Updated README with feature engineering additions.

README.md

@@ -16,7 +16,7 @@ The second step is Feature Selection:<br>
 <br>
 All are very important questions and you must be very careful using this feature_engg option in featurewiz. Otherwise, you can create a "garbage in, garbage out" problem. Caveat Emptor!
 <br>featurewiz uses the SULOV method and Recursive XGBoost to reduce features in order to select the best features for the model. Here is how.<br>
-SULOV -> SULOV means Searching for Uncorrelated List of Variables. The SULOV method is similar to the Minimum-redundancy-maximum-relevance (mRMR) <a href="https://en.wikipedia.org/wiki/Feature_selection#Minimum-redundancy-maximum-relevance_(mRMR)_feature_selection">algorithm explained in wikipedia</a> as one of the best feature selection methods. The SULOV algorithm is explained in this chart below.
+<b>SULOV<b>: SULOV means Searching for Uncorrelated List of Variables. The SULOV method is similar to the Minimum-redundancy-maximum-relevance (mRMR) <a href="https://en.wikipedia.org/wiki/Feature_selection#Minimum-redundancy-maximum-relevance_(mRMR)_feature_selection">algorithm explained in wikipedia</a> as one of the best feature selection methods. The SULOV algorithm is explained in this chart below.
 Here is a simple way of explaining how it works:
 <ol>
 <li>Find all the pairs of highly correlated variables exceeding a correlation threshold (say absolute(0.7)).
@@ -28,7 +28,7 @@ Here is a simple way of explaining how it works:
 ![sulov](SULOV.jpg)
 
 
-3. Recursive XGBoost: Once SULOV has selected variables that have high mutual information scores with least less correlation amongst them, we use XGBoost to repeatedly find best features among the remaining variables after SULOV. The Recursive XGBoost method is explained in this chart below.
+3. <b>Recursive XGBoost</b>: Once SULOV has selected variables that have high mutual information scores with least less correlation amongst them, we use XGBoost to repeatedly find best features among the remaining variables after SULOV. The Recursive XGBoost method is explained in this chart below.
 Here is how it works:
 <ol>
 <li>Select all variables in data set and the full data split into train and valid sets.