25 Decision Making Project Ideas For Student 2026-27

Decision making is a core skill for students across disciplines — from business, computer science and psychology to engineering and social studies.

Good decision-making combines data, reasoning, and context: students learn to identify options, weigh evidence, model outcomes, and choose actions that meet defined goals.

This article is written for students and presents 25 practical, well-explained Decision making project ideas you can pick for coursework, competitions, internship portfolios, or self-learning.

Each project includes objectives, required data or tools, suggested methodology, expected outcomes, evaluation metrics, and possible extensions.

Whether you want to build a simple rule-based model, use statistical tests, or create a machine-learning solution to support decisions, these projects will help you practice structured thinking, technical implementation, and clear reporting.

The projects range from low-resource classroom experiments to intermediate-level data projects suitable for a final-year thesis. Read the introduction and the project descriptions carefully, choose one that matches your skills and resources, and follow the suggested steps to complete a strong, demonstrable project.

Must Read: 25 Book Project Ideas — Student-Friendly Projects You Can Start Today

How to use these Decision making project ideas

• Pick a domain you care about. Finance, healthcare, education, transportation, sustainability, and HR are rich for decision problems.
• Define the decision. Be precise: what decision will be supported (e.g., accept/reject, choose top-K options, allocate budget)?
• Gather data and constraints. Even small datasets or surveys work if you model assumptions carefully.
• Select a method. Options include decision trees, analytic hierarchy process (AHP), multi-criteria decision analysis (MCDA), Bayesian inference, optimization, simulation, and machine learning.
• Validate and evaluate. Use accuracy, precision/recall, cost/benefit, regret, or user studies to measure success.
• Document thoroughly. Explain problem, assumptions, data, method, code, results, and limitations.

Methodologies you can use

• Rule-based / Heuristics: simple if-then rules for quick decisions.
• Decision Trees / Random Forests: interpretable models for classification.
• Multi-Criteria Decision Analysis (AHP, TOPSIS, VIKOR): for ranked choices with multiple criteria.
• Linear / Integer Programming: for allocation or scheduling decisions.
• Bayesian Methods: incorporate prior beliefs and update with evidence.
• Reinforcement Learning / Simulation: for sequential decisions and policies.
• Statistical Hypothesis Testing / Causal Inference: for policy decisions backed by evidence.
• Markov Decision Processes (MDP): for stochastic sequential decision problems.

25 Decision Making Project Ideas

Below are 25 Decision making project ideas, each explained in student-friendly detail.

1. Student Course Recommendation System (Rule-based → ML)

Summary: Build a system that suggests elective courses to students based on their past performance, interests, and career goals.

Objective: Help students choose electives that maximize learning and career relevance.

Data/Tools: Student grades, course descriptions, short interest survey, Python (pandas, scikit-learn), simple recommender libraries.

Method: Start with rules (e.g., recommend advanced math if grades in related subjects > threshold). Move to collaborative filtering or content-based recommendation using cosine similarity or matrix factorization.

Steps:

1. Collect anonymized data (grades, course tags).
2. Preprocess and encode features.
3. Implement rule-based baseline.
4. Train and evaluate a simple recommender.
5. Compare and present results.

Expected outcome: A dashboard or script that suggests top 3 electives with reasoning.

Evaluation: Precision@k (how often students rate a recommended course useful), user survey, offline metrics (RMSE).

Extensions: Add career alignment, prerequisites checking, and fairness constraints.

2. Admission Decision Support for Small College

Summary: Create a decision-support model for admission officers to rank applicants.

Objective: Prioritize applicants based on academic, extracurricular, and socioeconomic criteria.

Data/Tools: Mock applicant dataset or real anonymized data, Excel or Python, AHP or weighted scoring.

Method: Use Multi-Criteria Decision Analysis (AHP or weighted scoring) to convert qualitative attributes into a rank.

Steps:

1. Define criteria and weights (gpa, test scores, leadership, need).
2. Normalize scores.
3. Compute weighted sum and rank applicants.
4. Validate with historical outcomes if available.

Expected outcome: Ranked list and sensitivity analysis showing how weight changes affect decisions.

Evaluation: Compare ranking against historical admission decisions, and check bias indicators.

Extensions: Use logistic regression to predict success and incorporate it into ranking.

3. Campus Food Vendor Selection (TOPSIS)

Summary: Choose the best food vendor for campus contracts considering cost, hygiene, menu variety, and student satisfaction.

