https://updategadh.com/
Loan Approval Prediction System Using Python and Machine Learning
Loan Approval Prediction System is a machine learning-based web application developed using Python, Flask, and Scikit-learn. The system takes
💰 LOAN APPROVAL PREDICTION SYSTEM — Python & Machine Learning
A Flask web app that predicts whether a loan application gets Approved or Rejected — with 6 ML models compared and the best one auto-selected. Here's what's inside 👇
✨ KEY FEATURES
• Predicts loan approval using applicant income, credit history, education, dependents, loan amount/term & property area
• Compares 6 classification algorithms & auto-selects the best by F1-score
• Full preprocessing pipeline — missing value handling, one-hot encoding, standard scaling
• Prediction confidence score shown with each result
• SQLite-based prediction history with filtering & pagination
• Admin dashboard with charts (approval rate, property-area breakdown, model performance)
• Responsive Bootstrap 5 interface
🤖 MODELS COMPARED
Logistic Regression · Decision Tree · Random Forest · K-Nearest Neighbors · Support Vector Machine · Gradient Boosting
🏆 Best performer in testing: SVM, with an 81.48% F1-score
⚙️ STACK
Python 3 · Flask · Scikit-learn · Pandas · NumPy · SQLite · Bootstrap 5 · Chart.js · Matplotlib/Seaborn
🎓 GOOD FOR
BCA, MCA, B.Tech CS/IT & ML/Data Science students who want a genuine end-to-end ML project — training pipeline, model comparison, live prediction & a working dashboard, not just a notebook.
📦 What you get: Full Source Code + Project Report + Synopsis + PPT
🔗 Full write-up: https://updategadh.com/loan-approval-prediction-system/
💬 Which model would you have picked — SVM or Random Forest? 👇
#PythonProject #MachineLearning #Flask #FinalYearProject #DataScience
A Flask web app that predicts whether a loan application gets Approved or Rejected — with 6 ML models compared and the best one auto-selected. Here's what's inside 👇
✨ KEY FEATURES
• Predicts loan approval using applicant income, credit history, education, dependents, loan amount/term & property area
• Compares 6 classification algorithms & auto-selects the best by F1-score
• Full preprocessing pipeline — missing value handling, one-hot encoding, standard scaling
• Prediction confidence score shown with each result
• SQLite-based prediction history with filtering & pagination
• Admin dashboard with charts (approval rate, property-area breakdown, model performance)
• Responsive Bootstrap 5 interface
🤖 MODELS COMPARED
Logistic Regression · Decision Tree · Random Forest · K-Nearest Neighbors · Support Vector Machine · Gradient Boosting
🏆 Best performer in testing: SVM, with an 81.48% F1-score
⚙️ STACK
Python 3 · Flask · Scikit-learn · Pandas · NumPy · SQLite · Bootstrap 5 · Chart.js · Matplotlib/Seaborn
🎓 GOOD FOR
BCA, MCA, B.Tech CS/IT & ML/Data Science students who want a genuine end-to-end ML project — training pipeline, model comparison, live prediction & a working dashboard, not just a notebook.
📦 What you get: Full Source Code + Project Report + Synopsis + PPT
🔗 Full write-up: https://updategadh.com/loan-approval-prediction-system/
💬 Which model would you have picked — SVM or Random Forest? 👇
#PythonProject #MachineLearning #Flask #FinalYearProject #DataScience