Data Science Projects for CSE

In 2025, data science continues to be one of the most in-demand domains for CSE students. Final-year projects based on real-world data offer a powerful way to apply your Python, ML, and analytics skills.

Whether you’re aiming for a career in AI, business intelligence, or research, these data science project ideas will help you stand out.

From beginner-friendly datasets to advanced modeling, we’ve covered practical ideas every engineering student can build and learn from.

Data Science Project Ideas for Final Year CSE Students

1. Predictive Healthcare Analytics
   Analyze patient records to predict disease onset (e.g. diabetes, heart disease) using classification or survival models. Include feature selection, dashboards, and performance metrics.
2. E-Commerce Fraud Detection
   Model financial transactions to flag fraudulent activity using anomalies, ensemble models or clustering. Visualize risk levels and alert thresholds.
3. Social Media Sentiment Analysis
   Analyze X/Twitter, Facebook, or Reddit posts to gauge public sentiment on brands or events. Use NLP preprocessing and classification models.
4. Customer Churn Prediction
   Predict users likely to cancel subscriptions based on behavior and demographics. Train classifiers (like Random Forest, XGBoost), evaluate metrics, and visualize retention strategies.
5. Sales Forecasting Dashboard
   Forecast product sales with time-series methods (e.g., ARIMA, Prophet). Build visual analytics dashboard for inventory planning and trend insights.
6. Urban Crime Pattern Analysis
   Analyze city crime datasets to uncover spatial-temporal patterns or hotspots. Use clustering and visualization to inform law enforcement strategies.
7. Smart Farming Yield Analysis
   Leverage IoT and historical farm data to predict crop yields. Combine weather, soil and sensor metrics for actionable insights.
8. Environmental Noise Classification
   Build real-time urban sound classification (traffic, construction, nature) using audio processing and machine learning. Useful for pollution monitoring.
9. Student Performance Predictor
   Analyze academic records and behavior to forecast final grades. Apply classification/regression to identify at-risk students for targeted interventions.
10. Ride-Hailing Demand Forecasting
    Use ride-share trip data to forecast demand by location/hour. Useful for optimizing driver allocation using time-series analytics.
11. Topic Modeling of News Articles
    Use LDA or NMF on large text corpora to extract trending topics. Visualize topic distributions over time or categories.
12. Salesperson Performance Analysis
    Analyze CRM and sales data to predict top-performing reps. Use clustering and regression to align training and resource allocation.
13. Employee Attrition Modeling
    Predict which employees are likely to quit by analyzing HR metrics and feedback using classification models. Deploy dashboard for HR interventions.
14. Disease Outbreak Prediction
    Forecast disease case surges (e.g. flu, dengue) using historical health data and weather trends. Use time-series forecasting and anomaly detection.
15. Credit Risk Scoring
    Build models to assess loan default risk using financial history and socio-economic features. Useful for fintech applications.
16. Real-Time Analytics Dashboard on Streaming Data
    Track real-time event streams (e.g. IoT logs or Twitter hashtags). Use Spark/Flume pipeline with live visualization and anomaly detection.
17. Social Network Influence Scoring
    Analyze network graphs (e.g. Twitter or LinkedIn) to determine key influencers using centrality metrics and predictive analytics.
18. Movie & Music Recommendation Engine
    Build recommendation systems using collaborative filtering, content-based filtering, and hybrid methods for media platforms. Include explainability features.
19. Text Summarization Tool
    Create extractive or abstractive summarization models for long documents or news feeds using NLP and deep learning.
20. Anomaly Detection in Network Traffic
    Monitor logs or traffic streams to detect unusual behavior using unsupervised techniques or PyOD frameworks. Supports cybersecurity analytics.

21. Synthetic Data Generation Pipeline
    Generate synthetic datasets to augment scarce or sensitive data for AI training. Compare model accuracy using synthetic vs real data sources.
22. Predictive Energy Forecasting
    Combine smart meter and weather datasets to predict energy consumption peaks. Help grid scheduling and renewable integration.
23. Urban Heat Island Analysis
    Analyze temperature datasets across city zones to map heat islands. Recommend green infrastructure placement and cooling strategies.
24. Ethical AI Bias Assessment
    Evaluate fairness of ML models across demographic groups using bias metrics. Develop mitigation strategies to ensure equitable performance.
25. Geospatial Data Sentiment Mapping
    Use geo-tagged social media to map public satisfaction or grievances across city regions. Visualize sentiment heat maps for urban planning.
26. Materials Informatics Pipeline
    Use data science on materials datasets to predict properties (strength, conductivity). Speed up new material discovery.
27. Nanoinformatics for Bio-Applications
    Analyze nanoparticle datasets to predict effectiveness in drug delivery or diagnostics. Combine feature engineering with predictive models.
28. Datasphere Farming Analytics
    Use multi-source agricultural data (satellite, soil, weather) to optimize irrigation and crop yield forecasting. Integrated cloud data pipeline.
29. Living Intelligence Sensor Analytics
    Integrate AI with biological sensors to analyze adaptive environmental responses (e.g. wearable health + biofeedback loops). Real‑world prototype analytics.
30. Sustainable AI Impact Calculator
    Quantify carbon footprint of different data science models (GPT vs LSTM) and optimize for minimal environmental impact.
31. Healthcare Resource Allocation Model
    Analyze hospital usage data to allocate beds, staff, equipment efficiently and prioritizing fairness across demographics.
32. Climate Disaster Response Optimizer
    Use historical disaster and climate data to optimize emergency response planning—predict resource needs and logistics deployment.
33. Financial Sentiment-Stock Correlation
    Track social sentiment for stocks and correlate sentiment signals with price movements to improve forecasting accuracy.
34. Fitness Behavior Clustering
    Analyze wearable fitness data to cluster users based on exercise and health patterns and provide personalized wellness insights.
35. Retail Dynamic Pricing Model
    Use event, demand, and competitor pricing data to optimize ticket or product pricing in real-time for max revenue.
36. Virtual Event Engagement Analytics
    Measure attendee interactions (chat, polls, clicks) to predict engagement levels and tailor future virtual event formats.
37. Educational Learning Path Optimizer
    Use student academic records and psychometric data to suggest personalized learning resources and course paths.
38. Urban Soundscape Health Index
    Classify noise types via audio features and correlate with health outcome data to propose sound mitigation strategies.
39. Supply Chain Disruption Forecasting
    Analyze shipment, weather, and economic indicators to predict logistics disruptions and optimize contingency planning.
40. Interactive Data Visualization Dashboard
    Build dashboards with real-time interactive visualizations—for user journeys, city analytics, or environmental monitoring using tools like Dash or Power BI.

Conclusion

Choosing the right data science project can shape your career path and showcase your practical skills to recruiters. Start with a topic that excites you, use real-world datasets, and focus on clear outcomes.

Need a customized project or expert advice? Talk to our team now and get started with confidence!

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