production deployment. Applications include medical image analysis, quality control in manufacturing, or content moderation in social media platforms.

12. Comparative Analysis of Supervised Learning Algorithms for Medical Diagnosis Prediction Using Clinical Data and Patient Health Records

This project compares random forests, gradient boosting, and support vector machines for predicting diseases, evaluating performance metrics, interpretability, and clinical applicability. The research uses publicly available healthcare datasets, implements multiple algorithms, and conducts rigorous evaluation using cross-validation and stratified sampling. Analysis includes performance comparisons, feature importance analysis, and assessment of clinical validity to guide healthcare professionals in adopting predictive systems.

13. Implementation of Automated Machine Learning AutoML Framework for Non-Technical Users in Business Intelligence Applications

This research develops a user-friendly AutoML system that automatically selects algorithms, performs feature engineering, and optimizes models for business users without data science expertise. The project creates an interface that accepts datasets, automatically handles preprocessing, evaluates multiple algorithms, and recommends optimal models. Implementation democratizes machine learning, enabling business analysts to build predictive models without requiring deep technical knowledge, accelerating time-to-insight in organizations.

14. Deep Learning Pipeline for Natural Language Understanding and Question Answering System Development Using Transformer Models

This project builds a machine learning pipeline implementing BERT, GPT, or similar transformer architectures for understanding complex questions and generating accurate answers from documents. The research fine-tunes pre-trained language models on domain-specific data, implements question-answering architectures, and develops retrieval mechanisms for document-based QA systems. Applications include customer support chatbots, intelligent document search, and automated information retrieval systems for organizations.

15. Real-Time Machine Learning Model Deployment and Monitoring System for Continuous Performance Evaluation and Model Retraining

This research creates infrastructure for deploying ML models in production environments, implementing monitoring systems, drift detection, and automated retraining pipelines for sustained performance. The project develops containerized models using Docker, implements monitoring dashboards, and creates systems that detect data drift and performance degradation. This ensures models remain effective in production despite changing data distributions, maintaining business value over extended deployment periods.

Data Visualization and Business Intelligence Topics

16. Interactive Dashboard Development for Business Intelligence Using Advanced Data Visualization Techniques and Real-Time Data Integration

This project creates dynamic dashboards in Tableau, Power BI, or Python libraries, visualizing key performance indicators, business metrics, and actionable insights for decision-making. The research integrates data from multiple sources, develops real-time update mechanisms, and creates interactive visualizations that enable stakeholders to explore data and derive insights. Implementation includes drill-down capabilities, filtering mechanisms, and automated alerts for significant business events or threshold violations.

17. Geospatial Data Visualization and Mapping Analytics for Urban Planning and Infrastructure Development in Nigerian Cities

This research combines geospatial data with visualization tools to analyze urban infrastructure, traffic patterns, population density, and development opportunities for urban planners. The project uses mapping libraries like Folium or Mapbox, analyzes spatial relationships, and creates visualizations that inform urban planning decisions. Applications include identifying areas needing infrastructure investment, optimizing public transportation routes, or planning emergency response systems based on geographic distribution of services.

18. Network Analysis Visualization and Community Detection Using Graph Analytics for Social Network Structure Understanding

This project visualizes complex network structures, identifies communities and influential nodes using graph databases and network analysis algorithms for social media or organizational networks. The research implements community detection algorithms like modularity optimization and Louvain method, identifies central nodes, and creates interactive network visualizations. Applications inform understanding of information spread in social networks, identifying influential individuals, or analyzing organizational communication patterns.

19. Data Storytelling Through Advanced Visualization for Executive Decision-Making and Strategic Business Planning in Large Organizations

This research develops compelling visual narratives that communicate complex data insights effectively to non-technical stakeholders for informed strategic planning and resource allocation. The project combines data analysis with visualization design principles, creating presentation decks and interactive reports that guide executives through data-driven insights. Focus includes translating technical findings into business language, highlighting actionable recommendations, and using visual design to emphasize key messages and drive decision-making.

Big Data Analytics and Processing Topics

20. Big Data Processing Pipeline Development Using Apache Spark and Hadoop for Large-Scale Data Analysis and Real-Time Analytics

This project builds scalable data processing infrastructure using distributed computing frameworks to handle terabytes of data for analytics, modeling, and business intelligence applications. The research implements MapReduce jobs, develops Spark applications, and optimizes cluster configurations for processing performance. Applications include batch processing of historical data, real-time analytics on streaming information, and enabling organizations to derive insights from massive datasets that exceed traditional database capabilities.

21. Real-Time Stream Processing and Event-Driven Analytics Using Kafka and Apache Flink for Internet of Things IoT Data

This research implements streaming data pipelines to process sensor data, device metrics, and real-time events, enabling immediate anomaly detection and automated responses in IoT systems. The project develops Kafka producers from IoT devices, implements Flink processing jobs for real-time computations, and creates alerting systems for significant events. Applications include monitoring industrial equipment, smart city infrastructure management, or environmental monitoring systems that require immediate response to detected anomalies.

