From 283ef07cc5f0e88f6fbf4dcb071a611158eeea21 Mon Sep 17 00:00:00 2001
From: Kevin Markham <justmarkham@gmail.com>
Date: Fri, 29 Jun 2018 12:11:35 -0400
Subject: [PATCH] update notebooks to use scikit-learn 0.19.1 and Python 3.6

---
 01_machine_learning_intro.ipynb    |   21 +-
 02_machine_learning_setup.ipynb    |   47 +-
 03_getting_started_with_iris.ipynb |  379 +++++----
 04_model_training.ipynb            |   73 +-
 05_model_evaluation.ipynb          |  129 ++-
 06_linear_regression.ipynb         |  193 +++--
 07_cross_validation.ipynb          |  149 ++--
 08_grid_search.ipynb               | 1169 ++++++++++++++++++++++------
 09_classification_metrics.ipynb    |  296 +++----
 9 files changed, 1485 insertions(+), 971 deletions(-)

diff --git a/01_machine_learning_intro.ipynb b/01_machine_learning_intro.ipynb
index c8dea2c..f0c0a83 100644
--- a/01_machine_learning_intro.ipynb
+++ b/01_machine_learning_intro.ipynb
@@ -4,8 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# What is machine learning, and how does it work?\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "# What is machine learning, and how does it work? ([video #1](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1))\n",
+    "\n",
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos)."
    ]
   },
   {
@@ -143,9 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -238,23 +237,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/02_machine_learning_setup.ipynb b/02_machine_learning_setup.ipynb
index 77a5bb1..2662428 100644
--- a/02_machine_learning_setup.ipynb
+++ b/02_machine_learning_setup.ipynb
@@ -4,8 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Setting up Python for machine learning: scikit-learn and IPython Notebook\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "# Setting up Python for machine learning: scikit-learn and Jupyter Notebook ([video #2](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2))\n",
+    "\n",
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
+    "\n",
+    "**Note:** Since the video recording, the official name of the \"IPython Notebook\" was changed to \"Jupyter Notebook\". However, the functionality is the same."
    ]
   },
   {
@@ -16,7 +19,7 @@
     "\n",
     "- What are the benefits and drawbacks of scikit-learn?\n",
     "- How do I install scikit-learn?\n",
-    "- How do I use the IPython Notebook?\n",
+    "- How do I use the Jupyter Notebook?\n",
     "- What are some good resources for learning Python?"
    ]
   },
@@ -68,10 +71,10 @@
     "\n",
     "**Option 1:** [Install scikit-learn library](http://scikit-learn.org/stable/install.html) and dependencies (NumPy and SciPy)\n",
     "\n",
-    "**Option 2:** [Install Anaconda distribution](https://www.continuum.io/downloads) of Python, which includes:\n",
+    "**Option 2:** [Install Anaconda distribution](https://www.anaconda.com/download/) of Python, which includes:\n",
     "\n",
     "- Hundreds of useful packages (including scikit-learn)\n",
-    "- IPython and IPython Notebook\n",
+    "- IPython and Jupyter Notebook\n",
     "- conda package manager\n",
     "- Spyder IDE"
    ]
@@ -80,14 +83,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![IPython header](images/02_ipython_header.png)"
+    "![Jupyter logo](images/02_jupyter_logo.svg)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Using the IPython Notebook\n",
+    "## Using the Jupyter Notebook\n",
     "\n",
     "### Components:\n",
     "\n",
@@ -96,12 +99,12 @@
     "\n",
     "### Installation:\n",
     "\n",
-    "- **Option 1:** Install [IPython](http://ipython.org/install.html) and the [notebook](https://jupyter.readthedocs.io/en/latest/install.html)\n",
+    "- **Option 1:** [Install the Jupyter notebook](https://jupyter.readthedocs.io/en/latest/install.html) (includes IPython)\n",
     "- **Option 2:** Included with the Anaconda distribution\n",
     "\n",
     "### Launching the Notebook:\n",
     "\n",
-    "- Type **ipython notebook** at the command line to open the dashboard\n",
+    "- Type **jupyter notebook** at the command line to open the dashboard\n",
     "- Don't close the command line window while the Notebook is running\n",
     "\n",
     "### Keyboard shortcuts:\n",
@@ -119,11 +122,11 @@
     "- **Ctrl+Enter** to run a cell\n",
     "- Switch to Command mode using **Esc**\n",
     "\n",
-    "### IPython and Markdown resources:\n",
+    "### IPython, Jupyter, and Markdown resources:\n",
     "\n",
     "- [nbviewer](http://nbviewer.jupyter.org/): view notebooks online as static documents\n",
-    "- [IPython documentation](http://ipython.readthedocs.io/en/stable/): focuses on the interpreter\n",
-    "- [IPython Notebook tutorials](http://jupyter.readthedocs.io/en/latest/content-quickstart.html): in-depth introduction\n",
+    "- [IPython documentation](http://ipython.readthedocs.io/en/stable/)\n",
+    "- [Jupyter Notebook quickstart](http://jupyter.readthedocs.io/en/latest/content-quickstart.html)\n",
     "- [GitHub's Mastering Markdown](https://guides.github.com/features/mastering-markdown/): short guide with lots of examples"
    ]
   },
@@ -133,10 +136,10 @@
    "source": [
     "## Resources for learning Python\n",
     "\n",
-    "- [Codecademy's Python course](https://www.codecademy.com/learn/python): browser-based, tons of exercises\n",
+    "- [Codecademy's Python course](https://www.codecademy.com/learn/learn-python): browser-based, tons of exercises\n",
     "- [DataQuest](https://www.dataquest.io/): browser-based, teaches Python in the context of data science\n",
     "- [Google's Python class](https://developers.google.com/edu/python/): slightly more advanced, includes videos and downloadable exercises (with solutions)\n",
-    "- [Python for Informatics](http://www.pythonlearn.com/): beginner-oriented book, includes slides and videos"
+    "- [Python for Everybody](https://www.py4e.com/): beginner-oriented book, includes slides and videos"
    ]
   },
   {
@@ -153,9 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -248,23 +249,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/03_getting_started_with_iris.ipynb b/03_getting_started_with_iris.ipynb
index 02cc554..3c0a0ae 100644
--- a/03_getting_started_with_iris.ipynb
+++ b/03_getting_started_with_iris.ipynb
@@ -4,8 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Getting started in scikit-learn with the famous iris dataset\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "# Getting started in scikit-learn with the famous iris dataset ([video #3](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3))\n",
+    "\n",
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
+    "\n",
+    "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
    ]
   },
   {
@@ -45,9 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -63,7 +64,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x3ce33c8>"
+       "<IPython.lib.display.IFrame at 0x10caa2470>"
       ]
      },
      "execution_count": 2,
@@ -97,9 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# import load_iris function from datasets module\n",
@@ -109,14 +108,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "sklearn.datasets.base.Bunch"
+       "sklearn.utils.Bunch"
       ]
      },
      "execution_count": 4,
@@ -133,164 +130,162 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 5.1  3.5  1.4  0.2]\n",
-      " [ 4.9  3.   1.4  0.2]\n",
-      " [ 4.7  3.2  1.3  0.2]\n",
-      " [ 4.6  3.1  1.5  0.2]\n",
-      " [ 5.   3.6  1.4  0.2]\n",
-      " [ 5.4  3.9  1.7  0.4]\n",
-      " [ 4.6  3.4  1.4  0.3]\n",
-      " [ 5.   3.4  1.5  0.2]\n",
-      " [ 4.4  2.9  1.4  0.2]\n",
-      " [ 4.9  3.1  1.5  0.1]\n",
-      " [ 5.4  3.7  1.5  0.2]\n",
-      " [ 4.8  3.4  1.6  0.2]\n",
-      " [ 4.8  3.   1.4  0.1]\n",
-      " [ 4.3  3.   1.1  0.1]\n",
-      " [ 5.8  4.   1.2  0.2]\n",
-      " [ 5.7  4.4  1.5  0.4]\n",
-      " [ 5.4  3.9  1.3  0.4]\n",
-      " [ 5.1  3.5  1.4  0.3]\n",
-      " [ 5.7  3.8  1.7  0.3]\n",
-      " [ 5.1  3.8  1.5  0.3]\n",
-      " [ 5.4  3.4  1.7  0.2]\n",
-      " [ 5.1  3.7  1.5  0.4]\n",
-      " [ 4.6  3.6  1.   0.2]\n",
-      " [ 5.1  3.3  1.7  0.5]\n",
-      " [ 4.8  3.4  1.9  0.2]\n",
-      " [ 5.   3.   1.6  0.2]\n",
-      " [ 5.   3.4  1.6  0.4]\n",
-      " [ 5.2  3.5  1.5  0.2]\n",
-      " [ 5.2  3.4  1.4  0.2]\n",
-      " [ 4.7  3.2  1.6  0.2]\n",
-      " [ 4.8  3.1  1.6  0.2]\n",
-      " [ 5.4  3.4  1.5  0.4]\n",
-      " [ 5.2  4.1  1.5  0.1]\n",
-      " [ 5.5  4.2  1.4  0.2]\n",
-      " [ 4.9  3.1  1.5  0.1]\n",
-      " [ 5.   3.2  1.2  0.2]\n",
-      " [ 5.5  3.5  1.3  0.2]\n",
-      " [ 4.9  3.1  1.5  0.1]\n",
-      " [ 4.4  3.   1.3  0.2]\n",
-      " [ 5.1  3.4  1.5  0.2]\n",
-      " [ 5.   3.5  1.3  0.3]\n",
-      " [ 4.5  2.3  1.3  0.3]\n",
-      " [ 4.4  3.2  1.3  0.2]\n",
-      " [ 5.   3.5  1.6  0.6]\n",
-      " [ 5.1  3.8  1.9  0.4]\n",
-      " [ 4.8  3.   1.4  0.3]\n",
-      " [ 5.1  3.8  1.6  0.2]\n",
-      " [ 4.6  3.2  1.4  0.2]\n",
-      " [ 5.3  3.7  1.5  0.2]\n",
-      " [ 5.   3.3  1.4  0.2]\n",
-      " [ 7.   3.2  4.7  1.4]\n",
-      " [ 6.4  3.2  4.5  1.5]\n",
-      " [ 6.9  3.1  4.9  1.5]\n",
-      " [ 5.5  2.3  4.   1.3]\n",
-      " [ 6.5  2.8  4.6  1.5]\n",
-      " [ 5.7  2.8  4.5  1.3]\n",
-      " [ 6.3  3.3  4.7  1.6]\n",
-      " [ 4.9  2.4  3.3  1. ]\n",
-      " [ 6.6  2.9  4.6  1.3]\n",
-      " [ 5.2  2.7  3.9  1.4]\n",
-      " [ 5.   2.   3.5  1. ]\n",
-      " [ 5.9  3.   4.2  1.5]\n",
-      " [ 6.   2.2  4.   1. ]\n",
-      " [ 6.1  2.9  4.7  1.4]\n",
-      " [ 5.6  2.9  3.6  1.3]\n",
-      " [ 6.7  3.1  4.4  1.4]\n",
-      " [ 5.6  3.   4.5  1.5]\n",
-      " [ 5.8  2.7  4.1  1. ]\n",
-      " [ 6.2  2.2  4.5  1.5]\n",
-      " [ 5.6  2.5  3.9  1.1]\n",
-      " [ 5.9  3.2  4.8  1.8]\n",
-      " [ 6.1  2.8  4.   1.3]\n",
-      " [ 6.3  2.5  4.9  1.5]\n",
-      " [ 6.1  2.8  4.7  1.2]\n",
-      " [ 6.4  2.9  4.3  1.3]\n",
-      " [ 6.6  3.   4.4  1.4]\n",
-      " [ 6.8  2.8  4.8  1.4]\n",
-      " [ 6.7  3.   5.   1.7]\n",
-      " [ 6.   2.9  4.5  1.5]\n",
-      " [ 5.7  2.6  3.5  1. ]\n",
-      " [ 5.5  2.4  3.8  1.1]\n",
-      " [ 5.5  2.4  3.7  1. ]\n",
-      " [ 5.8  2.7  3.9  1.2]\n",
-      " [ 6.   2.7  5.1  1.6]\n",
-      " [ 5.4  3.   4.5  1.5]\n",
-      " [ 6.   3.4  4.5  1.6]\n",
-      " [ 6.7  3.1  4.7  1.5]\n",
-      " [ 6.3  2.3  4.4  1.3]\n",
-      " [ 5.6  3.   4.1  1.3]\n",
-      " [ 5.5  2.5  4.   1.3]\n",
-      " [ 5.5  2.6  4.4  1.2]\n",
-      " [ 6.1  3.   4.6  1.4]\n",
-      " [ 5.8  2.6  4.   1.2]\n",
-      " [ 5.   2.3  3.3  1. ]\n",
-      " [ 5.6  2.7  4.2  1.3]\n",
-      " [ 5.7  3.   4.2  1.2]\n",
-      " [ 5.7  2.9  4.2  1.3]\n",
-      " [ 6.2  2.9  4.3  1.3]\n",
-      " [ 5.1  2.5  3.   1.1]\n",
-      " [ 5.7  2.8  4.1  1.3]\n",
-      " [ 6.3  3.3  6.   2.5]\n",
-      " [ 5.8  2.7  5.1  1.9]\n",
-      " [ 7.1  3.   5.9  2.1]\n",
-      " [ 6.3  2.9  5.6  1.8]\n",
-      " [ 6.5  3.   5.8  2.2]\n",
-      " [ 7.6  3.   6.6  2.1]\n",
-      " [ 4.9  2.5  4.5  1.7]\n",
-      " [ 7.3  2.9  6.3  1.8]\n",
-      " [ 6.7  2.5  5.8  1.8]\n",
-      " [ 7.2  3.6  6.1  2.5]\n",
-      " [ 6.5  3.2  5.1  2. ]\n",
-      " [ 6.4  2.7  5.3  1.9]\n",
-      " [ 6.8  3.   5.5  2.1]\n",
-      " [ 5.7  2.5  5.   2. ]\n",
-      " [ 5.8  2.8  5.1  2.4]\n",
-      " [ 6.4  3.2  5.3  2.3]\n",
-      " [ 6.5  3.   5.5  1.8]\n",
-      " [ 7.7  3.8  6.7  2.2]\n",
-      " [ 7.7  2.6  6.9  2.3]\n",
-      " [ 6.   2.2  5.   1.5]\n",
-      " [ 6.9  3.2  5.7  2.3]\n",
-      " [ 5.6  2.8  4.9  2. ]\n",
-      " [ 7.7  2.8  6.7  2. ]\n",
-      " [ 6.3  2.7  4.9  1.8]\n",
-      " [ 6.7  3.3  5.7  2.1]\n",
-      " [ 7.2  3.2  6.   1.8]\n",
-      " [ 6.2  2.8  4.8  1.8]\n",
-      " [ 6.1  3.   4.9  1.8]\n",
-      " [ 6.4  2.8  5.6  2.1]\n",
-      " [ 7.2  3.   5.8  1.6]\n",
-      " [ 7.4  2.8  6.1  1.9]\n",
-      " [ 7.9  3.8  6.4  2. ]\n",
-      " [ 6.4  2.8  5.6  2.2]\n",
-      " [ 6.3  2.8  5.1  1.5]\n",
-      " [ 6.1  2.6  5.6  1.4]\n",
-      " [ 7.7  3.   6.1  2.3]\n",
-      " [ 6.3  3.4  5.6  2.4]\n",
-      " [ 6.4  3.1  5.5  1.8]\n",
-      " [ 6.   3.   4.8  1.8]\n",
-      " [ 6.9  3.1  5.4  2.1]\n",
-      " [ 6.7  3.1  5.6  2.4]\n",
-      " [ 6.9  3.1  5.1  2.3]\n",
-      " [ 5.8  2.7  5.1  1.9]\n",
-      " [ 6.8  3.2  5.9  2.3]\n",
-      " [ 6.7  3.3  5.7  2.5]\n",
-      " [ 6.7  3.   5.2  2.3]\n",
-      " [ 6.3  2.5  5.   1.9]\n",
-      " [ 6.5  3.   5.2  2. ]\n",
-      " [ 6.2  3.4  5.4  2.3]\n",
-      " [ 5.9  3.   5.1  1.8]]\n"
+      "[[5.1 3.5 1.4 0.2]\n",
+      " [4.9 3.  1.4 0.2]\n",
+      " [4.7 3.2 1.3 0.2]\n",
+      " [4.6 3.1 1.5 0.2]\n",
+      " [5.  3.6 1.4 0.2]\n",
+      " [5.4 3.9 1.7 0.4]\n",
+      " [4.6 3.4 1.4 0.3]\n",
+      " [5.  3.4 1.5 0.2]\n",
+      " [4.4 2.9 1.4 0.2]\n",
+      " [4.9 3.1 1.5 0.1]\n",
+      " [5.4 3.7 1.5 0.2]\n",
+      " [4.8 3.4 1.6 0.2]\n",
+      " [4.8 3.  1.4 0.1]\n",
+      " [4.3 3.  1.1 0.1]\n",
+      " [5.8 4.  1.2 0.2]\n",
+      " [5.7 4.4 1.5 0.4]\n",
+      " [5.4 3.9 1.3 0.4]\n",
+      " [5.1 3.5 1.4 0.3]\n",
+      " [5.7 3.8 1.7 0.3]\n",
+      " [5.1 3.8 1.5 0.3]\n",
+      " [5.4 3.4 1.7 0.2]\n",
+      " [5.1 3.7 1.5 0.4]\n",
+      " [4.6 3.6 1.  0.2]\n",
+      " [5.1 3.3 1.7 0.5]\n",
+      " [4.8 3.4 1.9 0.2]\n",
+      " [5.  3.  1.6 0.2]\n",
+      " [5.  3.4 1.6 0.4]\n",
+      " [5.2 3.5 1.5 0.2]\n",
+      " [5.2 3.4 1.4 0.2]\n",
+      " [4.7 3.2 1.6 0.2]\n",
+      " [4.8 3.1 1.6 0.2]\n",
+      " [5.4 3.4 1.5 0.4]\n",
+      " [5.2 4.1 1.5 0.1]\n",
+      " [5.5 4.2 1.4 0.2]\n",
+      " [4.9 3.1 1.5 0.1]\n",
+      " [5.  3.2 1.2 0.2]\n",
+      " [5.5 3.5 1.3 0.2]\n",
+      " [4.9 3.1 1.5 0.1]\n",
+      " [4.4 3.  1.3 0.2]\n",
+      " [5.1 3.4 1.5 0.2]\n",
+      " [5.  3.5 1.3 0.3]\n",
+      " [4.5 2.3 1.3 0.3]\n",
+      " [4.4 3.2 1.3 0.2]\n",
+      " [5.  3.5 1.6 0.6]\n",
+      " [5.1 3.8 1.9 0.4]\n",
+      " [4.8 3.  1.4 0.3]\n",
+      " [5.1 3.8 1.6 0.2]\n",
+      " [4.6 3.2 1.4 0.2]\n",
+      " [5.3 3.7 1.5 0.2]\n",
+      " [5.  3.3 1.4 0.2]\n",
+      " [7.  3.2 4.7 1.4]\n",
+      " [6.4 3.2 4.5 1.5]\n",
+      " [6.9 3.1 4.9 1.5]\n",
+      " [5.5 2.3 4.  1.3]\n",
+      " [6.5 2.8 4.6 1.5]\n",
+      " [5.7 2.8 4.5 1.3]\n",
+      " [6.3 3.3 4.7 1.6]\n",
+      " [4.9 2.4 3.3 1. ]\n",
+      " [6.6 2.9 4.6 1.3]\n",
+      " [5.2 2.7 3.9 1.4]\n",
