Then, final data can be used for predicting future sales using different machine learning techniques. We apply different machine learning models like 'linear regression', 'decision tree', 'random forest', 'ridge regression', and 'XGBoost model' to predict outlet sales. We found out that XGBoost...
Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted o... M Nikou,G Mansourfar,J Bagher...
Machine Learning Algorithms on Big Mart Sales Data using Python Programming Language - baluniranjan/Big-Mart-Sales-Prediction
Alro10/deep-learning-time-series Star2.6k List of papers, code and experiments using deep learning for time series forecasting deep-neural-networksdeep-learningtime-seriestensorflowpredictionpython3pytorchrecurrent-neural-networkslstmseries-analysisforecasting-modelslstm-neural-networksdemand-forecastingseries-for...
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014) Dai, Y., Huang, J.: A sales prediction method based on LSTM with hyper-parameter search. J. Phys. Conf. Ser. 1756, 012015 (2021) Google...
Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant ...
the quality of a model. For example, there might not have been enough data, or there wasn't enough variation in the data. You can view the quality of the model you're currently using on theLate Payment Prediction Setuppage. You can also specify a minimum threshold for the model quality...
The total number of data available for the analysis is 1442, since the data are large and the factors are more with different levels, the prediction done using the Machine Learning (ML) approach. Ridge regression, a supervised machine learning algorithm is used to predict the sales price of ...
Early-prediction of book success using the available pre-publication information could be instrumental in supporting decision makers. Indeed, we would like to predict performance of a book prior to its publication. To offer such predictions, here we focus on variables available before the actual ...
Using its powers of prediction, AI can make increasingly accurate estimates of how likely it is that leads in your database close. By analyzing vast amounts of historical and market data, AI can highlight which types of leads stand a better chance of closing and when. In turn, this lends...