🎓 College Placement Prediction Model

A machine learning-based project that predicts % chances for a student to likely be placed based on their academic records and other factors.

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📌 Context

This project started as a learning experiment, where I initially explored model training using basic datasets. Later, I scaled it by choosing a richer dataset, extracted required columns, and performed proper EDA and preprocessing. After training and validating my model, I created a web application using Flask to make it interactive and usable.

➡️ For now, I’ve added the GitHub repo — and soon the live web app link will be available too!

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🛠️ Tech Stack

• Language: Python
• Libraries: Pandas, NumPy, Matplotlib, Scikit-learn, Flask
• Deployment: Local (Web app soon to be deployed)

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🧠 Model Details

• Algorithm Used (Logistic Regression )
• Input Features: Stream ,Cgpa ,Internships, Project
• Target Output: Placement status (%)
• Accuracy Achieved: 82%

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💡 Features

• Clean and simple UI for user input
• Predicts placement chance based on input
• Easy to deploy and modify
• Modular structure for backend + frontend

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📂 Project Structure

College_placement_prediction_model/ ├── predict_system/ │ ├── Sample.csv │ ├── model.py │ ├── placement_model.pkl │ └── templates/ │ └── index.html └── README.md

👤 Author Piyush 🔗 GitHub Profile 📬 LinkedIn Coming Soon

📃 License This project is open-source and free to use for educational or personal use.

⚙️ How to Run Locally

# Clone the repo
git clone https://github.com/MeNoodie/College_placement_prediction_model.git

# Navigate to the folder
cd College_placement_prediction_model

# Install dependencies
pip install -r requirements.txt

# Run the app 
python app.py