Objective: Select a vendor using transparent multi-criteria analysis.

Data/Tools: Vendor proposals (mock), student preference surveys, TOPSIS method, Excel or Python.

Method: Apply TOPSIS to rank vendors based on normalized criteria and weights.

Steps:

1. Collect criteria and assign weights (survey students).
2. Normalize and compute distance to ideal solution.
3. Rank vendors.

Expected outcome: A ranked vendor list with justification.

Evaluation: Simulated contract period results or post-hire satisfaction survey.

Extensions: Add contract duration and dynamic pricing constraints.

4. Personal Finance Decision Tool (Budget Allocation)

Summary: Build a tool that recommends monthly budget allocation (savings, rent, food, entertainment) using goal-based decision logic.

Objective: Help students prioritize spending while meeting saving goals.

Data/Tools: User inputs, simple optimization (linear programming), Python (PuLP) or Excel Solver.

Method: Formulate a constrained optimization problem: maximize utility subject to income and required expenses.

Steps:

1. Ask user for income and goals.
2. Define utility function (e.g., decreasing marginal utility).
3. Solve LP to suggest allocations.

Expected outcome: Clear monthly budget breakdown and “what-if” analysis.

Evaluation: User feedback, feasibility of allocations, stress-tested scenarios.

Extensions: Add stochastic income, emergency funds, and investment suggestions.

5. Hiring Shortlist with Predictive Scoring

Summary: Design a predictive model to shortlist job applicants for an internship based on resume features and test scores.

Objective: Reduce hiring time and increase fit.

Data/Tools: Historical hiring data (mock/anonymized), resume parsing (spaCy), scikit-learn.

Method: Feature engineering from resumes (skills, experience), classification model (logistic regression), thresholding for shortlisting.

Steps:

1. Build feature set.
2. Train model and calibrate score threshold.
3. Create a shortlist and explain reasons.

Expected outcome: Shortlist tool with interpretability (feature importance).

Evaluation: Precision in shortlisting top candidates, real-world hire outcome tracking.

Extensions: Add bias audits and fairness constraints.

6. Traffic Signal Timing Optimization (Simulation + LP)

Summary: Optimize signal timing at a campus intersection to minimize average waiting time.

Objective: Reduce traffic congestion and waiting times.

Data/Tools: Traffic counts, arrival rates, basic simulation (SimPy) or linear programming.

Method: Model as queuing system and optimize green time allocation using LP or genetic algorithms.

Steps:

1. Collect sample arrival rates.
2. Build a simulation model.
3. Run optimization under different flow scenarios.
4. Compare baseline vs. optimized timings.

Expected outcome: Suggested signal timings and estimated time savings.

Evaluation: Mean waiting time reduction, throughput increase.

Extensions: Adaptive timing using sensor inputs and reinforcement learning.

7. Medical Triage Decision Support (Rule-based + ML)

Summary: Create a triage tool to prioritize patients in a simulated clinic based on symptoms and vital signs.

Objective: Help non-expert staff make safe, quick triage decisions.

Data/Tools: Public triage datasets (or simulated), decision tree models, clinician rules.

Method: Combine clinician rules (mandatory red flags) with decision-tree classification for moderate cases.

Steps:

1. Define triage categories and criteria.
2. Implement rule checks for emergencies.
3. Train a decision tree for other cases.
4. Validate with test cases.

Expected outcome: Triage flowchart and simple app or web form.

Evaluation: Sensitivity for urgent cases (false negatives unacceptable), clinician review.

Extensions: Add confidence scores and telemedicine integration.

8. Product Pricing Decision using Demand Estimation

Summary: Use historical sales data to recommend optimal price for a product to maximize revenue.

Objective: Make pricing decisions informed by demand elasticity.

Data/Tools: Sales and price history, regression models, Python.

Method: Estimate demand curve (price vs. quantity), compute revenue and profit across price points.

Steps:

1. Fit demand model (linear/log-log).
2. Compute revenue = price × predicted quantity.
3. Find price maximizing revenue or profit (account for costs).

Expected outcome: Price recommendation with expected revenue projection.

Evaluation: Holdout validation, A/B pricing tests if possible.

Extensions: Dynamic pricing, seasonality, competitor prices.

9. Event Location Decision for Club Activities

Summary: Choose a location for a college event balancing cost, capacity, convenience, and facilities.

Objective: Provide a ranked list of possible venues.

Data/Tools: Venue attributes, student preference weights, AHP or weighted scoring.

