Welcome to my Credit Score Classification project! In this project, I demonstrate my expertise in building and evaluating machine learning models. The goal of this project is to predict the credit score of an individual with high accuracy, based on their financial history and personal information.
The project is implemented in a Jupyter Notebook, using the Python programming language and several popular machine learning libraries such as NumPy, Pandas, Matplotlib, and Seaborn. The data used in this project is a simulated dataset of individuals and their credit scores, which I use to train and evaluate several different machine learning models.
I start by performing exploratory data analysis to gain insights into the data and understand the relationships between the various features and the credit score. I then use this understanding to preprocess the data and prepare it for modeling.
Next, I train and evaluate multiple machine learning models including Random Forest. I use various evaluation metrics such as accuracy, precision, recall, and F1-score to determine the performance of each model and select the best model based on the results.
Finally, I use the selected model to make predictions on new, unseen data and evaluate its performance on this data. I also visualize the results to gain additional insights into the performance of the model.
This project showcases my ability to work with real-world data, perform exploratory data analysis, build and evaluate machine learning models, and make informed decisions based on the results. I believe this project will impress hiring managers and demonstrate my skills in the field of machine learning and data science.
Thank you for your interest in this project. I hope you find it informative and impressive. If you have any questions or comments, please do not hesitate to reach out!