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Machine Learning · Mini Project
Campus Placement Prediction
Estimates placement chances from a student's academic record
1-2 weeks build
Overview
Uses the public Campus Recruitment dataset on Kaggle to estimate whether a student is likely to be placed, based on school and degree percentages, specialisation, work experience and aptitude test scores. Interpretable models come first, and the report discusses what the data can and cannot say about an individual. A Streamlit app turns it into a self-check tool.
What makes it stand up
- 01Exploratory analysis of placement rates by stream and score
- 02Logistic regression and decision tree kept deliberately interpretable
- 03Random forest compared for accuracy against interpretability
- 04Report section on the limits of a small, single-college dataset
- 05Streamlit self-check form with the factors behind each estimate
Module breakdown
- 01
EDA
Placement rates across marks, streams and experience.
- 02
Preprocessing
Encoding and scaling of academic features.
- 03
Models
Logistic regression, decision tree and random forest.
- 04
Evaluation
Cross-validated metrics and confusion matrices.
- 05
App
Streamlit form with an explained estimate.
After this, you will be able to
- Explain how logistic regression turns scores into a probability
- Defend why interpretability matters for this use case
- Demo an estimate from a profile the examiner provides
Technology stack
- Python
- Pandas
- scikit-learn
- Streamlit
- Matplotlib
- Level
- Mini Project
- Domain
- Machine Learning
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support