KCET Rank Prediction and College Recommendation System 🚀
Table of Contents Project Overview Dataset Acquisition and Preparation Exploratory Data Analysis Machine Learning Methodology Models and Hyperparameter Tuning Evaluation Metrics Streamlit Web Application Deployment
Running Locally
Future Enhancements
Disclaimer
Project Overview
This project delivers a robust ML solution for predicting KCET ranks and providing personalized college recommendations. Students can estimate competitive ranks before official results and explore colleges based on predicted ranks and branch preferences.
Technologies used: Python, Pandas, Scikit-learn, Streamlit
Example of KCET Rank Predictor & College Finder UI
Dataset Acquisition and Preparation
Sources: Official KCET datasets from KEA for years 2023–2025.
Data Cleaning:
Unified Excel format, handled missing values
Normalized column names
Standardized course and college names
Integration: Merged datasets to form a comprehensive foundation for model training.
Exploratory Data Analysis (EDA)
Distribution plots to visualize marks and rank spread
Correlation heatmaps for feature relationships
Statistical summaries to detect anomalies and biases
Machine Learning Methodology Models and Hyperparameter Tuning Model Best R² RMSE Key Hyperparameters Example Gradient Boosting 0.9996 1169.60 subsample=0.8, n_estimators=500 Random Forest 0.9996 1170.07 n_estimators=100, max_depth=30 Decision Tree 0.9996 1170.01 min_samples_split=8, max_depth=40 Extra Trees 0.9994 1345.15 n_estimators=100, max_depth=50 Ridge Regression 0.9214 15872.43 alpha=0.01, solver='lsqr'
Gradient Boosting performed best.
Linear models provided strong baselines.
Evaluation Metrics
R² Score – Explained variance
RMSE – Average prediction error in rank units
Streamlit Web Application
Features: Interactive input for KCET marks, board marks, and exam year
Real-time rank prediction College recommendations filtered by rank, branch(es), location, and type
Multi-branch selection with robust matching (ignores “engineering/engg”)
Clickable college websites Professional gradient background and readable UI Deployment
The application is deployed on Render:
Try the live KCET Rank Predictor & College Finder https://kcet-rank-prediction-college-tolm.onrender.com Running Locally
Clone the repository:
git clone https://github.com/yourusername/kcet-rank-prediction.git cd kcet-rank-prediction
Install dependencies:
pip install -r requirements.txt
Run the Streamlit app:
streamlit run streamlit_app.py
Future Enhancements
Category-wise seat reservations & quota-aware recommendations
Extended dataset with branch-specific cutoff trends
Interpretability using SHAP
Multi-modal input options (voice, image)
CSV/Excel download of eligible colleges
Disclaimer
The KCET Rank Predictor is trained on limited historical data and is intended for preliminary guidance only. Predicted ranks may differ from official results. Users should verify recommendations with official college sources.