Merge branch 'dengai' into develop

Conflicts:
	.gitignore

examples/dengue-fever-time-series/.gitignore

@@ -0,0 +1,9 @@
+*.pyc
+__pycache__
+*.bak
+*.orig
+/temp
+/.idea
+/results
+/temp*.py
+/mlruns

examples/dengue-fever-time-series/README.md

@@ -0,0 +1,38 @@
+# Dengue Fever Time Series Prediction with sensAI
+
+This example demonstrates how to use the sensAI library to predict the number of dengue fever cases in San Juan, 
+Puerto Rico and Iquitos, Peru on a weekly basis.
+The data is a slightly adapted version of the [DengAI dataset](https://www.kaggle.com/datasets/qianyigang129/dengai-dataset/data) from Kaggle, which is licensed under CC0 (public domain).
+
+This example demonstrates
+  * time series prediction with sensAI
+  * the use of vastly different models with entirely different input pipelines,
+    comparing complex dynamic models (that make use the preceding time series)
+    with simpler regression models
+  * how to use MLflow to record and compare the results of different models
+  * hyperparameter optimization with sensAI in conjunction with hyperopt
+  * recursive feature elimination
+
+## Setup
+
+Set up your Python virtual environment using [conda](https://docs.anaconda.com/miniconda/miniconda-install/):
+
+    conda env create -f environment.yml
+
+This will create a new environment called `dengai`.
+
+    conda activate dengai
+
+## Entry Points
+
+Initial data analysis with some insights on the dataset can be found in the Jupyter notebook `nb_data_analysis.ipynb`.
+
+Main scripts:
+ * `run_model_evaluation.py` to train and evaluate the models
+     * Before running it,
+        * edit the script and uncomment all the models you want to evaluate.
+        * configure the main script at the bottom to configure the concrete task to run.
+     * Run the script
+     * Inspect results in the `results/` folder or via the MLflow UI (run `mlflow ui`)
+ * `run_hyperopt.py` to perform hyperparameter optimization on an XGBoost model
+ * `run_rfe.py` to perform recursive feature elimination on an XGBoost model