diff --git a/Ch4/.ipynb_checkpoints/exercises-checkpoint.ipynb b/Ch4/.ipynb_checkpoints/exercises-checkpoint.ipynb
new file mode 100644
index 000000000..606665cc9
--- /dev/null
+++ b/Ch4/.ipynb_checkpoints/exercises-checkpoint.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 12**\n",
+    "\n",
+    "Implement batch gradient descent from scratch (no SKLearn!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris = datasets.load_iris()\n",
+    "list(iris.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _iris_dataset:\n",
+      "\n",
+      "Iris plants dataset\n",
+      "--------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "    :Number of Instances: 150 (50 in each of three classes)\n",
+      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
+      "    :Attribute Information:\n",
+      "        - sepal length in cm\n",
+      "        - sepal width in cm\n",
+      "        - petal length in cm\n",
+      "        - petal width in cm\n",
+      "        - class:\n",
+      "                - Iris-Setosa\n",
+      "                - Iris-Versicolour\n",
+      "                - Iris-Virginica\n",
+      "                \n",
+      "    :Summary Statistics:\n",
+      "\n",
+      "    ============== ==== ==== ======= ===== ====================\n",
+      "                    Min  Max   Mean    SD   Class Correlation\n",
+      "    ============== ==== ==== ======= ===== ====================\n",
+      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
+      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
+      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
+      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
+      "    ============== ==== ==== ======= ===== ====================\n",
+      "\n",
+      "    :Missing Attribute Values: None\n",
+      "    :Class Distribution: 33.3% for each of 3 classes.\n",
+      "    :Creator: R.A. Fisher\n",
+      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
+      "    :Date: July, 1988\n",
+      "\n",
+      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
+      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
+      "Machine Learning Repository, which has two wrong data points.\n",
+      "\n",
+      "This is perhaps the best known database to be found in the\n",
+      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
+      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
+      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
+      "type of iris plant.  One class is linearly separable from the other 2; the\n",
+      "latter are NOT linearly separable from each other.\n",
+      "\n",
+      ".. topic:: References\n",
+      "\n",
+      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
+      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
+      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
+      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
+      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
+      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
+      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
+      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
+      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
+      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
+      "     on Information Theory, May 1972, 431-433.\n",
+      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
+      "     conceptual clustering system finds 3 classes in the data.\n",
+      "   - Many, many more ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.DESCR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris[\"data\"][:, (2,3)]  # petal length and width\n",
+    "y = (iris[\"target\"])  # 1 if Iris virginica, else 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Important variables\n",
+    "\n",
+    "X_with_bias = np.c_[np.ones([len(X), 1]), X] # Add column of ones for theta intercept term\n",
+    "alpha = 0.1\n",
+    "iterations=1500\n",
+    "\n",
+    "print(X.shape)\n",
+    "\n",
+    "# NOTE: If ValueError: all input arrays must have the same shape appears then you may have run this cel multiple times\n",
+    "#    which will have added multiple collumns of ones to the matrix X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup our proportions\n",
+    "\n",
+    "test_ratio = .2\n",
+    "val_ratio = .2\n",
+    "total_size = len(X)\n",
+    "\n",
+    "# Calculate size of our splits\n",
+    "\n",
+    "test_size = int(test_ratio*total_size)\n",
+    "val_size = int(val_ratio*total_size)\n",
+    "train_size = total_size - test_size - val_size\n",
+    "\n",
+    "# Split our data\n",
+    "\n",
+    "rnd_indices = np.random.permutation(total_size) # Shuffle our input matrix\n",
+    "\n",
+    "X_train = X_with_bias[rnd_indices[:train_size]]\n",
+    "y_train = y[rnd_indices[:train_size]]\n",
+    "X_valid = X_with_bias[rnd_indices[train_size:-test_size]]\n",
+    "y_valid = y[rnd_indices[train_size:-test_size]]\n",
+    "X_test = X_with_bias[rnd_indices[-test_size:]]\n",
+    "y_test = y[rnd_indices[-test_size:]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(90, 3)\n",
+      "(30, 2)\n",
+      "(30, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train.shape)\n",
+    "print(X_val.shape)\n",
+    "print(X_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_one_hot(y):\n",
+    "    n_classes = y.max() + 1\n",
+    "    m = len(y)\n",
+    "    Y_one_hot = np.zeros((m, n_classes)) # Setup zero matrix with m rows and a column for each class\n",
+    "    Y_one_hot[np.arange(m), y] = 1 # Fill in ones\n",
+    "    return Y_one_hot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 0, 0, 0, 1, 2, 0, 2])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 1.],\n",
+       "       [0., 0., 1.],\n",
+       "       [0., 0., 1.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [0., 1., 0.],\n",
+       "       [0., 0., 1.],\n",
+       "       [1., 0., 0.],\n",
+       "       [0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_one_hot(y_train[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y_train_one_hot = to_one_hot(y_train)\n",
+    "Y_test_one_hot = to_one_hot(y_test)\n",
+    "Y_val_one_hot = to_one_hot(y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Softmax function = exp(X) / (sum of exp(X))\n",
+    "\n",
+    "def softmax(logits):\n",
+    "    exps = np.exp(logits)\n",
+    "    exp_sums = np.sum(exps, axis=1, keepdims=True)\n",
+    "    return exps / exp_sums"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_inputs = X_train.shape[1] # Number of features\n",
+    "n_outputs = len(np.unique(y_train)) # 3 uniqure values which will each be a possible output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1.4567897105648775\n",
+      "500 0.7451993577978241\n",
+      "1000 0.6279369677273878\n",
+      "1500 0.5572702696067121\n",
+      "2000 0.5111859948576022\n",
+      "2500 0.47856473219026296\n",
+      "3000 0.45387932862540925\n",
+      "3500 0.43422780377165426\n",
+      "4000 0.41797875623202274\n",
+      "4500 0.4041537521442775\n",
+      "5000 0.39213163561158126\n"
+     ]
+    }
+   ],
+   "source": [
+    "eta = 0.01\n",
+    "n_iterations = 5001\n",
+    "m = len(X_train)\n",
+    "epsilon = 1e-7\n",
+    "\n",
+    "Theta = np.random.randn(n_inputs, n_outputs)\n",
+    "\n",
+    "# Cycle through set to apply batch gradient descent\n",
+    "\n",
+    "for iteration in range(n_iterations):\n",
+    "    logits = X_train.dot(Theta) # Logits which are raw predictions from applying X to Theta\n",
+    "    p_hat = softmax(logits) # Apply softmax to logits to get our probabilities\n",
+    "    loss = -np.mean(np.sum(Y_train_one_hot * np.log(p_hat + epsilon), axis=1)) # Compute loss function\n",
+    "    error = p_hat - Y_train_one_hot # Compute error \n",
+    "    if iteration % 500 == 0:\n",
+    "        print(iteration, loss)\n",
+    "    Grad = 1/m * X_train.T.dot(error)\n",
+    "    Theta = Theta - eta * Grad\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3.61613128,  0.06856255, -2.86225561],\n",
+       "       [-0.2597962 ,  0.80558911,  0.70553675],\n",
+       "       [-0.90831271,  0.18903751,  2.43558706]])"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9666666666666667"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Predictions\n",
+    "\n",
+    "logits = X_valid.dot(Theta)\n",
+    "p_hat = softmax(logits)\n",
+    "y_pred = np.argmax(p_hat, axis=1)\n",
+    "\n",
+    "accuracy_score = np.mean(y_pred == y_valid)\n",
+    "accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ch4/datasets/housing/ex1data1.txt b/Ch4/datasets/housing/ex1data1.txt
new file mode 100644
index 000000000..0f88ccb61
--- /dev/null
+++ b/Ch4/datasets/housing/ex1data1.txt
@@ -0,0 +1,97 @@
+6.1101,17.592
+5.5277,9.1302
+8.5186,13.662
+7.0032,11.854
+5.8598,6.8233
+8.3829,11.886
+7.4764,4.3483
+8.5781,12
+6.4862,6.5987
+5.0546,3.8166
+5.7107,3.2522
+14.164,15.505
+5.734,3.1551
+8.4084,7.2258
+5.6407,0.71618
+5.3794,3.5129
+6.3654,5.3048
+5.1301,0.56077
+6.4296,3.6518
+7.0708,5.3893
+6.1891,3.1386
+20.27,21.767
+5.4901,4.263
+6.3261,5.1875
+5.5649,3.0825
+18.945,22.638
+12.828,13.501
+10.957,7.0467
+13.176,14.692
+22.203,24.147
+5.2524,-1.22
+6.5894,5.9966
+9.2482,12.134
+5.8918,1.8495
+8.2111,6.5426
+7.9334,4.5623
+8.0959,4.1164
+5.6063,3.3928
+12.836,10.117
+6.3534,5.4974
+5.4069,0.55657
+6.8825,3.9115
+11.708,5.3854
+5.7737,2.4406
+7.8247,6.7318
+7.0931,1.0463
+5.0702,5.1337
+5.8014,1.844
+11.7,8.0043
+5.5416,1.0179
+7.5402,6.7504
+5.3077,1.8396
+7.4239,4.2885
+7.6031,4.9981
+6.3328,1.4233
+6.3589,-1.4211
+6.2742,2.4756
+5.6397,4.6042
+9.3102,3.9624
+9.4536,5.4141
+8.8254,5.1694
+5.1793,-0.74279
+21.279,17.929
+14.908,12.054
+18.959,17.054
+7.2182,4.8852
+8.2951,5.7442
+10.236,7.7754
+5.4994,1.0173
+20.341,20.992
+10.136,6.6799
+7.3345,4.0259
+6.0062,1.2784
+7.2259,3.3411
+5.0269,-2.6807
+6.5479,0.29678
+7.5386,3.8845
+5.0365,5.7014
+10.274,6.7526
+5.1077,2.0576
+5.7292,0.47953
+5.1884,0.20421
+6.3557,0.67861
+9.7687,7.5435
+6.5159,5.3436
+8.5172,4.2415
+9.1802,6.7981
+6.002,0.92695
+5.5204,0.152
+5.0594,2.8214
+5.7077,1.8451
+7.6366,4.2959
+5.8707,7.2029
+5.3054,1.9869
+8.2934,0.14454
+13.394,9.0551
+5.4369,0.61705
diff --git a/Ch4/datasets/housing/ex1data2.txt b/Ch4/datasets/housing/ex1data2.txt
new file mode 100644
index 000000000..79e9a807e
--- /dev/null
+++ b/Ch4/datasets/housing/ex1data2.txt
@@ -0,0 +1,47 @@
+2104,3,399900
+1600,3,329900
+2400,3,369000
+1416,2,232000
+3000,4,539900
+1985,4,299900
+1534,3,314900
+1427,3,198999
+1380,3,212000
+1494,3,242500
+1940,4,239999
+2000,3,347000
+1890,3,329999
+4478,5,699900
+1268,3,259900
+2300,4,449900
+1320,2,299900
+1236,3,199900
+2609,4,499998
+3031,4,599000
+1767,3,252900
+1888,2,255000
+1604,3,242900
+1962,4,259900
+3890,3,573900
+1100,3,249900
+1458,3,464500
+2526,3,469000
+2200,3,475000
+2637,3,299900
+1839,2,349900
+1000,1,169900
+2040,4,314900
+3137,3,579900
+1811,4,285900
+1437,3,249900
+1239,3,229900
+2132,4,345000
+4215,4,549000
+2162,4,287000
+1664,2,368500
+2238,3,329900
+2567,4,314000
+1200,3,299000
+852,2,179900
+1852,4,299900
+1203,3,239500
diff --git a/Ch4/exercises.ipynb b/Ch4/exercises.ipynb
new file mode 100644
index 000000000..606665cc9
--- /dev/null
+++ b/Ch4/exercises.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 12**\n",
+    "\n",
+    "Implement batch gradient descent from scratch (no SKLearn!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris = datasets.load_iris()\n",
+    "list(iris.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _iris_dataset:\n",
+      "\n",
+      "Iris plants dataset\n",
+      "--------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "    :Number of Instances: 150 (50 in each of three classes)\n",
+      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
+      "    :Attribute Information:\n",
+      "        - sepal length in cm\n",
+      "        - sepal width in cm\n",
+      "        - petal length in cm\n",
+      "        - petal width in cm\n",
+      "        - class:\n",
+      "                - Iris-Setosa\n",
+      "                - Iris-Versicolour\n",
+      "                - Iris-Virginica\n",
+      "                \n",
+      "    :Summary Statistics:\n",
+      "\n",
+      "    ============== ==== ==== ======= ===== ====================\n",
+      "                    Min  Max   Mean    SD   Class Correlation\n",
+      "    ============== ==== ==== ======= ===== ====================\n",
+      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
+      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
