Nikita Dutta·Apr 22, 2025📌 Part 3: Feature Scaling & NormalizationScaling ensures that features contribute equally to distance-based algorithms like KNN, SVM, or gradient descent-based models. Tree-based…
Nikita Dutta·Apr 21, 2025📌 Part 2: Outlier Detection & TreatmentOutliers can distort model training, mislead interpretation, and affect overall performance. In this section, we’ll explore how to detect…
Nikita Dutta·Apr 20, 2025🧹 Part 1.3: When to Drop vs. Impute & Creating Binary Missing IndicatorsMissing data is inevitable, but handling it the right way can separate a winning model from the rest. In this post, we’ll explore strategic…
Nikita Dutta·Apr 20, 2025Part 1.2: Imputation Techniques for Missing Data (With Code & Visuals)When we encounter missing data in a dataset, it’s essential to decide how to handle it effectively. While dropping missing values might be…
Nikita Dutta·Apr 20, 2025📌 Part 1.1: Understanding the Types of Missing Data (with Code & Visuals)Before deciding whether to impute or drop missing values, it’s essential to ask why data is missing. This post will walk you through the…
Nikita Dutta·Apr 14, 2025Mastering Data Preprocessing for Machine Learning and Competitions: A Medium SeriesWhether you’re prepping for a Kaggle competition or building robust models in finance, data preprocessing is the bridge between raw data…
Nikita Dutta·Feb 2, 2025Monte Carlo Simulation and Value-at-Risk (VaR) for Swaps and OptionsIntroduction
