College_Recommendation_Predictor

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College_Recommendation_Predictor

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# College_Recommendation_Predictor

🚀 An interactive Streamlit-based dashboard for analyzing colleges based on ratings, placement-to-fee ratio, and other key factors.  
Deployed Link: https://collegerecommendationpredictor.streamlit.app/  
🔗 Linkedln Profile Link: https://www.linkedin.com/in/arshia05/
📢 Related Linkedln post Link: https://www.linkedin.com/posts/arshia05_machinelearning-ai-collegerecommendation-activity-7312715192029573120-W86S?utm_source=share&utm_medium=member_desktop&rcm=ACoAAET7I50BN_JsnnWbwEnbmzPIR-tOov4vBo4

🌟 Features
✔️ Dynamic Graphs & Visualizations – Compare colleges based on various parameters
🎯 College Recommendation – Get the best college suggestions based on location, ratings, and fees
📊 Real-Time Analysis – View placement trends and fee structures dynamically
⚙️ Interactive Sidebar – Customize inputs for tailored insights
🌗 Light & GitHub Mode – Toggle between different themes
📝 Summarized Insights – Auto-generated textual insights for better understanding

🏗️ Tech Stack
🔹 Frontend: Streamlit (Python)
🔹 Backend: Python (Pandas, NumPy, Matplotlib, Seaborn)
🔹 Machine Learning: Scikit-learn (if applicable)
🔹 Database/Storage: CSV/Excel
🔹 Deployment: Streamlit Cloud

📌 Usage
Use the sidebar to filter colleges based on state, stream, ratings, and fees.
View real-time visualizations to analyze college performance.
Get automated textual insights in the Summary tab.

PYTHON_PROJECT
│── .devcontainer/              # Development container settings
│── .vscode/                    # VS Code settings
│── app.py                      # Streamlit app for dashboard
│── categorizing.py             # Script for data categorization
│── data_cleaning.py            # Data preprocessing script
│── model_training.py           # ML model training script
│── cleaned_data.csv            # Preprocessed dataset
│── College_data.csv/xlsx       # Raw dataset files
│── gradient_boosting.pkl       # Gradient Boosting model
│── meta_model.pkl              # Meta-learning model
│── random_forest.pkl           # Random Forest model
│── requirements.txt            # List of required Python packages
│── README.md

🖥️ Screenshots

📌 Future Enhancements
🔹 Add more filtering options (e.g., course type, hostel fees)
🔹 Improve ML model accuracy for predictions
🔹 Integrate API for real-time placement data

Developed by Arshia Singh

College_Recommendation_Predictor

College_Recommendation_Predictor Latest Version Download

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College_Recommendation_Predictor

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