+      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
+      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
+      "    ============== ==== ==== ======= ===== ====================\n",
+      "\n",
+      "    :Missing Attribute Values: None\n",
+      "    :Class Distribution: 33.3% for each of 3 classes.\n",
+      "    :Creator: R.A. Fisher\n",
+      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
+      "    :Date: July, 1988\n",
+      "\n",
+      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
+      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
+      "Machine Learning Repository, which has two wrong data points.\n",
+      "\n",
+      "This is perhaps the best known database to be found in the\n",
+      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
+      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
+      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
+      "type of iris plant.  One class is linearly separable from the other 2; the\n",
+      "latter are NOT linearly separable from each other.\n",
+      "\n",
+      ".. topic:: References\n",
+      "\n",
+      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
+      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
+      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
+      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
+      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
+      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
+      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
+      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
+      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
+      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
+      "     on Information Theory, May 1972, 431-433.\n",
+      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
+      "     conceptual clustering system finds 3 classes in the data.\n",
+      "   - Many, many more ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.DESCR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris[\"data\"][:, (2,3)]  # petal length and width\n",
+    "y = (iris[\"target\"])  # 1 if Iris virginica, else 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Important variables\n",
+    "\n",
+    "X_with_bias = np.c_[np.ones([len(X), 1]), X] # Add column of ones for theta intercept term\n",
+    "alpha = 0.1\n",
+    "iterations=1500\n",
+    "\n",
+    "print(X.shape)\n",
+    "\n",
+    "# NOTE: If ValueError: all input arrays must have the same shape appears then you may have run this cel multiple times\n",
+    "#    which will have added multiple collumns of ones to the matrix X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup our proportions\n",
+    "\n",
+    "test_ratio = .2\n",
+    "val_ratio = .2\n",
+    "total_size = len(X)\n",
+    "\n",
+    "# Calculate size of our splits\n",
+    "\n",
+    "test_size = int(test_ratio*total_size)\n",
+    "val_size = int(val_ratio*total_size)\n",
+    "train_size = total_size - test_size - val_size\n",
+    "\n",
+    "# Split our data\n",
+    "\n",
+    "rnd_indices = np.random.permutation(total_size) # Shuffle our input matrix\n",
+    "\n",
+    "X_train = X_with_bias[rnd_indices[:train_size]]\n",
+    "y_train = y[rnd_indices[:train_size]]\n",
+    "X_valid = X_with_bias[rnd_indices[train_size:-test_size]]\n",
+    "y_valid = y[rnd_indices[train_size:-test_size]]\n",
+    "X_test = X_with_bias[rnd_indices[-test_size:]]\n",
+    "y_test = y[rnd_indices[-test_size:]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(90, 3)\n",
+      "(30, 2)\n",
+      "(30, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train.shape)\n",
+    "print(X_val.shape)\n",
+    "print(X_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_one_hot(y):\n",
+    "    n_classes = y.max() + 1\n",
+    "    m = len(y)\n",
+    "    Y_one_hot = np.zeros((m, n_classes)) # Setup zero matrix with m rows and a column for each class\n",
+    "    Y_one_hot[np.arange(m), y] = 1 # Fill in ones\n",
+    "    return Y_one_hot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 0, 0, 0, 1, 2, 0, 2])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 1.],\n",
+       "       [0., 0., 1.],\n",
+       "       [0., 0., 1.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [0., 1., 0.],\n",
+       "       [0., 0., 1.],\n",
+       "       [1., 0., 0.],\n",
+       "       [0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_one_hot(y_train[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y_train_one_hot = to_one_hot(y_train)\n",
+    "Y_test_one_hot = to_one_hot(y_test)\n",
+    "Y_val_one_hot = to_one_hot(y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Softmax function = exp(X) / (sum of exp(X))\n",
+    "\n",
+    "def softmax(logits):\n",
+    "    exps = np.exp(logits)\n",
+    "    exp_sums = np.sum(exps, axis=1, keepdims=True)\n",
+    "    return exps / exp_sums"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_inputs = X_train.shape[1] # Number of features\n",
+    "n_outputs = len(np.unique(y_train)) # 3 uniqure values which will each be a possible output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1.4567897105648775\n",
+      "500 0.7451993577978241\n",
+      "1000 0.6279369677273878\n",
+      "1500 0.5572702696067121\n",
+      "2000 0.5111859948576022\n",
+      "2500 0.47856473219026296\n",
+      "3000 0.45387932862540925\n",
+      "3500 0.43422780377165426\n",
+      "4000 0.41797875623202274\n",
+      "4500 0.4041537521442775\n",
+      "5000 0.39213163561158126\n"
+     ]
+    }
+   ],
+   "source": [
+    "eta = 0.01\n",
+    "n_iterations = 5001\n",
+    "m = len(X_train)\n",
+    "epsilon = 1e-7\n",
+    "\n",
+    "Theta = np.random.randn(n_inputs, n_outputs)\n",
+    "\n",
+    "# Cycle through set to apply batch gradient descent\n",
+    "\n",
+    "for iteration in range(n_iterations):\n",
+    "    logits = X_train.dot(Theta) # Logits which are raw predictions from applying X to Theta\n",
+    "    p_hat = softmax(logits) # Apply softmax to logits to get our probabilities\n",
+    "    loss = -np.mean(np.sum(Y_train_one_hot * np.log(p_hat + epsilon), axis=1)) # Compute loss function\n",
+    "    error = p_hat - Y_train_one_hot # Compute error \n",
+    "    if iteration % 500 == 0:\n",
+    "        print(iteration, loss)\n",
+    "    Grad = 1/m * X_train.T.dot(error)\n",
+    "    Theta = Theta - eta * Grad\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3.61613128,  0.06856255, -2.86225561],\n",
+       "       [-0.2597962 ,  0.80558911,  0.70553675],\n",
+       "       [-0.90831271,  0.18903751,  2.43558706]])"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9666666666666667"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Predictions\n",
+    "\n",
+    "logits = X_valid.dot(Theta)\n",
+    "p_hat = softmax(logits)\n",
+    "y_pred = np.argmax(p_hat, axis=1)\n",
+    "\n",
+    "accuracy_score = np.mean(y_pred == y_valid)\n",
+    "accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ch5/.ipynb_checkpoints/Exercises-checkpoint.ipynb b/Ch5/.ipynb_checkpoints/Exercises-checkpoint.ipynb
new file mode 100644
index 000000000..bc6125423
--- /dev/null
+++ b/Ch5/.ipynb_checkpoints/Exercises-checkpoint.ipynb
@@ -0,0 +1,657 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import os\n",
+    "\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
+    "y = iris[\"target\"]\n",
+    "\n",
+    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
+    "X = X[setosa_or_versicolor]\n",
+    "y = y[setosa_or_versicolor]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the data\n",
+    "\n",
+    "def plot_setosa_versicolor(X = X, y = y):\n",
+    "    plt.plot(X[:,0][y==0], X[:,1][y==0], 'bo', label='iris setosa')\n",
+    "    plt.plot(X[:,0][y==1], X[:,1][y==1], 'r^', label='iris verticolor')\n",
+    "\n",
+    "    plt.xlabel('petal length', fontsize=15)\n",
+    "    plt.ylabel('petal width', fontsize=15)\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_setosa_versicolor()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**EXERCISE 8**\n",
+    "\n",
+    "Train LinearSVC on linearly seperable data, then train SVC and SGCClassifier and compare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_decision_boundary(pipeline, X=X, y=y):\n",
+    "    # Get params for our decision boundary\n",
+    "    transfX = pipeline['scaler'].fit_transform(X)\n",
+    "\n",
+    "    Xmin = transfX.min()\n",
+    "    Xmax = transfX.max()\n",
+    "\n",
+    "    b = pipeline['clf'].intercept_\n",
+    "    w0 = pipeline['clf'].coef_[0][0]\n",
+    "    w1 = pipeline['clf'].coef_[0][1]\n",
+    "\n",
+    "    # get our input values to build line\n",
+    "    x0 = np.linspace(Xmin, Xmax, 200)\n",
+    "\n",
+    "    # Setup boundary\n",
+    "    # b + w0x + w1y = 0  ==> y = -w0/w1 * x - b/w1\n",
+    "    boundary = -(w0/w1) * x0 - b/w1\n",
+    "    margin = 1/w1\n",
+    "    top_gutter = boundary + margin\n",
+    "    bot_gutter = boundary - margin\n",
+    "\n",
+    "    # Plot our boundary and gutters\n",
+    "    plt.plot(x0, boundary, 'k-')\n",
+    "    plt.plot(x0, top_gutter, 'r--')\n",
+    "    plt.plot(x0, bot_gutter, 'b--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Preprocess the data\n",
+    "\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.svm import LinearSVC\n",
+    "\n",
+    "Linear_SVM_clf = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('clf', LinearSVC()) \n",
+    "])\n",
+    "\n",
+    "Linear_SVM_clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_setosa_versicolor(X = transfX)\n",
+    "plot_decision_boundary(Linear_SVM_clf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "         steps=[('scaler',\n",
+       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       "                ('clf',\n",
+       "                 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None,\n",
+       "                     coef0=0.0, decision_function_shape='ovr', degree=3,\n",
+       "                     gamma='scale', kernel='linear', max_iter=-1,\n",
+       "                     probability=False, random_state=None, shrinking=True,\n",
+       "                     tol=0.001, verbose=False))],\n",
+       "         verbose=False)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "SVC_clf = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('clf', SVC(kernel='linear')) \n",
+    "])\n",
+    "\n",
+    "SVC_clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sMfjxRxg6FIYMgapVnZYy+2RWfjIfl6bMjwRMOU7lcowxtG3bltmzZ3PgwAH++9//cvz4cQYNGkRYWBgPPfQQv//u0+ql+Y+MyjTeey9cdx08/zzUqAE9esCyZc7KmV0yKz+ppSkVUJ9CoOJyuWT58uXSt29fKVy4sABy4403yvTp0+Xs2bNOixfYZBXCuGOHre9QqZJ9FbFpvg8ccE5mT9CEckoa8IZPwRhzjTGmozGmW/rmG5WluMMYQ7t27fjwww85cOAAb7zxBkePHmXgwIGEhYXxyCOP8McffzgtZmCS9ok5mbRPzrVrwyuvwL598PTTdt+331rroWdP+OYb//Q9ZHZfWd2zkn/ISFOkb0Bd4DcgEXBl0BI9GccXLb9aChnhcrnk+++/l3vvvVcKFSokgLRu3VpmzJih1kN2yEmyuZgYkX/+U6RiRXtszZoiL70kkvZzz6w0ZWbk5LzslqzMbUI5jVoKOMjN4jVgJbAV6AFcDdRM3zwZxxdNlULGHD58WMaNGydXX321AFK2bFl55JFH5I/kQjWKb7hwwdYS6NTJKoaEBLt/+3aRunXtv1xWRejTk1nZSnfkdUI5TTYXcORWKZwBuntybF43VQqZ43K5ZNmyZdKnT58U66FNmzYya9YstR58TXI9jQsXRMqVu/QJ3NOn/szKVrojrxPKabK5gMSdUvDUp7ATCM3lTJXiAMYYOnTowJw5c9i/fz+vv/46f/31F/3796dq1ao89thjbNmyxWkxg5OSJe2rMZA+bLhNG1i/Pusx+vW7dPu++7I+xxdRRBq1lH/ISFOkb0Bn4FfgSk+Oz8umlkL2SUxMlCVLlkjv3r1TrIe2bdvK7Nmz5dy5c06LF3y4Kz85aZLtj40V+esvz8/LzFrI64RyGrUUsJDd6SNgHbA2TTsEXAS2pdu/FljrbhxfN1UKuePQoUPy6quvypVXXimAlCtXTh577DHZsmWL06IFD+7KT9ata/sfflikYEGRu+8W+e47kcTEzM/LzLeQ1wnlNNlcwOJOKbhd0WyMmQ54vNxZRAZl307JPflhRXNe4HK5WLZsGZGRkSxYsICEhATatWtHeHg4vXr1okiRIk6LGLgULWpXCacnNBTOnYNt2yAqCqZPh7g4G/L68MPwz39mfl5GXH89bNhw+f7GjT2brsrumOD96yl5grsVzZrmQrmMQ4cOMW3aNCZNmsSuXbsoX748AwcOZNiwYVx77bVOixe8nD8Pn31mFUTt2jBlit3/00/QsiWEaAICxXvkSikYY6YCY0VkdwZ9NYHnRWSwVyTNJqoUfIfL5WLp0qVERkby+eefk5CQQPv27YmIiKBnz55qPfiShASbkC86Gpo3t0oiPBwGDoRKlZyWTgkCcpv7aCBQ0U1fBWBADuVS/JiQkBBuvvlmPv30U/bt28d//vMfYmJi6Nu3L9WqVeOpp55i+/btTosZnBQsaF/r14fZsyEszE4nVasGffpAbKyz8ilBS3bsUXcmRX3giBdkUfyYypUr8/TTT7Njxw6+/fZb2rZty/jx47nmmmvo2LEjH3/8MRfTFzJRck9oKNx/P/zwA/zxB4wcCevWQZkytv+XX+CI/vsp3iMzR/MjwCNJmzWBv4AL6Q4LBa4ApovIEF8JmRk6feQcBw8eZOrUqUyaNImYmBgqVqzIwIEDCQ8P56qrrnJavODF5bL+BRGoVw927oS77rLTS+3b23URipIF2fYpGGNuBroABngc+BA4mO6wi8CfwFwRSa8w8gRVCs6TmJjId999R1RUFAsXLiQxMZGOHTsSERHBnXfeSeHChZ0WMXjZvNk6pmfMgBMn4Jpr4D//gV69nJZM8XNy62h+HpgsIgd8IVxuUKXgX8TGxqZYD3v37qVSpUoMGjSIYcOGUbt2bafFC17OnYNPP4XISHjoIet3OHzYKo127dR6UC4jYEJSjTFdgf8BBbCK6NXMjlel4J8kJiayePFiIiMj+fLLL0lMTKRz586Eh4fTo0cPtR58iYhVAq++alN7X3utnVp64AGoUMFp6RQ/ISfTR9kqKSUiHXMoW9prFsCumL4Z2I9dVd1XRDa7Oyc3SmH0aKhVy/rxSpfO0RCKBxw4cCDFeti3bx+VKlVi8ODBDBs2jCuvvNJp8YKXs2fhk0+s9bB6NRQubC2I6dN1zYOSo5DUuHTtGuAmoBg2a2oxoA02lfZRL8nZAtghIrtE5CIwB5uu2+vEx8P339tgjrAwW3Z33Tr7kKV4l6pVq/Kvf/2L3bt3s2jRIlq2bMnrr79O7dq16dKlC/PmzSM+Pt5pMYOPYsVgwAC7+G3TJmstJDupAebNg2PHnJVR8T8yyn2RvgFDgE1AjXT7ayTtH+bJOB5c527slFHydn9gQgbHhQPRQHSNGjVynPvD5RJZu1ZkyBCRYsVs2pZ3383xcEo22Ldvn7z44otSvXp1AeSKK66Qp59+Wnbt2uW0aPmDAwdEjBEpUkSkXz+RH36w/xBKvoFc1lPYBdzppq8nsNuTcTy4zj0ZKIV3MjvHWwnxTpwQee89kf377fbChSLDhomsW+eV4RU3JCQkyBdffCG33367hISEiDFGunTpIvPmzZOLFy86LV5ws3GjyMiRIqVKpSbo0z/4fIM7peDpxGJlwF1OgyKAt9bd7weqp9muBuTJ0s3SpWHECKha1W7v2gUffGAzDDRtaqP+Tp/OC0nyFwUKFKB79+4sXLiQPXv28Nxzz/HHH3/Qq1cvatSowTPPPMOePXucFjM4adgQJkywq6OnToWyZaF60r/fTz/BqlU6n5ofyUhTpG/AV8AeoFm6/c2BGOArT8bx4DoFsVbJP4DCwEagXmbn+DJ19okTdjqpYUP7INWkic8upaQhPj5eFi5cKLfddluK9dC1a1f57LPP1HrIK269NdV6eOstkWPHnJZI8TLkcvqoGrbITiL2yX1D0msisB6o5sk4Hl6rGzYCaSfwTFbH50U9BZdLZPVqka+/tttnz4p07CgSGZlacVHxDTExMfLcc89JWFiYAFKlShV59tlnZc+ePU6LFtycOSMyebJI8+b2ZyI0VOT5552WSvEi7pRCttYpGGO6JVkHlbFpL9aJyFc5MlG8hBPrFLZuhbvvht9/hxIlbEhrRIRNO6/4hoSEBL766isiIyP5+uuvAejatSvh4eF0796dgskJ5BTvs369nT9t2tSG6Z05Y8Na+/VLzcGkBBwBs3gtuzi1eE0E1qyxIeAff2xT4W/YAI0a5bko+Y6YmBimTJnClClTiI2NJSwsjCFDhjB06FBq1KjhtHjBz7x59qmoaFG77iE83NZ70FXTAUVOFq8VE5Gzye+zukDysXmNP6xoPn4cFi60C0aNgSefhL//ttZDcnEqxfskJCTw5ZdfEhUVxTfffAPArbfeSkREBN26dVPrwZf8+qu1Hj74wFoODRrYTK5qOQQMOVEKiUArEVlrjHGRRWlOESngFUmziT8ohfSMGmWLZp0/Dy1aWOXQpw8UL+60ZMHLnj17UqyHgwcPUrVq1RTroXr16lkPoOSM06dhzhy7YnrqVLtvyhSbvfWGG9R68GNyohQGAF+KSJwxZiBZK4UZ3hA0u/ijUgBrPcyaZaeXNm+GRx6Bt97y/PwPPoBnnoG9e6FGDXj5Zeu7UDInPj6eL7/8ksjISBYvXowxhm7duhEeHs6tt96q1oOvuXjRFgI6csSGvIaHW9+D5pHxO9wpBa9EDDnZ8iL6KDe4XCKrVons3Gm3f/hB5IYbRKZMsQEeGTF7duoK6+RWrJjdr3jOrl275P/+7/+kcuXKAki1atXk+eefl3379jktWnBz6pTIxIki11+f+sc7Z47TUinpIJchqWOxSepKeHJ8XjZ/Vwrp+eYbkeuus598qVJ2QenGjZceU7PmpQohudWs6YTEgc/Fixdl3rx50qVLFwEkJCREbr/9dvnyyy8lISHBafGCm+homxpgyxa7vXq1TR1w8qSzcim5C0k1xvwG1AVc2AVlK5PaKhE57B1jJmf46/RRZojYxaKRkTYFfqlScOAAFCpk+woUyHghqTE2n5mSc3bt2sXkyZOZOnUqhw4donr16gwdOpTBgwdTrVo1p8ULfp56Ct54wybr69vXOtyaNVPfgwPkOiTVGFMWmyU1uTXB1jzYDqwUkWHeE9dzAlEppCUuzpbebdvW/uA3awY7dmScUqNmTdCMD94hPj6ehQsXEhkZyXfffUdISAjdu3cnIiKCW265hQIFHImbCH5EIDraPhF99JFN792tGyxa5LRk+Q6vrlMwxhQGOgOjgbaAiEYf5ZoTJ2zk0ty5NrV3WooVsxGA6mz2Pjt37kyxHg4fPkyNGjUYOnQoQ4YMISwszGnxgpdTp2xERUiItRgSEuCf/7QWRLPL/Z+Kd8mVoxkoBdwK/Ac7bXQOOAIsAJ4AWngyji9aoPkUPOHoUZH77hMpWND6EipWtE7m5Onv2bOtf8EY+6oOaO9w4cIFmTt3rnTu3FkAKVCggPTo0UO++uor9T3kBRs2pEZYNGlindWaR8ZnkEtHcwLwN/AhEEEWSerysgWjUkjG5RJZsUIkPt5uP/OMyDXXiBQuLBqZ5GO2b98uo0ePlooVKwogNWvWlLFjx8qBAwecFi24SZ+FsnhxkT/+cFqqoMSdUvDU0bwa60M4BfyQZC2sADaIJwP4kGCaPsqKyZNteu+EhMv71N/gGy5evMiCBQuIiopi6dKlFChQgNtvv52IiAi6dOlCiJa19A0isHatnUsdN85OMb33no3G6NvXJh1TcoU3HM1FgZZYH8JNSe8TgJ+AFSLymvfE9Zz8pBTAfZCGMXYFdeHCeStPfmL79u1MmjSJadOmcfToUWrVqsWwYcMYNGgQVapUcVq84KdTJ1i2TLNQegmvLl7D+hh6AMuxYaqJORnHGy2Yp48ywt0ahrAwkXLlRB59VGTzZqelDG7Onz8vc+bMkQ4dOgggBQsWlLvuuku+/fZbSUxMdFq84MXlEvnpJ5EBA2wqb7BzqkqOIDeV14wxlY0x9xhj3jbGrAfigHlAWeBdoK8XFJfiAS+/bCOR0lKsGDz6KHTuDO++C3XrQrt2NrDjwgVn5AxmihQpQp8+fVi2bBlbt27l0Ucf5YcffuCWW27hqquu4pVXXuGvv/5yWszgwxho1cqm7Y6Nhbffhh49bN/vv9u51fXrHRUxKMhIU6RvWGvgPHaq6DXgNqCMJ+f6uvmLpZCTiKBOnS592u/UybPxRowQKVDAnlOggN1O5tAhkddeE6ld2z5MJRfM+vtvjVryJefPn5cPP/xQ2rdvn2I99OrVSxYvXqzWQ14wc2aq9dC8uS0Q5C6PjCIi7i0FT5VCByDUk2PzuvmDUshJrqL0CiGtYshsPE+vlZh4adDGlVeKhIRkT0YlZ/z555/yxBNPSPny5QWQK6+8Ul555RX566+/nBYtuImLE/nf/2wJ0eQ51eTQPeUy3CkFLbLjBWrVgpiYy/dnFhGU2ar+mjXdjwfZv1ZiIlSoYBfHZec8JXecP3+ezz77jKioKFasWEHBggW58847iYiIoGPHjhq55CtE4KefYNs2GDTIbg8caOdUNYd9Clp5zYeEhGQ/V1FmSsEY9+NBzvIiZSbj33/b/iJF3J+v5I4///yTqKgoZsyYwbFjx6hdu3ZK5FKlSpWcFi+4OXrU5pHZssUmGuvf30YuNWjgtGSO4k4p6KOKF3BXATKnlSEzGy+n18rsvEmTbAr8p56C7ds9l1PxnOuuu44333yTAwcOMHv2bMLCwhgzZgzVqlWjT58+LF26FJdmO/QNFSrYBGM//AB33GEX/DRsCF984bRk/klGc0qB1NSn4JlvILPzfvxRpFev1LQaHTuKfPyxjQBUfMfmzZvl0UcflbJlywogV111lbz++uty6NAhp0ULbo4eFXnrLZGzZ+32+++LjBolsmmTs3LlMeTG0ezPzR+UgkjeRh/lNIooq/NiY0VeflmkVi2Rtm1T9+tvlG85e/aszJo1S9q0aSOAFCpUSPr06SPLli0Tl2pm3zNmTGrumFatRKZPT1UYQYwqBR/jLkw0pz/8meHr0NLERJHkQJm//rL/L8nWw4UL3r2Wcim///67PIrEiTkAACAASURBVPLII1KmTBkB5Oqrr5Zx48bJkSNHnBYtuDlyROS//xW59lr7j9qjh9MS+ZxsKwVgHbDW0+ZuHF83f1AKI0Zc+sOfdpVxTqaIMiOvS3UeOyby0kupK6krVhQZPVrk4EHfXE+xnD17VmbMmCGtW7cWQAoXLiz33nuvfP/992o9+BKXS2T5clshTkRk/36Rm24SmTEj6KwHd0rBbfSRMWY64HFokogMyr5HI/f4Q/RRwYI27DM7ZBZ2mlmIaE7CX71BYiIsXmxro3z1lY32q1ULDh6E8uU155Iv+f3334mKimLmzJmcPHmSa665hvDwcAYMGECFChWcFi+4Wb3ahrNu2wZlysADD0B4ONSr57RkuUZDUn1ITioJZhZ2mtPQ0rwKXjl2DMqVs++7d7eFtAYNgmHD4Mor80aG/MjZs2f55JNPiIyMZPXq1RQuXJi7776b8PBw2rZti9GSlr5BBFassE9E8+bZf7TYWAjwUGK/D0lNyq30hzHGZYwJqLJLOanc6IvQ0rwiWSGArRTXsiW8/jrUrg233GItCsX7FCtWjAEDBvDTTz+xadMmwsPDWbRoEe3bt6dOnTq8+eabxMXFOS1m8GEMtG9vy4ceOAAff5yqEO69Fx57zK6BCBI8VgrGmFrGmGeNMTONMXPTNy/I8jtwF7ZeQ0ARHp7xfneVHDt1cp/Y7uWXM79WTs/zFV27woIFsHcvvPii/d9INtwuXIBdu5yRK9hp0KAB77zzDrGxsUybNo2yZcvyxBNPULVqVfr168fKlSsJ9FkAv6RiRejVy75PSLAKIzkLZdu2Ngvl+fPOyphbMnI0pG9AU+AMsAVIBNYDu7CJ8vYCyzwZx8NrLQeaeXp8ThzNOY3eySwRXZkylzp/y5Sx+zNyNCdTqNCl+wsVSu1L76QOC/NMDl/cd3ZISEj1x82ZY6/VpYvIvHkiFy96/3pKKhs3bpSRI0dKqVKlBJA6derI+PHjJS4uzmnRgpu0WShB5M037X4/DwgglwnxlgEzgAJJiqBJ0v4bgRigqyfjeHgtnyqFnEbvuIswGjEiNf+Wp61QIZGiRTPuK1rUfdRSWFjgRC2J2MCNF14QqVbNXq9yZZH/+z+Rc+d8d01F5MyZMzJlyhS54YYbBJAiRYpIv379ZOXKlRq55EsSE0WWLLGL40Rs5tZ27UQ++MAv/+hzqxSOAbcAJkkp3JimbzC2LKcn4yzBThOlbz3SHJOlUgDCgWggukaNGtn6INwVqalZM/Pzkp/M0zd3+33Vcip/Ts/zBvHxIgsXitx2m0i9eqkPUBs3ahJLX7NhwwYZMWKElCxZUgCpW7euvPXWW3IsOae64js+/NCmJwaR8uVFHn9c5M8/nZYqhdwqhTigQ9L7v4C+afpuBv72ZBwPr+VTS8GYjH8cjcnqA/SPllP5c3qetzl/3r6eOSNSqpRIlSoizz4rsmdP3sqR3zhz5oxMnjxZWrRoIYCEhoZK//79ZdWqVWo9+JLERJHvvhO5+26bR6ZRo0v7HMSdUvDU0bwZqJ30fjXwmDHmamNMTWA0sNPDcRwnp9E77iKMchJ5lBsCOWoJUjOxFikCs2ZBkybWSf6Pf0C3bvDrr3krT36hePHiDBkyhJ9//pn169czcOBAFixYQJs2bWjQoAFvv/02x48fd1rM4CMkxJZE/OQT2LcPpkyx+0+dsuF6Tz1l10D4ExlpivQN6A88m/S+DrAP63BOBE4BXTwZJ4tr9AT2AxeAQ8C3npynPgX/9Sl4SkyMyL/+Ze9v3Tq7b/dutR58zenTp2XSpEnSrFmzFOthwIAB8uOPP6r14GtiYi7NQtmhg8hHH6Wa0nkA3sx9BJTAThvdAVTKyRjeat6OPsppGcz0iqFu3eQP/vKWTPpKaCEhqX2ZRR/5a86k3JLWvzBokJWzWzeRzz9X34Ov+eWXXyQiIkJKlCghgNSvX1/eeecdOX78uNOiBTdps1CCdbSJ5MkffK6UAvAAUN5NXzngAU/G8UXzZu4jbz9NZ2ZdZNanWEvh2WetzwFEqlYVeeMNp6UKfk6dOiVRUVHStGlTAaRo0aIycOBAWb16tVoPviQxUWTVqtTtfv1skjQfZqHMrVJIBFq46WsKJHoyji+aN5WCtyN0MotYyqxPSSU+XmT+fJFbbxV59FG7z+US+eYbuyZC8R3R0dESHh6eYj00aNBAJkyYICdOnHBatOBn3LjLs1Bu3+7VS+RWKbgyUQpdgOOejOOL5k2l4O0InZxGGCkZk/yg+v339nOqVs2ug9i3z1Gxgp5Tp07JxIkT5frrr0+xHgYNGiRr1qxR68GXJCSIfP21yJ132qfFp56y+xMTvWI9uFMKmWVJ7QH0SNocCCwCjqQ7LBS4CdgiIl08c217F28mxPN2BlJ32VOTI5bc9SUkZP9a+Yn4eFtJMSrK5lkyBm67zW5Xruy0dMFNdHQ0kZGRfPTRR/z99980atSI8PBw7r//fkqXLu20eMFLbKz9cbjiCvj2W5ut9Zln4OGHczxkThLiVQIaJDWwIakN0rWawGIgIseS+RHezivkLidSeHjmfUrmFCoEd90F33wDO3fCmDE291Jyor4ffoD9+52VMVhp1qwZkyZNIjY2lvfffx9jDCNHjiQsLIwhQ4awdu1a3D1oKrkgLMwqBLC56lu18l1a5IzMh/QN+B6o48mxed28XWTH2xE6mUUs5TSHkXI5ybMYLpf93kJCRO64Q2TRIvU9+BKXyyVr166VIUOGSLFixQSQxo0by3vvvScnT550WjwlE8ju9JE7jE3aXgU4LCKOT3T4Qz0Fxb/YuRMmT4apU+HwYbtA74034J57nJYsuDl58iQffvghkZGRbNy4keLFi9O3b1/Cw8Np1qyZ1nvwM3JdT8EY080Y8zNwHrt4rWHS/knGmH5ek1RRcknt2vDKK3YB6SefwLXXQokStm/vXvj66+xXylOypnTp0owYMYL169fz888/06dPHz788ENatGhB06ZNiYyM5PTp006LqWSBR0rBGPMAsBD4E5uMLq3K3wYM8b5oipI7CheGu++2zuhbb7X7pkyx6TRq17a+ooMHnZUxGDHG0KJFC6ZMmUJsbCwTJkwgISGB4cOHU6VKFcLDw/nll1+cFlNxg6eWwjPAOBEZAMxO1/cHUNerUimKj3jmGZg7F666Cp59FqpXh759bTCw4n1Kly7NyJEj2bhxI6tXr6Z3797Mnj2bZs2a0bRpU6KiotR68DM8VQo1ge/c9J0HSnlHHEXxLYULW9/CkiU2D9kTT0CFCql1tqdNU+vBFxhjaNmyJVOnTiU2NpZ33nmHixcvEhERQVhYGBEREfyq2RD9Ak+Vwj7gejd9zYAd3hFHUfKOq6+G116Dd96x27t3w+DB1jHdq5cNB/dV1F9+pkyZMowaNYpNmzbx008/0atXL2bOnEnTpk1p3rw5kyZN4syZM06LmW/xVClMAZ5PcigXTdpnjDGdsKmzJ/lCOEXJS/7xD9i6FR591K516NrVTjPpA6xvMMbQqlUrpk+fTmxsLG+//Tbnzp0jPDycsLCwFKe1krd4FJKaFIY6ARiOzYNUEIjHlueMFJGRvhQyMzQkVfEFFy7A/PkwYwbMmQOlS8N3SROonTrZNPmK9xERVq9eTWRkJHPnzuX8+fM0b96ciIgI+vTpQ4nkMDIl17gLSc3WOgVjzFVAJ6A8tkTnMhFxtEKEKgUlr+jcGZYutZFLw4bBwIGpi0wV73Ps2DFmz55NZGQkmzdvpmTJkvTr14+IiAgaNWrktHgBj1eUgj+iSkHJK86ft9ZDZCSsWGHTbTz9NLz4otOSBTciwo8//khUVBRz587lwoULtGjRIsV6KF68uNMiBiTeWLxW2BgTboyZbIxZlPQ6zBhT2LuiKop/Ehpqw1eXL4ctW+Chh6BePdsXF2ed1ocPOypiUGKMoU2bNsycOZPY2FjGjx/P6dOnGTJkCGFhYYwcOZJNmzY5LWbQ4KlPoQ7wDRAG/AIcxibMawL8BXQVkc0+lNMtaiko/sBHH8F991nroWdPm9iwQwf1PfgKEWHVqlVERkby6aefcuHCBVq2bEl4eDh9+vShWPrMlspl5Gr6yBizEigNdBeRvWn218Cm1D4uIm29KK/HqFJQ/IUtW2z67hkz4PhxuOYa+OWX1BQbim+Ii4tj5syZREZGsnXrVkqXLk3//v0JDw+nQYMGWQ+QT8mtUjgH9BWRBRn09QQ+FJGil5/pe1QpKP7G+fMwb54NZf3vf+2+8eOhcWNo3z51oZziXUSElStXplgPFy9epFWrVoSHh9O7d2+1HtKRW5/CHmxBnYwIBfa66VOUfEdoKNx/f6pCOHvWJujr2NEm53vjDTiSvlyVkmuMMbRt25YPPviAAwcO8N///pe4uDgGDRpE1apVefjhh/n999+dFtPv8VQpjAFeMsbckHanMaYl8G/gn94WTFGChWLFbEW/mTOhUiV46imoVs1mcFV8Q4UKFXj88cf5888/Wb58ObfeeiuRkZE0aNCA1q1bM3PmTM6dO+e0mH6Jp9NH67D5j8pjnczJjuZKQBzWkkhBRFp4W1B36PSREmj88Yf1PTzxhE2psXQpbNgAAwbYPEyKbzh69CgzZswgKiqKbdu2UaZMGR544AEiIiKoWzf/5fTMrU9hWnYuJiKDsnN8blCloAQ6Tz5pp5qSU32Hh0Pbtup78BUiwvLly4mKimLevHnEx8fTpk0bwsPDufvuuyla1BH3aJ6ji9cUxY/57TeYNMlOMZ08aWs+LFrktFTBz5EjR5g+fTpRUVHs2LGDsmXLplgPderUcVo8n6JKQVECgLNnra+hcGG7UO78eXj4YejfH9q0UevBV7hcLpYvX05kZCTz588nPj6em266KcV6CA11F2cTuKhSUJQAJDra5lw6eRLq1LFTSw88AOXKOS1Z8HL48OEU62Hnzp2UK1cuxXq47rrrnBbPa+Q6zYWvMcaMM8b8aYzZZIyZb4wp47RMiuI0zZrBgQMwdSqUKgWPPQZhYbBDK5j4jEqVKjF69Gi2bdvGkiVL6NSpExMmTKBOnTq0a9eODz/8kPPnzzstps/wG0vBGNMFm3U1wRjzGoCIZBnqqpaCkp/YuBEWLIDnnrNTSa++CkWLWuuhbFmnpQteDh06lGI97Nq1i/LlyzNgwADCw8O59tprnRYvRwTU9FHSKum7ReT+rI5VpaDkV0SgSxdbWjQ01JYZjYiAG29U34OvcLlcLFu2jMjISBYsWEBCQgLt2rUjIiKCu+66iyJFijgtosf4/fRROgYDX7vrTMrWGm2MiT6iS0OVfIoxtvDP+vUwaJC1INq00VTeviQkJITOnTvzySefsG/fPv7zn/+wd+9e7rvvPqpWrcqTTz7Jtm2OlpjJNXlqKRhjlgCVM+h6RkQ+TzrmGWzd57vEA+HUUlAUy5kz8PHH0KoV1K0La9bAxInWOd2qlVoPvsLlcrFkyRKioqL4/PPPSUhIoH379kRERNCzZ0+/tR4CYvrIGDMAW/Kzk4ic9eQcVQqKkjEzZ8LIkVZZ1K9vlUP//lBGQzh8xsGDB5k2bRqTJk1iz549VKhQgUGDBjFs2DCuvvpqp8W7BL9XCsaYrsCbQDsR8XhOSJWCorjnzBlb6yEqyoa3Vqtm8zBpnQff4nK5+O6774iMjGThwoUkJibSsWNHwsPD6dmzJ4ULO1+bLBCUwg6gCDaXEsAaERme1XmqFBTFM379FXbtsqk0XC5bDKhLF+jXD0qXdlq64OXgwYNMnTqVSZMmERMTQ8WKFVOsh6uuusoxufxeKeQUVQqKkn0OHoTu3a2iKFoU7r3XRi61aKG+B1+RmJiYYj188cUXJCYm0qlTJyIiIujRo0eeWw+BFn2kKIoPqVLFVoWLjraWwty50LIlLF7stGTBS4ECBejatSvz588nJiaGsWPHsn37dnr37k316tUZM2YMO3fudFpMtRQURYHTp61ieOABW2f69ddh2zbrnG7eXK0HX5GYmMi3335LVFQUX3zxBS6Xi86dO6dYD4UKFfLZtdVSUBTFLSVLwpAhViEAnDoFc+bADTdAkybw/vt2n+JdChQoQLdu3ViwYAF79+7lxRdfZOvWrdxzzz1Ur16dp59+ml27duWpTKoUFEW5jJdegthYeO89u3L6wQet1aD4jqpVq/Lcc8+xe/duvvzyS1q0aMHrr79O7dq1ueWWW1JqP/ganT5SFCVTRGDdOptKo2FD2L7dOqbDw+G++6yVofiG/fv3M2XKFCZPnsz+/fupXLkygwcPZujQofzjH//I1dg6faQoSo4wxkYlNWxot48cgcREGD7cOqzDw63TWvE+1apV4/nnn2f37t188cUXNGvWjFdffZXatWvTtWtX1q9f7/VrqlJQFCVb3Hijzbe0Zg307g2zZ0Pr1nDihO0P8MkHv6RgwYJ0796dL774gj179vDcc8/x+++/E+KDVYg6faQoSq44eRJ+/tkuhAP7euWV1oJo0sRZ2YKZxMREChQokOPzdfpIURSfULp0qkK4eBGqVrV5l5o2teGskybZdBuKd8mNQsgMVQqKoniNwoVh2jQbufT223DunLUYPvjA9rtczsqnZI0qBUVRvE6ZMvDQQ/Dbb/DjjzZKCex6hxYtYMoUtR78FVUKiqL4DGOsYzo5bLViRTh7FoYOtbWmH3zQlhhV/AdVCoqi5Bm9e1vrYdUqm6V12jRb8yGZhATnZFMsqhQURclTjLEhrDNmwIED1hENdv1DWBiMGgWbNjkrY35GlYKiKI5RrhzUqWPfnzsHt9wCkydDo0a2hOi0aXa6Sck7VCkoiuIX1KgBs2ZZ62H8eLsYbvBg+Osv23/+vLPy5ReCcvFafHw8+/fv57z+FfkNoaGhVKtWzaepgJXgQsROIzVqZLd79LBTTOHh1jdRrJiz8gU67havFXRCGF+zf/9+SpYsSa1atTCaCN5xRIS4uDj279+f6yReSv7BmFSFANCpk83aOmgQPPYY9O9v8y/VreucjMFIUE4fnT9/nvLly6tC8BOMMZQvX14tNyVXPPwwbNkCK1ZAt24QGQnz5tm++Hjrk1ByT1AqBUAVgp+h34fiDYyBtm3tCukDB2ykEsDnn9vIpUcegT/+cFbGQCdolYKiKMFNhQpQtqx9f+WV0LWrXTFdvz60aWOd1omJzsoYiKhSwD511KoFISH2NTlPS2648cYbc9SXG5YvX85PP/3kk7EVxZ9p0gQ++shaD+PGweHDMHas/Z8GOHrUWfkCiXyvFD74wEYzxMTYaIeYmEsTeOWUjH6cE5MeW3z1w61KQcnvVKwITz4JW7fC99/b6aZz5+Caa+y00+zZGtqaFfleKTzzzOWLY86etftzQ4kSJQD7Q92hQwfuu+8+GjRocEnfwYMHadu2LY0bN6Z+/fqsXLnysnHGjBlD3bp1adiwIU8++SQAR44coVevXjRv3pzmzZvz448/smfPHiZOnMj48eNp3LgxK1euJCYmhk6dOtGwYUM6derE3r17Afjkk0+oX78+jRo1om3btgDs2bOHm266iSZNmtCkSRNVLkpAY4xN4Q12Cunpp+HgQRuxVLUqPP64fQBUMkBEAro1bdpU0rN58+bL9rnDGBFrI1zajPF4iAwpXry4iIh8//33UqxYMdm1a9dlfW+88Ya89NJLIiKSkJAgp06dumSMuLg4ueaaa8TlcomIyPHjx0VEpG/fvrJy5UoREYmJiZHrrrtORESef/55GTduXMr53bt3l+nTp4uIyJQpU6RHjx4iIlK/fn3Zv3//JWP+/fffcu7cORER2bZtm2T0ueaW7HwviuJtEhNFli4V6d1bpFAhkTVr7P4jR0SS/vTzFUC0ZPCbGpTrFLJDjRoZPzHUqOG9a7Ro0SLD+PzmzZszePBg4uPjufPOO2ncuPEl/aVKlSI0NJShQ4dy22230b17dwCWLFnC5s2bU447deoUp0+fvmz81atX89lnnwHQv39/Ro8eDUDr1q0ZOHAgvXv35q677gLsgr9Ro0axYcMGChQowLZt27xz84riJ4SEQMeOth09CuXL2/1PPw2ffQYDBtip4+uuc1ZOp8n300cvv3z5yshixex+b1G8ePEM97dt25YffviBqlWr0r9/f2bOnHlJf8GCBVm7di29evViwYIFdO3aFQCXy8Xq1avZsGEDGzZs4MCBA5RMzk2cCclhoRMnTuSll15i3759NG7cmLi4OMaPH88VV1zBxo0biY6O5uLFi7m8a0XxXypUsFNMAPffbxXFO+/YPEzt28P8+Y6K5yh+oxSMMWONMZuMMRuMMYuNMWF5cd3774eoKKhZ0/6R1Kxpt++/3/fXjomJoVKlSgwbNowhQ4bw66+/XtJ/5swZTp48Sbdu3XjrrbfYsGEDAF26dGHChAkpxyXvL1my5CUWw4033sicOXMA+OCDD2jTpg0AO3fu5IYbbuDf//43FSpUYN++fZw8eZIqVaoQEhLCrFmzUpziihLstG8Pn3wC+/bBK6/A3r2wdKntE4GdOx0VL+/JaE7JiQaUSvP+YWCiJ+fl1qfgK9L6FG677bYM+6ZPny716tWTxo0bS5s2bS7xO4iIxMbGSvPmzaVBgwZSv379FP/AkSNHpHfv3tKgQQOpU6eOREREiIjI1q1bpUGDBtKoUSP54YcfZPfu3dKhQwdp0KCBdOzYUWJiYkREpGfPnlK/fn2pV6+ePPzww+JyuWTbtm3SoEEDueGGG2TMmDEpMnoTf/heFCUrEhNFTp+271essD7GDh1EPvpI5Px5Z2XzJrjxKfhlQjxjzNNADREZkdWxGSXE27JlC3WS8/EqfoN+L0qgceSITeU9aRLs3m2nnQYOhGefhdKlnZYud7hLiOc300cAxpiXjTH7gPuB5zI5LtwYE22MiT5y5EjeCagoSr6iYkXriN6xA779NjXFRmio7f/zTwg291ueKgVjzBJjzO8ZtB4AIvKMiFQHPgBGuRtHRKJEpJmINKtYsWJeia8oSj4lJAS6dLEJ+HbuhCJFwOWyqTWqVYPRo63iCAbyVCmISGcRqZ9B+zzdoR8CvfJSNkVRFE8oWjT1/cSJtrTom2/C1VdD586QwRrUgMJvpo+MMVen2bwD+NMpWRRFUbIiJMRaCvPn24ilsWNh+3ZbMQ7g0KHAjFzyG6UAvJo0lbQJ6AI84rRAiqIonhAWZp3Pu3bZWg9g1z1cdRXcfDN8+qmt+RAI+M2KZhHR6SJFUQKaAgVS348YYX0PkyfDPffAFVdARAS8+KJz8nmCP1kKznLwILRrl1olPJc4kTo7I6ZPn05sbGzK9tChQy9JkZGdcUaNcuv7VxQlHVWrwr/+Za2HRYvghhvs9FIy33/vn9aDKoVkxo6FVavsqxdwInV2RtdLrxQmT55M3TwoapuQkODzayhKIFCggJ1S+vxzm7obbChrx45QvTr83//ZNRD+gioFsFbCtGk2xmzaNK9YC95Inf3111/Tu3fvlO3ly5dz++23A7B48WJatWpFkyZNuOeeezhz5gwAtWrV4t///jdt2rTho48+Ijo6mvvvv5/GjRtz7tw52rdvT/Jiv2+++YYmTZrQqFEjOnXqBMCxY8e48847adiwIS1btmTTpk2X3Zu7lNwDBw7k8ccfp0OHDvzzn//M9WeoKMFGctGfq6+GL76AFi3gtdegdm245RY/CWvNaJlzIDWvpLkYMUKkcGG7nr1wYZEHH8ze+RngjdTZ8fHxUr16dTlz5oyIiAwfPlxmzZolR44ckZtuuill/6uvviovvviiiIjUrFlTXnvttZQx2rVrJ+vWrbts+/Dhw1KtWrUUueLi4kREZNSoUfLCCy+IiMjSpUulUaNGIiIybdo0GTlypIi4T8k9YMAAue222yQhISHDz0TTXCjK5ezdK/L88yJXXy1y7Jjdt26dyO7dvr0ubtJcqKWQbCUkL0u8eNFr1kIymaXOnjZtGi+88AK//fbbZZlOCxYsSNeuXfniiy9ISEhg0aJF9OjRgzVr1rB582Zat25N48aNmTFjBjFp8n/36dMnS5nWrFlD27ZtU+QqV64cAKtWraJ///4AdOzYkbi4OE6ePHnJuatXr+a+++4DbEruVatWpfTdc889FEjrbVMUJVOqV4cXXrDV4pJrTo8aZetO33orLFgAeTkbq0ph7Fg7bZSWxESv+RYg56mzwf7Az507l2XLltG8eXNKliyJiHDzzTenpM7evHkzU6ZMyfJ6aRGRlFTa6fenJ6Pj3PV7cm1FUS4n7b/Z3LnWSf3bb9Czp63vMnly3sihSmH16suTl1y8CHngDM4qdTZA+/bt+fXXX5k0aVKKBdCyZUt+/PFHdiRNQJ49e9ZtUZz06bSTadWqFStWrGB3kofr2LFjgFVUHyQVqF6+fDkVKlSgVKlSl5zrLiW3oijeoUYNG7q6Z491UDdpkhruevy43ecr68Fv1ik4xvr1jl16+fLljBs3jkKFClGiRIkMLYUCBQrQvXt3pk+fzowZMwCoWLEi06dPp2/fvly4cAGAl156iWuuueay8wcOHMjw4cMpWrQoq1evTtlfsWJFoqKiuOuuu3C5XFSqVInvvvuOF154gUGDBtGwYUOKFSuWcs20vP322wwePJhx48ZRsWJFpk2b5q2PRFGUNBQsCHfcYVsyH39s10D07w8Z/GTkGr9MnZ0dNHV24KDfi6LknoQEu+7hiiugZcucj+MudbZaCoqiKAFEwYLQo4fvxlefgqIoipJC0CqFQJ8WCzb0+1CUwCAolUJoaChxcXH6Q+QniAhxcXGEJperUhTFbwlKn0K1atXYv38/WqrTfwgNDaVatWpOi6EoShYEpVIoVKhQhiuIFUVRlMwJyukjRVEUJWeoUlAURVFSUKWgKIqipBDwK5qNMUeAmCwP9B8qAEedFiKX6D34B8Fwq+WaigAAB/9JREFUDxAc9xGI91BTRCqm3xnwSiHQMMZEZ7S0PJDQe/APguEeIDjuIxjuIRmdPlIURVFSUKWgKIqipKBKIe+JcloAL6D34B8Ewz1AcNxHMNwDoD4FRVEUJQ1qKSiKoigpqFJQFEVRUlCl4GOMMfcYY/4wxriMMW5D1owxXY0xW40xO4wxY/JSxqwwxpQzxnxnjNme9FrWzXF7jDG/GWM2GGOiMzomr8nqczWWt5P6NxljmjghZ2Z4cA/tjTEnkz73DcaY55yQMzOMMVONMYeNMb+76Q+E7yGre/D778EjRESbDxtQB7gWWA40c3NMAWAncCVQGNgI1HVa9jTyvQ6MSXo/BnjNzXF7gApOy5udzxXoBnwNGKAl8LPTcufgHtoDXzotaxb30RZoAvzupt+vvwcP78HvvwdPmloKPkZEtojI1iwOawHsEJFdInIRmAP4sOBetukBzEh6PwO400FZsoMnn2sPYKZY1gBljDFV8lrQTPD3vw2PEJEfgGOZHOLv34Mn9xAUqFLwD6oC+9Js70/a5y9cISIHAZJeK7k5ToDFxphfjDHheSadezz5XP39s/dUvlbGmI3GmK+NMfXyRjSv4u/fg6cE+vcQnPUU8hpjzBKgcgZdz4jI554MkcG+PI0VzuwesjFMaxGJNcZUAr4zxvyZ9HTlFJ58ro5/9lngiXy/YvPYnDHGdAMWAFf7XDLv4u/fgycEw/egSsEbiEjnXA6xH6ieZrsaEJvLMbNFZvdgjDlkjKkiIgeTTPrDbsaITXo9bIyZj536cFIpePK5Ov7ZZ0GW8onIqTTvvzLGvGeMqSAigZSgzd+/hywJku9Bp4/8hHXA1caYfxhjCgP3AgsdliktC4EBSe8HAJdZP8aY4saYksnvgS5AhlEaeYgnn+tC4IGk6JeWwMnkqTI/Ict7MMZUNsaYpPctsP/XcXkuae7w9+8hS4Lke1BLwdcYY3oC7wAVgUXGmA0icosxJgyYLCLdRCTBGDMK+BYbbTJVRP5wUOz0vArMNcYMAfYC9wCkvQfgCmB+0v9EQeBDEfnGIXkBcPe5GmOGJ/VPBL7CRr7sAM4Cg5ySNyM8vIe7gRHGmATgHHCvJIXD+AvGmI+w0TkVjDH7geeBQhAY3wN4dA9+/z14gqa5UBRFUVLQ6SNFURQlBVUKiqIoSgqqFBRFUZQUVCkoiqIoKahSUBRFUVJQpaDkS4wx4caYHOVwMsZMzyoLrCfH+BpjzGhjTPsM9ktSmKuiXIYqBSW/Ek7gJPbLKaOxcfWK4jGqFBRFUZQUVCkoAUPylIwx5k5jzJ/GmPPGmFXGmLrpjgsxxoxJKthywRizzRgzIE3/cqApMCBpKkWMMQOT+h5IGvOYMea4MeZ7k0lxpGzKX8MYMydp7LPGmG+NMdem6a+VJEtvY0xkUsGW/caYF40xIenGusfYokfnkmS8Pt197AHKA8+nucf2aYYoYIz5jzHmiLGFY941xhTxxn0qgY0qBSXQqAm8CYwF7gNKA98aY0LTHPMO8CwQBdwGzAemGmO6J/U/CPyJTa3QKqktSuqrBczEpvK4D5uo7QdjzJW5EdoYUw5YhS24NBzoDRQHlhhjiqY7/HXgDDZtwmzguaT3yWM1w9ZV+BXoic0b9HG6MXoCJ4Epae7x1zT9TwBhQD9gHBABPJKbe1SCBKer/GjT5mkDpmPTKd+YZl9NIAEYnrR9FeACBqQ7dyawLs12NDA9i+uFYPM4/Qk8l06OaA9kjU6zPRabHK1cmn1lsT/cI5O2ayXd38x0Y20A5qTZ/gSbbNCk2Tc66dyBafYdBV7IQDYBfki3bwGwxunvWJvzTS0FJdA4LCI/JW+ISAzwCzZNN0AnrFKYb4wpmNyApUBjY0yBzAY3xtQxxsw3xhwCEoF47NP9NbmUuzPwHXAqjUynk2RPPz21ON32Zmwq6WSaA1+ISNrEZdnNqpvVNZR8imZJVQKNjGo5HAaSSzdWwGYTPenm/CrYKaHLSEr9vRg4BDwOxADngclAaEbnZIMK2NrDfTLoW5pu+0S67Yvprl8ZOJLumPTbWZHVNZR8iioFJdDIqBRoJSA51fgx7HRSa6zFkJ4MCwQl0Qr7tHyziPyZvNMYUzpnol7CMezT/NgM+k5nc6y/sKnY05J+W1FyhCoFJdCoZIy5MXkKyRhTA2gCTEvqX4a1FEqLyHeZjJPRk3Gyw/dC8g5jzI3Yuf5fcin3Uqxz+Q8ROZfLsdYBtxtj/i/NFNIdGRynT/9KtlGloAQaR4FZxph/YQuZ/Bv79D8dQES2GmMmAnOMMa9jHcqhQD3gGhEZmjTOn8AtxphbsA7g3cAabNTPpKRzqwEvAAe8IPeb2EifZcaYd5LGvAJoB6wSkY+yMdZrwM/Ye5wG1AGGJfWltY7+BG4zxnyDva+tIpJdq0TJZ6ijWQk0YoCnsD/Wc4BTwC0icj7NMSOx0zQPYMNOp2NDU9PWi34J2ALMJenJW0QOYUNRK2NLjj6KDR/dkVuhxdbpbYn9oR6P9V28jg2p3ZTNsaKBvti1FguAXsCIpO5TaQ59CvgbG267Lul4RckUrbymBAzGmOlAfRHxymKyYMIY0w+YBVwpIrudlkcJXHT6SFECEGPM+9gQ1+NYn8qzwCJVCEpuUaWgKIFJeeC9pNc47Irm0Y5KpAQFOn2kKIqipKCOZkVRFCUFVQqKoihKCqoUFEVRlBRUKSiKoigpqFJQFEVRUvh/g+2ZqDxXIRIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_setosa_versicolor(X = transfX)\n",
+    "plot_decision_boundary(SVC_clf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "         steps=[('scaler',\n",
+       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       "                ('clf',\n",
+       "                 SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
+       "                               early_stopping=False, epsilon=0.1, eta0=0.001,\n",
+       "                               fit_intercept=True, l1_ratio=0.15,\n",
+       "                               learning_rate='constant', loss='hinge',\n",
+       "                               max_iter=1000, n_iter_no_change=5, n_jobs=None,\n",
+       "                               penalty='l2', power_t=0.5, random_state=42,\n",
+       "                               shuffle=True, tol=0.001, validation_fraction=0.1,\n",
+       "                               verbose=0, warm_start=False))],\n",
+       "         verbose=False)"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import SGDClassifier\n",
+    "\n",
+    "sgd_clf = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('clf', SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001,\n",
+    "                        max_iter=1000, tol=1e-3, random_state=42)) \n",
+    "])\n",
+    "\n",
+    "sgd_clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_setosa_versicolor(X = transfX)\n",
+    "plot_decision_boundary(sgd_clf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**EXERCISE 10**\n",
+    "\n",
+    "Train SVR on California Housing Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "\n",
+    "housing = fetch_california_housing()\n",
+    "X = housing[\"data\"]\n",
+    "y = housing[\"target\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20640, 8)"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "         steps=[('scaler',\n",
+       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       "                ('model',\n",
+       "                 SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
+       "                     gamma='scale', kernel='rbf', max_iter=-1, shrinking=True,\n",
+       "                     tol=0.001, verbose=False))],\n",
+       "         verbose=False)"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVR\n",
+    "\n",
+    "SVR_model = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('model', SVR())\n",
+    "])\n",
+    "\n",
+    "SVR_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7275639524733043"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SVR_model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3570026426754465\n",
+      "0.5974969813107398\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "y_pred = SVR_model.predict(X_test)\n",
+    "mse = mean_squared_error(y_pred, y_test)\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(mse)\n",
+    "print(rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
+      "[CV] model__C=8.732501769442347, model__gamma=0.014138684138012492 ...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV]  model__C=8.732501769442347, model__gamma=0.014138684138012492, total=  10.9s\n",
+      "[CV] model__C=8.732501769442347, model__gamma=0.014138684138012492 ...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   10.8s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV]  model__C=8.732501769442347, model__gamma=0.014138684138012492, total=  10.7s\n",
+      "[CV] model__C=8.732501769442347, model__gamma=0.014138684138012492 ...\n",
+      "[CV]  model__C=8.732501769442347, model__gamma=0.014138684138012492, total=  10.9s\n",
+      "[CV] model__C=10.6073996426796, model__gamma=0.0010782720368618492 ...\n",
+      "[CV]  model__C=10.6073996426796, model__gamma=0.0010782720368618492, total=  10.1s\n",
+      "[CV] model__C=10.6073996426796, model__gamma=0.0010782720368618492 ...\n",
+      "[CV]  model__C=10.6073996426796, model__gamma=0.0010782720368618492, total=  10.1s\n",
+      "[CV] model__C=10.6073996426796, model__gamma=0.0010782720368618492 ...\n",
+      "[CV]  model__C=10.6073996426796, model__gamma=0.0010782720368618492, total=  10.0s\n",
+      "[CV] model__C=5.173843032530275, model__gamma=0.03912018707260784 ....\n",
+      "[CV]  model__C=5.173843032530275, model__gamma=0.03912018707260784, total=  11.1s\n",
+      "[CV] model__C=5.173843032530275, model__gamma=0.03912018707260784 ....\n",
+      "[CV]  model__C=5.173843032530275, model__gamma=0.03912018707260784, total=  11.1s\n",
+      "[CV] model__C=5.173843032530275, model__gamma=0.03912018707260784 ....\n",
+      "[CV]  model__C=5.173843032530275, model__gamma=0.03912018707260784, total=  11.2s\n",
+      "[CV] model__C=8.757343573995623, model__gamma=0.026503976649874594 ...\n",
+      "[CV]  model__C=8.757343573995623, model__gamma=0.026503976649874594, total=  11.2s\n",
+      "[CV] model__C=8.757343573995623, model__gamma=0.026503976649874594 ...\n",
+      "[CV]  model__C=8.757343573995623, model__gamma=0.026503976649874594, total=  11.6s\n",
+      "[CV] model__C=8.757343573995623, model__gamma=0.026503976649874594 ...\n",
+      "[CV]  model__C=8.757343573995623, model__gamma=0.026503976649874594, total=  11.8s\n",
+      "[CV] model__C=3.993757431556655, model__gamma=0.0037627657376435165 ..\n",
+      "[CV]  model__C=3.993757431556655, model__gamma=0.0037627657376435165, total=   9.9s\n",
+      "[CV] model__C=3.993757431556655, model__gamma=0.0037627657376435165 ..\n",
+      "[CV]  model__C=3.993757431556655, model__gamma=0.0037627657376435165, total=  10.1s\n",
+      "[CV] model__C=3.993757431556655, model__gamma=0.0037627657376435165 ..\n",
+      "[CV]  model__C=3.993757431556655, model__gamma=0.0037627657376435165, total=   7.3s\n",
+      "[CV] model__C=5.908725733130206, model__gamma=0.04722302780443009 ....\n",
+      "[CV]  model__C=5.908725733130206, model__gamma=0.04722302780443009, total=   6.4s\n",
+      "[CV] model__C=5.908725733130206, model__gamma=0.04722302780443009 ....\n",
+      "[CV]  model__C=5.908725733130206, model__gamma=0.04722302780443009, total=   8.2s\n",
+      "[CV] model__C=5.908725733130206, model__gamma=0.04722302780443009 ....\n",
+      "[CV]  model__C=5.908725733130206, model__gamma=0.04722302780443009, total=  11.8s\n",
+      "[CV] model__C=6.339380352838232, model__gamma=0.004194342545426101 ...\n",
+      "[CV]  model__C=6.339380352838232, model__gamma=0.004194342545426101, total=  10.3s\n",
+      "[CV] model__C=6.339380352838232, model__gamma=0.004194342545426101 ...\n",
+      "[CV]  model__C=6.339380352838232, model__gamma=0.004194342545426101, total=   5.7s\n",
+      "[CV] model__C=6.339380352838232, model__gamma=0.004194342545426101 ...\n",
+      "[CV]  model__C=6.339380352838232, model__gamma=0.004194342545426101, total=   9.1s\n",
+      "[CV] model__C=9.323582401718916, model__gamma=0.011028101996364465 ...\n",
+      "[CV]  model__C=9.323582401718916, model__gamma=0.011028101996364465, total=  10.7s\n",
+      "[CV] model__C=9.323582401718916, model__gamma=0.011028101996364465 ...\n",
+      "[CV]  model__C=9.323582401718916, model__gamma=0.011028101996364465, total=  11.0s\n",
+      "[CV] model__C=9.323582401718916, model__gamma=0.011028101996364465 ...\n",
+      "[CV]  model__C=9.323582401718916, model__gamma=0.011028101996364465, total=  11.6s\n",
+      "[CV] model__C=7.485779608851653, model__gamma=0.03852395227919697 ....\n",
+      "[CV]  model__C=7.485779608851653, model__gamma=0.03852395227919697, total=  11.9s\n",
