Time-series demand forecasting is constructed by using LSTM, GRU, LSTM with seq2seq architecture, and prophet models.
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airline-passengers
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Determining the important factors that influences the customer or passenger satisfaction of an airlines using CRISP-DM methodology in Python and RapidMiner.
python random-forest exploratory-data-analysis jupyter-notebook naive-bayes-classifier logistic-regression decision-tree-classifier customer-satisfaction factors knn-classification rapidminer xgboost-algorithm crisp-dm airline-passengers adaboost-classifier passenger-satisfaction airline-passenger-satisfaction
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In this section, we will examine the use of the prophet method, which is one of the time series analysis methods.
time-series prophet time-series-analysis time-series-prediction time-series-forecasting prophet-model airline-passengers prophet-facebook prophet-library prophet-facebook-model
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🛍️ Streamline retail operations with AI-driven demand forecasting and inventory management for improved sales and informed decision-making.
python demand time-series serverless keras jupyter-notebook forecast lstm prophet altair cme fsi lstm-attention xgboost-regression aws-sagemaker airline-passengers gluonts demand-forecast rcg
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- Python
Forecast monthly airline passenger demand using classical time series models — Holt-Winters, ARIMA, and SARIMAX. Analyze trends, seasonality, and model performance to identify the most accurate forecasting technique for real-world airline data.
python machine-learning time-series jupyter-notebook data-visualization forecasting data-analysis arima statsmodels sarimax holt-winters trend-analysis seasonality airline-passengers
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This project aims to provide an explanation of how to apply the ARIMA model using Python to assist readers.
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DL projects done in Python
deep-learning cross-validation recurrent-neural-networks neural-networks artificial-neural-networks data-preprocessing rnn-tensorflow breast-cancer-wisconsin breast-cancer chi-square-test time-series-forecasting one-hot-encoding airline-passengers
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LSTM Recurrent Neural Network for forecasting international airline passengers using TensorFlow/Keras. Time-series prediction on the classic Box-Jenkins dataset (1949–1960).
python machine-learning deep-learning tensorflow keras lstm recurrent-neural-network time-series-forecasting airline-passengers
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📈 Generate accurate demand forecasts with Python using synthetic data, featuring ARIMA/SARIMA modeling, evaluation metrics, and reproducible visualizations.
python demand energy time-series hls gru forecasting logistics regression-models mfg spatio-temporal-modeling airline-booking electricity-load-forecasting time-series-forecasting lstm-attention cnn-lstm airline-passengers gluonts
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- Python
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