{"id":25271313,"url":"https://github.com/relostar-devil/credit-score","last_synced_at":"2026-04-28T18:35:17.512Z","repository":{"id":277075239,"uuid":"931253443","full_name":"Relostar-Devil/Credit-Score","owner":"Relostar-Devil","description":"Automates the classification of individuals into credit score brackets—Poor, Standard, or Good—using machine learning. 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It demonstrates an end-to-end workflow—from data ingestion and cleaning to feature engineering, model building, hyperparameter tuning, and evaluation—using various machine learning algorithms such as Logistic Regression, Decision Trees, and Random Forest.\n\n## Problem Statement\n\nManual classification of credit scores in large-scale finance settings can be time-consuming and error-prone. This project provides an intelligent system that automatically classifies individuals into credit score brackets (e.g., Poor, Standard, Good) based on their credit-related information, thereby reducing manual efforts and increasing efficiency.\n\n## Dataset Information\n\n- **Records:** 100,000 entries\n- **Features:** 22 columns including:\n  - `Month`\n  - `Age`\n  - `Occupation`\n  - `Annual_Income`\n  - `Monthly_Inhand_Salary`\n  - `Num_Bank_Accounts`\n  - `Num_Credit_Card`\n  - `Interest_Rate`\n  - `Num_of_Loan`\n  - `Delay_from_due_date`\n  - `Num_of_Delayed_Payment`\n  - `Changed_Credit_Limit`\n  - `Num_Credit_Inquiries`\n  - `Credit_Mix`\n  - `Outstanding_Debt`\n  - `Credit_Utilization_Ratio`\n  - `Payment_of_Min_Amount`\n  - `Total_EMI_per_month`\n  - `Amount_invested_monthly`\n  - `Payment_Behaviour`\n  - `Monthly_Balance`\n  - **Target:** `Credit_Score`\n\n\n## Methodology\n\n- **Data Preprocessing:**\n  - Importing libraries (`pandas`, `numpy`, `matplotlib`, etc.).\n  - Cleaning data by replacing special characters, converting data types, and handling missing values.\n  - Feature engineering, including one-hot encoding for categorical variables and feature selection based on Variance Inflation Factor (VIF).\n\n- **Modeling:**\n  - **Logistic Regression:** Baseline model achieving ~61.8% accuracy.\n  - **Decision Tree:** Initial accuracy around 69.7%, improved to ~70.93% with hyperparameter tuning using GridSearchCV.\n  - **Random Forest:** Demonstrated superior performance with ~79.7% accuracy.\n\n- **Evaluation:**\n  - Splitting data into training and testing sets.\n  - Assessing models based on accuracy scores and other performance metrics.\n\n## Results and Insights\n\nUsing robust data cleaning and feature selection techniques, the Random Forest model in this project achieves the highest accuracy (~79.7%) for credit score classification. These insights underline the importance of model selection and hyperparameter tuning in building reliable predictive systems for finance applications.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frelostar-devil%2Fcredit-score","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frelostar-devil%2Fcredit-score","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frelostar-devil%2Fcredit-score/lists"}