+      "[CV] model__C=7.485779608851653, model__gamma=0.03852395227919697 ....\n",
+      "[CV]  model__C=7.485779608851653, model__gamma=0.03852395227919697, total=   6.6s\n",
+      "[CV] model__C=7.485779608851653, model__gamma=0.03852395227919697 ....\n",
+      "[CV]  model__C=7.485779608851653, model__gamma=0.03852395227919697, total=  11.9s\n",
+      "[CV] model__C=5.32139202170268, model__gamma=0.00373396867904038 .....\n",
+      "[CV]  model__C=5.32139202170268, model__gamma=0.00373396867904038, total=  10.0s\n",
+      "[CV] model__C=5.32139202170268, model__gamma=0.00373396867904038 .....\n",
+      "[CV]  model__C=5.32139202170268, model__gamma=0.00373396867904038, total=  10.0s\n",
+      "[CV] model__C=5.32139202170268, model__gamma=0.00373396867904038 .....\n",
+      "[CV]  model__C=5.32139202170268, model__gamma=0.00373396867904038, total=  10.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  5.1min finished\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "RandomizedSearchCV(cv=3, error_score=nan,\n",
+       "                   estimator=Pipeline(memory=None,\n",
+       "                                      steps=[('scaler',\n",
+       "                                              StandardScaler(copy=True,\n",
+       "                                                             with_mean=True,\n",
+       "                                                             with_std=True)),\n",
+       "                                             ('model',\n",
+       "                                              SVR(C=1.0, cache_size=200,\n",
+       "                                                  coef0=0.0, degree=3,\n",
+       "                                                  epsilon=0.1, gamma='scale',\n",
+       "                                                  kernel='rbf', max_iter=-1,\n",
+       "                                                  shrinking=True, tol=0.001,\n",
+       "                                                  verbose=False))],\n",
+       "                                      verbose=False),\n",
+       "                   iid='deprecated', n_iter=10, n_jobs=None,\n",
+       "                   param_distributions={'model__C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000022EDAD77B88>,\n",
+       "                                        'model__gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000022EDAD778C8>},\n",
+       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
+       "                   return_train_score=False, scoring=None, verbose=2)"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "from scipy.stats import reciprocal, uniform\n",
+    "\n",
+    "SVR_model = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('model', SVR())\n",
+    "])\n",
+    "\n",
+    "param_dists = {\n",
+    "    'model__gamma': reciprocal(0.001, 0.1),\n",
+    "    'model__C': uniform(1,10)\n",
+    "}\n",
+    "\n",
+    "rnd_search_cv = RandomizedSearchCV(SVR_model, param_dists, verbose=2, cv=3)\n",
+    "rnd_search_cv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "         steps=[('scaler',\n",
+       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       "                ('model',\n",
+       "                 SVR(C=5.908725733130206, cache_size=200, coef0=0.0, degree=3,\n",
+       "                     epsilon=0.1, gamma=0.04722302780443009, kernel='rbf',\n",
+       "                     max_iter=-1, shrinking=True, tol=0.001, verbose=False))],\n",
+       "         verbose=False)"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rnd_search_cv.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.37646952041010656\n",
+      "0.6135711209062129\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = rnd_search_cv.predict(X_test)\n",
+    "mse = mean_squared_error(y_pred, y_test)\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(mse)\n",
+    "print(rmse)\n",
+    "\n",
+    "# Note that our original model performed better thanks to its gamma = 'auto' default option\n",
+    "# To improce performance, further tuning of gamma would be required"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ch5/Exercises.ipynb b/Ch5/Exercises.ipynb
new file mode 100644
index 000000000..bc6125423
--- /dev/null
+++ b/Ch5/Exercises.ipynb
@@ -0,0 +1,657 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import os\n",
+    "\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
+    "y = iris[\"target\"]\n",
+    "\n",
+    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
+    "X = X[setosa_or_versicolor]\n",
+    "y = y[setosa_or_versicolor]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the data\n",
+    "\n",
+    "def plot_setosa_versicolor(X = X, y = y):\n",
+    "    plt.plot(X[:,0][y==0], X[:,1][y==0], 'bo', label='iris setosa')\n",
+    "    plt.plot(X[:,0][y==1], X[:,1][y==1], 'r^', label='iris verticolor')\n",
+    "\n",
+    "    plt.xlabel('petal length', fontsize=15)\n",
+    "    plt.ylabel('petal width', fontsize=15)\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_setosa_versicolor()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**EXERCISE 8**\n",
+    "\n",
+    "Train LinearSVC on linearly seperable data, then train SVC and SGCClassifier and compare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_decision_boundary(pipeline, X=X, y=y):\n",
+    "    # Get params for our decision boundary\n",
+    "    transfX = pipeline['scaler'].fit_transform(X)\n",
+    "\n",
+    "    Xmin = transfX.min()\n",
+    "    Xmax = transfX.max()\n",
+    "\n",
+    "    b = pipeline['clf'].intercept_\n",
+    "    w0 = pipeline['clf'].coef_[0][0]\n",
+    "    w1 = pipeline['clf'].coef_[0][1]\n",
+    "\n",
+    "    # get our input values to build line\n",
+    "    x0 = np.linspace(Xmin, Xmax, 200)\n",
+    "\n",
+    "    # Setup boundary\n",
+    "    # b + w0x + w1y = 0  ==> y = -w0/w1 * x - b/w1\n",
+    "    boundary = -(w0/w1) * x0 - b/w1\n",
+    "    margin = 1/w1\n",
+    "    top_gutter = boundary + margin\n",
+    "    bot_gutter = boundary - margin\n",
+    "\n",
+    "    # Plot our boundary and gutters\n",
+    "    plt.plot(x0, boundary, 'k-')\n",
+    "    plt.plot(x0, top_gutter, 'r--')\n",
+    "    plt.plot(x0, bot_gutter, 'b--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Preprocess the data\n",
+    "\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.svm import LinearSVC\n",
+    "\n",
+    "Linear_SVM_clf = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('clf', LinearSVC()) \n",
+    "])\n",
+    "\n",
+    "Linear_SVM_clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_setosa_versicolor(X = transfX)\n",
+    "plot_decision_boundary(Linear_SVM_clf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "         steps=[('scaler',\n",
+       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       "                ('clf',\n",
+       "                 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None,\n",
+       "                     coef0=0.0, decision_function_shape='ovr', degree=3,\n",
+       "                     gamma='scale', kernel='linear', max_iter=-1,\n",
+       "                     probability=False, random_state=None, shrinking=True,\n",
+       "                     tol=0.001, verbose=False))],\n",
+       "         verbose=False)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "SVC_clf = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('clf', SVC(kernel='linear')) \n",
+    "])\n",
+    "\n",
+    "SVC_clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_setosa_versicolor(X = transfX)\n",
+    "plot_decision_boundary(SVC_clf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "         steps=[('scaler',\n",
+       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       "                ('clf',\n",
+       "                 SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
+       "                               early_stopping=False, epsilon=0.1, eta0=0.001,\n",
+       "                               fit_intercept=True, l1_ratio=0.15,\n",
+       "                               learning_rate='constant', loss='hinge',\n",
+       "                               max_iter=1000, n_iter_no_change=5, n_jobs=None,\n",
+       "                               penalty='l2', power_t=0.5, random_state=42,\n",
+       "                               shuffle=True, tol=0.001, validation_fraction=0.1,\n",
+       "                               verbose=0, warm_start=False))],\n",
+       "         verbose=False)"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import SGDClassifier\n",
+    "\n",
+    "sgd_clf = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('clf', SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001,\n",
+    "                        max_iter=1000, tol=1e-3, random_state=42)) \n",
+    "])\n",
+    "\n",
+    "sgd_clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_setosa_versicolor(X = transfX)\n",
+    "plot_decision_boundary(sgd_clf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**EXERCISE 10**\n",
+    "\n",
+    "Train SVR on California Housing Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "\n",
+    "housing = fetch_california_housing()\n",
+    "X = housing[\"data\"]\n",
+    "y = housing[\"target\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20640, 8)"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "         steps=[('scaler',\n",
+       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       "                ('model',\n",
+       "                 SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
+       "                     gamma='scale', kernel='rbf', max_iter=-1, shrinking=True,\n",
+       "                     tol=0.001, verbose=False))],\n",
+       "         verbose=False)"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVR\n",
+    "\n",
+    "SVR_model = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('model', SVR())\n",
+    "])\n",
+    "\n",
+    "SVR_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7275639524733043"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SVR_model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3570026426754465\n",
+      "0.5974969813107398\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "y_pred = SVR_model.predict(X_test)\n",
+    "mse = mean_squared_error(y_pred, y_test)\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(mse)\n",
+    "print(rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
+      "[CV] model__C=8.732501769442347, model__gamma=0.014138684138012492 ...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV]  model__C=8.732501769442347, model__gamma=0.014138684138012492, total=  10.9s\n",
+      "[CV] model__C=8.732501769442347, model__gamma=0.014138684138012492 ...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   10.8s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV]  model__C=8.732501769442347, model__gamma=0.014138684138012492, total=  10.7s\n",
+      "[CV] model__C=8.732501769442347, model__gamma=0.014138684138012492 ...\n",
+      "[CV]  model__C=8.732501769442347, model__gamma=0.014138684138012492, total=  10.9s\n",
+      "[CV] model__C=10.6073996426796, model__gamma=0.0010782720368618492 ...\n",
+      "[CV]  model__C=10.6073996426796, model__gamma=0.0010782720368618492, total=  10.1s\n",
+      "[CV] model__C=10.6073996426796, model__gamma=0.0010782720368618492 ...\n",
+      "[CV]  model__C=10.6073996426796, model__gamma=0.0010782720368618492, total=  10.1s\n",
+      "[CV] model__C=10.6073996426796, model__gamma=0.0010782720368618492 ...\n",
+      "[CV]  model__C=10.6073996426796, model__gamma=0.0010782720368618492, total=  10.0s\n",
+      "[CV] model__C=5.173843032530275, model__gamma=0.03912018707260784 ....\n",
+      "[CV]  model__C=5.173843032530275, model__gamma=0.03912018707260784, total=  11.1s\n",
+      "[CV] model__C=5.173843032530275, model__gamma=0.03912018707260784 ....\n",
+      "[CV]  model__C=5.173843032530275, model__gamma=0.03912018707260784, total=  11.1s\n",
+      "[CV] model__C=5.173843032530275, model__gamma=0.03912018707260784 ....\n",
+      "[CV]  model__C=5.173843032530275, model__gamma=0.03912018707260784, total=  11.2s\n",
+      "[CV] model__C=8.757343573995623, model__gamma=0.026503976649874594 ...\n",
+      "[CV]  model__C=8.757343573995623, model__gamma=0.026503976649874594, total=  11.2s\n",
+      "[CV] model__C=8.757343573995623, model__gamma=0.026503976649874594 ...\n",
+      "[CV]  model__C=8.757343573995623, model__gamma=0.026503976649874594, total=  11.6s\n",
