diff --git a/.ipynb_checkpoints/Chapter_1-checkpoint.ipynb b/.ipynb_checkpoints/Chapter_1-checkpoint.ipynb
new file mode 100644
index 00000000..286dcb3d
--- /dev/null
+++ b/.ipynb_checkpoints/Chapter_1-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/.ipynb_checkpoints/Chapter_2-checkpoint.ipynb b/.ipynb_checkpoints/Chapter_2-checkpoint.ipynb
new file mode 100644
index 00000000..4fac1c04
--- /dev/null
+++ b/.ipynb_checkpoints/Chapter_2-checkpoint.ipynb
@@ -0,0 +1,353 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Classifying with Real-World Examples\n",
+    "\n",
+    "##Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "from sklearn.datasets import load_iris\n",
+    "from itertools import combinations\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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f5jbn9vb2La8zmQyZTKaOEYqINL9sNks2m635PLFPiWFmfYDbgcXuPqfI9p8B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\nJM//AiNA3E6zq7ItAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106d5f3d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Y5O43lC3viCN8EZF20pbj8Ks2roAvIlKzegN+V43SERGRwSngi4jkhAK+iEhO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\nIjmhgC8ikhP/H0KIhKyNOtAgAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x108abdbd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/cXc3M+/iNCe4+0tm9l7gXjN7yt1X1TtWERGJxty7+r2O8aJmTxG0VbxsZgcC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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107054810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107757990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RVNIY4e5b4tdbgBFl2jlwn5mtNbMvNCc0EREpp2HXNMzsXmCPEm/NLlxxdzcz\nL9PNke7+gpntDtxrZk+4+8p6xyoiImHMvdz3dQN3avYE0bWKF81sT2C5u3+wwu9cBrzm7leVeK/5\nH0JEJOPcverh/0Sqp4AlwNnAFfHPO4obmNlgoJ+7/83MhgBTgG+W6qyWDy4iItVL6kxjGPCfwN4U\nlNya2UjgWnc/3sz2AW6Lf6U/8FN3v7zpwYqISKdEkoaIiGRTZu4IN7PrzWyLmW0s837OzF41s/Xx\ncmmzYyzHzEab2XIze9TMfm9m55dpt9DM/mBmG8ysvdlxlhMSf8qP/yAzW21mD5vZY2ZW8ow1xce/\nYvxpPv4AZtYvjuvOMu+n8th36Cn+DBz7Z8zskTi235VpE3783T0TCzAJaAc2lnk/ByxJOs4yse0B\nHBy/3gV4EvhQUZtpwN3x68OBB5OOu8r4U3v84/gGxz/7Aw8CR2Xl+AfGn/bjPwv4aakY037sA+JP\n+7F/GhjWw/tVHf/MnGl4VGr7lwrNUnlB3N1fdPeH49evAY8DI4uadd7w6O6rgaFmVu7+laYKjB9S\nevwB3H1b/HIg0A/YWtQktccfguKHlB5/M9uL6Ivph5SOMdXHPiB+etieFj3FV9Xxz0zSCODAEfHp\n1d1mdkDSAZViZmOIzphWF701CthUsP4csFdzogrXQ/ypPv5mtpOZPUx0M+lyd3+sqEmqj39A/Gk+\n/vOBfwbeKfN+qo89leNP87GHyjdJV3X8+1LSWAeMdvePANdQoow3aWa2C/Bz4KvxX+w7NClaT1WV\nQoX4U3383f0ddz+Y6B/Dx8wsV6JZao9/QPypPP5mdgLwkruvp+e/dlN57APjT+WxL3Cku7cDU4GZ\nZjapRJvg499nkoa7/63jFN7dfwUMiEt7U8HMBgC3Aj9x91L/U20GRhes7xVvS4VK8af9+Hdw91eB\nXwITit5K9fHvUC7+FB//I4ATzexp4CbgE2Z2Y1GbNB/7ivGn+NgD4O4vxD//H3A7UDz5a1XHv88k\nDTMbYWYWvz6MqJy41Lhv08VxXQc85u4LyjRbAnwubj8ReMW75udKVEj8KT/+w81saPy6DZgMrC9q\nlubjXzH+tB5/d7/E3Ue7+/uB04Ffu/vnipql9tiHxJ/WYw/RTdJmtmv8uuMm6eIK1KqOf1J3hFfN\nzG4CjgaGm9km4DJgAIC7LwZOBb5kZm8D24j+A6fFkcA/AI+YWcc/9kuIbm7E3Re7+91mNs3MngJe\nBz6fTKheFuLBAAAEM0lEQVQlVYyfdB//PYEbzGwnoj+Ufuzu95vZuZCJ418xftJ9/As5QIaOfbEd\n4ifdx34EcHuc0zpukr6nN8dfN/eJiEiwPjM8JSIijaekISIiwZQ0REQkmJKGiIgEU9IQEZFgShoi\nIhJMSUOkjHjK65JTeVf4vZFmdkuZ9/Jmdkj8+pKC7WOszLT/Jfo4z8zOqTauEv2cb2Zn9bYfaS1K\nGiJ15u7Pu/uMcm8XvL642r7jO4//EfhJLbEV+RHwlTr0Iy1ESUMyy8yGmNkvLXo40UYzOy3ePj7+\ni36tmS01sz3i7XkzW2DRw2g2mtmh8fbDzOwBM1tnZr81s/0r7PcuMxsXv15vZv8rfv2vZvZP8VnD\n7+NtbWZ2s0UPT7oNaIs2278BbfHv/5gomfQzsx9Y9KCrZWY2qMTujwSecPe34/73NbP74mPwkJnt\nE58h/cbM7jCz/zKzfzOzs8zsdxY9jGcfiOZMAl42sw/39r+FtA4lDcmy44DN7n6wu48Dllo0seI1\nwCnuPoHor+l5cXsH2uIZP78MXB9vfxyY5O6HEE1P878r7HclMMnM3gVsJ5rUDuAo4DcF+wL4EvCa\nux8Q9z0ecHf/BvCGu7e7+1lEs4zuByxy9wOBV4BTSuz7KGBtwfpPgWviGXA/CrwQbz8IOBf4EHAW\n8AF3P4zomRCFZxe/Az5W4fOKdMrM3FMiJTwCXBn/1X6Xu68yswOBDxM9PwCiBxY9X/A7N0H0UC8z\ne1f8xf9u4EYz25foy35Ahf2uBM4neiLaL4Fj4okE3+/uf7DomSMdJgFXx/vcaGaP9NDv0+7e8f5D\nwJgSbfYGVgHEE9GNdPdfxP2/FW8HWNMx6Vw8p9Cy+Pd/D3y8oL/ngX0qfF6RTkoaklnxF3Q7cDww\n18zuJ5r6+VF3P6Ln3+7mW8D97n6ymb0PyFdov4ZoavI/AvcCw4Ev0v0MoFDoU93+u+D134mGsmrt\nr7CvdwrW36H7v3sjJc+ukGzQ8JRklpntCbzp7j8FriR6ouCTwO7xFM+Y2QDr/iS1z8TbjyKaAvqv\nwLvoOhupOMOqu28nerrZDOABojOPrwErSjRfAXw23ueBRMNGHbabWbV/uP2J6JntHdcknjOzT8f9\n7xyf8VRjT+CZKn9HWpiShmTZOGB1PF37vwBz4y/0U4ErLHo86nqisf4Ob5rZOuC7RFVIAP8HuDze\n3o/uf3mX+yt8BbDF3f+baLhoJFHyKP697wG7mNljwDfpfjbyA6Lp5jsuhBfvq9S+V9H9AUxnAeeb\n2Yb4vT3K9FXYZ+F7hxXFLdIjTY0uLcPMlgMXufu6pGOpVVxyuw44vOMaRi/6ehfRsNyhdQlOWoLO\nNEQyxKO/8q4FzqxDd+cQX6QXCaUzDRERCaYzDRERCaakISIiwZQ0REQkmJKGiIgEU9IQEZFgShoi\nIhLs/wPxtGDeaq2rtwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1071ba610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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DFBFpv5JMEn2AdXnbr4X7SrXZv8pxiYhIKMn1JOL2DxVeDhV936RJk3Y+z2Qy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\n/j8Gir0p2+OyNQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1071d9590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = load_iris()\n",
+    "\n",
+    "features = data.data\n",
+    "feature_names = data.feature_names\n",
+    "target = data.target\n",
+    "target_names = data.target_names\n",
+    "\n",
+    "def get_combos(li, r):\n",
+    "    return [c for c in combinations(li, r)]\n",
+    "\n",
+    "def plot_features(x, y):\n",
+    "    for t in range(3):\n",
+    "        if t==0:\n",
+    "            c = 'r'\n",
+    "            marker = '>'\n",
+    "        elif t==1:\n",
+    "            c = 'g'\n",
+    "            marker = 'o'\n",
+    "        elif t==2:\n",
+    "            c = 'b'\n",
+    "            marker = 'x'\n",
+    "        plt.scatter(features[target==t,x],\n",
+    "                    features[target==t,y],\n",
+    "                    marker=marker,\n",
+    "                    c=c)\n",
+    "        plt.gca().set_xlabel(feature_names[x])\n",
+    "        plt.gca().set_ylabel(feature_names[y])\n",
+    "\n",
+    "for x,y in get_combos(range(4), 2):\n",
+    "    plt.figure()\n",
+    "    plot_features(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "petal length allows us to separate out iris setosa."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum of setosa: 1.9.\n",
+      "Minimum of others: 3.0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = target_names[target] #array of strings\n",
+    "plength = features[:, 2]\n",
+    "is_setosa = (labels=='setosa')\n",
+    "\n",
+    "#important step:\n",