+      " [5.  2.  3.5 1. ]\n",
+      " [5.9 3.  4.2 1.5]\n",
+      " [6.  2.2 4.  1. ]\n",
+      " [6.1 2.9 4.7 1.4]\n",
+      " [5.6 2.9 3.6 1.3]\n",
+      " [6.7 3.1 4.4 1.4]\n",
+      " [5.6 3.  4.5 1.5]\n",
+      " [5.8 2.7 4.1 1. ]\n",
+      " [6.2 2.2 4.5 1.5]\n",
+      " [5.6 2.5 3.9 1.1]\n",
+      " [5.9 3.2 4.8 1.8]\n",
+      " [6.1 2.8 4.  1.3]\n",
+      " [6.3 2.5 4.9 1.5]\n",
+      " [6.1 2.8 4.7 1.2]\n",
+      " [6.4 2.9 4.3 1.3]\n",
+      " [6.6 3.  4.4 1.4]\n",
+      " [6.8 2.8 4.8 1.4]\n",
+      " [6.7 3.  5.  1.7]\n",
+      " [6.  2.9 4.5 1.5]\n",
+      " [5.7 2.6 3.5 1. ]\n",
+      " [5.5 2.4 3.8 1.1]\n",
+      " [5.5 2.4 3.7 1. ]\n",
+      " [5.8 2.7 3.9 1.2]\n",
+      " [6.  2.7 5.1 1.6]\n",
+      " [5.4 3.  4.5 1.5]\n",
+      " [6.  3.4 4.5 1.6]\n",
+      " [6.7 3.1 4.7 1.5]\n",
+      " [6.3 2.3 4.4 1.3]\n",
+      " [5.6 3.  4.1 1.3]\n",
+      " [5.5 2.5 4.  1.3]\n",
+      " [5.5 2.6 4.4 1.2]\n",
+      " [6.1 3.  4.6 1.4]\n",
+      " [5.8 2.6 4.  1.2]\n",
+      " [5.  2.3 3.3 1. ]\n",
+      " [5.6 2.7 4.2 1.3]\n",
+      " [5.7 3.  4.2 1.2]\n",
+      " [5.7 2.9 4.2 1.3]\n",
+      " [6.2 2.9 4.3 1.3]\n",
+      " [5.1 2.5 3.  1.1]\n",
+      " [5.7 2.8 4.1 1.3]\n",
+      " [6.3 3.3 6.  2.5]\n",
+      " [5.8 2.7 5.1 1.9]\n",
+      " [7.1 3.  5.9 2.1]\n",
+      " [6.3 2.9 5.6 1.8]\n",
+      " [6.5 3.  5.8 2.2]\n",
+      " [7.6 3.  6.6 2.1]\n",
+      " [4.9 2.5 4.5 1.7]\n",
+      " [7.3 2.9 6.3 1.8]\n",
+      " [6.7 2.5 5.8 1.8]\n",
+      " [7.2 3.6 6.1 2.5]\n",
+      " [6.5 3.2 5.1 2. ]\n",
+      " [6.4 2.7 5.3 1.9]\n",
+      " [6.8 3.  5.5 2.1]\n",
+      " [5.7 2.5 5.  2. ]\n",
+      " [5.8 2.8 5.1 2.4]\n",
+      " [6.4 3.2 5.3 2.3]\n",
+      " [6.5 3.  5.5 1.8]\n",
+      " [7.7 3.8 6.7 2.2]\n",
+      " [7.7 2.6 6.9 2.3]\n",
+      " [6.  2.2 5.  1.5]\n",
+      " [6.9 3.2 5.7 2.3]\n",
+      " [5.6 2.8 4.9 2. ]\n",
+      " [7.7 2.8 6.7 2. ]\n",
+      " [6.3 2.7 4.9 1.8]\n",
+      " [6.7 3.3 5.7 2.1]\n",
+      " [7.2 3.2 6.  1.8]\n",
+      " [6.2 2.8 4.8 1.8]\n",
+      " [6.1 3.  4.9 1.8]\n",
+      " [6.4 2.8 5.6 2.1]\n",
+      " [7.2 3.  5.8 1.6]\n",
+      " [7.4 2.8 6.1 1.9]\n",
+      " [7.9 3.8 6.4 2. ]\n",
+      " [6.4 2.8 5.6 2.2]\n",
+      " [6.3 2.8 5.1 1.5]\n",
+      " [6.1 2.6 5.6 1.4]\n",
+      " [7.7 3.  6.1 2.3]\n",
+      " [6.3 3.4 5.6 2.4]\n",
+      " [6.4 3.1 5.5 1.8]\n",
+      " [6.  3.  4.8 1.8]\n",
+      " [6.9 3.1 5.4 2.1]\n",
+      " [6.7 3.1 5.6 2.4]\n",
+      " [6.9 3.1 5.1 2.3]\n",
+      " [5.8 2.7 5.1 1.9]\n",
+      " [6.8 3.2 5.9 2.3]\n",
+      " [6.7 3.3 5.7 2.5]\n",
+      " [6.7 3.  5.2 2.3]\n",
+      " [6.3 2.5 5.  1.9]\n",
+      " [6.5 3.  5.2 2. ]\n",
+      " [6.2 3.4 5.4 2.3]\n",
+      " [5.9 3.  5.1 1.8]]\n"
      ]
     }
    ],
@@ -312,9 +307,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -332,9 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -356,9 +347,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -397,16 +386,14 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<type 'numpy.ndarray'>\n",
-      "<type 'numpy.ndarray'>\n"
+      "<class 'numpy.ndarray'>\n",
+      "<class 'numpy.ndarray'>\n"
      ]
     }
    ],
@@ -419,15 +406,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(150L, 4L)\n"
+      "(150, 4)\n"
      ]
     }
    ],
@@ -439,15 +424,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(150L,)\n"
+      "(150,)\n"
      ]
     }
    ],
@@ -459,9 +442,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# store feature matrix in \"X\"\n",
@@ -496,9 +477,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -591,23 +570,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/04_model_training.ipynb b/04_model_training.ipynb
index 5c19d84..b98bc62 100644
--- a/04_model_training.ipynb
+++ b/04_model_training.ipynb
@@ -4,8 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Training a machine learning model with scikit-learn\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "# Training a machine learning model with scikit-learn ([video #4](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4))\n",
+    "\n",
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
+    "\n",
+    "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
    ]
   },
   {
@@ -29,9 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -47,7 +48,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x3d50e80>"
+       "<IPython.lib.display.IFrame at 0x10fb4e4a8>"
       ]
      },
      "execution_count": 2,
@@ -131,9 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# import load_iris function from datasets module\n",
@@ -152,16 +151,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(150L, 4L)\n",
-      "(150L,)\n"
+      "(150, 4)\n",
+      "(150,)\n"
      ]
     }
    ],
@@ -188,9 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.neighbors import KNeighborsClassifier"
@@ -209,9 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "knn = KNeighborsClassifier(n_neighbors=1)"
@@ -229,9 +222,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -260,9 +251,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -294,9 +283,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -324,9 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -354,9 +339,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -390,9 +373,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -447,9 +428,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -542,23 +521,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/05_model_evaluation.ipynb b/05_model_evaluation.ipynb
index 81d6577..5f910c0 100644
--- a/05_model_evaluation.ipynb
+++ b/05_model_evaluation.ipynb
@@ -4,8 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Comparing machine learning models in scikit-learn\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "# Comparing machine learning models in scikit-learn ([video #5](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5))\n",
+    "\n",
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
+    "\n",
+    "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
    ]
   },
   {
@@ -50,9 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# read in the iris data\n",
@@ -74,20 +75,18 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n",
-       "       1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
-       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
-       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
       ]
      },
      "execution_count": 3,
@@ -112,9 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -148,9 +145,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -183,15 +178,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.966666666667\n"
+      "0.9666666666666667\n"
      ]
     }
    ],
@@ -213,9 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -276,16 +267,14 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(150L, 4L)\n",
-      "(150L,)\n"
+      "(150, 4)\n",
+      "(150,)\n"
      ]
     }
    ],
@@ -298,13 +287,11 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# STEP 1: split X and y into training and testing sets\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=4)"
    ]
   },
@@ -329,16 +316,14 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(90L, 4L)\n",
-      "(60L, 4L)\n"
+      "(90, 4)\n",
+      "(60, 4)\n"
      ]
     }
    ],
@@ -351,16 +336,14 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(90L,)\n",
-      "(60L,)\n"
+      "(90,)\n",
+      "(60,)\n"
      ]
     }
    ],
@@ -373,9 +356,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -400,9 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -430,15 +409,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.966666666667\n"
+      "0.9666666666666667\n"
      ]
     }
    ],
@@ -459,9 +436,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -488,9 +463,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# try K=1 through K=25 and record testing accuracy\n",
@@ -506,14 +479,12 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x8ffad68>"
+       "Text(0,0.5,'Testing Accuracy')"
       ]
      },
      "execution_count": 17,
@@ -522,9 +493,9 @@
     },
     {
      "data": {
-      "image/png": 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MHg1HHVXfuRxM8tFvMJF0GPAB4NXA4aXlEVH5jBEbIpwvsWZU+pLvLZhkdd9OmwZbtsCe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\nVOkTwAfTbf4M+GQNZS3/lf8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+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8872e48>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -563,9 +534,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -632,9 +601,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -727,23 +694,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/06_linear_regression.ipynb b/06_linear_regression.ipynb
index bdfbd43..89bdb77 100644
--- a/06_linear_regression.ipynb
+++ b/06_linear_regression.ipynb
@@ -4,8 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Data science pipeline: pandas, seaborn, scikit-learn\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "# Data science pipeline: pandas, seaborn, scikit-learn ([video #6](https://www.youtube.com/watch?v=3ZWuPVWq7p4&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=6))\n",
+    "\n",
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
+    "\n",
+    "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
    ]
   },
   {
@@ -47,9 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# conventional way to import pandas\n",
@@ -59,14 +60,25 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -132,8 +144,8 @@
     }
    ],
    "source": [
-    "# read CSV file directly from a URL and save the results\n",
-    "data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)\n",
+    "# read CSV file from the 'data' subdirectory using a relative path\n",
+    "data = pd.read_csv('data/Advertising.csv', index_col=0)\n",
     "\n",
     "# display the first 5 rows\n",
     "data.head()"
@@ -152,14 +164,25 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -232,9 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -278,15 +299,13 @@
     "**Seaborn:** Python library for statistical data visualization built on top of Matplotlib\n",
     "\n",
     "- Anaconda users: run **`conda install seaborn`** from the command line\n",
-    "- Other users: [installation instructions](http://stanford.edu/~mwaskom/software/seaborn/installing.html)"
+    "- Other users: [installation instructions](http://seaborn.pydata.org/installing.html)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# conventional way to import seaborn\n",
@@ -299,14 +318,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0xaaf8fd0>"
+       "<seaborn.axisgrid.PairGrid at 0x1a20378898>"
       ]
      },
      "execution_count": 7,
@@ -315,9 +332,9 @@
     },
     {
      "data": {
-      "image/png": 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ToBRzGPcNcOtKIoNVizWt6I5ulVgMGD9WTdOwEI62rR/DhRsJvPjOhcryNxuA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CAAAg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\nEBEREREREREt8wfBxGXLfvrNrgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83f3e80>"
+       "<Figure size 1058.4x504 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -374,14 +391,25 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -457,9 +485,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -479,9 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -513,16 +537,14 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.series.Series'>\n",
-      "(200L,)\n"
+      "(200,)\n"
      ]
     }
    ],
@@ -542,30 +564,26 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "(150, 3)\n",
-      "(150L,)\n",
+      "(150,)\n",
       "(50, 3)\n",
-      "(50L,)\n"
+      "(50,)\n"
      ]
     }
    ],
@@ -587,9 +605,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -623,16 +639,14 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.87696662232\n",
-      "[ 0.04656457  0.17915812  0.00345046]\n"
+      "2.8769666223179318\n",
+      "[0.04656457 0.17915812 0.00345046]\n"
      ]
     }
    ],
@@ -645,16 +659,14 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[('TV', 0.046564567874150288),\n",
-       " ('Radio', 0.17915812245088836),\n",
-       " ('Newspaper', 0.0034504647111804065)]"
+       "[('TV', 0.04656456787415029),\n",
+       " ('Radio', 0.17915812245088839),\n",
+       " ('Newspaper', 0.003450464711180378)]"
       ]
      },
      "execution_count": 16,
@@ -694,9 +706,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# make predictions on the testing set\n",
@@ -724,9 +734,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# define true and predicted response values\n",
@@ -746,9 +754,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -780,9 +786,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -813,16 +817,14 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12.2474487139\n",
-      "12.2474487139\n"
+      "12.24744871391589\n",
+      "12.24744871391589\n"
      ]
     }
    ],
@@ -856,15 +858,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.40465142303\n"
+      "1.404651423032895\n"
      ]
     }
    ],
@@ -886,15 +886,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.38790346994\n"
+      "1.3879034699382886\n"
      ]
     }
    ],
@@ -943,13 +941,14 @@
     "\n",
     "Pandas:\n",
     "\n",
+    "- [pandas Q&A video series](https://www.dataschool.io/easier-data-analysis-with-pandas/) by me\n",
     "- [Three-part pandas tutorial](http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/) by Greg Reda\n",
     "- [read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) and [read_table](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_table.html) documentation\n",
     "\n",
     "Seaborn:\n",
     "\n",
-    "- [Official seaborn tutorial](http://web.stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n",
-    "- [Example gallery](http://web.stanford.edu/~mwaskom/software/seaborn/examples/index.html)"
+    "- [Official seaborn tutorial](http://seaborn.pydata.org/tutorial.html)\n",
+    "- [Example gallery](http://seaborn.pydata.org/examples/index.html)"
    ]
   },
   {
@@ -966,9 +965,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1061,23 +1058,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/07_cross_validation.ipynb b/07_cross_validation.ipynb
index 7134c6c..a03ad07 100644