+      "[CV] model__C=8.757343573995623, model__gamma=0.026503976649874594 ...\n",
+      "[CV]  model__C=8.757343573995623, model__gamma=0.026503976649874594, total=  11.8s\n",
+      "[CV] model__C=3.993757431556655, model__gamma=0.0037627657376435165 ..\n",
+      "[CV]  model__C=3.993757431556655, model__gamma=0.0037627657376435165, total=   9.9s\n",
+      "[CV] model__C=3.993757431556655, model__gamma=0.0037627657376435165 ..\n",
+      "[CV]  model__C=3.993757431556655, model__gamma=0.0037627657376435165, total=  10.1s\n",
+      "[CV] model__C=3.993757431556655, model__gamma=0.0037627657376435165 ..\n",
+      "[CV]  model__C=3.993757431556655, model__gamma=0.0037627657376435165, total=   7.3s\n",
+      "[CV] model__C=5.908725733130206, model__gamma=0.04722302780443009 ....\n",
+      "[CV]  model__C=5.908725733130206, model__gamma=0.04722302780443009, total=   6.4s\n",
+      "[CV] model__C=5.908725733130206, model__gamma=0.04722302780443009 ....\n",
+      "[CV]  model__C=5.908725733130206, model__gamma=0.04722302780443009, total=   8.2s\n",
+      "[CV] model__C=5.908725733130206, model__gamma=0.04722302780443009 ....\n",
+      "[CV]  model__C=5.908725733130206, model__gamma=0.04722302780443009, total=  11.8s\n",
+      "[CV] model__C=6.339380352838232, model__gamma=0.004194342545426101 ...\n",
+      "[CV]  model__C=6.339380352838232, model__gamma=0.004194342545426101, total=  10.3s\n",
+      "[CV] model__C=6.339380352838232, model__gamma=0.004194342545426101 ...\n",
+      "[CV]  model__C=6.339380352838232, model__gamma=0.004194342545426101, total=   5.7s\n",
+      "[CV] model__C=6.339380352838232, model__gamma=0.004194342545426101 ...\n",
+      "[CV]  model__C=6.339380352838232, model__gamma=0.004194342545426101, total=   9.1s\n",
+      "[CV] model__C=9.323582401718916, model__gamma=0.011028101996364465 ...\n",
+      "[CV]  model__C=9.323582401718916, model__gamma=0.011028101996364465, total=  10.7s\n",
+      "[CV] model__C=9.323582401718916, model__gamma=0.011028101996364465 ...\n",
+      "[CV]  model__C=9.323582401718916, model__gamma=0.011028101996364465, total=  11.0s\n",
+      "[CV] model__C=9.323582401718916, model__gamma=0.011028101996364465 ...\n",
+      "[CV]  model__C=9.323582401718916, model__gamma=0.011028101996364465, total=  11.6s\n",
+      "[CV] model__C=7.485779608851653, model__gamma=0.03852395227919697 ....\n",
+      "[CV]  model__C=7.485779608851653, model__gamma=0.03852395227919697, total=  11.9s\n",
+      "[CV] model__C=7.485779608851653, model__gamma=0.03852395227919697 ....\n",
+      "[CV]  model__C=7.485779608851653, model__gamma=0.03852395227919697, total=   6.6s\n",
+      "[CV] model__C=7.485779608851653, model__gamma=0.03852395227919697 ....\n",
+      "[CV]  model__C=7.485779608851653, model__gamma=0.03852395227919697, total=  11.9s\n",
+      "[CV] model__C=5.32139202170268, model__gamma=0.00373396867904038 .....\n",
+      "[CV]  model__C=5.32139202170268, model__gamma=0.00373396867904038, total=  10.0s\n",
+      "[CV] model__C=5.32139202170268, model__gamma=0.00373396867904038 .....\n",
+      "[CV]  model__C=5.32139202170268, model__gamma=0.00373396867904038, total=  10.0s\n",
+      "[CV] model__C=5.32139202170268, model__gamma=0.00373396867904038 .....\n",
+      "[CV]  model__C=5.32139202170268, model__gamma=0.00373396867904038, total=  10.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  5.1min finished\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "RandomizedSearchCV(cv=3, error_score=nan,\n",
+       "                   estimator=Pipeline(memory=None,\n",
+       "                                      steps=[('scaler',\n",
+       "                                              StandardScaler(copy=True,\n",
+       "                                                             with_mean=True,\n",
+       "                                                             with_std=True)),\n",
+       "                                             ('model',\n",
+       "                                              SVR(C=1.0, cache_size=200,\n",
+       "                                                  coef0=0.0, degree=3,\n",
+       "                                                  epsilon=0.1, gamma='scale',\n",
+       "                                                  kernel='rbf', max_iter=-1,\n",
+       "                                                  shrinking=True, tol=0.001,\n",
+       "                                                  verbose=False))],\n",
+       "                                      verbose=False),\n",
+       "                   iid='deprecated', n_iter=10, n_jobs=None,\n",
+       "                   param_distributions={'model__C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000022EDAD77B88>,\n",
+       "                                        'model__gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000022EDAD778C8>},\n",
+       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
+       "                   return_train_score=False, scoring=None, verbose=2)"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "from scipy.stats import reciprocal, uniform\n",
+    "\n",
+    "SVR_model = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('model', SVR())\n",
+    "])\n",
+    "\n",
+    "param_dists = {\n",
+    "    'model__gamma': reciprocal(0.001, 0.1),\n",
+    "    'model__C': uniform(1,10)\n",
+    "}\n",
+    "\n",
+    "rnd_search_cv = RandomizedSearchCV(SVR_model, param_dists, verbose=2, cv=3)\n",
+    "rnd_search_cv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "         steps=[('scaler',\n",
+       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       "                ('model',\n",
+       "                 SVR(C=5.908725733130206, cache_size=200, coef0=0.0, degree=3,\n",
+       "                     epsilon=0.1, gamma=0.04722302780443009, kernel='rbf',\n",
+       "                     max_iter=-1, shrinking=True, tol=0.001, verbose=False))],\n",
+       "         verbose=False)"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rnd_search_cv.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.37646952041010656\n",
+      "0.6135711209062129\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = rnd_search_cv.predict(X_test)\n",
+    "mse = mean_squared_error(y_pred, y_test)\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(mse)\n",
+    "print(rmse)\n",
+    "\n",
+    "# Note that our original model performed better thanks to its gamma = 'auto' default option\n",
+    "# To improce performance, further tuning of gamma would be required"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ch6/.ipynb_checkpoints/Exercises-checkpoint.ipynb b/Ch6/.ipynb_checkpoints/Exercises-checkpoint.ipynb
new file mode 100644
index 000000000..5d445f323
--- /dev/null
+++ b/Ch6/.ipynb_checkpoints/Exercises-checkpoint.ipynb
@@ -0,0 +1,1552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from sklearn.datasets import make_moons\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV, ShuffleSplit\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.base import clone"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 7**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = make_moons(n_samples=10000, noise=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 2)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.25044937,  1.08585135])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000,)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17a55165b08>,\n",
+       " <matplotlib.lines.Line2D at 0x17a571cae48>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X[y==0], 'r+')\n",
+    "plt.plot(X[y==1], 'gx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17a5721f1c8>,\n",
+       " <matplotlib.lines.Line2D at 0x17a5728ba08>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X_train[y_train==0], 'r+')\n",
+    "plt.plot(X_train[y_train==1], 'gx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 145 candidates, totalling 725 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+      "[Parallel(n_jobs=1)]: Done 725 out of 725 | elapsed:    6.5s finished\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, error_score=nan,\n",
+       "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
+       "                                              criterion='gini', max_depth=None,\n",
+       "                                              max_features=None,\n",
+       "                                              max_leaf_nodes=None,\n",
+       "                                              min_impurity_decrease=0.0,\n",
+       "                                              min_impurity_split=None,\n",
+       "                                              min_samples_leaf=1,\n",
+       "                                              min_samples_split=2,\n",
+       "                                              min_weight_fraction_leaf=0.0,\n",
+       "                                              presort='deprecated',\n",
+       "                                              random_state=None,\n",
+       "                                              splitter='best'),\n",
+       "             iid='de...\n",
+       "       14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n",
+       "       27., 28., 29., 30., 31., 32.])},\n",
+       "                         {'min_samples_split': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])},\n",
+       "                         {'min_samples_leaf': array([0.1, 0.2, 0.3, 0.4, 0.5])},\n",
+       "                         {'max_leaf_nodes': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
+       "                                             13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
+       "                                             22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
+       "                                             31, ...]}],\n",
+       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
+       "             scoring=None, verbose=1)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid = [\n",
+    "    {'max_depth': np.linspace(1, 32, 32, endpoint=True)},\n",
+    "    {'min_samples_split': np.linspace(0.1, 1, 10)},\n",
+    "    {'min_samples_leaf': np.linspace(0.1, 0.5, 5)},\n",
+    "    {'max_leaf_nodes': list(range(2,100))}\n",
+    "]\n",
+    "\n",
+    "clf = DecisionTreeClassifier()\n",
+    "\n",
+    "grid_search = GridSearchCV(clf, param_grid, cv=5, verbose=1)\n",
+    "\n",
+    "grid_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
+       "                       max_depth=2.0, max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8565555555555555"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search.best_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.851"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = grid_search.predict(X_test)\n",
+    "accuracy_score(y_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 8**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_trees = 1000\n",
+    "n_instances = 100\n",
+    "\n",
+    "subsets=[]\n",
+    "\n",
+    "# Randomly split up the training set\n",
+    "rs = ShuffleSplit(n_splits=n_trees, test_size = len(X_train)-n_instances)\n",
+    "\n",
+    "for train_subset_index, test_subset_index in rs.split(X_train):\n",
+    "    X_mini_train =  X_train[train_subset_index]\n",
+    "    y_mini_train = y_train[train_subset_index]\n",
+    "    subsets.append((X_mini_train, y_mini_train))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a bunch of forest predictors based on our best estimator\n",
+    "forest = [clone(grid_search.best_estimator_) for _ in range(n_trees)]\n",
+    "\n",
+    "accuracy_scores = []\n",
+    "\n",
+    "# Fit each tree to its training subset and test accuracy\n",
+    "for tree, (X_mini_train, y_mini_train) in zip(forest, subsets):\n",
+    "    tree.fit(X_mini_train, y_mini_train)\n",
+    "    \n",
+    "    y_pred = tree.predict(X_test)\n",
+    "    accuracy_scores.append(accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8245450000000001"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(accuracy_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nTODO Finish it up with majority rule!\\n'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "TODO Finish it up with majority rule!\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import mode\n",
+    "\n",
+    "# Empty array for our predictions\n",
+    "y_pred = []\n",
+    "\n",
+    "for row in X_test:\n",
+    "    predictions = []\n",
+    "    \n",
+    "    # Get a prediction for our sample (row) from each tree\n",
