Join our experts on 7/18 to dive into the world of machine learning, specifically tailored for financial services. You'll learn how your organization can: ⚠️ Leverage machine learning models to manage credit risk 🏋️ Better monitor and retrain models 🔧 Incorporate cross-validation and hyper-parameter tuning Register now: https://lnkd.in/gXKkzFhQ Speakers: Sagar Bathla, Mark Soffietti
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🛩 Project Alert: Credit Card Default Prediction with LightGBM! 📊 I'm thrilled to share the success of our recent project, where we developed a robust Credit Card Default Prediction model using LightGBM. 📈 Here's a quick overview of what we achieved: ✅ Data-Driven Insights: We started with a comprehensive analysis of the dataset, understanding the key features that influence credit card defaults. This step allowed us to tailor our predictive model effectively. ✅ Model Selection: After careful evaluation, we selected LightGBM, a powerful gradient boosting framework. It offered a perfect balance of performance and accuracy. ✅ Hyperparameter Optimization: Through RandomizedSearchCV, we fine-tuned our model's hyperparameters, achieving the best results. ✅ Model Validation: To ensure the model's reliability, we performed k-fold cross-validation, confirming its consistency and accuracy. ✅ Result Analysis: Our final LightGBM model demonstrated impressive results with an accuracy of 83.15% on the test dataset. We also evaluated precision, recall, and F1-score for each class. ✅ Saving the Model: To make it available for future use, we saved the trained LightGBM model to a .sav file. 📊 Metrics: Log Loss: 0.4277 ROC-AUC: 0.7825 Final Model Accuracy: 83.15% This project showcases the power of data science and machine learning in risk assessment and decision-making. It's a fantastic example of how technology can enhance credit card default prediction. 🙌 Our team is thrilled with these results, and we're excited to continue pushing the boundaries of data science and machine learning in future projects. hashtag #DataScience hashtag #MachineLearning hashtag #CreditRisk hashtag #LightGBM hashtag #PredictiveAnalytics hashtag #LinkedInPost
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🚀 Exciting Developments in Credit Risk Modeling! 📈💳 As financial institutions continue to navigate the complex landscape of credit risk, innovative approaches powered by machine learning are revolutionizing the way we assess and manage risk. 🌐💡 🔍 Dive into the world of credit risk modeling with our latest insights: 🔹 Harnessing Machine Learning: Discover how cutting-edge ML algorithms are enhancing predictive accuracy and risk assessment, enabling lenders to make more informed decisions while minimizing exposure. 🔹 Feature Engineering Mastery: Learn about advanced feature engineering techniques that capture intricate relationships within data, unlocking deeper insights into borrower behavior and creditworthiness. 🔹 Interpretability and Transparency: Explore the importance of model interpretability in credit risk modeling, ensuring stakeholders understand the factors driving credit decisions and fostering trust in the model's predictions. 🔹 Regulatory Compliance: Stay informed about regulatory compliance requirements and best practices for developing credit risk models that adhere to industry standards and regulatory guidelines. #CreditRisk #MachineLearning #Finance #Innovation #DataScience #FinancialServices #RiskManagement #LinkedInPost
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Product Intern @superU || Building the bridge between data and decision-making | Data Strategist & Problem Solver.