--- a/07_cross_validation.ipynb
+++ b/07_cross_validation.ipynb
@@ -4,8 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Cross-validation for parameter tuning, model selection, and feature selection\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "# Cross-validation for parameter tuning, model selection, and feature selection ([video #7](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7))\n",
+    "\n",
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
+    "\n",
+    "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
    ]
   },
   {
@@ -49,13 +52,11 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "from sklearn import metrics"
    ]
@@ -63,9 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# read in the iris data\n",
@@ -79,15 +78,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.973684210526\n"
+      "0.9736842105263158\n"
      ]
     }
    ],
@@ -141,9 +138,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -160,13 +155,13 @@
    ],
    "source": [
     "# simulate splitting a dataset of 25 observations into 5 folds\n",
-    "from sklearn.cross_validation import KFold\n",
-    "kf = KFold(25, n_folds=5, shuffle=False)\n",
+    "from sklearn.model_selection import KFold\n",
+    "kf = KFold(n_splits=5, shuffle=False).split(range(25))\n",
     "\n",
     "# print the contents of each training and testing set\n",
     "print('{} {:^61} {}'.format('Iteration', 'Training set observations', 'Testing set observations'))\n",
     "for iteration, data in enumerate(kf, start=1):\n",
-    "    print('{:^9} {} {:^25}'.format(iteration, data[0], data[1]))"
+    "    print('{:^9} {} {:^25}'.format(iteration, data[0], str(data[1])))"
    ]
   },
   {
@@ -235,27 +230,23 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.cross_validation import cross_val_score"
+    "from sklearn.model_selection import cross_val_score"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.          0.93333333  1.          1.          0.86666667  0.93333333\n",
-      "  0.93333333  1.          1.          1.        ]\n"
+      "[1.         0.93333333 1.         1.         0.86666667 0.93333333\n",
+      " 0.93333333 1.         1.         1.        ]\n"
      ]
     }
    ],
@@ -269,15 +260,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.966666666667\n"
+      "0.9666666666666668\n"
      ]
     }
    ],
@@ -289,15 +278,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.95999999999999996, 0.95333333333333337, 0.96666666666666656, 0.96666666666666656, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.98000000000000009, 0.96666666666666656, 0.96666666666666656, 0.97333333333333338, 0.95999999999999996, 0.96666666666666656, 0.95999999999999996, 0.96666666666666656, 0.95333333333333337, 0.95333333333333337, 0.95333333333333337]\n"
+      "[0.96, 0.9533333333333334, 0.9666666666666666, 0.9666666666666666, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9800000000000001, 0.9666666666666666, 0.9666666666666666, 0.9733333333333334, 0.96, 0.9666666666666666, 0.96, 0.9666666666666666, 0.9533333333333334, 0.9533333333333334, 0.9533333333333334]\n"
      ]
     }
    ],
@@ -315,14 +302,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x8b69ef0>"
+       "Text(0,0.5,'Cross-Validated Accuracy')"
       ]
      },
      "execution_count": 10,
@@ -331,9 +316,9 @@
     },
     {
      "data": {
-      "image/png": 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fuD9v24dyHt8L3Fvk3Ge4tHMeVb3ZQ5mNKShyLM71m/10dVQ+8r2nq51rQ/0rYoZ7dInVC70aCQeZT2V4YnxyMR18ucFp0O+zfFurlJf/TV8FctOdpt1txjSVVDrDgeOJiuaP5BsOB4nG4mQavJhTNJZABHZ6zPhbTG4esUgsQVsF17RJiauXl0DS4c4DAcB9XHjNTWNWsJ+emub8wtK5o7wYGQoymUzx9JmZGpSscpHYOZ4z0Ee/r/yhzLk2B3xs6OsmEksQjcW5erCf3m4vDRYXhPw+xhO2uNVq5CWQTOT0aSAirwVO169IxtRHpfmoCsl2RDcyE7CqEh1N1OR+RJyld/fFzjkd7RVcc9Dv4/T0HPOplblei6kfL4HkPwO/JyLHRCSGM2/jN+pbLGNqLxqLE+jp5LL1a6q+1pUDffR2tTe0wz129jxnZ+ar7mjPGgkHODoxQ3x2oaJrZhe4WinDos3yWbLuqqpPAS8QkT5AVHWq/sUypvYisTjD4fIy/hbT3ibOkNkGBpJIjTras3KDRyX9SIOBCwtcDa2tPlib5uGpEVREfgG4HvBl/xOq6p11LJdpQaqKKp6SANbazFyKwyeneOX1oZpdcyS8ls/+y1HGEufpaFv+FRl+/PQZujvauCa0VJIJb3a5waOns52rB8vLIgy5a7fXv58kk1FEqMmXAlO9JQOJiPwFsAZ4OfBXOEkUf1zncpkW9K4v7UMVPvXm5y77ax88niCj3lN+eLF7a5CFtPLCj36/Ztcs157L1tLZXpsgFujp5Dkb+1jf20VHBddczjQpb/38I1y2bg1/eOuOur+WWZqXGsmLVHWXiOxX1T8QkT8Bvl7vgpnWkskoPzg8Aeo8Xu5aSS072rNuvnYjn3jDMOcX0jW7ZrlecPm6ml7vU//huXRXOMcmuKaTro62us8lSS6k+eFTp5k8X7svBaY6XgJJ9q9iVkQ2A2eAy+tXJNOKnj4zw1TSycd59PQ0z9lYm+YYr6KjccLreljfVzBZdEU629t4/fOGana9laCaZjIRYVPAx1id06Rksx3b5MeVw8tXj78TkSDwceAnwDNcmtbdmJJyO6UjDchRFY3VZiKiKW3QX/+127N/S6em5kg3eEKocZQMJG5CxQdVNa6qXwMuA67NTXNijBeRWJzernb6ujuWfVGoU1NJjsfP17RZyxQW8td/dnu2mTKdUc5M2wTIlaBkIFHVDPAnOc/n8tK8G+NJNBZn11CQXUOBZV8UqhYZf4032TQp9VxyNxqLs6arHaDuzWjGGy9NW98VkdvFxtmZCiUX0jzuLgU7HA5yaGyS5DJ2UEdjcdrbhOs3W+dsvQ36fcynMsRnF5Y+uALZbMcvv2YjYAtprRReAsnv4iRpnBORSRGZEpHJOpfLtJBDOUvBjoSDpDLKYyeW708oEotzbaifHvdbrKmf7BDgetUUstmOX7XDmQ9kHe4rg5eldvtVtU1Vu1TV7z6vfDEHs+pEc4bejoSXN0dVJqNER+M1SyNiSgvlzG6vh2y245uuGaCjTWwhrRXCy4TElxXarqr/VPvimFYUiV28FGzI71u2HFVHTzvDjkdsxNayWFxyt06BJBI7x1Ub+/D7Ohlcho59442XeSTvzXnsw1kj/VHAFpgynuRnqB0JB5dtUajF2lAZy8aaym3s70akPkvuZrMd/9x1Tv/IoL/baiQrhJemrV/K+XkFsAM4Wf+imVYQn53n6dMzFycEDAd59sws52bmS5xZG9FRZ9jxlQPl544y5etsb2N9b3ddmrZGz12c7dgW0lo5KsmFMIoTTIxZUnT00qG32ceRZaiVRNxhx+0NSBS5WoUC3XXpbN/n1i6zE0uXY/Kj8cZLH8n/BLKDwtuAESBaz0KZ1hGNxS9ZCnbnUAARZ192GGc9JBfSHBqb5O0vuaJur2EuFfL3MHputubXjcbi+DovZDsO+X3MzKeZSi5UvUKkqY6XPpK9OY9TwJdU9V/rVB7TYiKx+CVLwfZ1d3DVxr66d7hfGHZs80eWUyjQzd5nz9b8upFYnB2bA4vZjnNHiFkgaSwvgeReIKmqaQARaReRNapa+68cpqWoKtFYnJuvvbTWMRIO8r3HT6KqdVtT4kLG37V1ub4pLOT3EZ9dILmQxtdZm7k7C+kMB48n+JUXXHbR64AzZ2W5k4Cai3npI3kQ6Ml53gP8Y32KY1rJ6LnznCmyFOxwOMi52QViZ8/X7fWjecOOzfIY9Nd+LsmT41PMpTIX9bUtDjW2fpKG8xJIfKo6nX3iPrZ1NM2SSq0Bku0w3VfHBI7RUcv42wjZD/hadrgX+luqR8AylfESSGZEZHFJOxF5HlC/r5GmZURj8aJLwV4T6sfX2Va3BI7ZYcc2f2T5herwAR+NxVnX28XQ2guNI77OdoJrOm0I8ArgpY/kd4CvisgJ9/km4JfrVyTTKiKxODu2BAouBdvZ3saOzYG6pZRfHHZsNZJlV48mp0gszkg4eEl/WsjvW5Y14k1pXiYkPgJcC/wm8F+A61T10XoXzDS3hXSGgycSJVO3j4SDHDwxyUI6U/PXjxxzhx3XcI12402/r5Pervaa1RSmkgscmZgu2EzpTEq0BpJGWzKQiMhvAb2qelBVDwB9IvJf6l8008wOn5wiuZApmSxxOBxkPpXhyfGpmr9+dPTSYcdm+QwGfDVr2jpwPIEqDBcYxm01kpXBSx/Jr6vq4oB/VT0H/LqXi4vILSLypIgcEZH3F9h/mYg8KCL7ReRhERlyt79cRCI5P0kRudXdd7mI/EhEfioiXxaRLm+3apbTYudoiaalbG1lX43nk2SHHVvG38ZxPuBrE0hKDdoY9Ps4MzNXl1qt8c5LIGnLXdRKRNqBJT+83eM+Bbwa2A7cISLb8w77BHC3qu4C7gQ+CqCqD6nqiKqO4CSHnAW+657zx8AnVfUq4Bzwdg/3YJZZtnM0vK6n6DFDa3tY39tV85Ty2WHHtiJi49QykERjcbatX0NwzaUfO6GAD1Vn/XbTOF4CyQPAV0TkZ0XkZuBLwD94OO8G4IiqHlXVeeAe4LV5x2zHmacC8FCB/QCvB76jqrNuQLsZZ5IkwBeAWz2UxSyzaCzB8FCg5GRDEWE4HKx5ICn1DdYsj1DAx6mpOTKZ6pfcjcYSRWuX2RFiNpeksbwEkvfhfNj/JvBb7uP3ljzDsQWI5TwfdbfligK3u49vA/pFZH3eMW/CCV4A64G4qqZKXBMAEXmHiOwVkb0TExMeimtqZXouxeFTU56aloaHghyZmGYqWbulWUsNOzbLIxTwkcoop2eqqymMJ5KMTyaLfikYtECyIngZtZVR1b9Q1der6u3A/cC7PVy70FfR/K8n7wFuFJF9wI3AcZx8Xs4FRDYBO3FqRV6vmS33Z1R1j6ruGRgY8FBcUysHRp3OUS81gpGtQVSdc2ql1LBjszwWJwtW2RGerV0W+1Kyqc4LaRlvPP1PE5ENIvKbIvJPwMPAoIfTRoFwzvMh4ETuAap6QlVfp6q7gQ+623I/Ud4IfENVs19XTwNBEcnOf7nkmqbxInnpvksZdofn1qrDPTvs2Ga0N9Zik1OVH/CRWJzOdmH7psKrewfXdNLV0Waz2xusaCARkX4R+TUR+Qfgx8BzgCtU9UpVfY+Haz8CXOWOsurCaaK6L+81NohItgwfAD6Xd407uNCshaoqTl/K691NbwG+5aEsZhllO0fX9i49oC64povLN/TWrJ8kO+zYZrQ31oVJidXN8YjG4ly3yV80+aOI1LRj31SmVI3kFM6IqLuAK1X13YDnJe3cfox34jRLHQK+oqqPicidIvIa97CbgCdF5DBOLeeu7Pkisg2nRvODvEu/D/hdETmC02fyWa9lMssjOlre0NvhoUDNlt71MuzY1N+Gvm7a26SqGkk6oxw4vnTtMmRrtzdcqRQpv4dTi/g08Lci8uVyL66q9+P0qeRu+1DO43u5MAIr/9xnKNCRrqpHcUaEmRXo5GSSsUSyrKal4XCQb0ZOMJY4z6ZA8eHCXngZdmzqr71N2NjfXdVkwacmppmeSy3Z1zYY8LF/GVbbNMUVrZGo6idV9WeA1+B0cn8T2Cwi7xORq5ergKa5LNYIymhayn5Q1KJ5y8uwY7M8Bv3VzW5fqqM9a1PAx1giidPybRrBy6ito6p6l6ruBJ4PBIDv1L1kpilFYnE62op3jhZy3SY/ne1SdYd7OcOOTf1V2+QUicXp93VwxYbekscN+n3MpzLEZ2s3hNyUp6zxkap6QFV/T1WvrFeBTHNbqnO0EF9nO9dt8lddI8kOO7ZAsjKEAtV1gkdjcYaHgrS1la5d1mqEmKmcDbQ3NZPJKPtHS2f8LWYkHOTAaIJ0FTOhraN9ZRn0+5ieSzE9l1r64DzJhTRPjE8VTNSYLxToBiyQNJIFElMz2c7RSmoEw0NBZubTHDk1vfTBRURjcS7zOOzY1N+mKtYlOXjc+VLhZdDGhcmPFkgaxQKJqZlqclxlO+erad6KjsYtv9YKUs1SuOX8LW3sr/3SvqY8RYf/isgBiqQfAXAz9hqzKDoap7976c7RQi5f30u/r4PIaJw3Pj+89Al5Khl2bOqrmpUSo6MJNgd8bHSDUSldHW1s6Ou22e0NVGoeyS+6v3/L/f1/3N9vxknrbsxFIrE4u8KBJTtHC2lrE4aHgkSOVVYj8TpU1CyfajrBI7FzZb2XoUC39ZE0UKl5JM+q6rPAi1X1v7kjtg6o6vuBVy1fEU0zSC6keWJsqqqmpZFwkCdPTnF+Pl32uVF32PH1m70POzb11dPVjt/XUXaN5Mz0HLGz58v6W7I0KY3lpY+kV0Rekn0iIi8Cym+7MC3tsRMJUh47R4sZDgdJZ5SDJ8rPBBypYNixqb9NgZ6yawrZdDnl1EiqnfxoquMlkLwd+JSIPCMiTwP/C/iP9S2WaTaRmPPhX02NJDvUs9wO9+ywYy9DRc3yqmTt9kgsQZvAzi3e38+Q38e52QWSC+XXZk31SvWRAKCqjwLDIuIHJC/NuzGA8+HvtXO0mI39PrYEexb7O7w6ejqbk2ltxa9t6iPk7+aJscmyzonG4lw92E9v95IfTxdeJ3BhhNhl663BZLktWSMRkUER+SzwZVVNiMh2EbF10s1FIrHyMv4WMxwOlB1I9h3LDhW1GslKE/L7mJieYyGd8XS8qjrZo8tsIq1mhJipnpemrc/jpILf7D4/DPxOvQpkms/ZmXmOnZ2tyRyOkXCQ0XPnOT3tPWvshWHHfVW/vqmtwYAPVZiY8vZ+PntmlvjsQtnryVialMbyEkg2qOpXgAwsrjNiDZFmUbSGQ2+z30TL6SepZtixqa9yP+DLWV0z12Cg8smPpnpeAsmMiKzHnZwoIi8ArJ/ELIrE4mV3jhazY0uANvEeSLLDjm0i4sq02HfhsckpEovT09nO1YPl1S77uztY09Vus9sbxEtv1u/iLJF7pYj8KzAAvKGupTJNJTpafudoMb3dHVw92E9k1Nt3lcdOTJLKqKVGWaHKrZFER+Ps3BKgo7287E3ZJXetRtIYXt6tx4AbgRcBvwFcDzxRz0KZ5qGqi+m+a2UkHCQai3taqKia/F6m/tb1dtHV3uYpkMynMjx2YrLiYdzVpq03lfPyFfKHqvpcnIACgIj8BHhu3UrVwr5zYIz7oicaXYyaWUhnODe7UNPUJMPhIPc8EuM/fWEvXR2lv+s8dmKSTVUOOzb1IyJs9Hfz7egYx86Uzqw0O59mPpWp+G8p5Pfxo6fPVnQuwFf3xljf18XN1w5WfI1C1xzo7+amazbW7JorUamkjSGcNdN7RGQ3znK7AH5gzTKUrSV99l+e5tDYJFvWts6a4sNDAW66ZqBm17vpmgGGw0Fi55ZO6ebrbOOXK0jyaJbPbbu38MBj4zw1sfQSAc/dGuTFV26o6HWykx8zGS174IWq8pG/P8S2Db01CySZjPKH336c4XBw9QYSnHxabwWGgD/N2T4F/F4dy9TSxhJJXnl9iE/+8kiji7JibQr08K3fenGji2Fq5N2vvIZ3v/Kaur9OyO8jlVFOz8wtppb36tkzsyTOL3DoxCRzqTTdHdWn2nnmzAyTydSq6LcpGkhU9QvAF0TkdlX92jKWqWVPWac8AAAfnklEQVRlMsqpqeTiSBZjTO1cWOCq/ECS7WubT2c4VGXy0fxrroZ+Gy8pUr4mIr+A08nuy9l+Zz0L1orOzs6zkNbFkSzGmNpZXJFxMslOyuuwj7jZo1MZZ/BILQJJdgj7ZDLF+fk0PV2tm1DUS4qUvwB+GXgXTj/JG4DL6lyulpT9ZjJogcSYmgsFKp/dHh2N89ytaxno765qlc5cuUPYW33GvZfhvy9S1V8DzqnqHwAvBKx3swLZtlJr2jKm9jb0ddPeJmWv3Z4ddjyyNchIOFh2rrdC5lJpDp2YXJyk2+rNW14CyXn396yIbAYWgMvrV6TWlZ11a01bxtRee5sw0Ndd9uz2J8YnnWHHQ04gOXp6hsTsQlVlOTQ2xXw6wy07QgCMT55f4ozm5iWQfFtEgsDHgZ8AzwD31LNQrerkZJI2gQ19XY0uijEtqZL1Ty7kigtcyPU2Wl2tJHLsHACvut4ZSjye8J6EtBl56Wz/Q/fh10Tk24DP1iSpzHgiycZ+X9npH4wx3oT83Tw1MVPWOfticTb0dbMl2IO/pxNwgsvLrq58blR0NMHG/m6uHOijr7uj5YcAl5qQ+LoS+1DVry91cRG5Bfj/gXbgr1T1j/L2XwZ8Did/11ngV1R11N23FfgrnP4YBX5eVZ8Rkc/jpGzJBrO3qmpkqbKsBOOTycUspcaY2tsU6OHfjpwp6xxnlFYAEcHv6+TKgd6qayTZkV8iwqC/u+X7SErVSH7J/b0RJ8/W993nLwceBkoGEhFpBz4FvAIYBR4RkftU9fGcwz4B3K2qXxCRm4GPAr/q7rsbuEtVvycifbhp7F3vVdV7l7q5lebkZJLLN9jqbcbUy6Dfx9Rcipm5lKckopPJBZ6amOG23VsWt42E1/KDw6dQVUTKX5ogMbvA0dMz3P68IaCydeubTdE2FlV9m6q+Dac2sF1Vb1fV23Hmk3hxA3BEVY+q6jxOv8pr847ZDjzoPn4ou19EtgMdqvo9tyzTqrp0vowVbiyRtI52Y+ooFOgGvA+33R9zGjZy83uNhAOcnp7neLyyDvJsbSY7F2VwFWQl9tJYv01Vx3KenwSu9nDeFiCW83zU3ZYrCtzuPr4N6HfXPrkaiIvI10Vkn4h83K3hZN0lIvtF5JMi0l3oxUXkHSKyV0T2TkxMeChufc3Op5hKpqxpy5g6ys7R8tqUlP3Q35WTvTobVKKxyrqCo7E4IrBzyBn6Gwp0c2pqjnRm6WzWzcpLIHlYRB4QkbeKyFuAv8epPSylUJ0w/1/yPcCNIrIPp9/jOJDCaXJ7qbv/+cAVOHm/AD4AXOtuXwe8r9CLq+pnVHWPqu4ZGKhdQsFKjdvQX2PqLlRmINl3LM4VA70E3E52gGtDfro62ojEzlVUhkgszpUDffh9nYtlSme0rOWjm82SgURV3wn8b2AYGAE+o6rv8nDtUS6euDgEXJQ/XVVPqOrrVHU38EF3W8I9d5/bLJYCvombtl5Vx9QxB/w1ThPaijdukxGNqbtyZrerKpFYnJG8tXS6Otq4frO/ohqJqhIdvTjFSrm1pGbkaRyqqn5dVf+r+/MNj9d+BLhKRC4XkS7gTTgrLS4SkQ0iki3DB3BGcGXPXSsi2arEzcDj7jmb3N8C3Aoc9Fiehlqc1W41EmPqZk1XB36ft+G2Y4kkp6fnCq5/MjwU5MDxBKl0psCZxR2Pn+f09PxF19wUcJaMaOUO96KBRET+xf09JSKTOT9TIjK51IXdmsQ7gQeAQ8BXVPUxEblTRF7jHnYT8KSIHAYGgbvcc9M4zVoPisgBnGayv3TP+aK77QCwAfhI2XfdANkJSVYjMaa+vK6UWGp1zd1bg5xfSHP45NJrqBS8Zk4tZ9AdANDKHe6l0si/xP3dX+nFVfV+4P68bR/KeXwvUHAYrztia1eB7TdXWp5GGk+cp9/XwZqu6tc1N8YUN+j3efr2H43F6Wpv49pNl37E5c5w377Z7/m1o7E4XR0XX3NDbzcdbVJ26pZmUqpGsq7Uz3IWshWMT9rQX2OWQ8jvvUZy3WZ/wUWsLlu/huCaTiLHypuYGInF2bHZT2dO9oq2NmFjf3fZySSbSamvx4/ijLIqNvrqirqUqEWNT85Zs5YxyyAU8HF6eo5UOlM0HVE6oxw4nuCNewonMhcRhoeCZc1wT6UzHDie4I4btl6ybzDgrZbUrEpNSLxcVa9wf+f/WBAp00mbjGjMsggFfGQUJkoMt/3pqSlm59MMh4svgDUcDnL45BQzcylPr3v45DTJhUzBPpdNqzWQ5BKRtSJyg4i8LPtT74K1klQ6w8S01UiMWQ5e5pJEFzva1xY9Znc4SEbhwHFvw4BLdd4P+n0t3bTlZYXE/wT8E87oqz9wf3+4vsVqLaen50ln1FZGNGYZeJm3EYnF8fs62LZ+TdFjdrkz072umBiNxVm7ppOt6y69ZsjvY2Y+zVSyunVOViovNZLfxplF/qyqvhzYDTQ+50gTGbc5JMYsGy+TEiOxBMNudt5i1vd1E17X47mfJDoaL3rNxTK1aK3ESyBJqmoSQES6VfUJ4Jr6Fqu1LKZHsaYtY+pu3ZouOtulaCCZnU9x+OQUuws0QeUbCa/1NHJrZs655vBQ4Wsu1pJatJ/ESyAZdVdI/CbwPRH5FnmpTkxptla7McunrU1K9kkcPD5JOqMFZ7TnGx4KcCKR5NQSAeDA8QQZLdw/AuXnAGs2XlZIvM19+GEReQgIAP9Q11K1mPHJJJ3twro1tsSuMcshVGJS4oWldb3USJxjIrE4r7w+VPS4yBLXzH6JbNXZ7aUmJP69iLxZRBZXYlLVH6jqfe76Isajk+4Su21t5S+SY4wp32CJNCmRWJyhtT1s6Cu4AsVFdmwJ0N4mS/aTRGNxtq5bw7rewl8WfZ3tBNd0rsqmrc8Avwg8IyJfFpFb3eSLpkxjiaQ1axmzjLI1EtVL1wCJxOKeaiPgBIBrQ/1LZgLOLq27ZJkSrZlKvtSExG+p6h3AVpxldd8CHBORz4nIK5argK3gpKVHMWZZhfw+kgsZJs9fPJlwYmqO4/Hznjras0bCQaKxOJkiC1OdmkxyIpFcMjg5OcAqW3VxpfOyHsl5Vf2y21fySpzhv9ZH4pGqOnm2rEZizLIZLDIEuJz+kazhcJCpuRRHT88U3H9hImLxWfKwSmskWSIyKCLvEpF/xRm59V3geXUvWYuYmksxO5+2Gokxy2hTsUAyGqe9TdixufSHfq6RxaV3C/eTREfjdLQJ1y9xzVDAx5mZORbKXOOkGZTqbP91Efk+8BOcNdT/m5t7632qGlm2Eja57BBEW6vdmOVzYbjtxU1JkVicawb76em6NONvMVcO9NHX3bFY88gXicW5dlM/vs7S1wwFfKjCqanWq5WUqpG8CPgjIKyq71LVf12mMrWUMVur3Zhlt9HvjMjKbUrKZJRoGR3tWe1tws4tgYIjtzIZZX8sUXQiYq5WnktSqrP9bar6XVVdrIeJyIeXpVQtxNKjGLP8ujvaWdfbdVHT1jNnZphMppbsyyhkOBzk0NgkyYX0RduPnp5mai615IgtaO212z1l/83xmqUPMbmyTVvZb0jGmOUx6PddNAEwW6MolfG3mJFwkIW08vjYxauMR9xhwV4CiZccYM2q3EBiM+rKND6ZZF1v15Ltp8aY2tqUNykxcixOb1c7z9nYV/a1inW4R2Nx+ro7uGJg6WuuXdNJV0dbS85uLzeQ2GitMp2cTFr6eGMaIL9GEhlNsHPImalerlDAR8jvu6TDPRKLs8vjNUXE8zLAzcbL8N+PiYhfRDpxkjaeFpFfWYaytQRnrXZr1jJmuYX8Ps7MzDOXSjOXSnPoxGTZHe25hsOBi2okyYU0h8bKu2apHGDNzEuN5JWqOomTLmUUZyjwe+taqhYybulRjGmIUMD5Andqco5DY1PMpzOMeBhdVcxwOMgzZ2aJzzqpBh8fmySVUU8jtrIGA75V27TV6f7+eeBLqnq2juVpKfOpDKen561py5gGyF0DZHFp3a2VB5LcTMDA4jolu8u4ZsjfzViicA6wZuYlkPydiDwB7AEeFJEBoPVCah2cmrKhv8Y0yqZAD+C0CkRicTb2d1f1f3HnlgAiLCZwjI7GCfl9ZX1RDAV6mE9liM+21pK7XnJtvR94IbBHVReAGeC19S5YK7AFrYxpnGzQOOnWSJZaWncp/b5OnjPQRyR2DnBqJl6G/RYqU6v1k3jpbH8DkFLVtIj8PvA3wOa6l6wFZGfVWiAxZvn5ezrwdbbx5PgUR0/PlP2hX8hIOEh0NMHZmXmePTNbdud9tt9m1QUS4P9V1SkReQnwKuALwKfrW6zWMObm+bGmLWOWX3a47YNPnAK8TRpcynA4yNmZef7+wJj7vLxZ8tlmsGLLADcrL4EkmxPgF4BPq+q3AFvgyoOTk0m6O9oI9HQufbAxpuYG/T7OzswjAjuHyk+Nki8bjO7+t2cQgV1ljgLb2O8EkrFVGEiOi8j/Bt4I3C8i3R7PQ0RuEZEnReSIiLy/wP7LRORBEdkvIg+LyFDOvq0i8l0ROSQij4vINnf75SLyIxH5qbty44oNauOTc4QCvqraZY0xlcs2K1850IffV/0XumtC/XR3tPHTU9NctdHJClyOro42NvR1t9wQYC8B4Y3AA8AtqhoH1uFhHomItAOfAl4NbAfuEJHteYd9ArhbVXcBdwIfzdl3N/BxVb0OuAE45W7/Y+CTqnoVcA54u4d7aIiTCVsZ0ZhGygaScuZ6lNLZ3saOLYGqrhkKdK++PhJVnQWeAl4lIu8ENqrqdz1c+wbgiKoeVdV54B4uHe21HXjQffxQdr8bcDpU9XtuGaZVdVacr/Y3A/e653wBuNVDWSry9OkZfnLsXMXn28qIxjRW9otcNfNH8mWbtyq9ZiumSfEyauu3gS8CG92fvxGRd3m49hYglvN81N2WKwrc7j6+DegXkfU4s+fjIvJ1EdknIh93azjrgbiqpkpcM1vud4jIXhHZOzEx4aG4l/rQtw7y+984WNG5i0vsWo3EmIa5cqAPEbhh27qaXfNnLl+HCDy/wmvm5wBrBV6att4O/IyqfkhVPwS8APh1D+cV6hjIn875HuBGEdkH3AgcB1JAB/BSd//zgSuAt3q8prNR9TOqukdV9wwMDHgo7qWGh4I8eXKK8/PppQ/Oc252gflUxma1G9NAL71qAw+/5yauCfXX7Jqv2D7Iw++5iasHK7tmyO/j3OzCJWubNDMvgUS4MHIL97GX3uNRIJzzfAg4kXuAqp5Q1dep6m7gg+62hHvuPrdZLIWzVvxzgdNAUEQ6il2zlkbCQdIZ5eCJRNnnZquu1rRlTOOICJet711R18x+JrRSrcRLIPlr4Eci8mF3hcR/Bz7r4bxHgKvcUVZdwJuA+3IPEJENIpItwweAz+Wcu9ZNxwJOv8jj6iSoeQh4vbv9LcC3PJSlIrvcMeL5axB4kf0jsRqJMSbX4gJXLdRP4qWz/U+BtwFncUZJvU1V/8zDeSngnTgjvg4BX1HVx0TkThHJrrR4E/CkiBwGBoG73HPTOM1aD4rIAZwa0F+657wP+F0ROYLTZ+IlqFVkY7+PLcEe9lUQSLKjMjZZjcQYk6MV06SUHATt1hb2q+oO4CflXlxV7wfuz9v2oZzH93JhBFb+ud8DdhXYfhRnRNiyGAkHK6qRjCeSiMBAv61FYoy5YHC1NW2pagaIisjWZSrPijMcDjB67jynp+fKOm88kWRDXzed7eUuQmmMaWX93R2s6WpfzMXXCrxMy9wEPCYiP8bJ/AuAqr6m+CmtIzvpKBqL87PXDXo+z4b+GmMKyeYAa6UaiZdA8gd1L8UKtnMoQJuUH0hOTiYZWrumjiUzxjSrUMC3mNS1FRQNJCLyHGBQVX+Qt/1lOPM9VoU1XR1cPdhfdof7+GSSPdvW1qlUxphmFvL7+NHTrbPYbKkG/D8Dpgpsn3X3rRq7tzod7l6Xx0wupInPLiyu0GaMMbmya7dnMq2x5G6pQLJNVffnb1TVvcC2upVoBRoeCjKZTPHMmVlPx9scEmNMKSG/j1RGOTMz3+ii1ESpQFLqU3BVfdXOroKWXWJzKdm1Bqyz3RhTyKC/tYYAlwokj4jIJTm1ROTtwKP1K9LKc/VgP2u62onGvKVKubBWu80hMcZcKjtRuVUWuCo1aut3gG+IyJu5EDj24KyOeFu9C7aStLcJO7YEiHjscM+mPrCmLWNMIYtpUlqkRlI0kKjqSeBFIvJyYIe7+e9V9fvLUrIVZiQc5PP/+gxzqTTdHe0ljx2fTNLX3UF/DVZkM8a0ng193bS3Scus3b7kPBJVfQgnUeKqNhIOMp/O8MTY1GKfSTEnJ5MM+q1ZyxhTWHubMNDXOislWv4Ojy50uC/dvDWWsJURjTGlZYcAtwILJB5tDvgY6O/2lMDxZCJp/SPGmJI2tdCSuxZIPBIRhoeCREZLB5JMRjk1NWdDf40xJYUCFkhWpZFwgKMTMyRmF4oec3pmjlRGrWnLGFPSoN/H1FyKmblUo4tSNQskZRgJO7mz9h8vXis56aaGthqJMaaU7DyzVuhwt0BShp1DSy+9Oz5pa7UbY5a2OLu9BZq3LJCUIdDTyRUDvSVHbo27qaGtRmKMKSWb1NVqJKvQSDhIJJYomgl4fDJJe5uwvs/mkRhjimultdstkJRpJBzk9PQcx+OFF6UZT8yxsd+ZtWqMMcX0dLXj93W0xMgtCyRlGglnl94tnMDx5KRNRjTGeNMqQ4AtkJTp2pCfrvY2okXmk9ha7cYYrwZbZO12CyRl6upoY/tmP5FjRQKJzWo3xni0KeCzPpLVaiQc5MDxBKl05qLt03MppudS1rRljPEk5PcxMTV3yWdJs7FAUoGRcJDzC2kOn5y+aPu4rYxojCnDYMBHRmFieq7RRamKBZIKZDMB5/eT2FrtxphyLA4BbvIOdwskFdi2fg2Bns5LZrhn/xg2WdOWMcaDVlm73QJJBUSE4XDwkhnulh7FGFOO7JdOq5GUICK3iMiTInJERN5fYP9lIvKgiOwXkYdFZChnX1pEIu7PfTnbPy8iT+fsG6nnPRQzEg5y+OTURZk7xxNJAj2d+DpLL8VrjDEA63q76GpvY3zS+kgKEpF24FPAq4HtwB0isj3vsE8Ad6vqLuBO4KM5+86r6oj785q8896bsy9Sr3soZSQcIKNw8PiFiYk2h8QYUw4RYaO/25q2SrgBOKKqR1V1HrgHeG3eMduBB93HDxXYv2IND1269O7JySSD1qxljClDyO9jLFE45VKzqGcg2QLEcp6PuttyRYHb3ce3Af0ist597hORvSLy7yJya955d7nNYZ8UkYZkR1zf1014Xc9FI7fGE0lCfkvWaIzxzlm73Zq2iimUtTA/Ze57gBtFZB9wI3AcyHY6bFXVPcB/AP5MRK50t38AuBZ4PrAOeF/BFxd5hxuI9k5MTFR3J0UMDwUXZ7gvpDNMTM8RclNDG2OMFyF37fZiGcWbQT0DySgQznk+BJzIPUBVT6jq61R1N/BBd1siu8/9fRR4GNjtPh9Txxzw1zhNaJdQ1c+o6h5V3TMwMFDTG8saCQc5kUhyajLJxNQcqjYZ0RhTnk0BH+cX0kwmm3fJ3XoGkkeAq0TkchHpAt4E3Jd7gIhsEJFsGT4AfM7dvjbbZCUiG4AXA4+7zze5vwW4FThYx3soaTET8GgiZ+ivNW0ZY7xrhbkkdQskqpoC3gk8ABwCvqKqj4nInSKSHYV1E/CkiBwGBoG73O3XAXtFJIrTCf9Hqvq4u++LInIAOABsAD5Sr3tYyvWbA7S3CZHYucXlMm1WuzGmHNl5Z2NNPJeko54XV9X7gfvztn0o5/G9wL0Fzvs3YGeRa95c42JWrKernWtD/URjCTa4KyJa05YxphyhFli73Wa2V2k4HCQ6Gmc8kaSrvY11vV2NLpIxpolsdEd6NnM6eQskVRoZCjKVTPFvT51hMNCN03VjjDHedHe0s763ywLJajay1elwP3A8Yc1axpiKDPp91rS1ml050Edvl5NbyzrajTGVCDX5SokWSKrU3ibsctOlWI3EGFOJQXdSYrOyQFID2YWuLH28MaYSIb+PMzPzzKXSjS5KReo6/He1GAkHAGvaMsZUJrsuyav/7J9pb6vtgJ3PvuX5bF2/pqbXzGeBpAZuvHojv/7Sy3nZ1fVJxWKMaW03XjPAbbu31KVG0tVR/4YnaeZEYV7t2bNH9+7d2+hiGGNMUxGRR93kuSVZH4kxxpiqWCAxxhhTFQskxhhjqmKBxBhjTFUskBhjjKmKBRJjjDFVsUBijDGmKhZIjDHGVGVVTEgUkQng2bzNG4DTDShOvbTa/UDr3ZPdz8rXavdU7f1cpqpLpuxYFYGkEBHZ62XGZrNotfuB1rsnu5+Vr9Xuabnux5q2jDHGVMUCiTHGmKqs5kDymUYXoMZa7X6g9e7J7mfla7V7Wpb7WbV9JMYYY2pjNddIjDHG1MCqCyQicouIPCkiR0Tk/Y0uTy2IyDMickBEIiLSdAuviMjnROSUiBzM2bZORL4nIj91f69tZBnLVeSePiwix933KSIiP9/IMpZDRMIi8pCIHBKRx0Tkt93tTfk+lbifZn6PfCLyYxGJuvf0B+72y0XkR+579GUR6ar5a6+mpi0RaQcOA68ARoFHgDtU9fGGFqxKIvIMsEdVm3L8u4i8DJgG7lbVHe62jwFnVfWP3IC/VlXf18hylqPIPX0YmFbVTzSybJUQkU3AJlX9iYj0A48CtwJvpQnfpxL380aa9z0SoFdVp0WkE/gX4LeB3wW+rqr3iMhfAFFV/XQtX3u11UhuAI6o6lFVnQfuAV7b4DKteqr6T8DZvM2vBb7gPv4Czn/yplHknpqWqo6p6k/cx1PAIWALTfo+lbifpqWOafdpp/ujwM3Ave72urxHqy2QbAFiOc9HafI/HpcC3xWRR0XkHY0uTI0MquoYOP/pgY0NLk+tvFNE9rtNX03RDJRPRLYBu4Ef0QLvU979QBO/RyLSLiIR4BTwPeApIK6qKfeQunzmrbZAIgW2tULb3otV9bnAq4HfcptVzMrzaeBKYAQYA/6kscUpn4j0AV8DfkdVJxtdnmoVuJ+mfo9UNa2qI8AQTgvMdYUOq/XrrrZAMgqEc54PAScaVJaaUdUT7u9TwDdw/oCa3Um3HTvbnn2qweWpmqqedP+jZ4C/pMneJ7fd/WvAF1X16+7mpn2fCt1Ps79HWaoaBx4GXgAERaTD3VWXz7zVFkgeAa5yRzF0AW8C7mtwmaoiIr1uZyEi0gu8EjhY+qymcB/wFvfxW4BvNbAsNZH9wHXdRhO9T25H7meBQ6r6pzm7mvJ9KnY/Tf4eDYhI0H3cA/wcTt/PQ8Dr3cPq8h6tqlFbAO5wvj8D2oHPqepdDS5SVUTkCpxaCEAH8LfNdk8i8iXgJpxMpSeB/w58E/gKsBU4BrxBVZum87rIPd2E02SiwDPAb2T7F1Y6EXkJ8M/AASDjbv49nH6FpnufStzPHTTve7QLpzO9HaeS8BVVvdP9jLgHWAfsA35FVedq+tqrLZAYY4yprdXWtGWMMabGLJAYY4ypigUSY4wxVbFAYowxpioWSIwxxlTFAolpCSLysIi8Km/b74jI/1rivOlS+2tQrgE38+o+EXlp3r6HRWSP+3ibm531VQWu8XE3m+vHKyzDTSLy7ZznHxGRB0Sk2y3D3px9e0Tk4ZzzVER+KWf/t0XkpkrKYVqXBRLTKr6EM8E015vc7Y30s8ATqrpbVf+50AEiMgQ8ALxbVR8ocMhvAM9V1fd6ecGcWcyF9n0QeDFwa85cgo0i8uoip4wCH/Tyumb1skBiWsW9wC+KSDcsJuLbDPyLiPSJyIMi8hNx1m25JONzgW/tfy4ib3UfP09EfuAmxXwgb/Zz9vjL3NfY7/7eKiIjwMeAnxdnbYueAuUOAd8Ffl9VL8myICL3Ab3Aj0Tklwu9jnvc50XkT0XkIeCPC/0Dici7gZ8HfklVz+fs+jjw+4XOAaJAQkReUWS/MRZITGtQ1TPAj4Fb3E1vAr6szozbJHCbm9jy5cCfuCkyluTmY/qfwOtV9XnA54BCmQP+HGftkV3AF4H/oaoR4ENuOUbyPryz7gb+XFW/WuS+XgOcd8//cqHXyTn8auDnVPXdBS71YuA/A6/OSTWe9UNgTkReXqgMwEcoHmiMsUBiWkpu81Zus5YA/5+I7Af+ESeN9qDHa14D7AC+56bn/n2cxHf5Xgj8rfv4/wAv8Xj9fwR+VUTWeDy+1Ot8VVXTRc47gvPv8Moi+4sGi2yTXH4fjzFZFkhMK/km8LMi8lygJ7twEfBmYAB4npti+yTgyzs3xcX/H7L7BXjMrRGMqOpOVS32YZzLa+6hj+Hkq/pqqb4Nj68zU+K4kzjNWp8sVPNQ1e/j3PMLipx/F9ZXYoqwQGJahttk8zBO81NuJ3sAOKWqC+6H6GUFTn8W2O6OZArgdJIDPAkMiMgLwWnqEpHrC5z/b1yoDb0ZZ5lTr/4rMAl81kOTW8Wvo6qHgdcBf+P23+S7C/hvRc79LrAWGPb6emb1sEBiWs2XcD7s7snZ9kVgjzvM9c3AE/knqWoMJ4vtfvf4fe72eZwU3H8sIlEgAryowOv+P8Db3OazX8VZK9sTtx/nLcAmnBpKKRW/jvtajwBvA+4TkSvz9t0PTJQ4/S4KN+uZVc6y/xpjjKmK1UiMMcZUxQKJMcaYqlggMcYYUxULJMYYY6pigcQYY0xVLJAYY4ypigUSY4wxVbFAYowxpir/F19pSpGIa+FNAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87e1a90>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -367,15 +352,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.98\n"
+      "0.9800000000000001\n"
      ]
     }
    ],
@@ -388,15 +371,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.953333333333\n"
+      "0.9533333333333334\n"
      ]
     }
    ],
@@ -424,9 +405,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -437,21 +416,17 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# read in the advertising dataset\n",
-    "data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)"
+    "data = pd.read_csv('data/Advertising.csv', index_col=0)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# create a Python list of three feature names\n",
@@ -467,9 +442,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -483,23 +456,21 @@
    "source": [
     "# 10-fold cross-validation with all three features\n",
     "lm = LinearRegression()\n",
-    "scores = cross_val_score(lm, X, y, cv=10, scoring='mean_squared_error')\n",
+    "scores = cross_val_score(lm, X, y, cv=10, scoring='neg_mean_squared_error')\n",
     "print(scores)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 3.56038438  3.29767522  2.08943356  2.82474283  1.3027754   1.74163618\n",
-      "  8.17338214  2.11409746  3.04273109  2.45281793]\n"
+      "[3.56038438 3.29767522 2.08943356 2.82474283 1.3027754  1.74163618\n",
+      " 8.17338214 2.11409746 3.04273109 2.45281793]\n"
      ]
     }
    ],
@@ -512,16 +483,14 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.88689808  1.81595022  1.44548731  1.68069713  1.14139187  1.31971064\n",
-      "  2.85891276  1.45399362  1.7443426   1.56614748]\n"
+      "[1.88689808 1.81595022 1.44548731 1.68069713 1.14139187 1.31971064\n",
+      " 2.85891276 1.45399362 1.7443426  1.56614748]\n"
      ]
     }
    ],
@@ -534,15 +503,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.69135317081\n"
+      "1.6913531708051797\n"
      ]
     }
    ],
@@ -554,15 +521,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.67967484191\n"
+      "1.6796748419090768\n"
      ]
     }
    ],
@@ -570,7 +535,7 @@
     "# 10-fold cross-validation with two features (excluding Newspaper)\n",
     "feature_cols = ['TV', 'Radio']\n",
     "X = data[feature_cols]\n",
-    "print(np.sqrt(-cross_val_score(lm, X, y, cv=10, scoring='mean_squared_error')).mean())"
+    "print(np.sqrt(-cross_val_score(lm, X, y, cv=10, scoring='neg_mean_squared_error')).mean())"
    ]
   },
   {
@@ -610,7 +575,7 @@
     "\n",
     "- scikit-learn documentation: [Cross-validation](http://scikit-learn.org/stable/modules/cross_validation.html), [Model evaluation](http://scikit-learn.org/stable/modules/model_evaluation.html)\n",
     "- scikit-learn issue on GitHub: [MSE is negative when returned by cross_val_score](https://github.com/scikit-learn/scikit-learn/issues/2439)\n",
-    "- Section 5.1 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (11 pages) and related videos: [K-fold and leave-one-out cross-validation](https://www.youtube.com/watch?v=nZAM5OXrktY) (14 minutes), [Cross-validation the right and wrong ways](https://www.youtube.com/watch?v=S06JpVoNaA0) (10 minutes)\n",
+    "- Section 5.1 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (11 pages) and related videos: [K-fold and leave-one-out cross-validation](https://www.youtube.com/watch?v=nZAM5OXrktY&list=PL5-da3qGB5IA6E6ZNXu7dp89_uv8yocmf) (14 minutes), [Cross-validation the right and wrong ways](https://www.youtube.com/watch?v=S06JpVoNaA0&list=PL5-da3qGB5IA6E6ZNXu7dp89_uv8yocmf) (10 minutes)\n",
     "- Scott Fortmann-Roe: [Accurately Measuring Model Prediction Error](http://scott.fortmann-roe.com/docs/MeasuringError.html)\n",
     "- Machine Learning Mastery: [An Introduction to Feature Selection](http://machinelearningmastery.com/an-introduction-to-feature-selection/)\n",
     "- Harvard CS109: [Cross-Validation: The Right and Wrong Way](https://github.com/cs109/content/blob/master/lec_10_cross_val.ipynb)\n",
@@ -631,9 +596,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -726,23 +689,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/08_grid_search.ipynb b/08_grid_search.ipynb
index 8bd6ff4..aa0a82c 100644
--- a/08_grid_search.ipynb
+++ b/08_grid_search.ipynb
@@ -4,8 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Efficiently searching for optimal tuning parameters\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "# Efficiently searching for optimal tuning parameters ([video #8](https://www.youtube.com/watch?v=Gol_qOgRqfA&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8))\n",
+    "\n",
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
+    "\n",
+    "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
    ]
   },
   {
@@ -65,14 +68,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.model_selection import cross_val_score\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline"
    ]
@@ -80,9 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# read in the iris data\n",
@@ -96,16 +95,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.          0.93333333  1.          1.          0.86666667  0.93333333\n",
-      "  0.93333333  1.          1.          1.        ]\n"
+      "[1.         0.93333333 1.         1.         0.86666667 0.93333333\n",
+      " 0.93333333 1.         1.         1.        ]\n"
      ]
     }
    ],
@@ -119,15 +116,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.966666666667\n"
+      "0.9666666666666668\n"
      ]
     }
    ],
@@ -139,15 +134,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.95999999999999996, 0.95333333333333337, 0.96666666666666656, 0.96666666666666656, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.98000000000000009, 0.96666666666666656, 0.96666666666666656, 0.97333333333333338, 0.95999999999999996, 0.96666666666666656, 0.95999999999999996, 0.96666666666666656, 0.95333333333333337, 0.95333333333333337, 0.95333333333333337]\n"
+      "[0.96, 0.9533333333333334, 0.9666666666666666, 0.9666666666666666, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9800000000000001, 0.9666666666666666, 0.9666666666666666, 0.9733333333333334, 0.96, 0.9666666666666666, 0.96, 0.9666666666666666, 0.9533333333333334, 0.9533333333333334, 0.9533333333333334]\n"
      ]
     }
    ],
@@ -165,14 +158,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x86787b8>"
+       "Text(0,0.5,'Cross-Validated Accuracy')"
       ]
      },
      "execution_count": 7,
@@ -181,9 +172,9 @@
     },
     {
      "data": {
