MTech Projects for Data Science — 2026 Guide
Data Science remains one of the highest-demand specialisations. Modern MTech projects emphasise end-to-end pipelines, Explainable AI, MLOps, multimodal models and domain-specific applications in healthcare, finance, retail and climate.
Why choose Data Science projects at ProjectsatBangalore?
- IEEE / KDD / NeurIPS / ACL style base papers
- Complete Python notebooks & production code
- Ready datasets or generation scripts
- MLOps & deployment guidance
- VTU / Anna / JNTU format reports
- 20–25 slide PPT + 50+ viva Q&A
- Architecture & experiment diagrams
- Mentoring via WhatsApp / Zoom
Classical & Ensemble ML
XGBoost, LightGBM, stacking, feature engineering pipelines
Deep Learning
Transformers, CNNs, GNNs, multimodal models
MLOps
MLflow, Airflow, model registry, monitoring, CI/CD
Explainable AI
SHAP, LIME, counterfactuals, fairness audits
Tools & Frameworks Used
Industry-standard Data Science and ML stack for every project.
Python / Pandas
Scikit-learn
PyTorch
TensorFlow
Hugging Face
Spark MLlib
MLflow
65+ Latest MTech Data Science Project Topics (2026)
Research-oriented and industry-relevant topics with recommended tools. All projects include IEEE base paper, code, report, PPT and viva support.
| # | Project Title | Domain | Tools / Stack |
|---|---|---|---|
| 📈 Classical Machine Learning & Ensemble Methods | |||
| 1 | Credit Risk Scoring with Advanced Feature Engineering and Stacking Ensembles | ML | Scikit-learn · XGBoost · LightGBM · SHAP |
| 2 | Customer Churn Prediction with Interpretable Models and Uplift Modelling | ML | Scikit-learn · XGBoost · CausalML |
| 3 | Automated Feature Engineering Pipeline with Featuretools and AutoML | AutoML | Featuretools · Auto-sklearn · Optuna |
| 4 | Imbalanced Classification for Fraud Detection using SMOTE and Cost-Sensitive Learning | Imbalanced | Imbalanced-learn · XGBoost · Scikit-learn |
| 5 | Multi-Label Classification for Tag Recommendation Systems | Multi-Label | Scikit-learn · scikit-multilearn · XGBoost |
| 🧠 Deep Learning | |||
| 6 | Tabular Deep Learning with FT-Transformer and TabNet | Tabular DL | PyTorch · pytorch-tabular · TabNet |
| 7 | Graph Neural Networks for Node Classification and Link Prediction | GNN | PyTorch Geometric · DGL · NetworkX |
| 8 | Self-Supervised Learning for Tabular and Image Data (SimCLR, MAE) | Self-Supervised | PyTorch · Lightly · timm |
| 9 | Multimodal Fusion Models for Joint Text-Image Understanding | Multimodal | PyTorch · CLIP · Hugging Face |
| 10 | Efficient Transformers for Long-Sequence Modelling (Longformer, Performer) | Transformers | Hugging Face · PyTorch · FlashAttention |
| 📝 NLP & Large Language Models | |||
| 11 | Retrieval-Augmented Generation (RAG) System with Evaluation Framework | RAG | LangChain · LlamaIndex · FAISS · Hugging Face |
| 12 | Domain-Adaptive Fine-Tuning of LLMs with LoRA / QLoRA | LLM | Hugging Face · PEFT · bitsandbytes |
| 13 | Aspect-Based Sentiment Analysis and Opinion Summarisation | Sentiment | Transformers · spaCy · Scikit-learn |
| 14 | Named Entity Recognition and Relation Extraction for Knowledge Graphs | IE | spaCy · Hugging Face · Neo4j |
| 15 | Text Classification with Few-Shot and Prompt-Based Learning | Few-Shot | SetFit · Hugging Face · OpenPrompt |
| 16 | Multilingual NLP Pipeline for Low-Resource Languages | Multilingual | Hugging Face · SentencePiece · FastText |
| 👁️ Computer Vision for Data Science | |||
| 17 | Medical Image Classification and Segmentation with Vision Transformers | Medical CV | PyTorch · MONAI · timm · Detectron2 |
| 18 | Object Detection and Tracking Pipeline with YOLOv8 / RT-DETR | Detection | Ultralytics · OpenCV · TensorRT |
| 19 | Image Anomaly Detection for Industrial Quality Control | Anomaly | PyTorch · Anomalib · PatchCore |
| 20 | Document Understanding and Information Extraction from Scanned Forms | Doc AI | LayoutLMv3 · Hugging Face · OpenCV |
