diff --git a/docs/README.methods.md b/docs/README.methods.md
index 0c2c977..5fb42cd 100644
--- a/docs/README.methods.md
+++ b/docs/README.methods.md
@@ -53,75 +53,4 @@ A typical workflow written using `fsspark` can be divided roughly in four major
 
 ### 5. Feature selection pipeline example
 
-Check it out [here](fsspark/pipeline/fs_corr_rf.py) a full FS pipeline example.
-
-```python
-"""
-Example of a feature selection pipeline implemented in fsspark.
-
-After data import and pre-processing, the pipeline applies univariate correlation filter,
-multivariate correlation filter and Randon Forest classification.
-
-"""
-
-from fsspark.config.context import init_spark, stop_spark_session
-from fsspark.fs.core import FSDataFrame
-from fsspark.fs.ml import cv_rf_classification, get_accuracy, get_predictions
-from fsspark.fs.multivariate import multivariate_filter
-from fsspark.fs.univariate import univariate_filter
-from fsspark.fs.utils import (remove_features_by_missingness_rate,
-                              impute_missing)
-from fsspark.utils.datasets import get_tnbc_data_path
-from fsspark.utils.io import import_table_as_psdf
-
-# Init spark
-init_spark()
-
-# Import data
-fsdf = import_table_as_psdf(get_tnbc_data_path(),
-                            n_partitions=5)
-
-fsdf = FSDataFrame(fsdf, sample_col='Sample', label_col='label')
-
-# Step 1. Data pre-processing.
-
-# a) Filter missingness rate
-fsdf = remove_features_by_missingness_rate(fsdf, threshold=0.1)
-
-# b) Impute data frame
-fsdf = impute_missing(fsdf)
-
-# c) Scale features
-fsdf = fsdf.scale_features(scaler_method='standard')
-
-# Split dataset in training/testing
-training_df, testing_df = fsdf.split_df(label_type_cat=True,
-                                        split_training_factor=0.8)
-
-# Step 2. Apply univariate correlation filter
-training_df = univariate_filter(training_df,
-                                univariate_method='u_corr',
-                                corr_threshold=0.3)
-
-# Step 3. Apply multivariate correlation filter
-training_df = multivariate_filter(training_df,
-                                  multivariate_method='m_corr',
-                                  corr_threshold=0.7
-                                  )
-
-# Step 4. ML-algorithm with cross-validation
-cv_model = cv_rf_classification(training_df,
-                                binary_classification=False)
-
-# Print out some stats
-
-# Get accuracy from training
-acc = get_accuracy(model=cv_model)
-print(f"Training accuracy: {acc}")
-
-# Get predictions from training
-pred = get_predictions(model=cv_model)
-pred.show()
-
-stop_spark_session()
-```
+[FS pipeline example](../fsspark/pipeline/fs_pipeline_example.py)