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fuD9v24dyHt8L3Fvk3Ge4tHMeVb3ZQ5mNKShyLM71m/10dVQ+8r2nq51rQ/0rYoZ7dInVC70aCQeZT2V4YnxyMR18ucFp0O+zfFurlJf/TV8FctOdpt1txjSVVDrDgeOJiuaP5BsOB4nG4mQavJhTNJZABHZ6zPhbTG4esUgsQVsF17RJiauXl0DS4c4DAcB9XHjNTWNWsJ+emub8wtK5o7wYGQoymUzx9JmZGpSscpHYOZ4z0Ee/r/yhzLk2B3xs6OsmEksQjcW5erCf3m4vDRYXhPw+xhO2uNVq5CWQTOT0aSAirwVO169IxtRHpfmoCsl2RDcyE7CqEh1N1OR+RJyld/fFzjkd7RVcc9Dv4/T0HPOplblei6kfL4HkPwO/JyLHRCSGM2/jN+pbLGNqLxqLE+jp5LL1a6q+1pUDffR2tTe0wz129jxnZ+ar7mjPGgkHODoxQ3x2oaJrZhe4WinDos3yWbLuqqpPAS8QkT5AVHWq/sUypvYisTjD4fIy/hbT3ibOkNkGBpJIjTras3KDRyX9SIOBCwtcDa2tPlib5uGpEVREfgG4HvBl/xOq6p11LJdpQaqKKp6SANbazFyKwyeneOX1oZpdcyS8ls/+y1HGEufpaFv+FRl+/PQZujvauCa0VJIJb3a5waOns52rB8vLIgy5a7fXv58kk1FEqMmXAlO9JQOJiPwFsAZ4OfBXOEkUf1zncpkW9K4v7UMVPvXm5y77ax88niCj3lN+eLF7a5CFtPLCj36/Ztcs157L1tLZXpsgFujp5Dkb+1jf20VHBddczjQpb/38I1y2bg1/eOuOur+WWZqXGsmLVHWXiOxX1T8QkT8Bvl7vgpnWkskoPzg8Aeo8Xu5aSS072rNuvnYjn3jDMOcX0jW7ZrlecPm6ml7vU//huXRXOMcmuKaTro62us8lSS6k+eFTp5k8X7svBaY6XgJJ9q9iVkQ2A2eAy+tXJNOKnj4zw1TSycd59PQ0z9lYm+YYr6KjccLreljfVzBZdEU629t4/fOGana9laCaZjIRYVPAx1id06Rksx3b5MeVw8tXj78TkSDwceAnwDNcmtbdmJJyO6UjDchRFY3VZiKiKW3QX/+127N/S6em5kg3eEKocZQMJG5CxQdVNa6qXwMuA67NTXNijBeRWJzernb6ujuWfVGoU1NJjsfP17RZyxQW8td/dnu2mTKdUc5M2wTIlaBkIFHVDPAnOc/n8tK8G+NJNBZn11CQXUOBZV8UqhYZf4032TQp9VxyNxqLs6arHaDuzWjGGy9NW98VkdvFxtmZCiUX0jzuLgU7HA5yaGyS5DJ2UEdjcdrbhOs3W+dsvQ36fcynMsRnF5Y+uALZbMcvv2YjYAtprRReAsnv4iRpnBORSRGZEpHJOpfLtJBDOUvBjoSDpDLKYyeW708oEotzbaifHvdbrKmf7BDgetUUstmOX7XDmQ9kHe4rg5eldvtVtU1Vu1TV7z6vfDEHs+pEc4bejoSXN0dVJqNER+M1SyNiSgvlzG6vh2y245uuGaCjTWwhrRXCy4TElxXarqr/VPvimFYUiV28FGzI71u2HFVHTzvDjkdsxNayWFxyt06BJBI7x1Ub+/D7Ohlcho59442XeSTvzXnsw1kj/VHAFpgynuRnqB0JB5dtUajF2lAZy8aaym3s70akPkvuZrMd/9x1Tv/IoL/baiQrhJemrV/K+XkFsAM4Wf+imVYQn53n6dMzFycEDAd59sws52bmS5xZG9FRZ9jxlQPl544y5etsb2N9b3ddmrZGz12c7dgW0lo5KsmFMIoTTIxZUnT00qG32ceRZaiVRNxhx+0NSBS5WoUC3XXpbN/n1i6zE0uXY/Kj8cZLH8n/BLKDwtuAESBaz0KZ1hGNxS9ZCnbnUAARZ192GGc9JBfSHBqb5O0vuaJur2EuFfL3MHputubXjcbi+DovZDsO+X3MzKeZSi5UvUKkqY6XPpK9OY9TwJdU9V/rVB7TYiKx+CVLwfZ1d3DVxr66d7hfGHZs80eWUyjQzd5nz9b8upFYnB2bA4vZjnNHiFkgaSwvgeReIKmqaQARaReRNapa+68cpqWoKtFYnJuvvbTWMRIO8r3HT6KqdVtT4kLG37V1ub4pLOT3EZ9dILmQxtdZm7k7C+kMB48n+JUXXHbR64AzZ2W5k4Cai3npI3kQ6Ml53gP8Y32KY1rJ6LnznCmyFOxwOMi52QViZ8/X7fWjecOOzfIY9Nd+LsmT41PMpTIX9bUtDjW2fpKG8xJIfKo6nX3iPrZ1NM2SSq0Bku0w3VfHBI7RUcv42wjZD/hadrgX+luqR8AylfESSGZEZHFJOxF5HlC/r5GmZURj8aJLwV4T6sfX2Va3BI7ZYcc2f2T5herwAR+NxVnX28XQ2guNI77OdoJrOm0I8ArgpY/kd4CvisgJ9/km4JfrVyTTKiKxODu2BAouBdvZ3saOzYG6pZRfHHZsNZJlV48mp0gszkg4eEl/WsjvW5Y14k1pXiYkPgJcC/wm8F+A61T10XoXzDS3hXSGgycSJVO3j4SDHDwxyUI6U/PXjxxzhx3XcI12402/r5Pervaa1RSmkgscmZgu2EzpTEq0BpJGWzKQiMhvAb2qelBVDwB9IvJf6l8008wOn5wiuZApmSxxOBxkPpXhyfGpmr9+dPTSYcdm+QwGfDVr2jpwPIEqDBcYxm01kpXBSx/Jr6vq4oB/VT0H/LqXi4vILSLypIgcEZH3F9h/mYg8KCL7ReRhERlyt79cRCI5P0kRudXdd7mI/EhEfioiXxaRLm+3apbTYudoiaalbG1lX43nk2SHHVvG38ZxPuBrE0hKDdoY9Ps4MzNXl1qt8c5LIGnLXdRKRNqBJT+83eM+Bbwa2A7cISLb8w77BHC3qu4C7gQ+CqCqD6nqiKqO4CSHnAW+657zx8AnVfUq4Bzwdg/3YJZZtnM0vK6n6DFDa3tY39tV85Ty2WHHtiJi49QykERjcbatX0NwzaUfO6GAD1Vn/XbTOF4CyQPAV0TkZ0XkZuBLwD94OO8G4IiqHlXVeeAe4LV5x2zHmacC8FCB/QCvB76jqrNuQLsZZ5IkwBeAWz2UxSyzaCzB8FCg5GRDEWE4HKx5ICn1DdYsj1DAx6mpOTKZ6pfcjcYSRWuX2RFiNpeksbwEkvfhfNj/JvBb7uP3ljzDsQWI5TwfdbfligK3u49vA/pFZH3eMW/CCV4A64G4qqZKXBMAEXmHiOwVkb0TExMeimtqZXouxeFTU56aloaHghyZmGYqWbulWUsNOzbLIxTwkcoop2eqqymMJ5KMTyaLfikYtECyIngZtZVR1b9Q1der6u3A/cC7PVy70FfR/K8n7wFuFJF9wI3AcZx8Xs4FRDYBO3FqRV6vmS33Z1R1j6ruGRgY8FBcUysHRp3OUS81gpGtQVSdc2ql1LBjszwWJwtW2RGerV0W+1Kyqc4LaRlvPP1PE5ENIvKbIvJPwMPAoIfTRoFwzvMh4ETuAap6QlVfp6q7gQ+623I/Ud4IfENVs19XTwNBEcnOf7nkmqbxInnpvksZdofn1qrDPTvs2Ga0N9Zik1OVH/CRWJzOdmH7psKrewfXdNLV0Waz2xusaCARkX4R+TUR+Qfgx8BzgCtU9UpVfY+Haz8CXOWOsurCaaK6L+81NohItgwfAD6Xd407uNCshaoqTl/K691NbwG+5aEsZhllO0fX9i49oC64povLN/TWrJ8kO+zYZrQ31oVJidXN8YjG4ly3yV80+aOI1LRj31SmVI3kFM6IqLuAK1X13YDnJe3cfox34jRLHQK+oqqPicidIvIa97CbgCdF5DBOLeeu7Pkisg2nRvODvEu/D/hdETmC02fyWa9lMssjOlre0NvhoUDNlt71MuzY1N+Gvm7a26SqGkk6oxw4vnTtMmRrtzdcqRQpv4dTi/g08Lci8uVyL66q9+P0qeRu+1DO43u5MAIr/9xnKNCRrqpHcUaEmRXo5GSSsUSyrKal4XCQb0ZOMJY4z6ZA8eHCXngZdmzqr71N2NjfXdVkwacmppmeSy3Z1zYY8LF/GVbbNMUVrZGo6idV9WeA1+B0cn8T2Cwi7xORq5ergKa5LNYIymhayn5Q1KJ5y8uwY7M8Bv3VzW5fqqM9a1PAx1giidPybRrBy6ito6p6l6ruBJ4PBIDv1L1kpilFYnE62op3jhZy3SY/ne1SdYd7OcOOTf1V2+QUicXp93VwxYbekscN+n3MpzLEZ2s3hNyUp6zxkap6QFV/T1WvrFeBTHNbqnO0EF9nO9dt8lddI8kOO7ZAsjKEAtV1gkdjcYaHgrS1la5d1mqEmKmcDbQ3NZPJKPtHS2f8LWYkHOTAaIJ0FTOhraN9ZRn0+5ieSzE9l1r64DzJhTRPjE8VTNSYLxToBiyQNJIFElMz2c7RSmoEw0NBZubTHDk1vfTBRURjcS7zOOzY1N+mKtYlOXjc+VLhZdDGhcmPFkgaxQKJqZlqclxlO+erad6KjsYtv9YKUs1SuOX8LW3sr/3SvqY8RYf/isgBiqQfAXAz9hqzKDoap7976c7RQi5f30u/r4PIaJw3Pj+89Al5Khl2bOqrmpUSo6MJNgd8bHSDUSldHW1s6Ou22e0NVGoeyS+6v3/L/f1/3N9vxknrbsxFIrE4u8KBJTtHC2lrE4aHgkSOVVYj8TpU1CyfajrBI7FzZb2XoUC39ZE0UKl5JM+q6rPAi1X1v7kjtg6o6vuBVy1fEU0zSC6keWJsqqqmpZFwkCdPTnF+Pl32uVF32PH1m70POzb11dPVjt/XUXaN5Mz0HLGz58v6W7I0KY3lpY+kV0Rekn0iIi8Cym+7MC3tsRMJUh47R4sZDgdJZ5SDJ8rPBBypYNixqb9NgZ6yawrZdDnl1EiqnfxoquMlkLwd+JSIPCMiTwP/C/iP9S2WaTaRmPPhX02NJDvUs9wO9+ywYy9DRc3yqmTt9kgsQZvAzi3e38+Q38e52QWSC+XXZk31SvWRAKCqjwLDIuIHJC/NuzGA8+HvtXO0mI39PrYEexb7O7w6ejqbk2ltxa9t6iPk7+aJscmyzonG4lw92E9v95IfTxdeJ3BhhNhl663BZLktWSMRkUER+SzwZVVNiMh2EbF10s1FIrHyMv4WMxwOlB1I9h3LDhW1GslKE/L7mJieYyGd8XS8qjrZo8tsIq1mhJipnpemrc/jpILf7D4/DPxOvQpkms/ZmXmOnZ2tyRyOkXCQ0XPnOT3tPWvshWHHfVW/vqmtwYAPVZiY8vZ+PntmlvjsQtnryVialMbyEkg2qOpXgAwsrjNiDZFmUbSGQ2+z30TL6SepZtixqa9yP+DLWV0z12Cg8smPpnpeAsmMiKzHnZwoIi8ArJ/ELIrE4mV3jhazY0uANvEeSLLDjm0i4sq02HfhsckpEovT09nO1YPl1S77uztY09Vus9sbxEtv1u/iLJF7pYj8KzAAvKGupTJNJTpafudoMb3dHVw92E9k1Nt3lcdOTJLKqKVGWaHKrZFER+Ps3BKgo7287E3ZJXetRtIYXt6tx4AbgRcBvwFcDzxRz0KZ5qGqi+m+a2UkHCQai3taqKia/F6m/tb1dtHV3uYpkMynMjx2YrLiYdzVpq03lfPyFfKHqvpcnIACgIj8BHhu3UrVwr5zYIz7oicaXYyaWUhnODe7UNPUJMPhIPc8EuM/fWEvXR2lv+s8dmKSTVUOOzb1IyJs9Hfz7egYx86Uzqw0O59mPpWp+G8p5Pfxo6fPVnQuwFf3xljf18XN1w5WfI1C1xzo7+amazbW7JorUamkjSGcNdN7RGQ3znK7AH5gzTKUrSV99l+e5tDYJFvWts6a4sNDAW66ZqBm17vpmgGGw0Fi55ZO6ebrbOOXK0jyaJbPbbu38MBj4zw1sfQSAc/dGuTFV26o6HWykx8zGS174IWq8pG/P8S2Db01CySZjPKH336c4XBw9QYSnHxabwWGgD/N2T4F/F4dy9TSxhJJXnl9iE/+8kiji7JibQr08K3fenGji2Fq5N2vvIZ3v/Kaur9OyO8jlVFOz8wtppb36tkzsyTOL3DoxCRzqTTdHdWn2nnmzAyTydSq6LcpGkhU9QvAF0TkdlX92jKWqWVPWac8AAAfnklEQVRlMsqpqeTiSBZjTO1cWOCq/ECS7WubT2c4VGXy0fxrroZ+Gy8pUr4mIr+A08nuy9l+Zz0L1orOzs6zkNbFkSzGmNpZXJFxMslOyuuwj7jZo1MZZ/BILQJJdgj7ZDLF+fk0PV2tm1DUS4qUvwB+GXgXTj/JG4DL6lyulpT9ZjJogcSYmgsFKp/dHh2N89ytaxno765qlc5cuUPYW33GvZfhvy9S1V8DzqnqHwAvBKx3swLZtlJr2jKm9jb0ddPeJmWv3Z4ddjyyNchIOFh2rrdC5lJpDp2YXJyk2+rNW14CyXn396yIbAYWgMvrV6TWlZ11a01bxtRee5sw0Ndd9uz2J8YnnWHHQ04gOXp6hsTsQlVlOTQ2xXw6wy07QgCMT55f4ozm5iWQfFtEgsDHgZ8AzwD31LNQrerkZJI2gQ19XY0uijEtqZL1Ty7kigtcyPU2Wl2tJHLsHACvut4ZSjye8J6EtBl56Wz/Q/fh10Tk24DP1iSpzHgiycZ+X9npH4wx3oT83Tw1MVPWOfticTb0dbMl2IO/pxNwgsvLrq58blR0NMHG/m6uHOijr7uj5YcAl5qQ+LoS+1DVry91cRG5Bfj/gXbgr1T1j/L2XwZ8Did/11ngV1R11N23FfgrnP4YBX5eVZ8Rkc/jpGzJBrO3qmpkqbKsBOOTycUspcaY2tsU6OHfjpwp6xxnlFYAEcHv6+TKgd6qayTZkV8iwqC/u+X7SErVSH7J/b0RJ8/W993nLwceBkoGEhFpBz4FvAIYBR4RkftU9fGcwz4B3K2qXxCRm4GPAr/q7rsbuEtVvycifbhp7F3vVdV7l7q5lebkZJLLN9jqbcbUy6Dfx9Rcipm5lKckopPJBZ6amOG23VsWt42E1/KDw6dQVUTKX5ogMbvA0dMz3P68IaCydeubTdE2FlV9m6q+Dac2sF1Vb1fV23Hmk3hxA3BEVY+q6jxOv8pr847ZDjzoPn4ou19EtgMdqvo9tyzTqrp0vowVbiyRtI52Y+ooFOgGvA+33R9zGjZy83uNhAOcnp7neLyyDvJsbSY7F2VwFWQl9tJYv01Vx3KenwSu9nDeFiCW83zU3ZYrCtzuPr4N6HfXPrkaiIvI10Vkn4h83K3hZN0lIvtF5JMi0l3oxUXkHSKyV0T2TkxMeChufc3Op5hKpqxpy5g6ys7R8tqUlP3Q35WTvTobVKKxyrqCo7E4IrBzyBn6Gwp0c2pqjnRm6WzWzcpLIHlYRB4QkbeKyFuAv8epPSylUJ0w/1/yPcCNIrIPp9/jOJDCaXJ7qbv/+cAVOHm/AD4AXOtuXwe8r9CLq+pnVHWPqu4ZGKhdQsFKjdvQX2PqLlRmINl3LM4VA70E3E52gGtDfro62ojEzlVUhkgszpUDffh9nYtlSme0rOWjm82SgURV3wn8b2AYGAE+o6rv8nDtUS6euDgEXJQ/XVVPqOrrVHU38EF3W8I9d5/bLJYCvombtl5Vx9QxB/w1ThPaijdukxGNqbtyZrerKpFYnJG8tXS6Otq4frO/ohqJqhIdvTjFSrm1pGbkaRyqqn5dVf+r+/MNj9d+BLhKRC4XkS7gTTgrLS4SkQ0iki3DB3BGcGXPXSsi2arEzcDj7jmb3N8C3Aoc9Fiehlqc1W41EmPqZk1XB36ft+G2Y4kkp6fnCq5/MjwU5MDxBKl0psCZxR2Pn+f09PxF19wUcJaMaOUO96KBRET+xf09JSKTOT9TIjK51IXdmsQ7gQeAQ8BXVPUxEblTRF7jHnYT8KSIHAYGgbvcc9M4zVoPisgBnGayv3TP+aK77QCwAfhI2XfdANkJSVYjMaa+vK6UWGp1zd1bg5xfSHP45NJrqBS8Zk4tZ9AdANDKHe6l0si/xP3dX+nFVfV+4P68bR/KeXwvUHAYrztia1eB7TdXWp5GGk+cp9/XwZqu6tc1N8YUN+j3efr2H43F6Wpv49pNl37E5c5w377Z7/m1o7E4XR0XX3NDbzcdbVJ26pZmUqpGsq7Uz3IWshWMT9rQX2OWQ8jvvUZy3WZ/wUWsLlu/huCaTiLHypuYGInF2bHZT2dO9oq2NmFjf3fZySSbSamvx4/ijLIqNvrqirqUqEWNT85Zs5YxyyAU8HF6eo5UOlM0HVE6oxw4nuCNewonMhcRhoeCZc1wT6UzHDie4I4btl6ybzDgrZbUrEpNSLxcVa9wf+f/WBAp00mbjGjMsggFfGQUJkoMt/3pqSlm59MMh4svgDUcDnL45BQzcylPr3v45DTJhUzBPpdNqzWQ5BKRtSJyg4i8LPtT74K1klQ6w8S01UiMWQ5e5pJEFzva1xY9Znc4SEbhwHFvw4BLdd4P+n0t3bTlZYXE/wT8E87oqz9wf3+4vsVqLaen50ln1FZGNGYZeJm3EYnF8fs62LZ+TdFjdrkz072umBiNxVm7ppOt6y69ZsjvY2Y+zVSyunVOViovNZLfxplF/qyqvhzYDTQ+50gTGbc5JMYsGy+TEiOxBMNudt5i1vd1E17X47mfJDoaL3rNxTK1aK3ESyBJqmoSQES6VfUJ4Jr6Fqu1LKZHsaYtY+pu3ZouOtulaCCZnU9x+OQUuws0QeUbCa/1NHJrZs655vBQ4Wsu1pJatJ/ESyAZdVdI/CbwPRH5FnmpTkxptla7McunrU1K9kkcPD5JOqMFZ7TnGx4KcCKR5NQSAeDA8QQZLdw/AuXnAGs2XlZIvM19+GEReQgIAP9Q11K1mPHJJJ3twro1tsSuMcshVGJS4oWldb3USJxjIrE4r7w+VPS4yBLXzH6JbNXZ7aUmJP69iLxZRBZXYlLVH6jqfe76Isajk+4Su21t5S+SY4wp32CJNCmRWJyhtT1s6Cu4AsVFdmwJ0N4mS/aTRGNxtq5bw7rewl8WfZ3tBNd0rsqmrc8Avwg8IyJfFpFb3eSLpkxjiaQ1axmzjLI1EtVL1wCJxOKeaiPgBIBrQ/1LZgLOLq27ZJkSrZlKvtSExG+p6h3AVpxldd8CHBORz4nIK5argK3gpKVHMWZZhfw+kgsZJs9fPJlwYmqO4/Hznjras0bCQaKxOJkiC1OdmkxyIpFcMjg5OcAqW3VxpfOyHsl5Vf2y21fySpzhv9ZH4pGqOnm2rEZizLIZLDIEuJz+kazhcJCpuRRHT88U3H9hImLxWfKwSmskWSIyKCLvEpF/xRm59V3geXUvWYuYmksxO5+2Gokxy2hTsUAyGqe9TdixufSHfq6RxaV3C/eTREfjdLQJ1y9xzVDAx5mZORbKXOOkGZTqbP91Efk+8BOcNdT/m5t7632qGlm2Eja57BBEW6vdmOVzYbjtxU1JkVicawb76em6NONvMVcO9NHX3bFY88gXicW5dlM/vs7S1wwFfKjCqanWq5WUqpG8CPgjIKyq71LVf12mMrWUMVur3Zhlt9HvjMjKbUrKZJRoGR3tWe1tws4tgYIjtzIZZX8sUXQiYq5WnktSqrP9bar6XVVdrIeJyIeXpVQtxNKjGLP8ujvaWdfbdVHT1jNnZphMppbsyyhkOBzk0NgkyYX0RduPnp5mai615IgtaO212z1l/83xmqUPMbmyTVvZb0jGmOUx6PddNAEwW6MolfG3mJFwkIW08vjYxauMR9xhwV4CiZccYM2q3EBiM+rKND6ZZF1v15Ltp8aY2tqUNykxcixOb1c7z9nYV/a1inW4R2Nx+ro7uGJg6WuuXdNJV0dbS85uLzeQ2GitMp2cTFr6eGMaIL9GEhlNsHPImalerlDAR8jvu6TDPRKLs8vjNUXE8zLAzcbL8N+PiYhfRDpxkjaeFpFfWYaytQRnrXZr1jJmuYX8Ps7MzDOXSjOXSnPoxGTZHe25hsOBi2okyYU0h8bKu2apHGDNzEuN5JWqOomTLmUUZyjwe+taqhYybulRjGmIUMD5Andqco5DY1PMpzOMeBhdVcxwOMgzZ2aJzzqpBh8fmySVUU8jtrIGA75V27TV6f7+eeBLqnq2juVpKfOpDKen561py5gGyF0DZHFp3a2VB5LcTMDA4jolu8u4ZsjfzViicA6wZuYlkPydiDwB7AEeFJEBoPVCah2cmrKhv8Y0yqZAD+C0CkRicTb2d1f1f3HnlgAiLCZwjI7GCfl9ZX1RDAV6mE9liM+21pK7XnJtvR94IbBHVReAGeC19S5YK7AFrYxpnGzQOOnWSJZaWncp/b5OnjPQRyR2DnBqJl6G/RYqU6v1k3jpbH8DkFLVtIj8PvA3wOa6l6wFZGfVWiAxZvn5ezrwdbbx5PgUR0/PlP2hX8hIOEh0NMHZmXmePTNbdud9tt9m1QUS4P9V1SkReQnwKuALwKfrW6zWMObm+bGmLWOWX3a47YNPnAK8TRpcynA4yNmZef7+wJj7vLxZ8tlmsGLLADcrL4EkmxPgF4BPq+q3AFvgyoOTk0m6O9oI9HQufbAxpuYG/T7OzswjAjuHyk+Nki8bjO7+t2cQgV1ljgLb2O8EkrFVGEiOi8j/Bt4I3C8i3R7PQ0RuEZEnReSIiLy/wP7LRORBEdkvIg+LyFDOvq0i8l0ROSQij4vINnf75SLyIxH5qbty44oNauOTc4QCvqraZY0xlcs2K1850IffV/0XumtC/XR3tPHTU9NctdHJClyOro42NvR1t9wQYC8B4Y3AA8AtqhoH1uFhHomItAOfAl4NbAfuEJHteYd9ArhbVXcBdwIfzdl3N/BxVb0OuAE45W7/Y+CTqnoVcA54u4d7aIiTCVsZ0ZhGygaScuZ6lNLZ3saOLYGqrhkKdK++PhJVnQWeAl4lIu8ENqrqdz1c+wbgiKoeVdV54B4uHe21HXjQffxQdr8bcDpU9XtuGaZVdVacr/Y3A/e653wBuNVDWSry9OkZfnLsXMXn28qIxjRW9otcNfNH8mWbtyq9ZiumSfEyauu3gS8CG92fvxGRd3m49hYglvN81N2WKwrc7j6+DegXkfU4s+fjIvJ1EdknIh93azjrgbiqpkpcM1vud4jIXhHZOzEx4aG4l/rQtw7y+984WNG5i0vsWo3EmIa5cqAPEbhh27qaXfNnLl+HCDy/wmvm5wBrBV6att4O/IyqfkhVPwS8APh1D+cV6hjIn875HuBGEdkH3AgcB1JAB/BSd//zgSuAt3q8prNR9TOqukdV9wwMDHgo7qWGh4I8eXKK8/PppQ/Oc252gflUxma1G9NAL71qAw+/5yauCfXX7Jqv2D7Iw++5iasHK7tmyO/j3OzCJWubNDMvgUS4MHIL97GX3uNRIJzzfAg4kXuAqp5Q1dep6m7gg+62hHvuPrdZLIWzVvxzgdNAUEQ6il2zlkbCQdIZ5eCJRNnnZquu1rRlTOOICJet711R18x+JrRSrcRLIPlr4Eci8mF3hcR/Bz7r4bxHgKvcUVZdwJuA+3IPEJENIpItwweAz+Wcu9ZNxwJOv8jj6iSoeQh4vbv9LcC3PJSlIrvcMeL5axB4kf0jsRqJMSbX4gJXLdRP4qWz/U+BtwFncUZJvU1V/8zDeSngnTgjvg4BX1HVx0TkThHJrrR4E/CkiBwGBoG73HPTOM1aD4rIAZwa0F+657wP+F0ROYLTZ+IlqFVkY7+PLcEe9lUQSLKjMjZZjcQYk6MV06SUHATt1hb2q+oO4CflXlxV7wfuz9v2oZzH93JhBFb+ud8DdhXYfhRnRNiyGAkHK6qRjCeSiMBAv61FYoy5YHC1NW2pagaIisjWZSrPijMcDjB67jynp+fKOm88kWRDXzed7eUuQmmMaWX93R2s6WpfzMXXCrxMy9wEPCYiP8bJ/AuAqr6m+CmtIzvpKBqL87PXDXo+z4b+GmMKyeYAa6UaiZdA8gd1L8UKtnMoQJuUH0hOTiYZWrumjiUzxjSrUMC3mNS1FRQNJCLyHGBQVX+Qt/1lOPM9VoU1XR1cPdhfdof7+GSSPdvW1qlUxphmFvL7+NHTrbPYbKkG/D8Dpgpsn3X3rRq7tzod7l6Xx0wupInPLiyu0GaMMbmya7dnMq2x5G6pQLJNVffnb1TVvcC2upVoBRoeCjKZTPHMmVlPx9scEmNMKSG/j1RGOTMz3+ii1ESpQFLqU3BVfdXOroKWXWJzKdm1Bqyz3RhTyKC/tYYAlwokj4jIJTm1ROTtwKP1K9LKc/VgP2u62onGvKVKubBWu80hMcZcKjtRuVUWuCo1aut3gG+IyJu5EDj24KyOeFu9C7aStLcJO7YEiHjscM+mPrCmLWNMIYtpUlqkRlI0kKjqSeBFIvJyYIe7+e9V9fvLUrIVZiQc5PP/+gxzqTTdHe0ljx2fTNLX3UF/DVZkM8a0ng193bS3Scus3b7kPBJVfQgnUeKqNhIOMp/O8MTY1GKfSTEnJ5MM+q1ZyxhTWHubMNDXOislWv4Ojy50uC/dvDWWsJURjTGlZYcAtwILJB5tDvgY6O/2lMDxZCJp/SPGmJI2tdCSuxZIPBIRhoeCREZLB5JMRjk1NWdDf40xJYUCFkhWpZFwgKMTMyRmF4oec3pmjlRGrWnLGFPSoN/H1FyKmblUo4tSNQskZRgJO7mz9h8vXis56aaGthqJMaaU7DyzVuhwt0BShp1DSy+9Oz5pa7UbY5a2OLu9BZq3LJCUIdDTyRUDvSVHbo27qaGtRmKMKSWb1NVqJKvQSDhIJJYomgl4fDJJe5uwvs/mkRhjimultdstkJRpJBzk9PQcx+OFF6UZT8yxsd+ZtWqMMcX0dLXj93W0xMgtCyRlGglnl94tnMDx5KRNRjTGeNMqQ4AtkJTp2pCfrvY2okXmk9ha7cYYrwZbZO12CyRl6upoY/tmP5FjRQKJzWo3xni0KeCzPpLVaiQc5MDxBKl05qLt03MppudS1rRljPEk5PcxMTV3yWdJs7FAUoGRcJDzC2kOn5y+aPu4rYxojCnDYMBHRmFieq7RRamKBZIKZDMB5/eT2FrtxphyLA4BbvIOdwskFdi2fg2Bns5LZrhn/xg2WdOWMcaDVlm73QJJBUSE4XDwkhnulh7FGFOO7JdOq5GUICK3iMiTInJERN5fYP9lIvKgiOwXkYdFZChnX1pEIu7PfTnbPy8iT+fsG6nnPRQzEg5y+OTURZk7xxNJAj2d+DpLL8VrjDEA63q76GpvY3zS+kgKEpF24FPAq4HtwB0isj3vsE8Ad6vqLuBO4KM5+86r6oj785q8896bsy9Sr3soZSQcIKNw8PiFiYk2h8QYUw4RYaO/25q2SrgBOKKqR1V1HrgHeG3eMduBB93HDxXYv2IND1269O7JySSD1qxljClDyO9jLFE45VKzqGcg2QLEcp6PuttyRYHb3ce3Af0ist597hORvSLy7yJya955d7nNYZ8UkYZkR1zf1014Xc9FI7fGE0lCfkvWaIzxzlm73Zq2iimUtTA/Ze57gBtFZB9wI3AcyHY6bFXVPcB/AP5MRK50t38AuBZ4PrAOeF/BFxd5hxuI9k5MTFR3J0UMDwUXZ7gvpDNMTM8RclNDG2OMFyF37fZiGcWbQT0DySgQznk+BJzIPUBVT6jq61R1N/BBd1siu8/9fRR4GNjtPh9Txxzw1zhNaJdQ1c+o6h5V3TMwMFDTG8saCQc5kUhyajLJxNQcqjYZ0RhTnk0BH+cX0kwmm3fJ3XoGkkeAq0TkchHpAt4E3Jd7gIhsEJFsGT4AfM7dvjbbZCUiG4AXA4+7zze5vwW4FThYx3soaTET8GgiZ+ivNW0ZY7xrhbkkdQskqpoC3gk8ABwCvqKqj4nInSKSHYV1E/CkiBwGBoG73O3XAXtFJIrTCf9Hqvq4u++LInIAOABsAD5Sr3tYyvWbA7S3CZHYucXlMm1WuzGmHNl5Z2NNPJeko54XV9X7gfvztn0o5/G9wL0Fzvs3YGeRa95c42JWrKernWtD/URjCTa4KyJa05YxphyhFli73Wa2V2k4HCQ6Gmc8kaSrvY11vV2NLpIxpolsdEd6NnM6eQskVRoZCjKVTPFvT51hMNCN03VjjDHedHe0s763ywLJajay1elwP3A8Yc1axpiKDPp91rS1ml050Edvl5NbyzrajTGVCDX5SokWSKrU3ibsctOlWI3EGFOJQXdSYrOyQFID2YWuLH28MaYSIb+PMzPzzKXSjS5KReo6/He1GAkHAGvaMsZUJrsuyav/7J9pb6vtgJ3PvuX5bF2/pqbXzGeBpAZuvHojv/7Sy3nZ1fVJxWKMaW03XjPAbbu31KVG0tVR/4YnaeZEYV7t2bNH9+7d2+hiGGNMUxGRR93kuSVZH4kxxpiqWCAxxhhTFQskxhhjqmKBxBhjTFUskBhjjKmKBRJjjDFVsUBijDGmKhZIjDHGVGVVTEgUkQng2bzNG4DTDShOvbTa/UDr3ZPdz8rXavdU7f1cpqpLpuxYFYGkEBHZ62XGZrNotfuB1rsnu5+Vr9Xuabnux5q2jDHGVMUCiTHGmKqs5kDymUYXoMZa7X6g9e7J7mfla7V7Wpb7WbV9JMYYY2pjNddIjDHG1MCqCyQicouIPCkiR0Tk/Y0uTy2IyDMickBEIiLSdAuviMjnROSUiBzM2bZORL4nIj91f69tZBnLVeSePiwix933KSIiP9/IMpZDRMIi8pCIHBKRx0Tkt93tTfk+lbifZn6PfCLyYxGJuvf0B+72y0XkR+579GUR6ar5a6+mpi0RaQcOA68ARoFHgDtU9fGGFqxKIvIMsEdVm3L8u4i8DJgG7lbVHe62jwFnVfWP3IC/VlXf18hylqPIPX0YmFbVTzSybJUQkU3AJlX9iYj0A48CtwJvpQnfpxL380aa9z0SoFdVp0WkE/gX4LeB3wW+rqr3iMhfAFFV/XQtX3u11UhuAI6o6lFVnQfuAV7b4DKteqr6T8DZvM2vBb7gPv4Czn/yplHknpqWqo6p6k/cx1PAIWALTfo+lbifpqWOafdpp/ujwM3Ave72urxHqy2QbAFiOc9HafI/HpcC3xWRR0XkHY0uTI0MquoYOP/pgY0NLk+tvFNE9rtNX03RDJRPRLYBu4Ef0QLvU979QBO/RyLSLiIR4BTwPeApIK6qKfeQunzmrbZAIgW2tULb3otV9bnAq4HfcptVzMrzaeBKYAQYA/6kscUpn4j0AV8DfkdVJxtdnmoVuJ+mfo9UNa2qI8AQTgvMdYUOq/XrrrZAMgqEc54PAScaVJaaUdUT7u9TwDdw/oCa3Um3HTvbnn2qweWpmqqedP+jZ4C/pMneJ7fd/WvAF1X16+7mpn2fCt1Ps79HWaoaBx4GXgAERaTD3VWXz7zVFkgeAa5yRzF0AW8C7mtwmaoiIr1uZyEi0gu8EjhY+qymcB/wFvfxW4BvNbAsNZH9wHXdRhO9T25H7meBQ6r6pzm7mvJ9KnY/Tf4eDYhI0H3cA/wcTt/PQ8Dr3cPq8h6tqlFbAO5wvj8D2oHPqepdDS5SVUTkCpxaCEAH8LfNdk8i8iXgJpxMpSeB/w58E/gKsBU4BrxBVZum87rIPd2E02SiwDPAb2T7F1Y6EXkJ8M/AASDjbv49nH6FpnufStzPHTTve7QLpzO9HaeS8BVVvdP9jLgHWAfsA35FVedq+tqrLZAYY4yprdXWtGWMMabGLJAYY4ypigUSY4wxVbFAYowxpioWSIwxxlTFAolpCSLysIi8Km/b74jI/1rivOlS+2tQrgE38+o+EXlp3r6HRWSP+3ibm531VQWu8XE3m+vHKyzDTSLy7ZznHxGRB0Sk2y3D3px9e0Tk4ZzzVER+KWf/t0XkpkrKYVqXBRLTKr6EM8E015vc7Y30s8ATqrpbVf+50AEiMgQ8ALxbVR8ocMhvAM9V1fd6ecGcWcyF9n0QeDFwa85cgo0i8uoip4wCH/Tyumb1skBiWsW9wC+KSDcsJuLbDPyLiPSJyIMi8hNx1m25JONzgW/tfy4ib3UfP09EfuAmxXwgb/Zz9vjL3NfY7/7eKiIjwMeAnxdnbYueAuUOAd8Ffl9VL8myICL3Ab3Aj0Tklwu9jnvc50XkT0XkIeCPC/0Dici7gZ8HfklVz+fs+jjw+4XOAaJAQkReUWS/MRZITGtQ1TPAj4Fb3E1vAr6szozbJHCbm9jy5cCfuCkyluTmY/qfwOtV9XnA54BCmQP+HGftkV3AF4H/oaoR4ENuOUbyPryz7gb+XFW/WuS+XgOcd8//cqHXyTn8auDnVPXdBS71YuA/A6/OSTWe9UNgTkReXqgMwEcoHmiMsUBiWkpu81Zus5YA/5+I7Af+ESeN9qDHa14D7AC+56bn/n2cxHf5Xgj8rfv4/wAv8Xj9fwR+VUTWeDy+1Ot8VVXTRc47gvPv8Moi+4sGi2yTXH4fjzFZFkhMK/km8LMi8lygJ7twEfBmYAB4npti+yTgyzs3xcX/H7L7BXjMrRGMqOpOVS32YZzLa+6hj+Hkq/pqqb4Nj68zU+K4kzjNWp8sVPNQ1e/j3PMLipx/F9ZXYoqwQGJahttk8zBO81NuJ3sAOKWqC+6H6GUFTn8W2O6OZArgdJIDPAkMiMgLwWnqEpHrC5z/b1yoDb0ZZ5lTr/4rMAl81kOTW8Wvo6qHgdcBf+P23+S7C/hvRc79LrAWGPb6emb1sEBiWs2XcD7s7snZ9kVgjzvM9c3AE/knqWoMJ4vtfvf4fe72eZwU3H8sIlEgAryowOv+P8Db3OazX8VZK9sTtx/nLcAmnBpKKRW/jvtajwBvA+4TkSvz9t0PTJQ4/S4KN+uZVc6y/xpjjKmK1UiMMcZUxQKJMcaYqlggMcYYUxULJMYYY6pigcQYY0xVLJAYY4ypigUSY4wxVbFAYowxpir/F19pSpGIa+FNAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x3d6e198>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -214,20 +205,16 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.grid_search import GridSearchCV"