+    "    for tree in forest:\n",
+    "        predictions.append(tree.predict(row.reshape(1,-1)))\n",
+    "    \n",
+    "    # Find the 'best' predictors useing SciPy's mode\n",
+    "    y_pred.append(mode(predictions)[0][0][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       " 1]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.853"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ch6/Exercises.ipynb b/Ch6/Exercises.ipynb
new file mode 100644
index 000000000..5d445f323
--- /dev/null
+++ b/Ch6/Exercises.ipynb
@@ -0,0 +1,1552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from sklearn.datasets import make_moons\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV, ShuffleSplit\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.base import clone"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 7**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = make_moons(n_samples=10000, noise=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 2)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.25044937,  1.08585135])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000,)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17a55165b08>,\n",
+       " <matplotlib.lines.Line2D at 0x17a571cae48>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X[y==0], 'r+')\n",
+    "plt.plot(X[y==1], 'gx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17a5721f1c8>,\n",
+       " <matplotlib.lines.Line2D at 0x17a5728ba08>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X_train[y_train==0], 'r+')\n",
+    "plt.plot(X_train[y_train==1], 'gx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 145 candidates, totalling 725 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+      "[Parallel(n_jobs=1)]: Done 725 out of 725 | elapsed:    6.5s finished\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, error_score=nan,\n",
+       "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
+       "                                              criterion='gini', max_depth=None,\n",
+       "                                              max_features=None,\n",
+       "                                              max_leaf_nodes=None,\n",
+       "                                              min_impurity_decrease=0.0,\n",
+       "                                              min_impurity_split=None,\n",
+       "                                              min_samples_leaf=1,\n",
+       "                                              min_samples_split=2,\n",
+       "                                              min_weight_fraction_leaf=0.0,\n",
+       "                                              presort='deprecated',\n",
+       "                                              random_state=None,\n",
+       "                                              splitter='best'),\n",
+       "             iid='de...\n",
+       "       14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n",
+       "       27., 28., 29., 30., 31., 32.])},\n",
+       "                         {'min_samples_split': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])},\n",
+       "                         {'min_samples_leaf': array([0.1, 0.2, 0.3, 0.4, 0.5])},\n",
+       "                         {'max_leaf_nodes': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
+       "                                             13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
+       "                                             22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
+       "                                             31, ...]}],\n",
+       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
+       "             scoring=None, verbose=1)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid = [\n",
+    "    {'max_depth': np.linspace(1, 32, 32, endpoint=True)},\n",
+    "    {'min_samples_split': np.linspace(0.1, 1, 10)},\n",
+    "    {'min_samples_leaf': np.linspace(0.1, 0.5, 5)},\n",
+    "    {'max_leaf_nodes': list(range(2,100))}\n",
+    "]\n",
+    "\n",
+    "clf = DecisionTreeClassifier()\n",
+    "\n",
+    "grid_search = GridSearchCV(clf, param_grid, cv=5, verbose=1)\n",
+    "\n",
+    "grid_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
+       "                       max_depth=2.0, max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8565555555555555"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search.best_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.851"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = grid_search.predict(X_test)\n",
+    "accuracy_score(y_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 8**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_trees = 1000\n",
+    "n_instances = 100\n",
+    "\n",
+    "subsets=[]\n",
+    "\n",
+    "# Randomly split up the training set\n",
+    "rs = ShuffleSplit(n_splits=n_trees, test_size = len(X_train)-n_instances)\n",
+    "\n",
+    "for train_subset_index, test_subset_index in rs.split(X_train):\n",
+    "    X_mini_train =  X_train[train_subset_index]\n",
+    "    y_mini_train = y_train[train_subset_index]\n",
+    "    subsets.append((X_mini_train, y_mini_train))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a bunch of forest predictors based on our best estimator\n",
+    "forest = [clone(grid_search.best_estimator_) for _ in range(n_trees)]\n",
+    "\n",
+    "accuracy_scores = []\n",
+    "\n",
+    "# Fit each tree to its training subset and test accuracy\n",
+    "for tree, (X_mini_train, y_mini_train) in zip(forest, subsets):\n",
+    "    tree.fit(X_mini_train, y_mini_train)\n",
+    "    \n",
+    "    y_pred = tree.predict(X_test)\n",
+    "    accuracy_scores.append(accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8245450000000001"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(accuracy_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nTODO Finish it up with majority rule!\\n'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "TODO Finish it up with majority rule!\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import mode\n",
+    "\n",
+    "# Empty array for our predictions\n",
+    "y_pred = []\n",
+    "\n",
+    "for row in X_test:\n",
+    "    predictions = []\n",
+    "    \n",
+    "    # Get a prediction for our sample (row) from each tree\n",
+    "    for tree in forest:\n",
+    "        predictions.append(tree.predict(row.reshape(1,-1)))\n",
+    "    \n",
+    "    # Find the 'best' predictors useing SciPy's mode\n",
+    "    y_pred.append(mode(predictions)[0][0][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.853"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ch7/.ipynb_checkpoints/Exercises-checkpoint.ipynb b/Ch7/.ipynb_checkpoints/Exercises-checkpoint.ipynb
new file mode 100644
index 000000000..c3cab63fb
--- /dev/null
+++ b/Ch7/.ipynb_checkpoints/Exercises-checkpoint.ipynb
@@ -0,0 +1,521 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "import os\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.preprocessing import normalize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 8**\n",
+    "\n",
+    "Create hard/soft voting ensemble on mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mnist = fetch_openml('mnist_784', version=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'DESCR', 'details', 'categories', 'url'])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mnist.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(70000, 784)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X, y = mnist['data'], mnist['target']\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split into train, val, test sets of size 50k, 10k, 10k\n",
+    "\n",
+    "X_train = X[:50000]\n",
+    "y_train = y[:50000]\n",
+    "X_val = X[50000:60000]\n",
+    "y_val = y[50000:60000]\n",
+    "X_test = X[60000:]\n",
+    "y_test = y[60000:]\n",
+    "\n",
+    "# Normalize features\n",
+    "\n",
+    "X_train /= 255.0\n",
+    "X_val /= 255.0\n",
+    "X_test /= 255.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_test.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training our  RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
+      "                       criterion='gini', max_depth=None, max_features='auto',\n",
+      "                       max_leaf_nodes=None, max_samples=None,\n",
+      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+      "                       min_samples_leaf=1, min_samples_split=2,\n",
+      "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
+      "                       n_jobs=None, oob_score=False, random_state=None,\n",
+      "                       verbose=0, warm_start=False)\n",
+      "Training our  ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
+      "                     criterion='gini', max_depth=None, max_features='auto',\n",
+      "                     max_leaf_nodes=None, max_samples=None,\n",
+      "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
+      "                     min_samples_leaf=1, min_samples_split=2,\n",
+      "                     min_weight_fraction_leaf=0.0, n_estimators=100,\n",
+      "                     n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
+      "                     warm_start=False)\n",
+      "Training our  LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
+      "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
+      "          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
+      "          verbose=0)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tsb\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
+      "  \"the number of iterations.\", ConvergenceWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "rfc = RandomForestClassifier()\n",
+    "etc = ExtraTreesClassifier()\n",
+    "svc = LinearSVC()\n",
+    "\n",
+    "classifiers = [rfc, etc, svc]\n",
+    "scores = []\n",
+    "\n",
+    "# Fit each classifier to the training set and predict on X_val\n",
+    "for clf in classifiers:\n",
+    "    print('Training our ', clf)\n",
+    "    clf.fit(X_train, y_train)\n",
+    "    score = clf.score(X_val, y_val)\n",
+    "    scores.append(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.9719, 0.9741, 0.9208]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tsb\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
+      "  \"the number of iterations.\", ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "VotingClassifier(estimators=[('rf',\n",
+       "                              RandomForestClassifier(bootstrap=True,\n",
+       "                                                     ccp_alpha=0.0,\n",
+       "                                                     class_weight=None,\n",
+       "                                                     criterion='gini',\n",
+       "                                                     max_depth=None,\n",
+       "                                                     max_features='auto',\n",
+       "                                                     max_leaf_nodes=None,\n",
+       "                                                     max_samples=None,\n",
+       "                                                     min_impurity_decrease=0.0,\n",
+       "                                                     min_impurity_split=None,\n",
+       "                                                     min_samples_leaf=1,\n",
+       "                                                     min_samples_split=2,\n",
+       "                                                     min_weight_fraction_leaf=0.0,\n",
+       "                                                     n_estimators=100,\n",
+       "                                                     n_jobs=None,\n",
+       "                                                     oob_score...\n",
+       "                                                   n_estimators=100,\n",
+       "                                                   n_jobs=None, oob_score=False,\n",
+       "                                                   random_state=None, verbose=0,\n",
+       "                                                   warm_start=False)),\n",
+       "                             ('sv',\n",
+       "                              LinearSVC(C=1.0, class_weight=None, dual=True,\n",
+       "                                        fit_intercept=True, intercept_scaling=1,\n",
+       "                                        loss='squared_hinge', max_iter=1000,\n",
+       "                                        multi_class='ovr', penalty='l2',\n",
