A web dashboard that uses open-source machine learning to predict cost overruns and delays in infrastructure projects for policymakers.
MoSPI · Data Informatics & Innovation Division (DIID) · Software
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Decomposed from what the description asks for. Nothing added.
Cost Overrun Prediction
A machine learning model that forecasts if a project will exceed its approved budget using historical data.
Time Overrun Prediction
A predictive model that estimates schedule delays based on milestone tracking and past timelines.
Project Risk Scoring
A framework that calculates risk scores for ongoing infrastructure projects to flag vulnerabilities.
Early Warning Alerts
A notification system that generates warnings for emerging implementation challenges and delays.
AI Monitoring Dashboard
A web interface for administrators to view predictions, analytics, and decision-support insights.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The evaluators will check if you used open-source tools and software, whether your machine learning models outperform conventional statistical methods, and if your system successfully predicts cost and time overruns using project data fields.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.
Exact historical dataset samples or schema fields beyond the mention of Common Upload Form fields?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific performance accuracy targets that the models must meet?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for cost overrun prediction. How would you build that?
Decoded from SIH26103 itself — A machine learning model that forecasts if a project will exceed its approved budget using historical data. The brief asks for it by name.
Each one is quoted from a gap in the brief, not a guess about your team.
No measurable target
The text never specifies a required percentage for prediction accuracy or a minimum threshold for success.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
The brief asks for time overrun prediction. How would you build that?
Decoded from SIH26103 itself — A predictive model that estimates schedule delays based on milestone tracking and past timelines. The brief asks for it by name.
The brief asks for project risk scoring. How would you build that?
Decoded from SIH26103 itself — A framework that calculates risk scores for ongoing infrastructure projects to flag vulnerabilities. The brief asks for it by name.
The brief asks for early warning alerts. How would you build that?
Decoded from SIH26103 itself — A notification system that generates warnings for emerging implementation challenges and delays. The brief asks for it by name.
The brief asks for ai monitoring dashboard. How would you build that?
Decoded from SIH26103 itself — A web interface for administrators to view predictions, analytics, and decision-support insights. The brief asks for it by name.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the evaluators will check if you used open-source tools and software, whether your machine learning models outperform conventional statistical methods, and if your system successfully predicts cost and time overruns using project data fields.
This is the evaluator read for your problem statement, decoded from the brief's own wording.