+    "max_setosa = plength[is_setosa].max()\n",
+    "min_non_setosa = plength[~is_setosa].min()\n",
+    "print('Maximum of setosa: {0}.'.format(max_setosa))\n",
+    "print('Minimum of others: {0}.'.format(min_non_setosa))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Therefore, if petal length is smaller than 2, we can conclude this is an Iris Setosa.\n",
+    "\n",
+    "We can only look for \"best possible\" separation between other species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features =  features[~is_setosa]\n",
+    "labels = labels[~is_setosa]\n",
+    "is_virginica = (labels == 'virginica')\n",
+    "is_virginica[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "best_acc = -1.0\n",
+    "for fi in range(features.shape[1]):\n",
+    "    # We are going to test all possible thresholds\n",
+    "    thresh = features[:, fi]\n",
+    "    for t in thresh:\n",
+    "        feature_i =  features[:, fi]\n",
+    "        pred = (feature_i > t) #viginica is greater\n",
+    "        acc = (pred == is_virginica).mean()\n",
+    "        rev_acc = (pred == ~is_virginica).mean()\n",
+    "        if rev_acc > acc:\n",
+    "            reverse = True\n",
+    "            acc = rev_acc\n",
+    "        else:\n",
+    "            reverse = False\n",
+    "        \n",
+    "        if acc > best_acc:\n",
+    "            best_acc = acc\n",
+    "            best_fi = fi\n",
+    "            best_t = t\n",
+    "            best_reverse = reverse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def is_virginica(fi, t, reverse, example):\n",
+    "    \"Apply threshold model to a new example\"\n",
+    "    test = example[fi] > t\n",
+    "    if reverse:\n",
+    "        test = not test\n",
+    "    return test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AXfWmRCQZilmqY39gBnAS0WTex8xsWbb6hBQrRCWHo4+OAtTOnfD009G2nTuj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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10710cb50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Evaluation\n",
+    "\n",
+    "* separate into training and testing sets\n",
+    "* testing set usually has lower accuracy than training.\n",
+    "\n",
+    "###cross validation\n",
+    "Ideally we'd like to use all of the data for training and testing, but we can't.  Leave out a subset of the training set.  Train on the rest of the set and test on this subset.  Repeat for all elements in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "correct = 0.0\n",
+    "for ei in range(len(features)):\n",
+    "    # select all but the one at 'ei'\n",
+    "    training = np.ones(len(features), bool)\n",
+    "    training[ei] = False\n",
+    "    testing = ~training\n",
+    "    model =  fit_model(features[training], is_virginica[training])\n",
+    "    predictions = predict(model, features[testing])\n",
+    "    correct += np.sum(predictions == is_virginica[testing])\n",
+    "acc = correct/float(len(features))\n",
+    "print('Accuracy: {0:.1%}'.format(acc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is computationally expensive though.  **x-fold cross validation** attempts to solve this.  Instead of leaving one out, leave out say, 20%, in a 5 fold cross validation.\n",
+    "\n",
+    "With less data, use up to 10 or 20 folds.\n",
+    "\n",
+    "Never report your training accuracy.  You will look like a noob\n",
+    "\n",
+    "##More Complex Classifiers\n",
+    "What makes up a classification model?\n",
+    "* **The Structure of the Model** - how does it make decisions?\n",
+    "* **The search procedure** - How do we find the model we need to use?\n",
+    "* **gain or loss function** - Which possibilities tested should be returned?  Optimize so that model makes fewer erros of a specific kind.\n",
+    "\n",
+    "**feature engineering** - derive new combined features.  Should vary with what matters but be invariant with what does not.\n",
+    "\n",
+    "**feature selection** is choosing good features automatically.\n",
+    "\n",
+    "###Classifying with scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.3.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/.ipynb_checkpoints/chapter_3_clustering-checkpoint.ipynb b/.ipynb_checkpoints/chapter_3_clustering-checkpoint.ipynb
new file mode 100644
index 00000000..286dcb3d
--- /dev/null
+++ b/.ipynb_checkpoints/chapter_3_clustering-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Chapter_1.ipynb b/Chapter_1.ipynb
new file mode 100644
index 00000000..f06c597d
--- /dev/null
+++ b/Chapter_1.ipynb
@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Numpy and Scipy\n",
+    "* always try to move loops into np.... functions\n",
+    "* tilde (~) unary operator as in c[~np.isnan(c)] will give you all the values in c that are not nan\n",
+    "\n",
+    "##First ML Application"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  1.00000000e+00   2.27200000e+03]\n",
+      " [  2.00000000e+00              nan]\n",
+      " [  3.00000000e+00   1.38600000e+03]\n",
+      " [  4.00000000e+00   1.36500000e+03]\n",
+      " [  5.00000000e+00   1.48800000e+03]\n",
+      " [  6.00000000e+00   1.33700000e+03]\n",
+      " [  7.00000000e+00   1.88300000e+03]\n",
+      " [  8.00000000e+00   2.28300000e+03]\n",
+      " [  9.00000000e+00   1.33500000e+03]\n",
+      " [  1.00000000e+01   1.02500000e+03]]\n",
+      "(743, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "url = 'https://raw.githubusercontent.com/goodwordalchemy/BuildingMachineLearningSystemsWithPython/master/ch01/data/web_traffic.tsv'\n",
+    "import requests\n",
+    "from io import BytesIO\n",
+    "import scipy as sp\n",
+    "\n",
+    "r=requests.get(url)\n",
+    "data = sp.genfromtxt(BytesIO(r.content), delimiter='\\t')\n",
+    "print(data[:10])\n",
+    "print (data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = data[:,0]\n",
+    "y = data[:,1]\n",
+    "sp.sum(sp.isnan(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = x[~sp.isnan(y)]\n",
+    "y = y[~sp.isnan(y)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eeeaZho6bzOzLMhkcHNTBwcHU+n1ZJlk0QyaDg4NarU5XeFrhaa1Wp6fkv379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\nvzImw2SyvrzzDQwMxHwNixa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QEEFFnKwsk2HLPnNOPJotiP7asWOH51781/D7IoorwzIO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\nry8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1064728d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "def plot_web_traffic():\n",
