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Jul 7, 2024 - Jupyter Notebook
mape
Here are 25 public repositories matching this topic...
Compute the mean arctangent absolute percentage error (MAAPE) incrementally.
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Apr 12, 2024 - Makefile
Compute the mean absolute percentage error (MAPE) incrementally.
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Apr 12, 2024 - Makefile
Compute a moving arctangent mean absolute percentage error (MAAPE) incrementally.
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Apr 12, 2024 - Makefile
Compute a moving mean absolute percentage error (MAPE) incrementally.
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Apr 12, 2024 - Makefile
Sales forecasting is an essential task for the management of a store. Machine learning can help us discover the factors that influence sales in a retail store and estimate the number of sales in the near future.
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Sep 23, 2023 - Jupyter Notebook
This repository has the implementation of Performance Metrics (e.g. F1 score, AUC, Accuracy, etc) from scratch, without using Scikit Learn library.
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Feb 1, 2023 - Jupyter Notebook
Basic to complex prediction model using exhaustive selector & Lasso
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Jan 29, 2023 - Jupyter Notebook
Distributed and decentralized MAPE-K loops framework
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Dec 20, 2022 - Python
Swarm intelligence aims at exploring the complicated relationships among multi-agents to stimulate co-evolution and the emergence of intelligent decision-making. Based on Multi-agent Particle Environment and deep Reinforcement learning method, we propose ...
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Dec 17, 2022 - Python
Sober truths: Predict the number of fatalities and alcohol-impaired driving crashes
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Aug 17, 2022 - Jupyter Notebook
in this repository we intend to predict Google and Apple Stock Prices Using Long Short-Term Memory (LSTM) Model in Python. Long Short-Term Memory (LSTM) is one type of recurrent neural network which is used to learn order dependence in sequence prediction problems. Due to its capability of storing past information, LSTM is very useful in predict…
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Aug 15, 2022 - Jupyter Notebook
Project to predict production quantities for a given dataset using Machine Learning algorithms.
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Aug 1, 2022 - Jupyter Notebook
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May 8, 2022 - Jupyter Notebook
Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.
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May 7, 2022 - R
Implementation of a simple linear regression with single feature
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Apr 16, 2022 - Python
Using MS Excel and R, accurately forecasted total core deposit data from a Richmond Bank. The Holt’s Linear Exponential Smoothing had the overall lowest “Quick and Dirty” MAPE (1.2%), the lowest overall Maximum MAPE (3.49%), and consistently more accurate projections for each of the forecast horizons. Overall, the Unaided, Holts Linear Exponenti…
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Jan 21, 2022 - R
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Oct 16, 2021 - HTML
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Oct 16, 2021 - HTML
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