🚀 Exciting Project Alert: Credit Card Default Prediction with LightGBM! 📊 I'm thrilled to share the success of our recent project, where we developed a robust Credit Card Default Prediction model using LightGBM. 📈 Here's a quick overview of what we achieved: ✅ Data-Driven Insights: We started with a comprehensive analysis of the dataset, understanding the key features that influence credit card defaults. This step allowed us to tailor our predictive model effectively. ✅ Model Selection: After careful evaluation, we selected LightGBM, a powerful gradient boosting framework. It offered a perfect balance of performance and accuracy. ✅ Hyperparameter Optimization: Through RandomizedSearchCV, we fine-tuned our model's hyperparameters, achieving the best results. ✅ Model Validation: To ensure the model's reliability, we performed k-fold cross-validation, confirming its consistency and accuracy. ✅ Result Analysis: Our final LightGBM model demonstrated impressive results with an accuracy of 83.15% on the test dataset. We also evaluated precision, recall, and F1-score for each class. ✅ Saving the Model: To make it available for future use, we saved the trained LightGBM model to a .sav file. 📊 Metrics: Log Loss: 0.4277 ROC-AUC: 0.7825 Final Model Accuracy: 83.15% This project showcases the power of data science and machine learning in risk assessment and decision-making. It's a fantastic example of how technology can enhance credit card default prediction. 🙌 Our team is thrilled with these results, and we're excited to continue pushing the boundaries of data science and machine learning in future projects. #DataScience #MachineLearning #CreditRisk #LightGBM #PredictiveAnalytics #LinkedInPost https://lnkd.in/gBijab76 https://lnkd.in/g8WXGPT2
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Former Intern at State Bank of India || SXUK, Eco (MA) '24 || SXUK, Eco (BA) '22 || Passionate about Data Analysis ||
In today's financial landscape, mitigating credit risk is paramount for lending institutions to ensure their financial stability and profitability. With the advent of machine learning techniques, predictive modeling has emerged as a powerful tool for forecasting the likelihood of credit default among borrowers. In this project, we embark on a journey to develop predictive models that can accurately predict whether a customer will default on their bank credit based on their credentials. This project aims to use machine learning algorithms to predict credit failure, enabling lending institutions to make informed decisions about credit extension and risk management. By analyzing historical data and using advanced predictive modeling techniques, models can accurately identify individuals at high risk. I want to thank CodersArts for this great project idea and also for helping by providing work samples. 📊 Here's a sneak peek into the chapters: 1️⃣ Getting Started with Credit Risk Prediction: Setting the stage for our predictive modeling journey. 2️⃣ Import Libraries: The essential step is to equip ourselves with the necessary data analysis and modeling tools. 3️⃣ Working with Data: Data cleaning and preparation to ensure we have a robust dataset for analysis. 4️⃣ Visualize Data: Utilizing data visualization techniques to gain insights and understand patterns in our dataset. 5️⃣ Train Test Split: Dividing our data into training and testing sets for model evaluation. 6️⃣ Creating Model: Implementing the Random Forest Classifier to build our predictive model. 7️⃣ SVM: Exploring Support Vector Machine algorithm for credit risk prediction. 8️⃣ Logistic Regression: Leveraging Logistic Regression with optimal parameters for accurate predictions. Through rigorous experimentation and analysis, we have developed a predictive model capable of accurately forecasting customer credit failure. By employing mathematical algorithms such as Random Forest Classifier, Support Vector Machine, and Logistic Regression, along with meticulous data cleaning and visualization, we strive to achieve the highest prediction accuracy possible. I am excited to share the insights and results from this project, showcasing the power of machine learning in tackling real-world challenges like credit risk assessment! GitHub Link- https://lnkd.in/dGXddAQN #CreditRisk #PredictiveModeling #FinancialStability #RiskManagement #DataVisualization #RandomForest #SupportVectorMachine #LogisticRegression #Kaggle #CreditRiskManagement
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Co-Founder, Chief AI & Analytics Advisor @ InstaDataHelp | Innovator and Patent-Holder in Gen AI and LLM | Data Science Thought Leader and Blogger | FRSS(UK) FSASS FROASD | 16+ Years of Excellence
Machine Learning in Finance: Predictive Analytics and Risk Management 🎉 Exciting news! 📢 We have just published a new blog post titled "Machine Learning in Finance: Predictive Analytics and Risk Management" on our website. 🚀 In this post, we explore how Machine Learning (ML) is revolutionizing the financial sector by enabling predictive analytics and enhancing risk management. 💡 ML algorithms can analyze vast amounts of data to make accurate predictions about stock prices, credit risk, and customer behavior. They can also identify potential risks in real-time, such as fraud or market volatility, allowing financial institutions to take proactive measures. 🔍 However, there are some challenges and limitations to consider, such as data quality, interpretability of ML algorithms, and overfitting. We discuss these challenges and provide insights on how to address them to fully harness the potential of ML in finance. 🛠️ If you're interested in learning more about how ML is transforming the finance industry, check out the blog post here: [link](https://ift.tt/uhLsS3z) 📚 Stay ahead of the game in finance with the power of Machine Learning! 💪📊 #MLinFinance #PredictiveAnalytics #RiskManagement #FinanceIndustry #DataAnalytics https://ift.tt/uhLsS3z