+    "    plt.scatter(x,y,s=10)\n",
+    "    plt.title(\"Web traffic over the last month\")\n",
+    "    plt.xlabel(\"Time\")\n",
+    "    plt.ylabel(\"Hits/hour\")\n",
+    "\n",
+    "    #the following sets the locations and names in the x-axis\n",
+    "    plt.xticks([w*7*24 for w in range(10)],\n",
+    "               ['week %i' % w for w in range(10)])\n",
+    "    plt.autoscale(tight=True)\n",
+    "    plt.grid(True, linestyle='-', color='0.75')\n",
+    "plot_web_traffic()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model parameters: [   2.59619213  989.02487106]\n",
+      "[  3.17389767e+08]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#for a learned model function f:\n",
+    "def error(f, x, y):\n",
+    "    return sp.sum((f(x)-y)**2)\n",
+    "\n",
+    "fp1, residuals, rank, sv, rcond = sp.polyfit(x,y,1,full=True)\n",
+    "print('Model parameters: %s' %fp1)\n",
+    "print(residuals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "317389767.34\n"
+     ]
+    }
+   ],
+   "source": [
+    "f1 = sp.poly1d(fp1)\n",
+    "print(error(f1, x, y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WsG1ffLYik/WJyJ+AJcDdqrqsrqWqEEvWV102N5lks1kGDx6el1Bt48a1PRXO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\n2d65Sl2zf7vcM5SLjHN+pwpSgETR1yrDetDXZGI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HRx+LtEiOIKTFCECSI0k+bO3nk7yV\n5NMk3yd5JMmxTBPlnGwVQP+zTLQ0iglCWq47meYOeRh4h6nQ271M5RseamXA/61MtDTMrq/mKq2w\nAk5W1e9JvgT2VNVHbd0XwAHgIJcvEy0NZYKQlus3+Ku09fbM8otMv3/h8mWipaG8xCQtz04mf/kG\n2JfkMEwzzyU5tNzDknbGBCEtRs0899rwzzLOtZMy0dIo/purJKnLEYQkqcsEIUnqMkFIkrpMEJKk\nLhOEJKnLBCFJ6jJBSJK6TBCSpK4/AGPyJNyGsiBtAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1064eb390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fx = sp.linspace(0, x[-1], 1000)\n",
+    "plot_web_traffic()\n",
+    "plt.plot(fx, f1(fx), linewidth=4)\n",
+    "plt.legend(['d=%i'%f1.order], loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "degree: 1 317389767.34\n",
+      "degree: 2 179983507.878\n",
+      "degree: 3 139350144.032\n",
+      "degree: 10 121942326.363\n",
+      "degree: 53 109452403.681\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/davidgoldberg/anaconda/envs/py3k/lib/python3.3/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
+      "  warnings.warn(msg, RankWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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hsW8f40jCEVpUbcEbbd4olH604nCDet076QbktbwvvuRcOnbUqFE89dRTLF26lD59+jBm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\nbNiwYcNGUvh/SCAeFHcD1swAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10654a310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#fitting higher degrees:\n",
+    "def plot_higher_degrees():\n",
+    "    degrees = [1,2,3,10,53]\n",
+    "    fx = sp.linspace(0, x[-1]+24*7*2, 1000)\n",
+    "    plot_web_traffic()\n",
+    "    for d in degrees:\n",
+    "        fp = sp.polyfit(x,y,d)\n",
+    "        f = sp.poly1d(fp)\n",
+    "        print('degree:',d,error(f, x, y))\n",
+    "        \n",
+    "        plt.plot(fx, f(fx), linewidth=2, label='d=%d'%f.order)\n",
+    "    plt.ylim(0,10000)\n",
+    "    plt.legend(loc='upper left')\n",
+    "plot_higher_degrees()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error inflection=132950348.197616\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AZgDLH1vZbW+qeiFWQKjqm6p6i6oeg+Peerv7e7+I/E1Ezip0YlVtBQ5W1d2B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\nOworFZZrJrO9rl4dH8bDr9IR6Rc5yVM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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106eb8bd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#separating data at inflection point ~3.5 weeks\n",
+    "inflection = 3.5*7*24\n",
+    "xa = x[:inflection]\n",
+    "ya = y[:inflection]\n",
+    "xb = x[inflection:]\n",
+    "yb = y[inflection:]\n",
+    "\n",
+    "fa = sp.poly1d(sp.polyfit(xa, ya, 1))\n",
+    "fb = sp.poly1d(sp.polyfit(xb, yb, 1))\n",
+    "\n",
+    "fa_error = error(fa, xa, ya)\n",
+    "fb_error = error(fb, xb, yb)\n",
+    "print(\"Error inflection=%f\" % (fa_error + fb_error))\n",
+    "\n",
+    "plot_web_traffic()\n",
+    "fxa = sp.linspace(xa[0], xa[-1], 500)\n",
+    "fxb = sp.linspace(xb[0], xb[-1], 500)\n",
+    "plt.plot(fxa, fa(fxa), linewidth=5)\n",
+    "plt.plot(fxb, fb(fxb), linewidth=5)\n",
+    "plt.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* underfit data might capture future data better\n",
+    "\n",
+    "###Training and Testing\n",
+    "We can't look into the future, but we can remove a certain percentage of the data and train on the remaining part.  Use the held-out set to calculate the rror\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error d=1: 1.8e+07\n",
+      "Error d=2: 1.7e+07\n",
+      "Error d=3: 1.8e+07\n",
+      "Error d=10: 2.8e+07\n",
+      "Error d=53: 1.3e+09\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/davidgoldberg/anaconda/envs/py3k/lib/python3.3/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
+      "  warnings.warn(msg, RankWarning)\n",
+      "/Users/davidgoldberg/anaconda/envs/py3k/lib/python3.3/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
+      "  warnings.warn(msg, RankWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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1wwNBKY4oxIzSsd988w1r167F6XRSUlLCfffdR8+ePenmR9bRaKBrujZOf8nO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\nUoIhDgMDAwODlPC/fz10lUg5XCIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106606a10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frac = 0.3\n",
+    "split_idx = int(frac * len(xb))\n",
+    "shuffled = sp.random.permutation(list(range(len(xb))))\n",
+    "test = sorted(shuffled[split_idx:])\n",
+    "train = sorted(shuffled[:split_idx])\n",
+    "plot_web_traffic()\n",
+    "fx = sp.linspace(0, x[-1]+24*7*2, 1000)\n",
+    "for d in degrees:\n",
+    "    f = sp.poly1d(sp.polyfit(xb[train], yb[train],d))\n",
+    "    print(\"Error d=%d: %0.2g\"%(d,error(f, xb[test], yb[test])))\n",
+    "    plt.plot(fx, f(fx), linewidth=2, label=\"d=%d\"%d)\n",