+    "from sklearn.model_selection import GridSearchCV"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -246,9 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -267,13 +252,11 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# instantiate the grid\n",
-    "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')"
+    "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False)"
    ]
   },
   {
@@ -286,9 +269,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -297,9 +278,10 @@
        "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
        "           metric_params=None, n_jobs=1, n_neighbors=30, p=2,\n",
        "           weights='uniform'),\n",
-       "       fit_params={}, iid=True, n_jobs=1,\n",
+       "       fit_params=None, iid=True, n_jobs=1,\n",
        "       param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]},\n",
-       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
+       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
+       "       scoring='accuracy', verbose=0)"
       ]
      },
      "execution_count": 12,
@@ -315,43 +297,251 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.053333</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.052068</td>\n",
+       "      <td>{'n_neighbors': 2}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 3}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 5}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 6}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 8}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 9}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 11}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 12}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'n_neighbors': 14}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 15}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 17}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 18}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 19}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 20}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>{'n_neighbors': 21}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>{'n_neighbors': 22}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 23}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'n_neighbors': 24}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>{'n_neighbors': 25}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'n_neighbors': 26}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 27}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.042687</td>\n",
+       "      <td>{'n_neighbors': 28}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.042687</td>\n",
+       "      <td>{'n_neighbors': 29}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.042687</td>\n",
+       "      <td>{'n_neighbors': 30}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "[mean: 0.96000, std: 0.05333, params: {'n_neighbors': 1},\n",
-       " mean: 0.95333, std: 0.05207, params: {'n_neighbors': 2},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 3},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 4},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 5},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 6},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 7},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 8},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 9},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 10},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 11},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 12},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 13},\n",
-       " mean: 0.97333, std: 0.04422, params: {'n_neighbors': 14},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 15},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 16},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 17},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 18},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 19},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 20},\n",
-       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 21},\n",
-       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 22},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 23},\n",
-       " mean: 0.96000, std: 0.04422, params: {'n_neighbors': 24},\n",
-       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 25},\n",
-       " mean: 0.96000, std: 0.04422, params: {'n_neighbors': 26},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 27},\n",
-       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 28},\n",
-       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 29},\n",
-       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 30}]"
+       "    mean_test_score  std_test_score               params\n",
+       "0          0.960000        0.053333   {'n_neighbors': 1}\n",
+       "1          0.953333        0.052068   {'n_neighbors': 2}\n",
+       "2          0.966667        0.044721   {'n_neighbors': 3}\n",
+       "3          0.966667        0.044721   {'n_neighbors': 4}\n",
+       "4          0.966667        0.044721   {'n_neighbors': 5}\n",
+       "5          0.966667        0.044721   {'n_neighbors': 6}\n",
+       "6          0.966667        0.044721   {'n_neighbors': 7}\n",
+       "7          0.966667        0.044721   {'n_neighbors': 8}\n",
+       "8          0.973333        0.032660   {'n_neighbors': 9}\n",
+       "9          0.966667        0.044721  {'n_neighbors': 10}\n",
+       "10         0.966667        0.044721  {'n_neighbors': 11}\n",
+       "11         0.973333        0.032660  {'n_neighbors': 12}\n",
+       "12         0.980000        0.030551  {'n_neighbors': 13}\n",
+       "13         0.973333        0.044222  {'n_neighbors': 14}\n",
+       "14         0.973333        0.032660  {'n_neighbors': 15}\n",
+       "15         0.973333        0.032660  {'n_neighbors': 16}\n",
+       "16         0.973333        0.032660  {'n_neighbors': 17}\n",
+       "17         0.980000        0.030551  {'n_neighbors': 18}\n",
+       "18         0.973333        0.032660  {'n_neighbors': 19}\n",
+       "19         0.980000        0.030551  {'n_neighbors': 20}\n",
+       "20         0.966667        0.033333  {'n_neighbors': 21}\n",
+       "21         0.966667        0.033333  {'n_neighbors': 22}\n",
+       "22         0.973333        0.032660  {'n_neighbors': 23}\n",
+       "23         0.960000        0.044222  {'n_neighbors': 24}\n",
+       "24         0.966667        0.033333  {'n_neighbors': 25}\n",
+       "25         0.960000        0.044222  {'n_neighbors': 26}\n",
+       "26         0.966667        0.044721  {'n_neighbors': 27}\n",
+       "27         0.953333        0.042687  {'n_neighbors': 28}\n",
+       "28         0.953333        0.042687  {'n_neighbors': 29}\n",
+       "29         0.953333        0.042687  {'n_neighbors': 30}"
       ]
      },
      "execution_count": 13,
@@ -360,67 +550,63 @@
     }
    ],
    "source": [
-    "# view the complete results (list of named tuples)\n",
-    "grid.grid_scores_"
+    "# view the results as a pandas DataFrame\n",
+    "import pandas as pd\n",
+    "pd.DataFrame(grid.cv_results_)[['mean_test_score', 'std_test_score', 'params']]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "{'n_neighbors': 1}\n",
-      "[ 1.          0.93333333  1.          0.93333333  0.86666667  1.\n",
-      "  0.86666667  1.          1.          1.        ]\n",
       "0.96\n"
      ]
     }
    ],
    "source": [
-    "# examine the first tuple\n",
-    "print(grid.grid_scores_[0].parameters)\n",
-    "print(grid.grid_scores_[0].cv_validation_scores)\n",
-    "print(grid.grid_scores_[0].mean_validation_score)"
+    "# examine the first result\n",
+    "print(grid.cv_results_['params'][0])\n",
+    "print(grid.cv_results_['mean_test_score'][0])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.95999999999999996, 0.95333333333333337, 0.96666666666666667, 0.96666666666666667, 0.96666666666666667, 0.96666666666666667, 0.96666666666666667, 0.96666666666666667, 0.97333333333333338, 0.96666666666666667, 0.96666666666666667, 0.97333333333333338, 0.97999999999999998, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.97999999999999998, 0.97333333333333338, 0.97999999999999998, 0.96666666666666667, 0.96666666666666667, 0.97333333333333338, 0.95999999999999996, 0.96666666666666667, 0.95999999999999996, 0.96666666666666667, 0.95333333333333337, 0.95333333333333337, 0.95333333333333337]\n"
+      "[0.96       0.95333333 0.96666667 0.96666667 0.96666667 0.96666667\n",
+      " 0.96666667 0.96666667 0.97333333 0.96666667 0.96666667 0.97333333\n",
+      " 0.98       0.97333333 0.97333333 0.97333333 0.97333333 0.98\n",
+      " 0.97333333 0.98       0.96666667 0.96666667 0.97333333 0.96\n",
+      " 0.96666667 0.96       0.96666667 0.95333333 0.95333333 0.95333333]\n"
      ]
     }
    ],
    "source": [
-    "# create a list of the mean scores only\n",
-    "grid_mean_scores = [result.mean_validation_score for result in grid.grid_scores_]\n",
+    "# print the array of mean scores only\n",
+    "grid_mean_scores = grid.cv_results_['mean_test_score']\n",
     "print(grid_mean_scores)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x93352e8>"
+       "Text(0,0.5,'Cross-Validated Accuracy')"
       ]
      },
      "execution_count": 16,
@@ -429,9 +615,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92fcc50>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -448,9 +634,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -490,9 +674,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# define the parameter values that should be searched\n",
@@ -503,9 +685,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -524,9 +704,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -535,9 +713,10 @@
        "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
        "           metric_params=None, n_jobs=1, n_neighbors=30, p=2,\n",
        "           weights='uniform'),\n",
-       "       fit_params={}, iid=True, n_jobs=1,\n",
+       "       fit_params=None, iid=True, n_jobs=1,\n",
        "       param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], 'weights': ['uniform', 'distance']},\n",
-       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
+       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
+       "       scoring='accuracy', verbose=0)"
       ]
      },
      "execution_count": 20,
@@ -547,80 +726,530 @@
    ],
    "source": [
     "# instantiate and fit the grid\n",
-    "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n",
+    "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False)\n",
     "grid.fit(X, y)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.053333</td>\n",
+       "      <td>{'n_neighbors': 1, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.053333</td>\n",
+       "      <td>{'n_neighbors': 1, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.052068</td>\n",
+       "      <td>{'n_neighbors': 2, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.053333</td>\n",
+       "      <td>{'n_neighbors': 2, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 3, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 3, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 4, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 4, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 5, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 5, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 6, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 6, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 7, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 7, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 8, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 8, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 9, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 9, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 10, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 10, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 11, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 11, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 12, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'n_neighbors': 12, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 13, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 13, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'n_neighbors': 14, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 14, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 15, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 15, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 16, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 16, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 17, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 17, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 18, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 18, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 19, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 19, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 20, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 20, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>{'n_neighbors': 21, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 21, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>{'n_neighbors': 22, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 22, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 23, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 23, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'n_neighbors': 24, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 24, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>{'n_neighbors': 25, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 25, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'n_neighbors': 26, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 26, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'n_neighbors': 27, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'n_neighbors': 27, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.042687</td>\n",