Method: Use AHP to capture pairwise comparisons of criteria and generate a consistent ranking.

Steps:

1. Define criteria and collect venue data.
2. Conduct AHP pairwise comparisons.
3. Rank venues and perform sensitivity analysis.

Expected outcome: Final location choice with reasoning.

Evaluation: Post-event satisfaction and cost adherence.

Extensions: Incorporate transport arrangements and contingency plans.

10. College Library Book Purchase Decision (Budgeted Selection)

Summary: Help librarians decide which books to buy under a limited budget to maximize student benefit.

Objective: Optimize selection given budget and multiple criteria (demand, relevance, price).

Data/Tools: Demand estimates (checkout requests), book cost, knapsack problem solver (0-1 knapsack), Python.

Method: Formulate as 0-1 knapsack: maximize total utility subject to budget.

Steps:

1. Assign utility scores to each title.
2. Solve knapsack for the budget.
3. Present selected titles and rationale.

Expected outcome: Optimal list of books within budget.

Evaluation: Usage statistics post-purchase, circulation rates.

Extensions: Multi-period budgeting and subscription models.

11. Ad Campaign Decision for Student Product (A/B Testing)

Summary: Run A/B tests to decide which ad version brings more signups for a student product or event.

Objective: Measure and choose the best creative or landing page.

Data/Tools: Ad variants, user tracking, statistical test (chi-square, t-test), Google Analytics or simple logging.

Method: Randomized A/B test with hypothesis testing.

Steps:

1. Create two ad versions.
2. Split audience randomly.
3. Run for sufficient sample size.
4. Compare conversion rates statistically.

Expected outcome: Data-driven selection of best ad.

Evaluation: P-value, confidence intervals, practical significance.

Extensions: Multi-armed bandits for continuous optimization.

12. Portfolio Selection for Student Investors (Markowitz)

Summary: Build a small investment portfolio and select weights to minimize risk for a target return.

Objective: Teach mean-variance optimization basics.

Data/Tools: Historical stock returns (mock or real), Python (numpy, cvxopt), Excel.

Method: Use Markowitz mean-variance optimization to compute efficient frontier.

Steps:

1. Collect return series.
2. Estimate expected returns and covariance.
3. Solve for weights given return target.
4. Visualize frontier.

Expected outcome: Suggested portfolio weights and risk-return plot.

Evaluation: Backtest on holdout period; Sharpe ratio improvements.

Extensions: Add constraints (no shorting), transaction costs.

13. Disaster Response Resource Allocation (Integer Programming)

Summary: Allocate limited relief resources (food, water, medical) to affected areas to minimize unmet need.

Objective: Learn optimization under constraints and humanitarian decision making.

Data/Tools: Simulated need per zone, cost and availability of resources, Python (PuLP).

Method: Integer programming to allocate discrete units under supply constraints.

Steps:

1. Define zones, needs, resource units.
2. Build IP model minimizing unmet need.
3. Solve and analyze scenarios.

Expected outcome: Resource allocation plan and scenario analysis.

Evaluation: Reduction in unmet need metrics, robustness tests.

Extensions: Time-dynamic allocation and logistics routing.

14. Student Mental Health Support Prioritization (Scoring + Clustering)

Summary: Prioritize outreach to students at risk based on survey responses and activity indicators.

Objective: Help counseling centers use limited resources effectively.

Data/Tools: Anonymous survey data, attendance logs, clustering (k-means), Python.

Method: Score students using weighted indicators, cluster to find high-risk groups, and prioritize outreach.

Steps:

1. Define risk indicators and weights.
2. Compute risk score for each student.
3. Cluster to identify groups.
4. Create outreach schedule.

Expected outcome: Prioritized list and contact plan.

Evaluation: Follow-up assessment improvement and uptake rate.

Extensions: Build a dashboard for counselors with privacy safeguards.

15. Green Campus Decision — Solar Panel Placement

Summary: Decide the best buildings or areas to install solar panels to maximize energy savings and payback.

Objective: Optimize investment in renewable infrastructure for campus sustainability.

Data/Tools: Roof areas, solar insolation data (local), energy consumption, cost estimates, Python, basic optimization.

Method: Compute potential generation for candidate locations and select combination under budget constraint.

Steps:

1. Estimate generation per site.
2. Compute cost and payback period.
3. Solve selection problem given budget.
4. Present cost-benefit analysis.

Expected outcome: Ranked list of installation sites and expected savings.