| 21 | Video Action Recognition and Temporal Modelling | Video | PyTorch · MMAction2 · SlowFast |
| ⏱️ Time Series & Forecasting | |||
| 22 | Multivariate Time-Series Forecasting with Temporal Fusion Transformers | Forecasting | PyTorch Forecasting · TFT · GluonTS |
| 23 | Anomaly Detection in Sensor / IoT Time Series using Autoencoders | Anomaly | PyTorch · Prophet · Isolation Forest |
| 24 | Demand Forecasting for Retail with Hierarchical and Intermittent Demand Models | Demand | Prophet · LightGBM · HierarchicalForecast |
| 25 | Energy Load Forecasting with Hybrid Statistical + Deep Learning Models | Energy | Prophet · LSTM · N-BEATS · PyTorch |
| 26 | Causal Impact Analysis and Intervention Effect Estimation on Time Series | Causal | CausalImpact · DoWhy · Prophet |
| 🎯 Recommendation Systems | |||
| 27 | Hybrid Recommendation System with Collaborative Filtering + Content Features | RecSys | Surprise · LightFM · Implicit |
| 28 | Session-Based Recommendation with Transformers (BERT4Rec / SASRec) | Session | PyTorch · RecBole · Transformers |
| 29 | Graph-based Recommendation with Knowledge Graph Embeddings | Graph Rec | PyTorch Geometric · DGL · Neo4j |
| 30 | Multi-Objective Recommendation Optimising Accuracy, Diversity and Fairness | Fair Rec | RecBole · Cornac · Fairness metrics |
| ⚙️ MLOps & Production Systems | |||
| 31 | End-to-End MLOps Pipeline with MLflow, Airflow and Model Registry | MLOps | MLflow · Airflow · Docker · FastAPI |
| 32 | Feature Store Design and Real-time Feature Serving | Feature Store | Feast · Redis · Spark · Kafka |
| 33 | Model Monitoring, Drift Detection and Automated Retraining | Monitoring | Evidently · WhyLabs · MLflow · Prometheus |
| 34 | CI/CD for Machine Learning with GitHub Actions and Model Testing | CI/CD | GitHub Actions · pytest · Great Expectations |
| 35 | Scalable Batch and Online Inference Serving with FastAPI / BentoML / Seldon | Serving | FastAPI · BentoML · Seldon · Kubernetes |
| 🔍 Explainable AI & Responsible AI | |||
| 36 | Explainable Credit Scoring with SHAP, LIME and Counterfactual Explanations | XAI | SHAP · LIME · DiCE · Scikit-learn |
| 37 | Fairness Audit and Bias Mitigation for Classification Models | Fairness | AIF360 · Fairlearn · SHAP |
| 38 | Concept-Based Explanations and TCAV for Deep Neural Networks | XAI | Captum · TCAV · PyTorch |
| 39 | Privacy-Preserving Machine Learning with Differential Privacy | Privacy | Opacus · PyDP · TensorFlow Privacy |
| 40 | Model Card and Dataset Card Generation for Responsible AI Documentation | Documentation | Hugging Face Hub · Model Cards Toolkit |
| ✨ Generative AI for Data Science | |||
| 41 | Synthetic Tabular Data Generation with CTGAN / TVAE and Utility Evaluation | Synthetic Data | SDV · CTGAN · Synthcity |
| 42 | Text-to-SQL and Natural Language Query Interfaces for Databases | Text-to-SQL | LangChain · Hugging Face · SQL parsers |
| 43 | Automated Report Generation from Data Insights using LLMs | Auto Report | LangChain · Llama / Mistral · Templates |
| 44 | Data Augmentation for Imbalanced Datasets using Generative Models | Augmentation | CTGAN · Diffusion · SMOTE variants |
| 🏥 Domain Applications (Healthcare, Finance, Retail, Climate) | |||
| 45 | Predictive Modelling of Patient Readmission Risk with Clinical Notes | Healthcare | PyTorch · Hugging Face · EHR data |
| 46 | Stock / Cryptocurrency Price Prediction with Multimodal Signals | Finance | PyTorch · Transformers · Alternative data |
| 47 | Customer Lifetime Value and Next-Best-Action Modelling for Retail | Retail | Lifetimes · XGBoost · CausalML |
| 48 | Climate and Weather Extremes Prediction with Spatio-Temporal Models | Climate | PyTorch · GraphCast-style · xarray |
| 49 | Supply Chain Demand Sensing and Inventory Optimisation | Supply Chain | Prophet · OR-Tools · LightGBM |
| 50 | Fraud Detection in Digital Payments with Graph and Sequence Models | FinTech | PyTorch Geometric · LSTM · XGBoost |
| 🚀 Advanced & Research-Oriented Topics | |||