+    "plt.ylim(0,10000)\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        2\n",
+      "0.0988 x - 113.4 x - 6.537e+04\n",
+      "100000 hits/hour expected at week 9.342779\n"
+     ]
+    }
+   ],
+   "source": [
+    "fbt2 = sp.poly1d(sp.polyfit(xb[train], yb[train], 2))\n",
+    "print(fbt2-100000)\n",
+    "from scipy.optimize import fsolve\n",
+    "reached_max = fsolve(fbt2-100000, x0=800)/(7*24)\n",
+    "print(\"100000 hits/hour expected at week %f\"%reached_max[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Conclusions:\n",
+    "* understand your data\n",
+    "* correct experiment setup - training and testing"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.3.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Chapter_2.ipynb b/Chapter_2.ipynb
new file mode 100644
index 00000000..f0236761
--- /dev/null
+++ b/Chapter_2.ipynb
@@ -0,0 +1,419 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Classifying with Real-World Examples\n",
+    "\n",
+    "##Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "from sklearn.datasets import load_iris\n",
+    "from itertools import combinations\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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f5jbn9vb2La8zmQyZTKaOEYqINL9sNks2m635PLFPiWFmfYDbgcXuPqfI9p8B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\nJM//AiNA3E6zq7ItAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106d5f3d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Y5O43lC3viCN8EZF20pbj8Ks2roAvIlKzegN+V43SERGRwSngi4jkhAK+iEhO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\nIjmhgC8ikhP/H0KIhKyNOtAgAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x108abdbd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/cXc3M+/iNCe4+0tm9l7gXjN7yt1X1TtWERGJxty7+r2O8aJmTxG0VbxsZgcC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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107054810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107757990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RVNIY4e5b4tdbgBFl2jlwn5mtNbMvNCc0EREpp2HXNMzsXmCPEm/NLlxxdzcz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\nIhLs/wPxtGDeaq2rtwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1071ba610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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DFBFpv5JMEn2AdXnbr4X7SrXZv8pxiYhIKMn1JOL2DxVeDhV936RJk3Y+z2Qy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\n/j8Gir0p2+OyNQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1071d9590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = load_iris()\n",
+    "\n",
+    "features = data.data\n",
+    "feature_names = data.feature_names\n",
+    "target = data.target\n",
+    "target_names = data.target_names\n",
+    "\n",
+    "def get_combos(li, r):\n",
+    "    return [c for c in combinations(li, r)]\n",
+    "\n",
+    "def plot_features(x, y):\n",
+    "    for t in range(3):\n",
+    "        if t==0:\n",
+    "            c = 'r'\n",
+    "            marker = '>'\n",
+    "        elif t==1:\n",
+    "            c = 'g'\n",
+    "            marker = 'o'\n",
+    "        elif t==2:\n",
+    "            c = 'b'\n",
+    "            marker = 'x'\n",
+    "        plt.scatter(features[target==t,x],\n",
+    "                    features[target==t,y],\n",
+    "                    marker=marker,\n",
+    "                    c=c)\n",
+    "        plt.gca().set_xlabel(feature_names[x])\n",
+    "        plt.gca().set_ylabel(feature_names[y])\n",
+    "\n",
+    "for x,y in get_combos(range(4), 2):\n",
+    "    plt.figure()\n",
+    "    plot_features(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "petal length allows us to separate out iris setosa."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum of setosa: 1.9.\n",
+      "Minimum of others: 3.0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = target_names[target] #array of strings\n",
+    "plength = features[:, 2]\n",
+    "is_setosa = (labels=='setosa')\n",
+    "\n",
+    "#important step:\n",
+    "max_setosa = plength[is_setosa].max()\n",
+    "min_non_setosa = plength[~is_setosa].min()\n",
+    "print('Maximum of setosa: {0}.'.format(max_setosa))\n",
+    "print('Minimum of others: {0}.'.format(min_non_setosa))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Therefore, if petal length is smaller than 2, we can conclude this is an Iris Setosa.\n",
+    "\n",
+    "We can only look for \"best possible\" separation between other species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features =  features[~is_setosa]\n",
+    "labels = labels[~is_setosa]\n",
+    "is_virginica = (labels == 'virginica')\n",
+    "is_virginica[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "best_acc = -1.0\n",
+    "for fi in range(features.shape[1]):\n",
+    "    # We are going to test all possible thresholds\n",
+    "    thresh = features[:, fi]\n",
+    "    for t in thresh:\n",
+    "        feature_i =  features[:, fi]\n",
+    "        pred = (feature_i > t) #viginica is greater\n",
+    "        acc = (pred == is_virginica).mean()\n",
+    "        rev_acc = (pred == ~is_virginica).mean()\n",
+    "        if rev_acc > acc:\n",
+    "            reverse = True\n",
+    "            acc = rev_acc\n",
+    "        else:\n",
+    "            reverse = False\n",
+    "        \n",
+    "        if acc > best_acc:\n",
+    "            best_acc = acc\n",
+    "            best_fi = fi\n",
+    "            best_t = t\n",
+    "            best_reverse = reverse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def is_virginica(fi, t, reverse, example):\n",
+    "    \"Apply threshold model to a new example\"\n",
+    "    test = example[fi] > t\n",
+    "    if reverse:\n",
+    "        test = not test\n",
+    "    return test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AXfWmRCQZilmqY39gBnAS0WTex8xsWbb6hBQrRCWHo4+OAtTOnfD009G2nTuj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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10710cb50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Evaluation\n",
+    "\n",
+    "* separate into training and testing sets\n",
+    "* testing set usually has lower accuracy than training.\n",
+    "\n",
+    "###cross validation\n",
