CodeAlpha_Credit_Scoring_Model

★ 13 Open GitHub ↗

Develop a credit scoring model to predict the creditworthiness of individuals based on historical financial data. Utilize classification algorithms and assess the model's accuracy.

Explore Similar Repositories

Fibro-CoSANet : Idiopathic pulmonary fibrosis (IPF) is a restrictive interstitial lung disease that causes lung function decline by lung tissue scarring. Although lung function decline is assessed by the forced vital capacity (FVC), determining the accurate progression of IPF remains a challenge. To address this challenge, we proposed Fibro-CoSANet, a novel end-to-end multi-modal learning-based approach, to predict the FVC decline. Fibro-CoSANet utilized CT images and demographic information in convolutional neural network frameworks with a stacked attention layer. Extensive experiments on the OSIC Pulmonary Fibrosis Progression Dataset demonstrated the superiority of our proposed Fibro-CoSANet by achieving the new state-of-the-art modified Laplace Log-Likelihood score of -6.68. This network may benefit research areas concerned with designing networks to improve the prognostic accuracy of IPF.
Movie-Revenue-Prediction : Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name, rating, genre, year, released, score, votes, director, writer, star, country, budget, company and runtime using gradient boosting______________________________ Training Accuracy: 91.58%____________ Testing Accuracy: 82.42%.
LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna : A hybrid AI-based stock market prediction system using LSTM, Random Forest, and XGBoost, built for real-world deployment with Optuna-powered tuning, feature-rich engineering, and ensemble prediction logic. Designed to optimize F1 score and accuracy, this system aims to generate reliable buy/sell signals on stocks.
// repository documentation