Reset a pandas DataFrame index
A guide on how to use OpenAI embeddings and clustering techniques to analyze survey data and extract meaningful topics and actionable insights from the responses.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
A complete walkthrough on constructing a Genetic Algorithm in Python, inspired by natural selection, with a real-world application. Includes steps to build a Genetic Algorithm, including creating a population, defining fitness functions, applying selection, crossover, and mutation operators, and iterating these processes until an optimal solution is reached. T
PCA (principal component analysis) can be effectively used for outlier detection by transforming data into a space where outliers are more easily identifiable due to the reduction in dimensionality and reshaping of data patterns.
A deep dive into time series analysis and forecasting methods, providing foundational knowledge and exploring various techniques used for understanding past data and predicting future outcomes.
This article explores the accuracy and affordability of using a Brix refractometer compared to a more expensive Atago refractometer for measuring Total Dissolved Solids (TDS) in espresso. The author conducted experiments to compare the two methods and found that the cheaper Brix meter provided comparable results.
A detailed overview of the architecture, Python implementation, and future of autoencoders, focusing on their use in feature extraction and dimension reduction in unsupervised learning.
This article explores NDCG — Normalized Discounted Cumulative Gain, a rank-aware metric for evaluating recommendation system models.
A step-by-step guide to making data-driven decisions with practical Python examples, covering the process of hypothesis testing, different types of tests, understanding p-values, and interpreting the results of a hypothesis test.
Support Vector Machine (SVM) algorithm with a focus on classification tasks, using a simple 2D dataset for illustration. It explains key concepts like hard and soft margins, support vectors, kernel tricks, and optimization probles.