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Text Classification using Custom Data and PyTorch

Text classification is a fundamental natural language processing (NLP) task that involves assigning predefined categories or labels to text documents. PyTorch, a popular deep learning framework, provides powerful tools for building and training text classification models. In this blog post, we will explore how to perform text classification using PyTorch and the WikiText2 dataset, a widely used benchmark for language modeling. Read More ›

Advanced Techniques for Handling Missing Data

Missing data is a common issue in real-world datasets. It can occur for various reasons, such as data collection errors, equipment malfunction, or participant non-response. However, missing data can significantly impact the accuracy and reliability of our analyses. To address this challenge, imputation methods come to our rescue. Read More ›

Performance metrics used in Credit Risk Scoring Models

In the financial sector, credit risk scoring models are frequently employed to evaluate a borrower’s creditworthiness. These models use a variety of data sources, including credit history, income, and employment details, to forecast the risk that a borrower will default on a loan. However, it is crucial to assess these models’ performance using the right metrics in order to guarantee their accuracy and efficacy. Read More ›