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

Stock Market Forecasting Neural Networks for Multi-Output Regression in Python

Multi-output regression Python Neural Networks

Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and … Read more

Image Classification with Convolutional Neural Networks – Classifying Cats and Dogs in Python

image classification with neural networks Python machine learning

This tutorial shows how to use Convolutional Neural Networks (CNNs) with Python for image classification. CNNs belong to the field of deep learning, a subarea of machine learning, and have become a cornerstone to many exciting innovations. There are endless applications, from self-driving cars over biometric security to automated tagging … Read more

Feature Engineering for Multivariate Stock Market Prediction with Python

feature engineering for stock market prediction, multivariate time series modelling

Multivariate forecasting models rely not exclusively on historical time series data but use additional features (multivariate = multiple input variables) such as moving averages or momentum indicators. The underlying assumption is that various variables increase the accuracy of a forecast by helping the model identify patterns in the historical data … 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