* Improved typing in the example notebook. (#117)

* Added support to open the notebook directly in Google Colab and Mineo.

Author: diegogm

README.md

@@ -175,6 +175,9 @@ __verbose__ : int, default=0
 
 ## Examples ##
 
+[![Open example notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/https://raw.githubusercontent.com/scikit-learn-contrib/boruta_py/master/boruta/examples/Madalon_Data_Set.ipynb)
+[![Open example notebook](https://b.mineo.app/static/img/open_in_mineo.svg)](https://b.mineo.app/import/https://raw.githubusercontent.com/scikit-learn-contrib/boruta_py/master/boruta/examples/Madalon_Data_Set.ipynb)
+
 ```python
 import pandas as pd
 from sklearn.ensemble import RandomForestClassifier

boruta/examples/Madalon_Data_Set.ipynb

@@ -7,7 +7,7 @@
     "### Using Boruta on the Madalon Data Set\n",
     "Author: [Mike Bernico]([email protected])\n",
     "\n",
-    "This example demonstrates using Boruta to find all relevant features in the Madalon dataset, which is an artificial dataset used in NIPS2003 and cited in the [Boruta paper](https://www.jstatsoft.org/article/view/v036i11/v36i11.pdf)\n",
+    "This example demonstrates using Boruta to find all relevant features in the Madalon dataset, which is an artificial dataset used in NIPS2003 and cited in the [Boruta paper](https://www.jstatsoft.org/article/view/v036i11/v36i11.pdf).\n",
     "\n",
     "This dataset has 2000 observations and 500 features.  We will use Boruta to identify the features that are relevant to the classification task.\n",
     "\n",
@@ -24,7 +24,7 @@
    "outputs": [],
    "source": [
     "# Installation\n",
-    "#!pip install boruta"
+    "!pip install boruta"
    ]
   },
   {
@@ -271,9 +271,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Boruta conforms to the sklearn api and can be used in a Pipeline as well as on it's own. Here we will demonstrate stand alone operation.\n",
+    "Boruta conforms to the sklearn api and can be used in a Pipeline as well as on it's own. Here we will demonstrate stand-alone operation.\n",
     "\n",
-    "First we will instantiate an estimator that Boruta will use.  Then we will instantiate a Boruta Object."
+    "First we will instantiate an estimator that Boruta will use. Then we will instantiate a Boruta Object."
    ]
   },
   {
@@ -309,9 +309,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Boruta has confirmed only a few features as useful.   When our run ended, Boruta was undecided on 2 features.   '\n",
+    "Boruta has confirmed only a few features as useful. When our run ended, Boruta was undecided on 2 features.\n",
     "\n",
-    "We can interrogate .support_ to understand which features were selected.   .support_ returns an array of booleans that we can use to slice our feature matrix to include only relevant columns.   Of course, .transform can also be used, as expected in the scikit API."
+    "We can interrogate `.support_` to understand which features were selected. `.support_` returns an array of booleans that we can use to slice our feature matrix to include only relevant columns. Of course, `.transform` can also be used, as expected in the scikit API."
    ]
   },
   {