Evaluation: Payback period, ROI, CO₂ reduction estimates.

Extensions: Add battery storage and time-of-use tariffs analysis.

16. Peer Grading Decision System (Consensus + Outlier Detection)

Summary: Design a system to combine peer grades into final marks while detecting unreliable graders.

Objective: Make peer assessment fair and robust.

Data/Tools: Peer grading data, statistical methods, Python.

Method: Use median/trimmed mean, detect outlier graders using correlation or z-scores, and reweight grades.

Steps:

1. Collect peer grades.
2. Measure grader reliability.
3. Compute adjusted grades.
4. Provide feedback on grading consistency.

Expected outcome: Finalized grades and grader reliability report.

Evaluation: Correlation with instructor grades, reduction of variance due to outliers.

Extensions: Weighted Bayesian aggregation and incentive mechanisms.

17. Loan Approval Decision Model (Logistic Regression)

Summary: Create a model predicting loan default risk to help small lenders make approval decisions.

Objective: Build a risk model for credit decisions.

Data/Tools: Borrower features (income, credit history) — use synthetic data if needed, scikit-learn.

Method: Train a logistic regression classifier, calibrate probabilities, and choose an approval threshold based on acceptable risk and expected loss.

Steps:

1. Generate or obtain dataset.
2. Preprocess and train model.
3. Determine threshold via expected profit/loss analysis.
4. Build simple decision policy.

Expected outcome: Approval rules and an interface to input applicant details.

Evaluation: ROC-AUC, confusion matrix, expected loss computation.

Extensions: Use explainable AI (SHAP) to justify decisions to applicants.

18. Course Scheduling Decision (Constraint Satisfaction)

Summary: Create a timetable for classes that minimizes conflicts and satisfies room, instructor, and student constraints.

Objective: Produce a conflict-free schedule that optimizes preferences.

Data/Tools: Class requirements, constraints, CSP solver or heuristic algorithms (simulated annealing), Python.

Method: Formulate as constraint satisfaction problem with optimization for preferences.

Steps:

1. Define variables and constraints.
2. Implement CSP solver or heuristic scheduler.
3. Run and iterate until feasible schedule found.
4. Analyze unmet preferences.

Expected outcome: Timetable, conflict report, and trade-offs.

Evaluation: Number of conflicts eliminated, satisfaction score.

Extensions: Dynamic re-scheduling and student-driven preference input.

19. Energy-Efficient Room Assignment (Greedy + Optimization)

Summary: Assign students to dorm rooms to reduce heating/cooling energy based on occupancy patterns.

Objective: Save energy while respecting preferences and roommate compatibility.

Data/Tools: Room sizes, building thermal zones, occupancy data, Python.

Method: Use greedy heuristic with constraints or integer programming to minimize estimated energy consumption.

Steps:

1. Estimate per-room energy given occupancy.
2. Formulate optimization objective and constraints.
3. Solve and compare to current assignment.

Expected outcome: New room assignment with projected energy savings.

Evaluation: Predicted energy reduction and resident satisfaction.

Extensions: Incorporate behavioral nudges and thermostat scheduling.

20. Choosing Research Topics (AHP + Topic Modeling)

Summary: Help final-year students choose research topics by combining personal interest, supervisor availability, and topic novelty.

Objective: Provide ranked suggestions of thesis topics tailored to the student.

Data/Tools: List of possible topics, faculty list, student interest survey, AHP, topic modeling (if many topics), Python.

Method: AHP for criteria weighting; optionally use LDA topic modeling to cluster many topic proposals.

Steps:

1. Define criteria (interest, skills match, supervisor availability).
2. Apply AHP to score topics.
3. Present top topics and reasons.

Expected outcome: Shortlist of recommended research topics matched to student profile.

Evaluation: Student satisfaction and supervisor acceptance rate.

Extensions: Include expected resources and timeline estimates.

21. Public Transport Route Decision (Network Analysis)

Summary: Propose a new shuttle route or improve existing routes based on demand and travel time.

Objective: Improve coverage and reduce travel time for campus commuters.

Data/Tools: Origin-destination data, network graph libraries (NetworkX), GIS optional.

Method: Analyze demand clusters, compute centrality, and plan route covering high-demand nodes while minimizing travel time.

Steps:

1. Map demand points and frequencies.
2. Use clustering to find hubs.
3. Design route and simulate travel times.
4. Optimize using heuristics.