+    "Ideally we'd like to use all of the data for training and testing, but we can't.  Leave out a subset of the training set.  Train on the rest of the set and test on this subset.  Repeat for all elements in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "correct = 0.0\n",
+    "for ei in range(len(features)):\n",
+    "    # select all but the one at 'ei'\n",
+    "    training = np.ones(len(features), bool)\n",
+    "    training[ei] = False\n",
+    "    testing = ~training\n",
+    "    model =  fit_model(features[training], is_virginica[training])\n",
+    "    predictions = predict(model, features[testing])\n",
+    "    correct += np.sum(predictions == is_virginica[testing])\n",
+    "acc = correct/float(len(features))\n",
+    "print('Accuracy: {0:.1%}'.format(acc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is computationally expensive though.  **x-fold cross validation** attempts to solve this.  Instead of leaving one out, leave out say, 20%, in a 5 fold cross validation.\n",
+    "\n",
+    "With less data, use up to 10 or 20 folds.\n",
+    "\n",
+    "Never report your training accuracy.  You will look like a noob\n",
+    "\n",
+    "##More Complex Classifiers\n",
+    "What makes up a classification model?\n",
+    "* **The Structure of the Model** - how does it make decisions?\n",
+    "* **The search procedure** - How do we find the model we need to use?\n",
+    "* **gain or loss function** - Which possibilities tested should be returned?  Optimize so that model makes fewer erros of a specific kind.\n",
+    "\n",
+    "**feature engineering** - derive new combined features.  Should vary with what matters but be invariant with what does not.\n",
+    "\n",
+    "**feature selection** is choosing good features automatically.\n",
+    "\n",
+    "###Classifying with scikit-learn\n",
+    "**k neighbors**\n",
+    "* normalize to z-scores -- how far from mean inunits of std deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean accuracy: 94.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.cross_validation import KFold\n",
+    "\n",
+    "classifier = KNeighborsClassifier(n_neighbors=1)\n",
+    "kf = KFold(len(features), n_folds=5, shuffle=True)\n",
+    "means = []\n",
+    "for training, testing in kf:\n",
+    "    classifier.fit(features[training], labels[training])\n",
+    "    prediction = classifier.predict(features[testing])\n",
+    "    curmean = np.mean(prediction == labels[testing])\n",
+    "    means.append(curmean)\n",
+    "print('Mean accuracy: {:.1%}'.format(np.mean(means)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean accuracy: 94.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "classifier = KNeighborsClassifier(n_neighbors=1)\n",
+    "\n",
+    "# each step gets (str,clf) pair.  str is name of step\n",
+    "# second is object that performs the transformation.\n",
+    "classifier = Pipeline([('norm', StandardScaler()),\n",
+    "        ('knn', classifier)])\n",
+    "\n",
+    "for training, testing in kf:\n",
+    "    classifier.fit(features[training], labels[training])\n",
+    "    prediction = classifier.predict(features[testing])\n",
+    "    curmean = np.mean(prediction == labels[testing])\n",
+    "    means.append(curmean)\n",
+    "print('Mean accuracy: {:.1%}'.format(np.mean(means)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.3.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/chapter_3_clustering.ipynb b/chapter_3_clustering.ipynb
new file mode 100644
index 00000000..4adf7e91
--- /dev/null
+++ b/chapter_3_clustering.ipynb
@@ -0,0 +1,1123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Bag of Words** -  order of words is ignored and word counts collected.\n",
+    "1.  Extract salient features from each post and store it as a vector per post\n",
+    "2.  Compute clustering on the vectors\n",
+    "3.  From this cluster, fetch a handful of posts having a similarity to the post in question. (diversity)\n",
+    "\n",
+    "#Preprocessing\n",
+    "###Converting raw text into a bag of words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
+       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
+       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
+       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
+       "        tokenizer=None, vocabulary=None)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "vectorizer = CountVectorizer(min_df=1)\n",
+    "vectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['disk', 'format', 'hard', 'how', 'my', 'problems', 'to']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "content = [\"How to format my hard disk\", \"Hard disk format problems\"]\n",
+    "X = vectorizer.fit_transform(content)\n",
+    "vectorizer.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 1]\n",
+      " [1 1]\n",
+      " [1 1]\n",
+      " [1 0]\n",
+      " [1 0]\n",
+      " [0 1]\n",
+      " [1 0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X.toarray().transpose())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Counting Words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Post Filename</th>\n",
+       "      <th>Post Content</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01.txt</td>\n",
+       "      <td>This is a toy post about machine learning. Act...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02.txt</td>\n",
+       "      <td>Imaging databases provide storage capabilities.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>03.txt</td>\n",
+       "      <td>Most imaging databases save images permanently.\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>04.txt</td>\n",
+       "      <td>Imaging databases store data.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>05.txt</td>\n",
+       "      <td>Imaging databases store data. Imaging database...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Post Filename                                       Post Content\n",
+       "0        01.txt  This is a toy post about machine learning. Act...\n",
+       "1        02.txt    Imaging databases provide storage capabilities.\n",
+       "2        03.txt  Most imaging databases save images permanently.\\n\n",
+       "3        04.txt                      Imaging databases store data.\n",
+       "4        05.txt  Imaging databases store data. Imaging database..."