+       "      <td>{'n_neighbors': 28, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 28, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.042687</td>\n",
+       "      <td>{'n_neighbors': 29, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'n_neighbors': 29, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.042687</td>\n",
+       "      <td>{'n_neighbors': 30, 'weights': 'uniform'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>{'n_neighbors': 30, 'weights': 'distance'}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "[mean: 0.96000, std: 0.05333, params: {'n_neighbors': 1, 'weights': 'uniform'},\n",
-       " mean: 0.96000, std: 0.05333, params: {'n_neighbors': 1, 'weights': 'distance'},\n",
-       " mean: 0.95333, std: 0.05207, params: {'n_neighbors': 2, 'weights': 'uniform'},\n",
-       " mean: 0.96000, std: 0.05333, params: {'n_neighbors': 2, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 3, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 3, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 4, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 4, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 5, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 5, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 6, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 6, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 7, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 7, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 8, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 8, 'weights': 'distance'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 9, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 9, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 10, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 10, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 11, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 11, 'weights': 'distance'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 12, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.04422, params: {'n_neighbors': 12, 'weights': 'distance'},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 13, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 13, 'weights': 'distance'},\n",
-       " mean: 0.97333, std: 0.04422, params: {'n_neighbors': 14, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 14, 'weights': 'distance'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 15, 'weights': 'uniform'},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 15, 'weights': 'distance'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 16, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 16, 'weights': 'distance'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 17, 'weights': 'uniform'},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 17, 'weights': 'distance'},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 18, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 18, 'weights': 'distance'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 19, 'weights': 'uniform'},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 19, 'weights': 'distance'},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 20, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 20, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 21, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 21, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 22, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 22, 'weights': 'distance'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 23, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 23, 'weights': 'distance'},\n",
-       " mean: 0.96000, std: 0.04422, params: {'n_neighbors': 24, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 24, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 25, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 25, 'weights': 'distance'},\n",
-       " mean: 0.96000, std: 0.04422, params: {'n_neighbors': 26, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 26, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 27, 'weights': 'uniform'},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 27, 'weights': 'distance'},\n",
-       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 28, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 28, 'weights': 'distance'},\n",
-       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 29, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 29, 'weights': 'distance'},\n",
-       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 30, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 30, 'weights': 'distance'}]"
+       "    mean_test_score  std_test_score  \\\n",
+       "0          0.960000        0.053333   \n",
+       "1          0.960000        0.053333   \n",
+       "2          0.953333        0.052068   \n",
+       "3          0.960000        0.053333   \n",
+       "4          0.966667        0.044721   \n",
+       "5          0.966667        0.044721   \n",
+       "6          0.966667        0.044721   \n",
+       "7          0.966667        0.044721   \n",
+       "8          0.966667        0.044721   \n",
+       "9          0.966667        0.044721   \n",
+       "10         0.966667        0.044721   \n",
+       "11         0.966667        0.044721   \n",
+       "12         0.966667        0.044721   \n",
+       "13         0.966667        0.044721   \n",
+       "14         0.966667        0.044721   \n",
+       "15         0.966667        0.044721   \n",
+       "16         0.973333        0.032660   \n",
+       "17         0.973333        0.032660   \n",
+       "18         0.966667        0.044721   \n",
+       "19         0.973333        0.032660   \n",
+       "20         0.966667        0.044721   \n",
+       "21         0.973333        0.032660   \n",
+       "22         0.973333        0.032660   \n",
+       "23         0.973333        0.044222   \n",
+       "24         0.980000        0.030551   \n",
+       "25         0.973333        0.032660   \n",
+       "26         0.973333        0.044222   \n",
+       "27         0.973333        0.032660   \n",
+       "28         0.973333        0.032660   \n",
+       "29         0.980000        0.030551   \n",
+       "30         0.973333        0.032660   \n",
+       "31         0.973333        0.032660   \n",
+       "32         0.973333        0.032660   \n",
+       "33         0.980000        0.030551   \n",
+       "34         0.980000        0.030551   \n",
+       "35         0.973333        0.032660   \n",
+       "36         0.973333        0.032660   \n",
+       "37         0.980000        0.030551   \n",
+       "38         0.980000        0.030551   \n",
+       "39         0.966667        0.044721   \n",
+       "40         0.966667        0.033333   \n",
+       "41         0.966667        0.044721   \n",
+       "42         0.966667        0.033333   \n",
+       "43         0.966667        0.044721   \n",
+       "44         0.973333        0.032660   \n",
+       "45         0.973333        0.032660   \n",
+       "46         0.960000        0.044222   \n",
+       "47         0.973333        0.032660   \n",
+       "48         0.966667        0.033333   \n",
+       "49         0.973333        0.032660   \n",
+       "50         0.960000        0.044222   \n",
+       "51         0.966667        0.044721   \n",
+       "52         0.966667        0.044721   \n",
+       "53         0.980000        0.030551   \n",
+       "54         0.953333        0.042687   \n",
+       "55         0.973333        0.032660   \n",
+       "56         0.953333        0.042687   \n",
+       "57         0.973333        0.032660   \n",
+       "58         0.953333        0.042687   \n",
+       "59         0.966667        0.033333   \n",
+       "\n",
+       "                                        params  \n",
+       "0     {'n_neighbors': 1, 'weights': 'uniform'}  \n",
+       "1    {'n_neighbors': 1, 'weights': 'distance'}  \n",
+       "2     {'n_neighbors': 2, 'weights': 'uniform'}  \n",
+       "3    {'n_neighbors': 2, 'weights': 'distance'}  \n",
+       "4     {'n_neighbors': 3, 'weights': 'uniform'}  \n",
+       "5    {'n_neighbors': 3, 'weights': 'distance'}  \n",
+       "6     {'n_neighbors': 4, 'weights': 'uniform'}  \n",
+       "7    {'n_neighbors': 4, 'weights': 'distance'}  \n",
+       "8     {'n_neighbors': 5, 'weights': 'uniform'}  \n",
+       "9    {'n_neighbors': 5, 'weights': 'distance'}  \n",
+       "10    {'n_neighbors': 6, 'weights': 'uniform'}  \n",
+       "11   {'n_neighbors': 6, 'weights': 'distance'}  \n",
+       "12    {'n_neighbors': 7, 'weights': 'uniform'}  \n",
+       "13   {'n_neighbors': 7, 'weights': 'distance'}  \n",
+       "14    {'n_neighbors': 8, 'weights': 'uniform'}  \n",
+       "15   {'n_neighbors': 8, 'weights': 'distance'}  \n",
+       "16    {'n_neighbors': 9, 'weights': 'uniform'}  \n",
+       "17   {'n_neighbors': 9, 'weights': 'distance'}  \n",
+       "18   {'n_neighbors': 10, 'weights': 'uniform'}  \n",
+       "19  {'n_neighbors': 10, 'weights': 'distance'}  \n",
+       "20   {'n_neighbors': 11, 'weights': 'uniform'}  \n",
+       "21  {'n_neighbors': 11, 'weights': 'distance'}  \n",
+       "22   {'n_neighbors': 12, 'weights': 'uniform'}  \n",
+       "23  {'n_neighbors': 12, 'weights': 'distance'}  \n",
+       "24   {'n_neighbors': 13, 'weights': 'uniform'}  \n",
+       "25  {'n_neighbors': 13, 'weights': 'distance'}  \n",
+       "26   {'n_neighbors': 14, 'weights': 'uniform'}  \n",
+       "27  {'n_neighbors': 14, 'weights': 'distance'}  \n",
+       "28   {'n_neighbors': 15, 'weights': 'uniform'}  \n",
+       "29  {'n_neighbors': 15, 'weights': 'distance'}  \n",
+       "30   {'n_neighbors': 16, 'weights': 'uniform'}  \n",
+       "31  {'n_neighbors': 16, 'weights': 'distance'}  \n",
+       "32   {'n_neighbors': 17, 'weights': 'uniform'}  \n",
+       "33  {'n_neighbors': 17, 'weights': 'distance'}  \n",
+       "34   {'n_neighbors': 18, 'weights': 'uniform'}  \n",
+       "35  {'n_neighbors': 18, 'weights': 'distance'}  \n",
+       "36   {'n_neighbors': 19, 'weights': 'uniform'}  \n",
+       "37  {'n_neighbors': 19, 'weights': 'distance'}  \n",
+       "38   {'n_neighbors': 20, 'weights': 'uniform'}  \n",
+       "39  {'n_neighbors': 20, 'weights': 'distance'}  \n",
+       "40   {'n_neighbors': 21, 'weights': 'uniform'}  \n",
+       "41  {'n_neighbors': 21, 'weights': 'distance'}  \n",
+       "42   {'n_neighbors': 22, 'weights': 'uniform'}  \n",
+       "43  {'n_neighbors': 22, 'weights': 'distance'}  \n",
+       "44   {'n_neighbors': 23, 'weights': 'uniform'}  \n",
+       "45  {'n_neighbors': 23, 'weights': 'distance'}  \n",
+       "46   {'n_neighbors': 24, 'weights': 'uniform'}  \n",
+       "47  {'n_neighbors': 24, 'weights': 'distance'}  \n",
+       "48   {'n_neighbors': 25, 'weights': 'uniform'}  \n",
+       "49  {'n_neighbors': 25, 'weights': 'distance'}  \n",
+       "50   {'n_neighbors': 26, 'weights': 'uniform'}  \n",
+       "51  {'n_neighbors': 26, 'weights': 'distance'}  \n",
+       "52   {'n_neighbors': 27, 'weights': 'uniform'}  \n",
+       "53  {'n_neighbors': 27, 'weights': 'distance'}  \n",
+       "54   {'n_neighbors': 28, 'weights': 'uniform'}  \n",
+       "55  {'n_neighbors': 28, 'weights': 'distance'}  \n",
+       "56   {'n_neighbors': 29, 'weights': 'uniform'}  \n",
+       "57  {'n_neighbors': 29, 'weights': 'distance'}  \n",
+       "58   {'n_neighbors': 30, 'weights': 'uniform'}  \n",
+       "59  {'n_neighbors': 30, 'weights': 'distance'}  "
       ]
      },
      "execution_count": 21,
@@ -629,16 +1258,14 @@
     }
    ],
    "source": [
-    "# view the complete results\n",
-    "grid.grid_scores_"
+    "# view the results\n",
+    "pd.DataFrame(grid.cv_results_)[['mean_test_score', 'std_test_score', 'params']]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -665,9 +1292,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -692,9 +1317,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -730,20 +1353,16 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.grid_search import RandomizedSearchCV"
+    "from sklearn.model_selection import RandomizedSearchCV"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# specify \"parameter distributions\" rather than a \"parameter grid\"\n",
@@ -760,23 +1379,111 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'weights': 'distance', 'n_neighbors': 16}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>{'weights': 'uniform', 'n_neighbors': 22}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.030551</td>\n",
+       "      <td>{'weights': 'uniform', 'n_neighbors': 18}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'weights': 'uniform', 'n_neighbors': 27}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.953333</td>\n",
+       "      <td>0.042687</td>\n",
+       "      <td>{'weights': 'uniform', 'n_neighbors': 29}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'weights': 'distance', 'n_neighbors': 10}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>{'weights': 'distance', 'n_neighbors': 22}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'weights': 'uniform', 'n_neighbors': 14}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.044222</td>\n",
+       "      <td>{'weights': 'distance', 'n_neighbors': 12}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>{'weights': 'uniform', 'n_neighbors': 15}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "[mean: 0.97333, std: 0.03266, params: {'n_neighbors': 18, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 8, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 24, 'weights': 'distance'},\n",
-       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 20, 'weights': 'uniform'},\n",
-       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 28, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 9, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 5, 'weights': 'distance'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 5, 'weights': 'uniform'},\n",
