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Campus Placement Predictor with Explainable Machine Learning

Approx. component budget: Rs. 0 - 1,000

Objective

Train and compare several classifiers and explain each prediction in simple language, so a student knows what to improve.

What you will build

A web app where a student enters CGPA, backlogs, internships, projects and skill scores, and the model predicts placement chances and shows which factors helped or hurt, using a public or self-collected anonymous dataset.

Results you can expect

• Accuracy
• Precision
• Recall and F1 for at least 4 models
• Cross-validation scores
• A feature-importance chart and per-student explanations
• With a demo on 20 unseen records

What you will learn

Data cleaning, train/test split, model comparison, overfitting, explainability with SHAP or LIME, and deploying a small model as a web page.

Tools and notes

Python, pandas, scikit-learn, SHAP, Streamlit or Flask. Use only anonymous data and state clearly that the output is guidance, not a guarantee.

Resume points from this project

• Built and tested Campus Placement Predictor with Explainable Machine Learning; objective: train and compare several classifiers and explain each prediction in simple language, so a student knows what to improve.
• Measured and analysed accuracy and precision; wrote the report and presented the results in a viva. Add your own numbers: [x].

Replace [x] with your own measured results. Write only what you really did.

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