diff --git a/exploration/ml_pipeline_feature_selection.py b/exploration/ml_pipeline_feature_selection.py
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+++ b/exploration/ml_pipeline_feature_selection.py
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+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.13.0
+#   kernelspec:
+#     display_name: straw2analysis
+#     language: python
+#     name: straw2analysis
+# ---
+
+# %%
+# %matplotlib inline
+
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# %%
+""" Feature selection pipeline: a methods that can be used in the wrapper metod alongside other wrapper contents (hyperparameter tuning etc.).
+
+(1) Establish methods for each of the steps in feature selection protocol: 
+    (a) feature selection inside specific sensors (sklearn method): returns most important features from all sensors 
+    (b) feature selection between "tuned" sensors: returns filtered sensors, containing most important features retured with (a)
+(2) Ensure that above methods are given only a part of data and use appropriate random seeds - to later simulate use case in production. 
+(3) Implement a method which gives graphical exploration of (1) (a) and (b) steps of the feature selection.
+(4) Prepare a core method that can be fit into a wrapper (see sklearn wrapper methods) and integrates methods from (1)
+
+"""
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