Project IV
Semester VII · BCA 404 · 3 credits
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The syllabus asks for
- What changes in Project IV
- The running example: SikshyaSignal
- The pipeline and where the computing lives
- Building the dataset and meeting the accuracy trap
- Should this project use machine learning at all?
- Eight reasons capstone data projects fail, and the cure
Key points
- A capstone is judged on the quality of the decision and on how honestly it is measured, not only on whether the code runs.
- Write a four-part problem statement and measurable objectives that name the metric.
- A data project is a pipeline of six stages; each arrow can fail, so name the failure of each.
- Accuracy can lie. With 23 percent failures, doing nothing is 77 percent accurate and useless.
- Try the cheapest method first and screen the idea for consent, bias and human review before coding.
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The syllabus asks for
- Where the data comes from, and the data sheet
- Five data faults and a cleaning script
- Features: scaling, derived columns and a correlation check
- Train, validation and test: three sets with three jobs
- Data leakage: when a perfect score is a warning
- Eight data habits that examiners notice
Key points
- Document every column in a data sheet: meaning, unit, range, source and whether it is known at prediction time.
- Five faults appear in nearly all data: missing, impossible, duplicate, inconsistent and outlier values; clean them in code and print counts.
- Scale features with z-scores and check correlations; a weak feature is a finding, not a failure.
- Split into train, validation and test with stratification; use the test set once.
- A suspiciously good feature signals leakage: information that is not available when the prediction is made.
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The syllabus asks for
- Logistic regression in plain words
- Training the model and reading the weights
- Choosing a threshold and reporting the test set once
- Cross-validation and a group-fairness check
- Eight ways a model can mislead, and the cure
Key points
- Logistic regression turns a weighted score into a probability with the sigmoid; gradient descent learns the weights by reducing log loss.
- Read the loss curve and both training and validation scores; a large gap signals over-fitting.
- Weights of correlated features are associations, not causes.
- Tune the threshold on validation, then report the test set once with the confusion matrix, precision, recall, F1, AUC and precision at the top, next to the baselines.
- Use cross-validation for a steadier estimate and a bootstrap interval to judge group gaps.
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The syllabus asks for
- From notebook to service
- Automated tests: eight checks that run in a second
- Monitoring: how the college knows when the model is going stale
- Privacy, consent and security
- Deployment options and the run file
- Eight deployment habits that prevent embarrassing failures
Key points
- Save the model as a small versioned file and wrap it in a function that validates input and returns the model version.
- Eight fast tests, including behaviour tests and a save-load round trip, make the service trustworthy.
- Drift has two forms: inputs change (covariate) or the relationship changes (concept). PSI watches inputs; only outcomes reveal concept drift.
- An alarm means investigate; retrain by a rule written in advance.
- A system about students needs consent, minimisation, access control, retention limits and human review.
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The syllabus asks for
- The report map
- The poster and the slides
- The eight-minute talk and the demo
- Originality: citing, checking and using AI tools honestly
- Twelve viva questions with model answers
- An illustrative rubric
- Eight habits for a strong defence and a useful reflection
Key points
- Follow your college's report format first; use the 70-page map as a budget check that favours data, evaluation and ethics.
- Posters and slides use claims as titles, big numbers, baselines beside results, and a limit.
- Plan an 8-minute talk with a protected question slot and a controlled demo with a backup.
- A similarity checker cannot prove originality; understand and cite everything, and be open about AI assistance.
- Prepare twelve viva answers, including honest ones about limits, leakage, fairness and privacy.
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The paper
TU assessment: 60% final examination set by the university, 40% internal (assignments, lab work, attendance). Confirm the split in your college's notice.