Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk
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Updated
Jan 4, 2023 - Jupyter Notebook
Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk
Compared the metrics and performance of different classification algorithms on Heart Failure dataset from UCI ML Repository
Evaluate a detection model performance
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Trained MATLAB models for 82% precision/80% recall, optimized with blob analysis for 25% performance boost. User-friendly alarm system with 500+ engaged users.
Amex Analyze This is a data science competition held by American Express across all the Indian Institute of Technology Institutes across India. I had participated in this competition in 2018, it was based on predictive modelling where we need to train a model to solve a bank problem - Analyze This 2018
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Insurance Cross Sell Opportunity Forecast through machine learning algorithm
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Implementation of a search engine using TF-IDF and Word Embedding-based vectorization techniques for efficient document retrieval
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Resample precision-recall curves correctly!
Supervised Machine Learning and Credit Risk
ML-FinFraud-Detector is a machine learning project for detecting financial transaction fraud. Utilizing XGBoost, precision-recall, and ROC curves, it provides accurate fraud detection. Explore feature importance, evaluate model performance, and enhance financial security with this comprehensive fraud detection solution.
The goal of this project is to develop a machine learning model that can help banks to identify customers who are likely to churn and take appropriate measures to retain them
Understanding and implementation of the following topics in Machine Learning: Evaluation Metrics, Precision, recall, and f1 score by hand, Classification evaluation metrics using sklearn, Regression metrics
A Swift implementation of mAP computation for Yolo-style detections
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