Darshan Divate’s Post

🚀 Excited to share my latest presentation occurred over online between 15 past 10 and 11 o'clock (45 mins) in the morning in front of diverse background professionals (Data Engineering, Finance, Data Science) on Saturday, August 3rd, 2024 about a new ML algorithm designed to predict loan defaults! 🌟 This innovative 'divide and rule' approach revolutionizes the management of large and complex datasets. I applied it to a well-known dataset, Irish loans downloaded from (https://lnkd.in/ecG3tDVP), commonly used to evaluate a borrower's credit risk. 🔍 Key Highlights: Dataset Breakdown: The dataset is segmented into six smaller parts, ranging from simple to complex. Tackle issues like: Similar patterns in different categories Extreme values from unique behaviors Missing values Unified Results: After individual processing, results are combined to deliver a straightforward decision on loan defaults. 💡 Unlike single AI models that often struggle with complex data, this method significantly enhances efficiency and effectiveness, offering a robust solution for large datasets. Thanks to my professor,Nonso Alex Nnamoko for providing the guidance throughout the project, Roman Golovnya, Krzysztof Lechowski for sharing feedback on my presentation! Eager to hear your thoughts and feedback on this approach! 🚀 #MachineLearning #DataScience #FinTech #LoanPrediction #AI #BigData #Innovation #TechTrends #DataAnalytics #IrishLoans #CreditRisk #DefaultRisk

CEO Melissa Koide is announcing our newest research project at the Office of the Comptroller of the Currency’s REACh Summit in Washington, D.C. today. The groundbreaking empirical analysis will evaluate the impact of machine learning models with and without bank account data on credit access. Our research will focus on providing responsible access to credit for consumers who may struggle to obtain safe and affordable loans. Read more about the project and how these valuable insights may benefit the entire lending ecosystem. #FinancialInclusion #MachineLearning #ResponsibleLending

Excited to share a major milestone in my data science journey! 🌟📊 Recently, I completed a comprehensive project focused on developing a credit scoring system for a banking institution, utilizing machine learning techniques. 🏦💻 This involved extensive data analysis, feature engineering, and model training to create a robust predictive model. What's more, I brought this model to life by developing a dynamic web application using the framework Flask, allowing seamless integration into the bank's existing infrastructure. 🚀🔍 The application provides real-time credit risk assessment, empowering decision-makers with valuable insights for loan approvals. This project has been a culmination of months of dedication, learning, and problem-solving. I'm incredibly proud of the results and eager to apply this experience to future endeavors. Here's to the exciting intersection of data science and finance! 💡📈 #DataScience #MachineLearning #Finance #Flask #CreditScoring #BankingTech

#Quantitativemodelling and analysis (more commonly called as quant) in varied forms have been an integral part of #financialservices industry for ages. Core of #quant analysis relies on mathematical and statistical concepts and financial modelling for trend analysis, discovery of patterns and forecasting of diverse business factors, besides prediction of future events and outcomes. In data-driven ecosystem, #Bigdata and #datascience techniques completely reshapes the capability profile of traditional quant - mostly relying on functional-centric and hard structured data alone. Connecting quant with data science widens the spectrum of discovery of non-conventional business values in the enterprise realm. My recent blog: The shifting trajectory of Quant in financial services https://lnkd.in/gHkHdb2M

🚀 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

🚀 Title: Credit Risk Analysis with Advanced Machine Learning 🌐 Introduction: Thrilled to share insights from my latest project on credit risk analysis at MoneyMe. This endeavor was a deep dive into the world of finance, where I tackled the challenging yet vital task of predicting loan defaults. 📈 The Challenge: Faced with a skewed dataset, my goal was to develop a predictive model using machine learning techniques. The focus was on enhancing decision-making in lending by reducing financial risk and improving accuracy in identifying potential defaulters. 🔍 Approach and Insights: Data Analysis: Utilized a robust dataset encompassing 2247 individual records, focusing on demographic and financial behaviors. Exploratory Data Analysis (EDA): Uncovered crucial trends in gender distribution, employment sectors, and marital status, influencing our risk models. Modeling Techniques: Employed decision tree algorithms, prioritizing recall to effectively identify loan defaulters. Key Findings: Achieved an impressive recall rate of 86.3% and an overall accuracy of 86.3%, significantly enhancing our loan approval process. 🚀 Impact and Future Directions: Strategic Decision-Making: Our models now contribute to more informed and strategic lending decisions. Future Plans: Exploring the integration of additional variables and advanced machine learning models like Random Forest or Gradient Boosting. 🔗 Learn More: For a deeper dive into the technical aspects and methodologies, check out the detailed explanation in the appendix of my presentation (https://lnkd.in/eWSUeVMH). #MachineLearning #CreditRisk #DataScience #FinancialAnalysis #RiskManagement #PredictiveAnalytics #FinTech #ArtificialIntelligence #finance #wealthmanagement #bank #analytics

🛩 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

Here are some Machine Learning project ideas on Finance you should try: 1. Anomaly Detection in Transactions (An Example: https://lnkd.in/d5xVYYnG) 2. Credit Scoring & Segmentation (An Example: https://lnkd.in/dP2dXDRt) 3. Loan Approval Prediction (An Example: https://lnkd.in/dQ-Kw5wW) 4. Currency Exchange Rate Forecasting (An Example: https://lnkd.in/dtzvkuDx) #datascience #dataanalytics #machinelearning #dataanalysis #machinelearningalgorithms #finance #projectideas

"Revolutionizing Financial Forecasting: A Deep Dive into Bank Nifty Prediction" In the fast-paced realm of finance, accurate predictions are pivotal for successful strategies. Our team leads this charge, employing data science and predictive analytics to forecast Bank Nifty trends with unparalleled precision. Central to our strategy is the NSEPython library, granting direct access to real-time data from the National Stock Exchange (NSE). We meticulously clean and preprocess this data to ensure its integrity. Using Seaborn and Plotly, we visualize the data, revealing insights into Bank Nifty movements. ARIMA and Prophet algorithms further refine our predictions, capturing temporal nuances and guiding future market trends. Beyond historical data, we integrate metrics like put-call ratio (PCR) and call/put percentages to gauge market sentiment accurately. Our goal is not just accuracy but empowering users with Streamlit-powered interactive applications for real-time decision-making. Our efforts culminate in a 70% accuracy rate in Bank Nifty predictions, a testament to data science's transformative potential in deciphering financial markets. As we refine our models, our mission persists: revolutionizing financial forecasting for a brighter future in finance. Join us on this journey toward unparalleled excellence. visit : https://lnkd.in/dcQGkHJh

Excited to unveil my latest mini Data Science project! 🚀 Dive into the world of predictive analytics as I showcase a powerful loan eligibility prediction model. From in-depth data analysis to captivating visualizations, witness firsthand the impact of machine learning in the finance sector. The Loan Eligibility Prediction project utilized machine learning techniques to develop a model capable of determining an individual's loan eligibility based on diverse attributes. It involved analyzing two datasets: one for training and testing the model, and another for making predictions. Overall, this project laid a robust groundwork for exploring the potential of machine learning in predicting financial outcomes. Future iterations could focus on refining the model to enhance accuracy and extend its utility across various scenarios.#DataAnalysis #Visualization #Finance #MiniProject