+       "                                        random_state=None, tol=0.0001,\n",
+       "                                        verbose=0))],\n",
+       "                 flatten_transform=True, n_jobs=None, voting='hard',\n",
+       "                 weights=None)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import VotingClassifier\n",
+    "\n",
+    "# Hard vote ensmeble\n",
+    "voting_clf = VotingClassifier(\n",
+    "    estimators=[('rf', rfc), ('et', etc), ('sv', svc)],\n",
+    "    voting='hard'\n",
+    ")\n",
+    "\n",
+    "voting_clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9719"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "voting_clf.score(X_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Try without SVC\n",
+    "\n",
+    "del voting_clf.estimators_[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9732"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "voting_clf.score(X_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9752"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set to soft voting and check if better\n",
+    "\n",
+    "voting_clf.voting='soft'\n",
+    "\n",
+    "voting_clf.score(X_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9707"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check on Test Set\n",
+    "\n",
+    "voting_clf.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 9**\n",
+    "\n",
+    "train a stacking ensemble on our previous classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Round up our predictions\n",
+    "\n",
+    "X_val_predictions = np.empty((len(X_val), len(classifiers)), dtype=np.float32)\n",
+    "\n",
+    "for index, clf in enumerate(classifiers):\n",
+    "    X_val_predictions[:, index] = clf.predict(X_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3. 3. 3.]\n",
+      " [8. 8. 8.]\n",
+      " [6. 6. 6.]\n",
+      " ...\n",
+      " [5. 5. 5.]\n",
+      " [6. 6. 6.]\n",
+      " [8. 8. 8.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_val_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
+       "                       criterion='gini', max_depth=None, max_features='auto',\n",
+       "                       max_leaf_nodes=None, max_samples=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
+       "                       n_jobs=None, oob_score=True, random_state=None,\n",
+       "                       verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train a classifier which will take as input our predictions matrix\n",
+    "blender = RandomForestClassifier(n_estimators=200, oob_score=True)\n",
+    "blender.fit(X_val_predictions, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9727"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check our out of bag score to get an idea of accuracy\n",
+    "blender.oob_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Round up predictions for X_test\n",
+    "X_test_predictions = np.empty((len(X_val), len(classifiers)), dtype=np.float32)\n",
+    "\n",
+    "for index, clf in enumerate(classifiers):\n",
+    "    X_test_predictions[:, index] = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use our blender to predict based on our predictions matrix\n",
+    "y_pred = blender.predict(X_test_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.968"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ch7/Exercises.ipynb b/Ch7/Exercises.ipynb
new file mode 100644
index 000000000..c3cab63fb
--- /dev/null
+++ b/Ch7/Exercises.ipynb
@@ -0,0 +1,521 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "import os\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.preprocessing import normalize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 8**\n",
+    "\n",
+    "Create hard/soft voting ensemble on mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mnist = fetch_openml('mnist_784', version=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'DESCR', 'details', 'categories', 'url'])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mnist.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(70000, 784)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X, y = mnist['data'], mnist['target']\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split into train, val, test sets of size 50k, 10k, 10k\n",
+    "\n",
+    "X_train = X[:50000]\n",
+    "y_train = y[:50000]\n",
+    "X_val = X[50000:60000]\n",
+    "y_val = y[50000:60000]\n",
+    "X_test = X[60000:]\n",
+    "y_test = y[60000:]\n",
+    "\n",
+    "# Normalize features\n",
+    "\n",
+    "X_train /= 255.0\n",
+    "X_val /= 255.0\n",
+    "X_test /= 255.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_test.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training our  RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
+      "                       criterion='gini', max_depth=None, max_features='auto',\n",
+      "                       max_leaf_nodes=None, max_samples=None,\n",
+      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+      "                       min_samples_leaf=1, min_samples_split=2,\n",
+      "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
+      "                       n_jobs=None, oob_score=False, random_state=None,\n",
+      "                       verbose=0, warm_start=False)\n",
+      "Training our  ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
+      "                     criterion='gini', max_depth=None, max_features='auto',\n",
+      "                     max_leaf_nodes=None, max_samples=None,\n",
+      "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
+      "                     min_samples_leaf=1, min_samples_split=2,\n",
+      "                     min_weight_fraction_leaf=0.0, n_estimators=100,\n",
+      "                     n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
+      "                     warm_start=False)\n",
+      "Training our  LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
+      "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
+      "          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
+      "          verbose=0)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tsb\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
+      "  \"the number of iterations.\", ConvergenceWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "rfc = RandomForestClassifier()\n",
+    "etc = ExtraTreesClassifier()\n",
+    "svc = LinearSVC()\n",
+    "\n",
+    "classifiers = [rfc, etc, svc]\n",
+    "scores = []\n",
+    "\n",
+    "# Fit each classifier to the training set and predict on X_val\n",
+    "for clf in classifiers:\n",
+    "    print('Training our ', clf)\n",
+    "    clf.fit(X_train, y_train)\n",
+    "    score = clf.score(X_val, y_val)\n",
+    "    scores.append(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.9719, 0.9741, 0.9208]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tsb\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
+      "  \"the number of iterations.\", ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "VotingClassifier(estimators=[('rf',\n",
+       "                              RandomForestClassifier(bootstrap=True,\n",
+       "                                                     ccp_alpha=0.0,\n",
+       "                                                     class_weight=None,\n",
+       "                                                     criterion='gini',\n",
+       "                                                     max_depth=None,\n",
+       "                                                     max_features='auto',\n",
+       "                                                     max_leaf_nodes=None,\n",
+       "                                                     max_samples=None,\n",
+       "                                                     min_impurity_decrease=0.0,\n",
+       "                                                     min_impurity_split=None,\n",
+       "                                                     min_samples_leaf=1,\n",
+       "                                                     min_samples_split=2,\n",
+       "                                                     min_weight_fraction_leaf=0.0,\n",
+       "                                                     n_estimators=100,\n",
+       "                                                     n_jobs=None,\n",
+       "                                                     oob_score...\n",
+       "                                                   n_estimators=100,\n",
+       "                                                   n_jobs=None, oob_score=False,\n",
+       "                                                   random_state=None, verbose=0,\n",
+       "                                                   warm_start=False)),\n",
+       "                             ('sv',\n",
+       "                              LinearSVC(C=1.0, class_weight=None, dual=True,\n",
+       "                                        fit_intercept=True, intercept_scaling=1,\n",
+       "                                        loss='squared_hinge', max_iter=1000,\n",
+       "                                        multi_class='ovr', penalty='l2',\n",
+       "                                        random_state=None, tol=0.0001,\n",
+       "                                        verbose=0))],\n",
+       "                 flatten_transform=True, n_jobs=None, voting='hard',\n",
+       "                 weights=None)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import VotingClassifier\n",
+    "\n",
+    "# Hard vote ensmeble\n",
+    "voting_clf = VotingClassifier(\n",
+    "    estimators=[('rf', rfc), ('et', etc), ('sv', svc)],\n",
+    "    voting='hard'\n",
+    ")\n",
+    "\n",
+    "voting_clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9719"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "voting_clf.score(X_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Try without SVC\n",
+    "\n",
+    "del voting_clf.estimators_[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9732"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "voting_clf.score(X_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9752"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set to soft voting and check if better\n",
+    "\n",
+    "voting_clf.voting='soft'\n",
+    "\n",
+    "voting_clf.score(X_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9707"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check on Test Set\n",
+    "\n",
+    "voting_clf.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 9**\n",
+    "\n",
+    "train a stacking ensemble on our previous classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Round up our predictions\n",
+    "\n",
+    "X_val_predictions = np.empty((len(X_val), len(classifiers)), dtype=np.float32)\n",
+    "\n",
+    "for index, clf in enumerate(classifiers):\n",
+    "    X_val_predictions[:, index] = clf.predict(X_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3. 3. 3.]\n",
+      " [8. 8. 8.]\n",
+      " [6. 6. 6.]\n",
+      " ...\n",
+      " [5. 5. 5.]\n",
+      " [6. 6. 6.]\n",
+      " [8. 8. 8.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_val_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
+       "                       criterion='gini', max_depth=None, max_features='auto',\n",
+       "                       max_leaf_nodes=None, max_samples=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
+       "                       n_jobs=None, oob_score=True, random_state=None,\n",
+       "                       verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train a classifier which will take as input our predictions matrix\n",
+    "blender = RandomForestClassifier(n_estimators=200, oob_score=True)\n",
+    "blender.fit(X_val_predictions, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9727"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check our out of bag score to get an idea of accuracy\n",
+    "blender.oob_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Round up predictions for X_test\n",
+    "X_test_predictions = np.empty((len(X_val), len(classifiers)), dtype=np.float32)\n",
+    "\n",
+    "for index, clf in enumerate(classifiers):\n",
+    "    X_test_predictions[:, index] = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use our blender to predict based on our predictions matrix\n",
+    "y_pred = blender.predict(X_test_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.968"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}