Building credit risk assessment model and predicting credit score with logistic regression, random forest, and KNN
What you will learn
Learn how to build credit risk assessment model using logistic regression
Learn how to build credit risk assessment model using random forest
Learn how to build credit risk assessment model using K Nearest Neighbor
Learn how to predict credit score using decision tree regressor
Learn how to find correlation between debt to income ratio and default rate
Learn how to analyze relationship between loan intent, loan amount, and default rate
Learn how to analyze relationship between outstanding debt and credit score
Learn how to deploy machine learning model using Gradio
Learn the basic fundamentals of credit risk analysis, technical limitations in credit risk modelling, and credit risk assessment use cases in banking industries
Learn how credit risk assessment models work. This section will cover data collection, preprocessing, feature selection, train test split, and model training
Learn about factors that affect credit score, such as payment history, credit utilization ratio, length of credit history, outstanding debt, and credit mix
Learn how to evaluate the accuracy and performance of the model using precision, recall, and cross validation
Learn how to find and download credit dataset from Kaggle
Learn how to clean dataset by removing missing values and duplicates
Why take this course?
π Credit Risk Modelling & Credit Scoring with Machine Learning π³
Course Headline:
Unlock the secrets of Building credit risk assessment models and predicting credit scores with cutting-edge machine learning techniques! Dive into the world of logistic regression, random forest, and K Nearest Neighbors to master credit risk analysis. ππ
π Welcome to Your Machine Learning Odyssey! π
Embark on a journey through Credit Risk Modelling & Credit Scoring with Machine Learning, where you will transform raw data into actionable insights that drive decision-making in the financial sector. This project-based course is a treasure trove for data science enthusiasts and professionals aiming to refine their skills in risk management and predictive modeling.
Course Overview:
π Understanding Credit Risk Analysis:
We start by setting the stage with the fundamentals of credit risk analysis, exploring its pivotal role in banking and finance. You’ll gain insights into the limitations and challenges faced in this domain, setting a solid foundation for your learning journey.
π Diving into Predictive Modeling:
Get hands-on experience with every step of building a credit scoring system: from data collection and preprocessing, to feature selection and model training. You’ll learn the ins and outs of using logistic regression, random forest, and K Nearest Neighbors algorithms to assess credit risk.
π Evaluating Model Performance:
Master the art of evaluating your model’s accuracy with precision, recall, and cross-validation techniques. Understanding these methods is crucial for fine-tuning your model to achieve the best predictive performance.
Why Master Credit Risk Modelling & Scoring?
Credit risk assessment and scoring are vital components of a bank’s or financial institution’s decision-making process. In a world where financial markets are increasingly complex and customer behaviors are ever-evolving, the traditional methods may fall short. By integrating machine learning algorithms with vast amounts of data, we can significantly enhance the accuracy of credit risk assessment, effectively mitigate risks, and optimize lending practices. This expertise opens doors to a multitude of exciting career opportunities in the burgeoning field of financial technology (FinTech).
Key Learning Outcomes:
- Credit Risk Fundamentals: Understand the core concepts, use cases, and challenges in credit risk analysis within the banking and financial sectors.
- Model Workflow: Gain a comprehensive understanding of the end-to-end process in building a credit risk assessment model, including data collection, preprocessing, feature selection, and deployment.
- Credit Score Factors: Learn how various factors such as payment history, credit utilization ratio, and new credit inquiries contribute to an individual’s credit score.
- Data Acquisition & Preparation: Find and download credit datasets from platforms like Kaggle and clean your data by handling missing values and duplicates.
- Model Building with ML Algorithms: Construct credit risk assessment models using logistic regression, random forest, and K Nearest Neighbors, and explore the relationship between outstanding debt and credit score.
- Predictive Analytics: Use decision tree regressor techniques to predict credit scores and understand the impact of loan intents and amounts on default rates.
- Model Deployment & Evaluation: Deploy your machine learning model using Gradio for real-time application and evaluate its performance using precision, recall, and cross-validation to ensure reliability and accuracy.
Your Learning Path:
- Introduction to Credit Risk Analysis π
- Setting Up Your Environment with Python & ML Libraries π
- Data Collection & Cleaning β
- Exploratory Data Analysis (EDA) π
- Feature Engineering βοΈ
- Model Selection: Logistic Regression π―
- Model Selection: Random Forest π³
- Model Selection: K Nearest Neighbors π°
- Credit Scoring with Decision Tree Regressor π²
- Model Evaluation & Optimization β
- Model Deployment using Gradio π
Embark on this transformative learning experience today and become a master of Credit Risk Modelling & Credit Scoring with Machine Learning! π
Enroll now and join a community of forward-thinking professionals who are shaping the future of financial risk management. Your data science journey awaits! πβ¨