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "def read_directory_to_list(directory='./repo/ch03/data/toy'):\n",
+    "    import os\n",
+    "    result = pd.DataFrame(columns=['Post Filename','Post Content'])\n",
+    "    for fn in os.listdir(directory):\n",
+    "        filename = directory+'/'+fn\n",
+    "        with open(filename, 'r') as f:\n",
+    "            result = result.append({'Post Filename': fn,\n",
+    "                                    'Post Content': f.read()},\n",
+    "                          ignore_index=True)\n",
+    "    return result\n",
+    "posts = read_directory_to_list()\n",
+    "posts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#samples: 5, #features: 25\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "vectorizer = CountVectorizer(min_df=1)\n",
+    "\n",
+    "#We have to notify the vectorizer about the full dataset up front\n",
+    "#so that it knows what words are to be expected.\n",
+    "posts = posts['Post Content']\n",
+    "X_train = vectorizer.fit_transform(posts)\n",
+    "num_samples, num_features = X_train.shape\n",
+    "print(\"#samples: %d, #features: %d\"%(num_samples, num_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Returns sparse array\n",
+      "  (0, 5)\t1\n",
+      "  (0, 7)\t1\n",
+      "\n",
+      "to array:\n",
+      "[[0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#We can now vectorize our new post.\n",
+    "new_post = \"imaging databases\"\n",
+    "new_post_vec = vectorizer.transform([new_post]) #note [list]\n",
+    "\n",
+    "#returns a sparse matrix\n",
+    "print(\"Returns sparse array\")\n",
+    "print(new_post_vec)\n",
+    "print()\n",
+    "\n",
+    "new_post_vec = new_post_vec.toarray()\n",
+    "print('to array:')\n",
+    "print(new_post_vec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###Naive Version - Euclidean distance\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New post: imaging databases\n",
+      "\n",
+      "=== Post 0 with dist=4.00: This is a toy post about machine learning. Actually, it contains not much interesting stuff.\n",
+      "=== Post 1 with dist=1.73: Imaging databases provide storage capabilities.\n",
+      "=== Post 2 with dist=2.00: Most imaging databases save images permanently.\n",
+      "\n",
+      "=== Post 3 with dist=1.41: Imaging databases store data.\n",
+      "=== Post 4 with dist=5.10: Imaging databases store data. Imaging databases store data. Imaging databases store data.\n",
+      "Best post is 3 with dist=1.41\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy as sp\n",
+    "def dist_raw(v1, v2):\n",
+    "    delta = v1-v2\n",
+    "    return sp.linalg.norm(delta) #calculates euclidean difference\n",
+    "\n",
+    "def find_euclidean_closest(new_post, dist_func, X_train=X_train):\n",
+    "    print(\"New post: %s\"%(new_post))\n",
+    "    print()\n",
+    "    new_post_vec = vectorizer.transform([new_post])\n",
+    "    new_post_vec = new_post_vec.toarray()\n",
+    "    \n",
+    "    best_doc = None\n",
+    "    best_dist = sp.inf\n",
+    "    best_i = None\n",
+    "    for i, post in enumerate(posts):\n",
+    "        if post == new_post:\n",
+    "            continue\n",
+    "        post_vec = X_train.getrow(i).toarray()\n",
+    "        d = dist_func(post_vec, new_post_vec)\n",
+    "        print(\"=== Post %i with dist=%.2f: %s\"%(i,d,post))\n",
+    "        if d<best_dist:\n",
+    "            best_dist = d\n",
+    "            best_i = i\n",
+    "\n",
+    "    print(\"Best post is %i with dist=%.2f\"%(best_i, best_dist))\n",
+    "    \n",
+    "find_euclidean_closest(\"imaging databases\", dist_raw)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But...check out post 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]]\n",
+      "[[0 0 0 0 3 3 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0]]\n",
+      "<class 'scipy.sparse.csr.csr_matrix'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train.getrow(3).toarray())\n",
+    "print(X_train.getrow(4).toarray())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Normalizing Word Count Vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New post: imaging databases\n",
+      "\n",
+      "=== Post 0 with dist=1.41: This is a toy post about machine learning. Actually, it contains not much interesting stuff.\n",
+      "=== Post 1 with dist=0.86: Imaging databases provide storage capabilities.\n",
+      "=== Post 2 with dist=0.92: Most imaging databases save images permanently.\n",
+      "\n",
+      "=== Post 3 with dist=0.77: Imaging databases store data.\n",
+      "=== Post 4 with dist=0.77: Imaging databases store data. Imaging databases store data. Imaging databases store data.\n",
+      "Best post is 3 with dist=0.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "def dist_norm(v1, v2):\n",
+    "    v1_normalized = v1/sp.linalg.norm(v1)\n",
+    "    v2_normalized = v2/sp.linalg.norm(v2)\n",
+    "    delta = v1_normalized - v2_normalized\n",
+    "    return sp.linalg.norm(delta)\n",
+    "\n",
+    "find_euclidean_closest(\"imaging databases\", dist_norm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Removing Less Important Words\n",
+    "**stop words** words like \"most\" that appear in all sorts of different contexts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['anyone', 'myself', 'cannot', 'my', 'off', 'via', 'also', 'un', 'even', 'sometime', 'whither', 'ever', 'between', 'wherein', 'everyone', 'some', 'someone', 'toward', 'ltd', 'through']\n",
+      "318\n"
+     ]
+    }
+   ],
+   "source": [
+    "vectorizer = CountVectorizer(min_df=1, stop_words='english')\n",
+    "print(list(vectorizer.get_stop_words())[:20])\n",
+    "print(len(list(vectorizer.get_stop_words())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#samples: 5, #features: 18\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train = vectorizer.fit_transform(posts)\n",
+    "num_samples, num_features = X_train.shape\n",
+    "print(\"#samples: %d, #features: %d\"%(num_samples, num_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New post: imaging databases\n",
+      "\n",
+      "=== Post 0 with dist=1.41: This is a toy post about machine learning. Actually, it contains not much interesting stuff.\n",
+      "=== Post 1 with dist=0.86: Imaging databases provide storage capabilities.\n",
+      "=== Post 2 with dist=0.86: Most imaging databases save images permanently.\n",
+      "\n",
+      "=== Post 3 with dist=0.77: Imaging databases store data.\n",
+      "=== Post 4 with dist=0.77: Imaging databases store data. Imaging databases store data. Imaging databases store data.\n",
+      "Best post is 3 with dist=0.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "find_euclidean_closest(\"imaging databases\", dist_norm, X_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that post 2 is now on par with post 1, whereas before removing the stop words, it was up at .94\n",
+    "\n",
+    "##Stemming\n",
+    "We currently counts similar words in different variants as different words.  E.g. \"imaging\" and \"images.\"  **Stemming** reuces words to their specific word stem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "graphic\n",
+      "imag\n",
+      "imag\n",
+      "imagin\n",
+      "imagin\n",
+      "buy\n",
+      "buy\n",
+      "bought\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nltk.stem\n",
+    "\n",
+    "s = nltk.stem.SnowballStemmer('english')\n",
+    "print(s.stem('graphics'))\n",
+    "print(s.stem('imaging'))\n",
+    "print(s.stem('image'))\n",