-       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 19, 'weights': 'uniform'},\n",
-       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 20, 'weights': 'distance'}]"
+       "   mean_test_score  std_test_score                                      params\n",
+       "0         0.973333        0.032660  {'weights': 'distance', 'n_neighbors': 16}\n",
+       "1         0.966667        0.033333   {'weights': 'uniform', 'n_neighbors': 22}\n",
+       "2         0.980000        0.030551   {'weights': 'uniform', 'n_neighbors': 18}\n",
+       "3         0.966667        0.044721   {'weights': 'uniform', 'n_neighbors': 27}\n",
+       "4         0.953333        0.042687   {'weights': 'uniform', 'n_neighbors': 29}\n",
+       "5         0.973333        0.032660  {'weights': 'distance', 'n_neighbors': 10}\n",
+       "6         0.966667        0.044721  {'weights': 'distance', 'n_neighbors': 22}\n",
+       "7         0.973333        0.044222   {'weights': 'uniform', 'n_neighbors': 14}\n",
+       "8         0.973333        0.044222  {'weights': 'distance', 'n_neighbors': 12}\n",
+       "9         0.973333        0.032660   {'weights': 'uniform', 'n_neighbors': 15}"
       ]
      },
      "execution_count": 27,
@@ -786,24 +1493,22 @@
    ],
    "source": [
     "# n_iter controls the number of searches\n",
-    "rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state=5)\n",
+    "rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state=5, return_train_score=False)\n",
     "rand.fit(X, y)\n",
-    "rand.grid_scores_"
+    "pd.DataFrame(rand.cv_results_)[['mean_test_score', 'std_test_score', 'params']]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "0.98\n",
-      "{'n_neighbors': 20, 'weights': 'uniform'}\n"
+      "{'weights': 'uniform', 'n_neighbors': 18}\n"
      ]
     }
    ],
@@ -816,15 +1521,13 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.98, 0.973, 0.98, 0.973, 0.973, 0.98, 0.98, 0.98, 0.973, 0.98, 0.98, 0.973, 0.98, 0.973, 0.973, 0.98, 0.98, 0.98, 0.98, 0.98]\n"
+      "[0.973, 0.98, 0.98, 0.98, 0.973, 0.98, 0.98, 0.973, 0.98, 0.973, 0.973, 0.98, 0.98, 0.98, 0.98, 0.973, 0.98, 0.98, 0.98, 0.973]\n"
      ]
     }
    ],
@@ -832,7 +1535,7 @@
     "# run RandomizedSearchCV 20 times (with n_iter=10) and record the best score\n",
     "best_scores = []\n",
     "for _ in range(20):\n",
-    "    rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10)\n",
+    "    rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10, return_train_score=False)\n",
     "    rand.fit(X, y)\n",
     "    best_scores.append(round(rand.best_score_, 3))\n",
     "print(best_scores)"
@@ -844,8 +1547,8 @@
    "source": [
     "## Resources\n",
     "\n",
-    "- scikit-learn documentation: [Grid search](http://scikit-learn.org/stable/modules/grid_search.html), [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html), [RandomizedSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.RandomizedSearchCV.html)\n",
-    "- Timed example: [Comparing randomized search and grid search](http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html)\n",
+    "- scikit-learn documentation: [Grid search](http://scikit-learn.org/stable/modules/grid_search.html), [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html), [RandomizedSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)\n",
+    "- Timed example: [Comparing randomized search and grid search](http://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html)\n",
     "- scikit-learn workshop by Andreas Mueller: [Video segment on randomized search](https://youtu.be/0wUF_Ov8b0A?t=17m38s) (3 minutes), [related notebook](https://github.com/amueller/pydata-nyc-advanced-sklearn/blob/master/Chapter%203%20-%20Randomized%20Hyper%20Parameter%20Search.ipynb)\n",
     "- Paper by Yoshua Bengio: [Random Search for Hyper-Parameter Optimization](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf)"
    ]
@@ -864,9 +1567,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -959,23 +1660,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/09_classification_metrics.ipynb b/09_classification_metrics.ipynb
index 41702a1..4d37c9e 100644
--- a/09_classification_metrics.ipynb
+++ b/09_classification_metrics.ipynb
@@ -4,9 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Evaluating a classification model\n",
+    "# Evaluating a classification model ([video #9](https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9))\n",
     "\n",
-    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
+    "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
+    "\n",
+    "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
    ]
   },
   {
@@ -69,35 +71,44 @@
    "source": [
     "## Classification accuracy\n",
     "\n",
-    "[Pima Indian Diabetes dataset](https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes) from the UCI Machine Learning Repository"
+    "[Pima Indians Diabetes dataset](https://www.kaggle.com/uciml/pima-indians-diabetes-database) originally from the UCI Machine Learning Repository"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# read the data into a Pandas DataFrame\n",
+    "# read the data into a pandas DataFrame\n",
     "import pandas as pd\n",
-    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data'\n",
+    "path = 'data/pima-indians-diabetes.data'\n",
     "col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']\n",
-    "pima = pd.read_csv(url, header=None, names=col_names)"
+    "pima = pd.read_csv(path, header=None, names=col_names)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -207,9 +218,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# define X and y\n",
@@ -221,22 +230,18 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# split X and y into training and testing sets\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -262,9 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# make class predictions for the testing set\n",
@@ -281,15 +284,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.692708333333\n"
+      "0.6927083333333334\n"
      ]
     }
    ],
@@ -309,9 +310,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -334,9 +333,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -357,9 +354,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -380,9 +375,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -403,9 +396,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -434,9 +425,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -449,7 +438,6 @@
    ],
    "source": [
     "# print the first 25 true and predicted responses\n",
-    "from __future__ import print_function\n",
     "print('True:', y_test.values[0:25])\n",
     "print('Pred:', y_pred_class[0:25])"
    ]
@@ -477,9 +465,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -526,9 +512,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -548,9 +532,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# save confusion matrix and slice into four pieces\n",
@@ -585,16 +567,14 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.692708333333\n",
-      "0.692708333333\n"
+      "0.6927083333333334\n",
+      "0.6927083333333334\n"
      ]
     }
    ],
@@ -615,16 +595,14 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.307291666667\n",
-      "0.307291666667\n"
+      "0.3072916666666667\n",
+      "0.30729166666666663\n"
      ]
     }
    ],
@@ -646,16 +624,14 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.241935483871\n",
-      "0.241935483871\n"
+      "0.24193548387096775\n",
+      "0.24193548387096775\n"
      ]
     }
    ],
@@ -676,15 +652,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.907692307692\n"
+      "0.9076923076923077\n"
      ]
     }
    ],
@@ -702,15 +676,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0923076923077\n"
+      "0.09230769230769231\n"
      ]
     }
    ],
@@ -730,16 +702,14 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.555555555556\n",
-      "0.555555555556\n"
+      "0.5555555555555556\n",
+      "0.5555555555555556\n"
      ]
     }
    ],
@@ -781,14 +751,12 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1], dtype=int64)"
+       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1])"
       ]
      },
      "execution_count": 24,
@@ -804,23 +772,21 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.63247571,  0.36752429],\n",
-       "       [ 0.71643656,  0.28356344],\n",
-       "       [ 0.71104114,  0.28895886],\n",
-       "       [ 0.5858938 ,  0.4141062 ],\n",
-       "       [ 0.84103973,  0.15896027],\n",
-       "       [ 0.82934844,  0.17065156],\n",
-       "       [ 0.50110974,  0.49889026],\n",
-       "       [ 0.48658459,  0.51341541],\n",
-       "       [ 0.72321388,  0.27678612],\n",
-       "       [ 0.32810562,  0.67189438]])"
+       "array([[0.63247571, 0.36752429],\n",
+       "       [0.71643656, 0.28356344],\n",
+       "       [0.71104114, 0.28895886],\n",
+       "       [0.5858938 , 0.4141062 ],\n",
+       "       [0.84103973, 0.15896027],\n",
+       "       [0.82934844, 0.17065156],\n",
+       "       [0.50110974, 0.49889026],\n",
+       "       [0.48658459, 0.51341541],\n",
+       "       [0.72321388, 0.27678612],\n",
+       "       [0.32810562, 0.67189438]])"
       ]
      },
      "execution_count": 25,
@@ -836,15 +802,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.36752429,  0.28356344,  0.28895886,  0.4141062 ,  0.15896027,\n",
-       "        0.17065156,  0.49889026,  0.51341541,  0.27678612,  0.67189438])"
+       "array([0.36752429, 0.28356344, 0.28895886, 0.4141062 , 0.15896027,\n",
+       "       0.17065156, 0.49889026, 0.51341541, 0.27678612, 0.67189438])"
       ]
      },
      "execution_count": 26,
@@ -860,9 +824,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# store the predicted probabilities for class 1\n",
@@ -872,28 +834,23 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# allow plots to appear in the notebook\n",
     "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.rcParams['font.size'] = 14"
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0xab7bda0>"
+       "Text(0,0.5,'Frequency')"
       ]
      },
      "execution_count": 29,
@@ -902,9 +859,9 @@
     },
     {
      "data": {
-      "image/png": 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QlJfLwmXhshj5NVYGeiUi6Suk31zcRbrC+BzwLBb84Od44FBJN5Fu+nYYqQ/9mYOM08zM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\nKnMSMTOzypxEzMysMicRMzOr7P8DZygQ6cD29GEAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xab553c8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -930,9 +887,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# predict diabetes if the predicted probability is greater than 0.3\n",
@@ -943,15 +898,13 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.36752429,  0.28356344,  0.28895886,  0.4141062 ,  0.15896027,\n",
-       "        0.17065156,  0.49889026,  0.51341541,  0.27678612,  0.67189438])"
+       "array([0.36752429, 0.28356344, 0.28895886, 0.4141062 , 0.15896027,\n",
+       "       0.17065156, 0.49889026, 0.51341541, 0.27678612, 0.67189438])"
       ]
      },
      "execution_count": 31,
@@ -967,14 +920,12 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  1.,  0.,  1.])"
+       "array([1., 0., 0., 1., 0., 0., 1., 1., 0., 1.])"
       ]
      },
      "execution_count": 32,
@@ -990,9 +941,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1011,9 +960,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1032,15 +979,13 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.741935483871\n"
+      "0.7419354838709677\n"
      ]
     }
    ],
@@ -1052,15 +997,13 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.615384615385\n"
+      "0.6153846153846154\n"
      ]
     }
    ],
@@ -1094,15 +1037,13 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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EjsF1keC6SHBd5E8l99dzhCnXjyQsO/uxhWQkrUlYJ/pWSceY2SXNEdNxnPmVgQOD66nd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\nc8hgVMysYxbpcveX4zhO7bR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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xadf99b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1132,9 +1073,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# define a function that accepts a threshold and prints sensitivity and specificity\n",
@@ -1146,16 +1085,14 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sensitivity: 0.241935483871\n",
-      "Specificity: 0.907692307692\n"
+      "Sensitivity: 0.24193548387096775\n",
+      "Specificity: 0.9076923076923077\n"
      ]
     }
    ],
@@ -1166,16 +1103,14 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sensitivity: 0.725806451613\n",
-      "Specificity: 0.615384615385\n"
+      "Sensitivity: 0.7258064516129032\n",
+      "Specificity: 0.6153846153846154\n"
      ]
     }
    ],
@@ -1193,15 +1128,13 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.724565756824\n"
+      "0.7245657568238213\n"
      ]
     }
    ],
@@ -1222,14 +1155,12 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.73782336182336183"
+       "0.7378233618233618"
       ]
      },
      "execution_count": 42,
@@ -1239,7 +1170,7 @@
    ],
    "source": [
     "# calculate cross-validated AUC\n",
-    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.model_selection import cross_val_score\n",
     "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
    ]
   },
@@ -1272,7 +1203,6 @@
     "\n",
     "## ROC and AUC Resources\n",
     "\n",
-    "- Lesson notes: [ROC Curves](http://ebp.uga.edu/courses/Chapter%204%20-%20Diagnosis%20I/8%20-%20ROC%20curves.html) (from the University of Georgia)\n",
     "- Video: [ROC Curves and Area Under the Curve](https://www.youtube.com/watch?v=OAl6eAyP-yo) (14 minutes) by me, including [transcript and screenshots](http://www.dataschool.io/roc-curves-and-auc-explained/) and a [visualization](http://www.navan.name/roc/)\n",
     "- Video: [ROC Curves](https://www.youtube.com/watch?v=21Igj5Pr6u4) (12 minutes) by Rahul Patwari\n",
     "- Paper: [An introduction to ROC analysis](http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf) by Tom Fawcett\n",
@@ -1299,9 +1229,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1394,23 +1322,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }