📝 Fix capital letter

Author: maks-sh

Readme.rst

@@ -42,7 +42,7 @@ scikit-uplift
 
 Uplift prediction aims to estimate the causal impact of a treatment at the individual level.
 
-More about uplift modelling problem read in `User guide <https://scikit-uplift.readthedocs.io/en/latest/user_guide/index.html>`__, and also in russian on habr.com: `Part 1`_ and `Part 2`_.
+More about uplift modelling problem read in `User Guide <https://scikit-uplift.readthedocs.io/en/latest/user_guide/index.html>`__, and also in russian on habr.com: `Part 1`_ and `Part 2`_.
 
 **Features**:
 
@@ -206,7 +206,7 @@ Important links
 - Official source code repo: https://github.com/maks-sh/scikit-uplift/
 - Issue tracker: https://github.com/maks-sh/scikit-uplift/issues
 - Documentation: https://scikit-uplift.readthedocs.io/en/latest/
-- User guide: https://scikit-uplift.readthedocs.io/en/latest/user_guide/index.html
+- User Guide: https://scikit-uplift.readthedocs.io/en/latest/user_guide/index.html
 - Contributing guide: https://scikit-uplift.readthedocs.io/en/latest/contributing.html
 - Release History: https://scikit-uplift.readthedocs.io/en/latest/changelog.html
 

docs/api/models/index.rst

@@ -2,7 +2,7 @@
 `sklift <../>`_.models
 **********************
 
-See :ref:`Models <models>` section of the User guide for further details.
+See :ref:`Models <models>` section of the User Guide for further details.
 
 .. toctree::
    :maxdepth: 3

docs/index.rst

@@ -15,7 +15,7 @@ scikit-uplift
 
 Uplift prediction aims to estimate the causal impact of a treatment at the individual level.
 
-Read more about uplift modelling problem in :ref:`User guide <user_guide>`, also in russian on habr.com: `Part 1`_ and `Part 2`_.
+Read more about uplift modelling problem in :ref:`User Guide <user_guide>`, also in russian on habr.com: `Part 1`_ and `Part 2`_.
 
 Features
 #########

docs/user_guide/index.rst

@@ -1,11 +1,11 @@
 .. _user_guide:
 
 **********
-User guide
+User Guide
 **********
 
 .. image:: https://habrastorage.org/webt/hf/7i/nu/hf7inuu3agtnwl1yo0g--mznzno.jpeg
-   :alt: User guide for uplift modeling and causal inference.
+   :alt: Cover of User Guide for uplift modeling and causal inference
 
 Uplift modeling estimates the effect of communication action on some customer outcome and gives an opportunity to efficiently target customers which are most likely to respond to a marketing campaign.
 It is relatively easy to implement, but surprisingly poorly covered in the machine learning courses and literature.
@@ -29,18 +29,18 @@ Credits
 **Acknowledgements:**
 
 - `Kirill Liksakov <https://github.com/kirrlix1994>`_ - uplift metrics research
-- `Alina Zhukova <https://www.linkedin.com/in/alina-zhukova-a307b677/>`_ - artwork: user guide cover and key pictures
+- `Alina Zhukova <https://www.linkedin.com/in/alina-zhukova-a307b677/>`_ - artwork: User Guide cover and key pictures
 
 Citations
 ----------
 
-If you find this User guide useful for your research, please consider citing:
+If you find this User Guide useful for your research, please consider citing:
 
 .. code:: latex
 
     @misc{user-guide-for-uplift-modeling,
       author = {Maksim Shevchenko, Irina Elisova},
-      title = {User guide for uplift modeling and casual inference},
+      title = {User Guide for uplift modeling and casual inference},
       year = {2020},
       publisher = {GitHub},
       journal = {GitHub repository},