Updated tutorial

Author: mGalarnyk

.DS_Store

@@ -35,27 +35,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Make information about dataset?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Parameters | Number\n",
-    "--- | ---\n",
-    "Classes | 3\n",
-    "Samples per class | [59, 71, 48]\n",
-    "Samples total | 178\n",
-    "Dimensionality | 13\n",
-    "Features | Real Positive"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will be using the [House Sales in King County dataset](https://www.kaggle.com/harlfoxem/housesalesprediction)."
+    "The Iris dataset is one of datasets scikit-learn comes with that do not require the downloading of any file from some external website. The code below will load the iris dataset."
    ]
   },
   {
@@ -66,22 +46,11 @@
    },
    "outputs": [],
    "source": [
-    "url = 'https://raw.githubusercontent.com/mGalarnyk/Python_Tutorials/master/Kaggle/HousingSalesKC/kc_house_data.csv'\n",
-    "\n",
-    "df = pd.read_csv(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "df.drop(['date', 'id', 'yr_renovated', 'zipcode', 'lat', 'long']\n",
-    "        , axis = 1\n",
-    "        , inplace = True)"
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_iris\n",
+    "data = load_iris()\n",
+    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
+    "df['target'] = data.target"
    ]
   },
   {

Sklearn/.DS_Store

@@ -0,0 +1,12 @@
+digraph Tree {
+node [shape=box, style="filled", color="black"] ;
+0 [label = "Root Node", fillcolor="cyan"] ;
+1 [label = "Leaf/Terminal\nNode", fillcolor="springgreen"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label="Decision Node", fillcolor="pink"] ;
+0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+3 [label = "Leaf/Terminal\nNode", fillcolor="springgreen"] ;
+2 -> 3 ;
+4 [label = "Leaf/Terminal\nNode", fillcolor="springgreen"] ;
+2 -> 4 ;
+}

Sklearn/DecisionTrees/.DS_Store

@@ -0,0 +1,12 @@
+digraph Tree {
+node [shape=box] ;
+0 [label="petal length (cm) <= 2.45\ngini = 0.667\nsamples = 150\nvalue = [50, 50, 50]\nclass = setosa"] ;
+1 [label="gini = 0.0\nsamples = 50\nvalue = [50, 0, 0]\nclass = setosa"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label="petal width (cm) <= 1.75\ngini = 0.5\nsamples = 100\nvalue = [0, 50, 50]\nclass = versicolor"] ;
+0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+3 [label="gini = 0.168\nsamples = 54\nvalue = [0, 49, 5]\nclass = versicolor"] ;
+2 -> 3 ;
+4 [label="gini = 0.043\nsamples = 46\nvalue = [0, 1, 45]\nclass = virginica"] ;
+2 -> 4 ;
+}