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Ever spent hours building a complex model, only to find a simpler solution works just as well? At Deepzest, we first solve problems with simple analysis and apply complex models to difficult problems. Reflecting on past experiences is a valuable exercise. Today, I'd like to share a lesson learned from building risk models for banks. In the past, my team and I explored various avenues – simple statistical models, complex machine learning algorithms, the whole gamut. Finally, D-day arrived - our presentation to the regulator. We confidently walked them through our intricate models, expecting a grilling session. To our surprise, the regulator simply looked at our models and said, "While these are impressive, a simple model would be easier to interpret and could likely explain 80% of the risk." The lesson? Sometimes, the simplest solution is the best. Complex models can be dazzling, but clear communication and interpretability are crucial. This experience taught me a valuable principle: basic data analysis can solve 80% of business problems. Don't underestimate the power of exploring your data with simple tools! Start by diving into your business data. What insights can you uncover through basic analysis? #DataAnalysis #BusinessInsights #Simplicity #LessonsLearned Image Source : https://inside5am.com/
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🚀 Excited to share my latest blog on Strategic ML Analytics in Finance: Manual Approach to NPA Predictions! 📊💡 Explore the journey of predicting Non-Performing Assets (Bad Loans) with hands-on analytics, crafting features, and building a powerful logistic regression model. 🛠️✨ Read the full blog on Medium: https://lnkd.in/deRPWA9K #Analytics #MachineLearning #Finance #DataScience #RiskPrediction #StrategicAnalytics #zucisystems #oldschoolway
Strategic ML Analytics in Finance: Steps on Manual Approach to NPA Predictions
rameshponnusamy.medium.com
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🚀 Excited to share a deep dive into my latest project: "Credit Risk Analyzer"! 📊 As a data enthusiast, I took on the challenge of developing a predictive model tailored for assessing credit risk in lending scenarios. Leveraging machine learning techniques, I aimed to empower financial institutions with data-driven insights to make informed decisions on loan applications. 💼 Key Highlights: 1) Developed a robust predictive model using advanced machine learning algorithms. 2) Analyzed vast amounts of historical data to identify patterns and trends in credit risk assessment. 3) Implemented an intuitive user interface for easy integration into existing financial systems. This project was not just about numbers and algorithms; it was about bridging the gap between data science and financial decision-making, ultimately helping businesses mitigate risks and improve lending strategies. 💪 💰 #CreditRisk #MachineLearning #DataAnalytics #MachineLearningModel #DataScience #PredictiveAnalytics #DataDrivenInsights
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Minimizing quantization error while rounding involves selecting a rounding method or strategy that is well-suited to the characteristics of your data and application. Here are some strategies to help minimize quantization error: 1 - Round to Nearest with Ties to Even (Bankers' Rounding): Bankers' rounding, or rounding to the nearest even number if the fractional part is exactly 0.5, is designed to reduce bias. This method is commonly used in financial calculations. 2- Round to Nearest Integer with Adjustable Offset: Adjust the rounding threshold based on the specific characteristics of your data. For example, you might round to the nearest integer with an offset that depends on the magnitude of the number. 3- Use Higher Precision During Calculations: Perform intermediate calculations with higher precision (more decimal places) before rounding. This can help reduce cumulative errors during complex computations. 4- Quantization Aware Training (QAT) for Machine Learning Models: If you're working with machine learning models that involve quantization, consider using techniques like Quantization Aware Training. This involves training a model with awareness of the quantization levels, potentially reducing the impact of quantization errors. 5- Dynamic Rounding: Adjust the rounding strategy dynamically based on the characteristics of the numbers being rounded. For example, you might choose a different rounding method for small numbers compared to large numbers. What can be more interesting ways of handling quantization losses ? #quantization #HW #FixedPoint
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AVP Consumer Lending Services and Decision Management
1wHey Chris, can you set up a link for me and my team, and Christine's that I can send around on this?