+    "print(s.stem('imagination'))\n",
+    "print(s.stem('imagine'))\n",
+    "print(s.stem('buys'))\n",
+    "print(s.stem('buying'))\n",
+    "print(s.stem('bought'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###Extending the vectorizer with NLTK's stemmer\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import nltk.stem\n",
+    "english_stemmer = nltk.stem.SnowballStemmer('english')\n",
+    "class StemmedCountVectorizer(CountVectorizer):\n",
+    "    def build_analyzer(self):\n",
+    "        analyzer = super(StemmedCountVectorizer, self).build_analyzer()\n",
+    "        return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))\n",
+    "vectorizer = StemmedCountVectorizer(min_df=1, stop_words=\"english\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#samples: 5, #features: 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train = vectorizer.fit_transform(posts)\n",
+    "num_samples, num_features = X_train.shape\n",
+    "print(\"#samples: %d, #features: %d\"%(num_samples, num_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New post: imaging databases\n",
+      "\n",
+      "=== Post 0 with dist=1.41: This is a toy post about machine learning. Actually, it contains not much interesting stuff.\n",
+      "=== Post 1 with dist=0.86: Imaging databases provide storage capabilities.\n",
+      "=== Post 2 with dist=0.63: Most imaging databases save images permanently.\n",
+      "\n",
+      "=== Post 3 with dist=0.77: Imaging databases store data.\n",
+      "=== Post 4 with dist=0.77: Imaging databases store data. Imaging databases store data. Imaging databases store data.\n",
+      "Best post is 2 with dist=0.63\n"
+     ]
+    }
+   ],
+   "source": [
+    "find_euclidean_closest(\"imaging databases\", dist_norm, X_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the stemmed vectorizer collapses imaging and images, post 2 becomes the most similar because \"imag\" appears twice.\n",
+    "\n",
+    "###Stop words on Steroids\n",
+    "\n",
+    "*  `max_df` parameter can be set to fraction, e.g. .9, so that any word that occurs in more than 90 percent of of all posts will be ignored. But how low to set it?\n",
+    "\n",
+    "* **Term frequency - inverse document frequency (TF_IDF)** - counting term frequencies for every post and discount those that appear in many posts.  We want a high value for a given term in a given post if that term occurs often in that post and very seldom anywhere else.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a:\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "\n",
+      "0.270310072072\n",
+      "\n",
+      "0.135155036036\n",
+      "0.366204096223\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Naive implementation\n",
+    "import scipy as sp\n",
+    "def tfidf(term, doc, corpus):\n",
+    "    #normalize so longer docs don't have advantage:\n",
+    "    tf = doc.count(term) / len(doc) \n",
+    "    num_docs_with_term = len([d for d in corpus if term in d])\n",
+    "    idf = sp.log(len(corpus) / num_docs_with_term)\n",
+    "    return tf * idf\n",
+    "\n",
+    "a, abb, abc = ['a'], ['a','b','b'], ['a','b','c']\n",
+    "D = [a, abb, abc]\n",
+    "print(\"a:\")\n",
+    "print(tfidf(\"a\", a, D))\n",
+    "print(tfidf(\"a\", abb, D))\n",
+    "print(tfidf(\"a\", abc, D))\n",
+    "print()\n",
+    "print(tfidf(\"b\", abb, D))\n",
+    "print()\n",
+    "print(tfidf(\"b\", abc, D))\n",
+    "print(tfidf(\"c\", abc, D))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SciKit implements this with TfidfVectorizer, which is inherited from CountVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "import nltk.stem\n",
+    "english_stemmer = nltk.stem.SnowballStemmer('english')\n",
+    "class StemmedTfidfVectorizer(TfidfVectorizer):\n",
+    "    def build_analyzer(self):\n",
+    "        analyzer = super(TfidfVectorizer, self).build_analyzer()\n",
+    "        return lambda doc: (\n",
+    "            english_stemmer.stem(w) for w in analyzer(doc))\n",
+    "vectorizer = StemmedTfidfVectorizer(min_df=1,\n",
+    "                stop_words='english', decode_error='ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#samples: 5, #features: 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train = vectorizer.fit_transform(posts)\n",
+    "num_samples, num_features = X_train.shape\n",
+    "print(\"#samples: %d, #features: %d\"%(num_samples, num_features))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###So far\n",
+    "1. Tokenize the text\n",
+    "    * lowercase the text\n",
+    "    * Throw away words that only occur once (min_df=1)\n",
+    "    * Throw away stop words\n",
+    "    * Collapse words onto their stems\n",
+    "2. Toss words that occur way too often to be of any help detcting related posts\n",
+    "3. Toss words that occur so seldom that they probably won't appear again.\n",
+    "4. Count Remaining words\n",
+    "5. Calculate TF-IDF values from counts, considering the whole text corpus\n",
+    "\n",
+    "###Limits:\n",
+    "* **Does not cover word relations** \"Car hits wall\" and \"Wall hits car\" will have same feature vector\n",
+    "* **Does not capture negations**.  This can be fixed by also counting \"bigrams\" and \"trigrams\" in addition to \"unigrams\"\n",
+    "* **Cannot account for misspelled words**\n",
+    "\n",
+    "#Clustering\n",
+    "* flat - i.e. key means.  Divides posts into a set of clusters without relating the clusters to each other.  Partitioning.  Needs up front number of clusters.\n",
+    "* hierarchical - clusters get clustered recursively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18846\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['alt.atheism',\n",
+       " 'comp.graphics',\n",
+       " 'comp.os.ms-windows.misc',\n",
+       " 'comp.sys.ibm.pc.hardware',\n",
+       " 'comp.sys.mac.hardware',\n",
+       " 'comp.windows.x',\n",
+       " 'misc.forsale',\n",
+       " 'rec.autos',\n",
+       " 'rec.motorcycles',\n",
+       " 'rec.sport.baseball',\n",
+       " 'rec.sport.hockey',\n",
+       " 'sci.crypt',\n",
+       " 'sci.electronics',\n",
+       " 'sci.med',\n",
+       " 'sci.space',\n",
+       " 'soc.religion.christian',\n",
+       " 'talk.politics.guns',\n",
+       " 'talk.politics.mideast',\n",
+       " 'talk.politics.misc',\n",
+       " 'talk.religion.misc']"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sklearn.datasets\n",
+    "all_data = sklearn.datasets.fetch_20newsgroups(subset='all')\n",
+    "print(len(all_data.filenames))\n",
+    "all_data.target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3529\n"
+     ]
+    }
+   ],
+   "source": [
+    "groups = ['comp.graphics', 'comp.os.ms-windows.misc',\n",
+    "          'comp.sys.ibm.pc.hardware','comp.sys.mac.hardware',\n",
+    "          'comp.windows.x','sci.space']\n",
+    "train_data = sklearn.datasets.fetch_20newsgroups(\n",
+    "        subset='train', categories=groups)\n",
+    "print(len(train_data.filenames))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2349\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_data = sklearn.datasets.fetch_20newsgroups(\n",
+    "        subset='test', categories=groups)\n",
+    "print(len(test_data.filenames))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#samples: 3529, #features: 4712\n"
+     ]
+    }
+   ],
+   "source": [
+    "vectorizer = StemmedTfidfVectorizer(\n",
+    "        min_df=10, max_df=0.5,\n",
+    "        stop_words='english',\n",
+    "        decode_error='ignore')\n",
+    "vectorized = vectorizer.fit_transform(train_data.data)\n",
