Machine-learning

The article delves into the world of two-sample proportion tests, offering insights into comparing proportions between distinct populations. It explores both manual calculation and a more streamlined approach using Python's statsmodels library. From understanding the theoretical foundation of two-sample proportion tests to implementing them practically, readers gain a comprehensive understanding of how to assess significant differences in proportions within categorical data.

This blog aims to show you how to predict stock prices using machine learning techniques, Apache Spark, and Apache Cassandra. The goal is to build a data pipeline that collects stock data, preprocesses it, engineers features, and trains a machine learning model to predict future prices.