Welcome to the Credit Default Prediction project! 📈 💳 This project focuses on predicting credit defaults using the Decision Tree and K-Nearest Neighbors (KNN) machine learning models. The goal is to analyze historical credit data and build predictive models that can accurately classify whether a borrower is likely to default on their credit obligations or not based on their credit history.
Further work will be continued to improve the accuracy by feature engineering the dataset further.
🌳 Decision Tree model for credit default prediction
🔢 K-Nearest Neighbors (KNN) model for credit default prediction
📊 Data analysis and preprocessing techniques
📁 Exploratory Data Analysis (EDA) for understanding the dataset
📉 Model evaluation and performance metrics
The following dependencies are required to run the YOLOv5 and Segment Anything Sticker Generator:
- Python 3.x
- Scikit-learn Library
- Numpy
- Pandas
- Matplotlib
- Seaborn
You can install these dependencies manually using the package manager of your choice.