Expected outcome: Route plan with estimated service metrics.

Evaluation: Reduced average travel time, coverage ratio.

Extensions: Dynamic routing using real-time data.

22. Water Usage Reduction Decision for Hostel (Behavior + Tech)

Summary: Recommend interventions (behavioral campaigns vs. sensor installs) to reduce water usage in hostels.

Objective: Decide where to invest budget to get the most water savings.

Data/Tools: Historical water use data, costs for interventions, simple cost-benefit model, Python or Excel.

Method: Estimate expected reduction and cost per liter saved for each intervention, then rank by cost-effectiveness.

Steps:

1. List possible interventions and estimated impacts.
2. Compute cost-effectiveness metrics.
3. Recommend prioritized investments.

Expected outcome: Ranked intervention list and projected savings.

Evaluation: Measured reduction after implementation.

Extensions: Pilot studies and split testing.

23. Textbook vs. e-Book Decision for Library (MCDA)

Summary: Decide whether to purchase physical textbooks or e-book licenses based on cost, accessibility, and durability.

Objective: Maximize student access under budget constraints.

Data/Tools: Price and usage estimates, MCDA methods, Excel.

Method: Multi-criteria scoring with weights for cost per student, accessibility, durability, and shelf space.

Steps:

1. Collect price and usage data.
2. Score options using weighted criteria.
3. Present decision and sensitivity checks.

Expected outcome: A procurement recommendation and break-even analysis.

Evaluation: Student satisfaction and circulation/usage metrics.

Extensions: Hybrid models and subscription options.

24. Food Waste Reduction Decision (Nudge + Forecasting)

Summary: Decide what portion sizes or menu rotations reduce plate waste while maintaining satisfaction.

Objective: Lower food waste via data-backed menu planning.

Data/Tools: Plate waste measurements, menu items, forecasting models, simple experiments.

Method: Forecast demand per menu item, try portion-size adjustments and run randomized trials to compare waste and satisfaction.

Steps:

1. Collect baseline waste data.
2. Forecast demand for each dish.
3. Run controlled trials for portion or menu changes.
4. Evaluate difference and scale.

Expected outcome: Recommendations for portioning and menu planning.

Evaluation: Waste reduction per meal and student satisfaction.

Extensions: Predictive ordering to reduce overproduction.

25. Choosing a Major/Career Path (Decision Aid App)

Summary: Build a simple decision aid app that helps students choose a major by mapping interests, strengths, and job prospects.

Objective: Provide structured guidance for a major/career choice.

Data/Tools: Interest inventory (survey), skill self-assessment, job market indicators, simple scoring model, web app (Flask or HTML + JS).

Method: Combine self-assessment scores with external data (skill demand) and present ranked majors with explanations and next steps.

Steps:

1. Create short survey for interests and skills.
2. Map survey to major options.
3. Use weighted scoring to rank choices.
4. Build UI to show results and career pathways.

Expected outcome: Personalized list of recommended majors and action plan.

Evaluation: Follow-up survey of student satisfaction and fit.

Extensions: Add psychometric testing and local internship matching.

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Practical Tips for Completing These Projects

1. Start Small: If data is scarce, simulate realistic datasets and clearly state assumptions.
2. Document Assumptions: Decision projects rely on assumptions — list them in your report.
3. Use Baselines: Always compare your method to a simple baseline (random choice, rule-based).
4. Explainability Matters: For decisions affecting people, prefer interpretable models or provide explanations.
5. Ethics and Bias: Evaluate decisions for fairness and unintended impacts.
6. Visualization: Use charts and simple dashboards to communicate results to non-technical reviewers.
7. Validation: Use holdout sets, cross-validation, A/B tests, or pilot runs to validate your approach.
8. Reproducibility: Share code, data (or generation scripts), and README so others can reproduce results.

Conclusion

These Decision making project ideas provide a broad selection of real-world problems you can tackle as a student.

Each project blends practical skills — data collection, modeling, optimization, and evaluation — with core decision-making concepts such as criteria weighting, trade-offs, and ethical considerations.

Pick an idea that matches your interests and resources, start with a clear problem statement, and iterate from a simple baseline toward a more advanced solution.

Remember that strong reporting and clear assumptions are as important as the model or algorithm you build.

Good documentation, transparent evaluation, and thoughtful discussion of limitations will make your project stand out.

If you’d like, I can help you pick one idea based on your course, available data, and timeline, and provide a step-by-step plan (including a template report and starter