Feature Engineering and Selection for Regression Models with Python and Scikit-learn

exploratory feature analysis python car price prediction

Training a machine learning model is like baking a cake: the quality of the end result depends on the ingredients you put in. If your input data is poor, your predictions will be too. But with the right ingredients – in this case, carefully selected input features – you can … Read more

Uncover Hidden Patterns in Financial Markets using Affinity Propagation Clustering in Python

Affinity Propagation Time Series Clustering Stock market Prediction

This article introduces affinity propagation – an unsupervised clustering technique that stands out from other clustering approaches by its capacity to resolve the number of clusters in a dataset. This tutorial shows how the affinity propagation model works by applying it to the cryptocurrency market. If you have followed recent … Read more

Leveraging Distributed Computing for Weather Analytics with PySpark

pyspark tutorial

Apache Spark is a popular distributed computing framework for Big Data processing and analytics. In this tutorial, we will work hands-on with PySpark, Spark’s Python-specific interface. We built on the conceptual knowledge gained in a previous tutorial: Introduction to BigData Analytics with Apache Spark, in which we learned about the … Read more

Getting Started with Big Data Analytics – Apache Spark Concepts and Architecture

Distributed Computing with PySpark

Apache Spark is a powerful open-source Big Data processing and analytics engine that is widely used for data processing, machine learning, and real-time stream processing. Its distributed architecture allows it to process workloads in a highly parallelized manner, making it highly efficient when working with large data sets. In this … Read more

Stock Market Prediction using Multivariate Time Series and Recurrent Neural Networks in Python

stock market prediction Python tutorial

Regression models based on recurrent neural networks (RNN) can recognize patterns in time series data, making them an exciting technology for stock market forecasting. What distinguishes these RNNs from traditional neural networks is their architecture. It consists of multiple layers of long-term, short-term memory (LSTM). These LSTM layers allow the … Read more