Pinned Repositories
12-Month-Forecast-With-LSTM
This is an example of LSTM used to forecast the next 12 months on the Air Passengers dataset.
Binary-Classification-with-Deep-Learning
This notebook is an example of binary classification with deep learning. A basic grid search is added at the end. The dataset can be found on Kaggle:
DataScienceTools
Useful Data Science and Machine Learning Tools,Libraries and Packages
Excel-Regression-Analysis
PowerBIAPIs
Quarterly-Sales-Forecast-with-Holt-Winters
This is a simple demonstration of using Holt-Winters to forecast quarterly sales amounts that have both trend and seasonality. The data can be found here: https://www.census.gov/retail/index.html
RFM-Segmentation-with-Quartiles-Jenks-Natural-Breaks-and-HDBSCAN
Run-through-of-Holt-Winters-SARIMA-and-FBProphet
This is a simple notebook comparing the output of Holt-Winters, SARIMA and FBProphet. Information about parameter tuning has not been included.
SARIMA-Param-Search-for-Python
This notebook includes a simple function to find the best SARIMA parameters and fit a model to them.
gianfelton's Repositories
gianfelton/12-Month-Forecast-With-LSTM
This is an example of LSTM used to forecast the next 12 months on the Air Passengers dataset.
gianfelton/RFM-Segmentation-with-Quartiles-Jenks-Natural-Breaks-and-HDBSCAN
gianfelton/Run-through-of-Holt-Winters-SARIMA-and-FBProphet
This is a simple notebook comparing the output of Holt-Winters, SARIMA and FBProphet. Information about parameter tuning has not been included.
gianfelton/DataScienceTools
Useful Data Science and Machine Learning Tools,Libraries and Packages
gianfelton/Binary-Classification-with-Deep-Learning
This notebook is an example of binary classification with deep learning. A basic grid search is added at the end. The dataset can be found on Kaggle:
gianfelton/Excel-Regression-Analysis
gianfelton/PowerBIAPIs
gianfelton/Quarterly-Sales-Forecast-with-Holt-Winters
This is a simple demonstration of using Holt-Winters to forecast quarterly sales amounts that have both trend and seasonality. The data can be found here: https://www.census.gov/retail/index.html
gianfelton/SARIMA-Param-Search-for-Python
This notebook includes a simple function to find the best SARIMA parameters and fit a model to them.