typos corrected

.gitignore

@@ -72,6 +72,8 @@ target/
 
 # Jupyter Notebook
 .ipynb_checkpoints
+examples/.ipynb_checkpoints
+docs/experiments
 
 # pyenv
 .python-version

README.md

@@ -5,7 +5,6 @@
 [![Pipi version](https://img.shields.io/pypi/v/retentioneering)](https://pypi.org/project/retentioneering/)
 [![Telegram](https://img.shields.io/badge/channel-on%20telegram-blue)](https://t.me/retentioneering_meetups)
 [![Python version](https://img.shields.io/pypi/pyversions/retentioneering)](https://pypi.org/project/retentioneering/)
-[![License](https://img.shields.io/pypi/l/retentioneering)](https://www.mozilla.org/en-US/MPL/)
 [![Travis Build Status](https://travis-ci.com/retentioneering/retentioneering-tools.svg)](https://travis-ci.com/github/retentioneering/retentioneering-tools)
 [![Downloads](https://pepy.tech/badge/retentioneering)](https://pepy.tech/project/retentioneering)
 
@@ -15,8 +14,8 @@
 
 Retentioneering is a Python framework to process and analyze clickstreams, 
 event streams, trajectories, and event logs. You can segment users, clients (agents),
- build ML pipelines to predict agent category or probability of target event based 
- on historical data.
+explore user behavior patterns, build ML pipelines to predict agent category or 
+probability of target event based on historical data.
 
 Retentioneering extends Pandas, NetworkX, Scikit-learn for in-depth processing of 
 event sequences data, specifically Retentioneering provides a powerful environment 
@@ -41,15 +40,17 @@ Retentioneering is currently installable from PyPI:
 pip3 install retentioneering
 ```
 
-If you use Pandas dataframes to work with user behaviour data you can try Retentioneering with just a few lines of code!
+If you use Pandas dataframes to work with user behaviour data you can start using
+Retentioneering with just a few lines of code:
 
 ```python
 import retentioneering
 
-# load sample data
+# load sample user behavior data as a pandas dataframe: 
 from retentioneering import datasets
 data = datasets.load_simple_shop()
 
+# update config to pass columns names:
 retentioneering.config.update({
     'event_col':'event',
     'event_time_col':'timestamp',
@@ -58,9 +59,10 @@ retentioneering.config.update({
 ```
 
 Above we imported sample dataset, which is regular pandas dataframe containing raw user
-behavior data from hypothetical web-site or app in form of sequence {'client_id', 'event', 'timestamp'},
-and pass those column names to retentioneering.config. Now, let's plot the graph to visualize 
-user behaviour from the dataset (read more about graphs here):
+behavior data from hypothetical web-site or app in form of sequence of records
+{'client_id', 'event', 'timestamp'}, and pass those column names to retentioneering.config.
+Now, let's plot the graph to visualize user behaviour from the dataset 
+(read more about graphs here):
 
 <div align="left">
 
@@ -96,11 +98,11 @@ data.rete.get_clusters(method='kmeans',
 
 <div align="left">
 
-Users with similar behavior grouped in the same clluster. Clusters with low convertion rate
-can represent systematic problem in the product: specific behavior pattern which systematically 
-does not lead to product goals. Obtained user segments can be explored deeper to understand 
-problematic behavior pattern. In the example above cluster 4 has low convertion rate to purchase 
-but high convertion rate to cart visit.
+Users with similar behavior grouped in the same cluster. Clusters with low conversion rate
+can represent systematic problem in the product: specific behavior pattern which does not 
+lead to product goals. Obtained user segments can be explored deeper to understand 
+problematic behavior pattern. In the example above for instance, cluster 4 has low 
+conversion rate to purchase but high conversion rate to cart visit.
 
 ```python
 clus_4 = data.rete.filter_cluster(4)