22. Data Warehouse Architecture Design and Implementation for Enterprise-Level Data Integration and Analytics Using SQL Server or Snowflake

This project designs and implements a comprehensive data warehouse incorporating data modeling, ETL processes, and optimization techniques for organizational analytics and reporting. The research develops dimensional models for analytical queries, creates ETL workflows that integrate data from multiple sources, and optimizes warehouse performance for reporting and OLAP operations. Implementation provides organizations with centralized repositories for historical data, enabling trend analysis and business intelligence across all corporate functions.

23. Cloud-Based Big Data Analytics Platform Development Using AWS, Azure, or Google Cloud for Scalable Data Science Operations

This research leverages cloud platforms to build elastic, scalable data science infrastructure for processing large datasets, running distributed algorithms, and deploying production models. The project explores cloud services like AWS S3 for storage, Lambda for computing, or managed analytics services, creating cost-effective platforms for data science operations. Implementation enables organizations to scale resources based on demand, reduce infrastructure costs, and access advanced tools without maintaining on-premises data centers.

Advanced Analytics and Specialized Applications Topics

24. Recommendation System Development Using Collaborative Filtering and Content-Based Filtering for Personalized Product and Content Suggestions

This project builds recommendation engines implementing matrix factorization, deep learning, and hybrid approaches to predict user preferences and suggest relevant products or media. The research develops collaborative filtering systems that identify similar users or items, content-based systems that match user preferences to item features, and hybrid approaches combining multiple techniques. Applications increase user engagement on e-commerce platforms, streaming services, or social media by presenting personalized content recommendations.

25. Time Series Decomposition and Seasonal Pattern Analysis for Inventory Management and Demand Forecasting in Retail Supply Chains

This research analyzes temporal patterns in sales data, decomposing trends and seasonality to optimize inventory levels, reduce stockouts, and minimize carrying costs for retailers. The project implements time series decomposition techniques, identifies seasonal patterns and trend components, and develops forecasts incorporating these patterns. Applications help retailers maintain optimal inventory levels, reduce waste from unsold perishable goods, and ensure product availability during peak demand periods.

26. Customer Lifetime Value Prediction Using Machine Learning for Strategic Marketing Resource Allocation and Customer Retention Planning

This project develops CLV models combining historical purchasing behavior, engagement metrics, and demographic data to identify high-value customers and optimize retention strategies. The research implements predictive models that estimate future revenue from customers, enabling marketing teams to prioritize retention efforts on high-value individuals. Applications include personalized loyalty programs, targeted marketing investments, and strategic decisions about customer acquisition versus retention spending.

27. Explainable Artificial Intelligence XAI Implementation for Interpretable Machine Learning Model Predictions in High-Stakes Healthcare Decisions

This research develops transparent ML models using LIME, SHAP, or attention mechanisms, ensuring medical professionals can understand and trust AI-driven diagnostic recommendations. The project implements explainability techniques that highlight which features influenced model predictions, creating transparency essential for healthcare applications. This addresses regulatory requirements and builds clinician trust in AI systems, enabling adoption of machine learning in medical decision-making where explainability is legally and ethically required.

28. Bayesian Analysis and Probabilistic Modeling for Uncertainty Quantification in Predictive Analytics and Decision-Making Under Uncertainty

This project implements Bayesian methods, Monte Carlo simulations, and probabilistic models to quantify prediction uncertainty and support risk-aware decision-making in business contexts. The research develops Bayesian networks that model relationships between variables, implements MCMC sampling for posterior inference, and creates uncertainty distributions for predictions. Applications enable decision-makers to understand confidence levels in predictions and make risk-adjusted decisions that account for uncertainty.

29. Natural Language Processing for Resume Screening and Talent Acquisition Automation in Human Resource Management Systems

This research applies NLP techniques to automatically parse resumes, extract key qualifications, match candidates with job requirements, and rank applicants for HR efficiency improvement. The project develops resume parsing algorithms using named entity recognition and information extraction, creates job requirement matching systems, and implements candidate ranking based on qualifications. Applications streamline recruitment processes, reduce time-to-hire, and improve candidate experience by automating initial screening stages.

30. Computer Vision Application Development for Quality Control and Defect Detection in Manufacturing Using Deep Learning Image Recognition

This project implements CNN-based systems to automatically inspect products on production lines, identifying defects, anomalies, and quality issues for manufacturing process optimization. The research collects training data from manufacturing processes, develops and trains deep learning models for defect classification, and integrates systems with production line cameras. Applications improve product quality, reduce waste, enable faster defect identification than manual inspection, and provide real-time feedback for process optimization.

Conclusion

The 30 final year project topics for data science students presented in this guide represent the cutting edge of what’s achievable and relevant