Stock Market Forecasting in Python – LSTM model using EuStockMarket dataset In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Stock Market Forecasting in Python – LSTM model using EuStockMarket dataset. Forecasting is required …
How to do Stock Market Forecasting in Python – ARMA model using EuStockMarket dataset Stock market forecasting is the process of using historical data and statistical models to predict future movements of stock prices. One of the methods for stock market forecasting is the ARMA model which stands for Autoregressive Moving Average. The ARMA …
How to do Time Series Forecasting in R – Neural Network model using lynx dataset Neural Network (NN) is a method for time series forecasting that is particularly well-suited for data that have complex patterns and non-linear relationships. Neural Networks are a set of algorithms, modeled loosely after the human brain, that are …
Time Series Forecasting in R – Seasonal ARIMA model using lynx dataset Seasonal ARIMA (SARIMA) is a method for time series forecasting that is particularly well-suited for data that exhibit both a trend and a seasonality pattern, such as regular fluctuations that occur at specific time intervals. The SARIMA model is an extension …
Time Series Forecasting in R – Seasonal Random Walk model using lynx dataset Seasonal Random Walk (SRW) is a method for time series forecasting that is particularly well-suited for data that exhibit a strong seasonality pattern, such as regular fluctuations that occur at specific time intervals. The SRW model assumes that the future …
Time Series Forecasting in R – Auto ARIMA model using lynx dataset Auto ARIMA is a method for time series forecasting that automatically selects the best parameters for an ARIMA model, which stands for Auto-Regressive Integrated Moving Average. ARIMA models are a commonly used method for time series forecasting and are particularly well-suited for …
End-to-End Machine Learning: bagging in R Bagging, short for bootstrap aggregating, is a technique used in machine learning to improve the performance of a model by averaging the results of multiple models. It works by training multiple versions of the same model on different subsets of the data, and then averaging their predictions. In R, …
End-to-End Machine Learning: model selection in R using xyplot When training multiple machine learning models, it’s important to select the best one to use on new, unseen data. One way to do this is by using a visual tool called an “xyplot”. An xyplot is a type of scatter plot that is used to compare …
End-to-End Machine Learning: model selection in R using summary results When training multiple machine learning models, it’s important to select the best one to use on new, unseen data. One way to do this is by using a tabular method called “summary results”. Summary results are a collection of statistics that summarize the performance of …
End-to-End Machine Learning: logloss metric in R When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model is by using a metric called “log loss” or “cross-entropy loss”. Log loss is a …