Welcome to my Credit Score Classification with Machine Learning project! In this project, I demonstrate my expertise in building and evaluating machine learning models to predict an individual's credit score with high accuracy based on their financial history and personal information. Project Overview
The project is implemented in a Jupyter Notebook, using the Python programming language along with popular machine learning libraries such as NumPy, Pandas, Matplotlib, and Seaborn. The dataset utilized consists of simulated information about individuals and their credit scores, serving as the foundation for training and evaluating various machine learning models. Project Workflow
Exploratory Data Analysis (EDA): Conducted a comprehensive analysis to gain insights into the data and understand the relationships between different features and the credit score.
Data Preprocessing: Utilized insights from EDA to preprocess the data and prepare it for model training.
Model Training and Evaluation: Employed various machine learning models, including Random Forest, and evaluated their performance using metrics such as accuracy, precision, recall, and F1 score.
Model Deployment: Selected the best-performing model and applied it to make predictions on new and unseen data.
Results Visualization: Visualized the model results to provide additional insights into its performance.
Technical Details
Implementation: Jupyter Notebook
Programming Language: Python
Libraries Used: NumPy, Pandas, Matplotlib, Seaborn, and other standard Python machine learning libraries.
Usage
Clone the repository:
bash
git clone https://github.com/natalia-dias01/credit-score-classification.git cd credit-score-classification
Install the required dependencies:
bash
pip install -r requirements.txt
Open and run the Jupyter Notebook:
bash
jupyter notebook credit_score_classification.ipynb
Feel free to reach out if you have any questions or comments! Thank you for your interest in this project.