| 51 | Causal Machine Learning for Treatment Effect Estimation | Causal ML | DoWhy · EconML · CausalML |
| 52 | Active Learning and Human-in-the-Loop Annotation Pipelines | Active Learning | modAL · ALiPy · Label Studio |
| 53 | Continual / Lifelong Learning for Streaming Data | Continual | Avalanche · PyTorch · EWC / Replay |
| 54 | Federated Learning for Privacy-Preserving Collaborative Modelling | Federated | Flower · PySyft · TensorFlow Federated |
| 55 | Neural Architecture Search and AutoML for Custom Domains | NAS | Optuna · Ray Tune · Auto-PyTorch |
| 56 | Uncertainty Quantification and Calibration of Predictive Models | Uncertainty | MAPIE · Uncertainty Toolbox · PyTorch |
| 57 | Multi-Task and Transfer Learning across Related Prediction Problems | Transfer | PyTorch · Hugging Face · AdapterHub |
| 58 | Data-Centric AI: Systematic Data Quality Improvement Pipelines | Data-Centric | Cleanlab · Great Expectations · Pandera |
| 59 | Interpretable Clustering and Topic Modelling for Unstructured Data | Unsupervised | BERTopic · HDBSCAN · UMAP |
| 60 | Online Learning and Concept Drift Adaptation for Streaming Analytics | Streaming | River · scikit-multiflow · Kafka |
| 61 | Knowledge Distillation and Model Compression for Edge Deployment | Compression | PyTorch · ONNX · TensorRT · Distil* |
| 62 | Automated A/B Testing and Experimentation Platform Design | Experimentation | Statsmodels · CausalML · Feature flags |
| 63 | Spatio-Temporal Graph Neural Networks for Traffic / Mobility Prediction | Spatio-Temporal | PyTorch Geometric · DGL · Traffic datasets |
| 64 | Large-Scale Embedding Learning and Approximate Nearest Neighbour Search | Embeddings | FAISS · Annoy · Sentence-Transformers |
| 65 | End-to-End Data Science Platform: Ingestion → Feature Store → Training → Serving → Monitoring | Platform | Airflow · Feast · MLflow · FastAPI · Evidently |
★ All 65 MTech Data Science project topics are sourced from IEEE Xplore, KDD, NeurIPS, ICML, ACL, CVPR and leading open-source project roadmaps (2022–2026). Each project includes the base paper, complete source code / notebooks, datasets or generation scripts, architecture diagrams, university-format report for VTU / Anna University / JNTU, PPT (20–25 slides) and 50+ viva Q&A specific to the topic.
FAQ — MTech Data Science Projects
Top topics include RAG systems, Time-series Transformers, Explainable AI for regulated domains, Graph-based recommendation, MLOps end-to-end pipelines, Synthetic data generation, Multimodal models, Federated learning, and Causal ML. All come with IEEE base paper, code, report, PPT and viva support.
Core: Python, Pandas, NumPy, Scikit-learn, XGBoost/LightGBM. Deep Learning: PyTorch, TensorFlow, Hugging Face. Scale: Spark MLlib. MLOps: MLflow, Airflow, Feast, Evidently, FastAPI. XAI: SHAP, LIME, Captum, AIF360. Deployment: Docker, Kubernetes, cloud ML platforms.
Yes. Every project includes IEEE / conference base paper, complete source code and notebooks, datasets or generation scripts, architecture diagrams, university-format report (VTU, Anna University, JNTU), PPT (20–25 slides) and 50+ viva Q&A.
Typical completion is 7–21 working days. Classical ML and basic DL projects are ready in 5–10 days. Full MLOps, multimodal or large-scale research projects take 12–21 days. Contact +91 95919 12372 with your deadline.
Data Science Project Lab — Bangalore
GPU workstations, Jupyter / VS Code environments, MLflow tracking servers, and dedicated mentoring rooms for MTech and PhD Data Science scholars.
GPU
Workstations
PyTorch · TensorFlowWorkstations
Classical ML
& Ensembles
XGBoost · LightGBM& Ensembles
NLP / LLM
Pipelines
Hugging Face · RAGPipelines
Computer
Vision
YOLO · MONAI · ViTVision
Time Series
Forecasting
TFT · Prophet · N-BEATSForecasting
MLOps
Stack
MLflow · Airflow · FeastStack
Explainable
AI
SHAP · LIME · FairnessAI
Generative
AI
CTGAN · Diffusion · LLMAI
Report & PPT
Preparation
VTU / Anna / JNTUPreparation
Viva & Review
Sessions
MTech / PhD DSSessions