+    "num_samples, num_features = vectorized.shape\n",
+    "print(\"#samples: %d, #features: %d\"% (num_samples, num_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialization complete\n",
+      "Iteration  0, inertia 5899.560\n",
+      "Iteration  1, inertia 3218.298\n",
+      "Iteration  2, inertia 3184.333\n",
+      "Iteration  3, inertia 3164.867\n",
+      "Iteration  4, inertia 3152.004\n",
+      "Iteration  5, inertia 3143.111\n",
+      "Iteration  6, inertia 3136.256\n",
+      "Iteration  7, inertia 3129.325\n",
+      "Iteration  8, inertia 3124.567\n",
+      "Iteration  9, inertia 3121.900\n",
+      "Iteration 10, inertia 3120.210\n",
+      "Iteration 11, inertia 3118.627\n",
+      "Iteration 12, inertia 3117.363\n",
+      "Iteration 13, inertia 3116.811\n",
+      "Iteration 14, inertia 3116.588\n",
+      "Iteration 15, inertia 3116.417\n",
+      "Iteration 16, inertia 3115.760\n",
+      "Iteration 17, inertia 3115.374\n",
+      "Iteration 18, inertia 3115.155\n",
+      "Iteration 19, inertia 3114.949\n",
+      "Iteration 20, inertia 3114.515\n",
+      "Iteration 21, inertia 3113.937\n",
+      "Iteration 22, inertia 3113.720\n",
+      "Iteration 23, inertia 3113.548\n",
+      "Iteration 24, inertia 3113.475\n",
+      "Iteration 25, inertia 3113.447\n",
+      "Converged at iteration 25\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "KMeans(copy_x=True, init='random', max_iter=300, n_clusters=50, n_init=1,\n",
+       "    n_jobs=1, precompute_distances='auto', random_state=3, tol=0.0001,\n",
+       "    verbose=1)"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_clusters = 50\n",
+    "from sklearn.cluster import KMeans\n",
+    "km = KMeans(n_clusters=num_clusters, init='random', \n",
+    "            n_init=1, verbose=1, random_state=3)\n",
+    "km.fit(vectorized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[38 17 47 ..., 41 14 16]\n",
+      "(3529,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# each cluster has been assigned a label\n",
+    "print(km.labels_)\n",
+    "print(km.labels_.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Putting it all together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "166\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_post = \"\"\"\n",
+    "Disk drive problems.  Hi, I have a problem with my hard disk.\n",
+    "After 1 year it is working only sporadically now.\n",
+    "I tried to format it, but now it doessn't boot any more.\n",
+    "Any ideas?  Thanks.\n",
+    "\"\"\"\n",
+    "\n",
+    "new_post_vec = vectorizer.transform([new_post])\n",
+    "new_post_label = km.predict(new_post_vec)[0]\n",
+    "\n",
+    "#Now that we have a clustering and a label...\n",
+    "# `nonzero` makes an array of indices of nonzero numbers\n",
+    "similar_indices = (km.labels_==new_post_label).nonzero()[0]\n",
+    "\n",
+    "similar = []\n",
+    "for i in similar_indices:\n",
+    "    dist = sp.linalg.norm(\n",
+    "            new_post_vec - vectorized[i].toarray())\n",
+    "    similar.append((dist, train_data.data[i]))\n",
+    "similar = sorted(similar)\n",
+    "print(len(similar))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.0236597431380716,\n",
+       " \"From: Thomas Dachsel <GERTHD@mvs.sas.com>\\nSubject: BOOT PROBLEM with IDE controller\\nNntp-Posting-Host: sdcmvs.mvs.sas.com\\nOrganization: SAS Institute Inc.\\nLines: 25\\n\\nHi,\\nI've got a Multi I/O card (IDE controller + serial/parallel\\ninterface) and two floppy drives (5 1/4, 3 1/2) and a\\nQuantum ProDrive 80AT connected to it.\\nI was able to format the hard disk, but I could not boot from\\nit. I can boot from drive A: (which disk drive does not matter)\\nbut if I remove the disk from drive A and press the reset switch,\\nthe LED of drive A: continues to glow, and the hard disk is\\nnot accessed at all.\\nI guess this must be a problem of either the Multi I/o card\\nor floppy disk drive settings (jumper configuration?)\\nDoes someone have any hint what could be the reason for it.\\nPlease reply by email to GERTHD@MVS.SAS.COM\\nThanks,\\nThomas\\n+-------------------------------------------------------------------+\\n| Thomas Dachsel                                                    |\\n| Internet: GERTHD@MVS.SAS.COM                                      |\\n| Fidonet:  Thomas_Dachsel@camel.fido.de (2:247/40)                 |\\n| Subnet:   dachsel@rnivh.rni.sub.org (UUCP in Germany, now active) |\\n| Phone:    +49 6221 4150 (work), +49 6203 12274 (home)             |\\n| Fax:      +49 6221 415101                                         |\\n| Snail:    SAS Institute GmbH, P.O.Box 105307, D-W-6900 Heidelberg |\\n| Tagline:  One bad sector can ruin a whole day...                  |\\n+-------------------------------------------------------------------+\\n\")"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "show_at_1 = similar[0]\n",
+    "show_at_2 = similar[int(len(similar)/10)]\n",
+    "show_at_3 = similar[int(len(similar)/ 2)]\n",
+    "show_at_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Another look at noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(245, 'From: SITUNAYA@IBM3090.BHAM.AC.UK\\nSubject: test....(sorry)\\nOrganization: The University of Birmingham, United Kingdom\\nLines: 1\\nNNTP-Posting-Host: ibm3090.bham.ac.uk\\n\\n==============================================================================\\n', 'comp.graphics')\n"
+     ]
+    }
+   ],
+   "source": [
+    "post_group = zip(train_data.data, train_data.target)\n",
+    "all = [(len(post[0]), post[0], train_data.target_names[post[1]])\n",
+    "       for post in post_group]\n",
+    "graphics = sorted([post for post in all if \n",
+    "                  post[2]=='comp.graphics'])\n",
+    "print(graphics[5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['situnaya', 'ibm3090', 'bham', 'ac', 'uk', 'subject', 'test', 'sorri', 'organ', 'univers', 'birmingham', 'unit', 'kingdom', 'line', 'nntp', 'post', 'host', 'ibm3090', 'bham', 'ac', 'uk']\n"
+     ]
+    }
+   ],
+   "source": [
+    "noise_post = graphics[5][1]\n",
+    "analyzer = vectorizer.build_analyzer()\n",
+    "print(list(analyzer(noise_post)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ac', 'birmingham', 'host', 'kingdom', 'nntp', 'sorri', 'test', 'uk', 'unit', 'univers']\n"
+     ]
+    }
+   ],
+   "source": [
+    "useful = set(analyzer(noise_post)).intersection(\n",
+    "        vectorizer.get_feature_names())\n",
+    "print(sorted(useful))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IDF(ac)=3.51\n",
+      "IDF(birmingham)=6.77\n",
+      "IDF(host)=1.74\n",
+      "IDF(kingdom)=6.68\n",
+      "IDF(nntp)=1.77\n",
+      "IDF(sorri)=4.14\n",
+      "IDF(test)=3.83\n",
+      "IDF(uk)=3.70\n",
+      "IDF(unit)=4.42\n",
+      "IDF(univers)=1.91\n"
+     ]
+    }
+   ],
+   "source": [
+    "#notice that all of these idf scores are pretty low.\n",
+    "#also, that the highest rated have nothing to do with \n",
+    "#computer graphics\n",
+    "for term in sorted(useful):\n",
+    "    print('IDF(%s)=%.2f'%(term,\n",
+    "            vectorizer._tfidf.idf_[vectorizer.vocabulary_[term]]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Further Reading:\n",
+    "* Python 3 Text Processing with NLTK 3 Cookbook\n",
+    "* [sklearn clustering docs](http://scikit-learn.org/dev/modules/clustering.html)\n",
+    "* sklearn metrics docs - to learn more about measuring kmeans."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.3.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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