Project Overview
Project Objective
Developed an advanced machine learning model to predict the probability of credit card client defaults, leveraging data-driven insights to mitigate financial risks.
Key Methodologies
- Comprehensive data preprocessing
- Feature engineering
- Multiple machine learning algorithms
- Predictive modeling
- Performance evaluation
Technical Implementation
- Utilized Python for data analysis and model development
- Implemented various machine learning algorithms
- Conducted thorough data cleaning and feature selection
- Achieved 85% accuracy in default prediction
Environment
The analysis has been fully conducted with Python language, exploiting several machine learning and statistical frameworks available such as scikit-learn, numpy, pandas, imblearn together with other data visualization libraries (matplotlib and seaborn).
In particular, Logistic Regression, Random Forest and Support Vector Machines algorithms have been applied.
Skills & Technologies
