🎓 AdmitWise

An AI-powered interactive web application built with Streamlit that predicts whether a candidate will get placed in a job (or admitted) based on academic performance and other features.
The model simplifies decision-making for students, HR teams, and academic advisors by providing data-driven placement predictions.

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🚀 Demo

🔗 Live Demo on Streamlit

🚀 Video Demo

https://github.com/user-attachments/assets/7c82904a-b4d1-4444-aae5-ed887995955d

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📌 Features

• Predicts placement or admission status instantly.
• User-friendly Streamlit UI with number inputs and dropdowns.
• One-hot encoding of categorical variables to match the model’s training data.
• Lightweight and easy to use for non-technical users.
• Easily extendable: retrain the model with new data or add new features.
• Shows binary prediction: “Placed” or “Not Placed” along with probability.

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🔍 How It Works

1. The user inputs academic and background features, including:
   • SSC percentage
   • HSC percentage
   • Degree percentage
   • MBA percentage
   • Work experience
   • Specialization
   • Gender
   • And more
2. Inputs are one-hot encoded for categorical features.
3. A Logistic Regression model (trained offline) is loaded using Pickle.
4. The model outputs a binary prediction: “Placed” or “Not Placed”.
5. The result is displayed on the Streamlit app in a clear, user-friendly format.

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⚙️ Tech Stack

Component	Technology / Library
Frontend / Web	Streamlit
Model	Logistic Regression
ML Framework	Scikit-learn
Data Handling	Pandas, NumPy