Campus Placement Predictor

📌 Project: Campus Placement Predictor

👨‍💻 Author: Shailesh Gupta

🔗 GitHub: View Source Code

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💡 Q: What problem does this project solve?

Many students go through their campus years wondering: "Will I get placed?"

This project aims to answer that question — by building a machine learning model that predicts the likelihood of campus placement based on a student’s academic profile and background.

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🛠️ Q: What kind of data is used?

The model uses structured data containing:

• Academic scores (SSC, HSC, degree, MBA)
• Work experience
• Specialization
• Gender, streams, and placement status

It’s a real-world classification task where the output is binary: Placed ✅ or Not Placed ❌

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🔍 Q: How does the model work?

After cleaning and preprocessing the data, I trained models using:

• Logistic Regression
• Decision Trees

Evaluation metrics like accuracy, confusion matrix, and ROC-AUC were used to validate performance.

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💬 Q: Can I try it live?

Absolutely! Here's the app in action 👇

🎓 Try the Placement Predictor App

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📁 Q: What’s in the GitHub repo?

• Cleanly structured Jupyter notebook
• Preprocessing, model building & evaluation steps
• Streamlit app for real-time prediction

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🎯 Q: What did I learn?

• Handling imbalanced data in classification problems
• Importance of feature encoding and selection
• Deploying real-life educational ML models for user interaction

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🙌 Stay Connected

If you liked this project, feel free to star the GitHub repo ⭐ or connect with me on LinkedIn.

More education and career-focused AI tools coming soon — stay tuned 🎓

🔖 Tags:
Campus Placement, Career Prediction, Logistic Regression, Machine Learning, Education AI, Streamlit, Python, Classification, Student Projects