diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb
index e69de29b..121fae3e 100644
--- a/2-Regression/1-Tools/notebook.ipynb
+++ b/2-Regression/1-Tools/notebook.ipynb
@@ -0,0 +1,593 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Welcome to your notebook"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "source": [
+    "print('Hello notebook')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Hello notebook\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "source": [
+    "import matplotlib.pyplot as plt\r\n",
+    "import numpy as np\r\n",
+    "from sklearn import datasets, linear_model, model_selection"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "X = the set of attributes - age, sex, bmi, bp, and 6 medical measurements\r\n",
+    "y = the target - a quantitative measure of disease progression one year after baseline"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\r\n",
+    "print(X.ndim, X.shape)\r\n",
+    "print(X[0])"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2 (442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990842 -0.01764613]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`.shape` is a numpy property which says how deep the sets go and how many in each.\r\n",
+    "\r\n",
+    "(442,10) means 442 arrays of 10 items each\r\n",
+    "\r\n",
+    "Each 'level' of array is called an axis in numpy, so we have 2 axes here, \r\n",
+    "the first has 442 entries and the second has 10 \r\n",
+    "(i.e. all 442 have 10, no variation allowed)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "X is 442 entries of the 10 attributes\r\n",
+    "\r\n",
+    "y is the 442 results"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "source": [
+    "Xview = X.view()\r\n",
+    "X = Xview[:, np.newaxis, 2]\r\n",
+    "print(X.shape)\r\n",
+    "print(X[0])"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(442, 1)\n",
+      "[0.06169621]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "This picks the second attribute from each set.\r\n",
+    "\r\n",
+    ": - leave outermost dimension alone\r\n",
+    "\r\n",
+    "np.newaxis - wrap inner one in a new array\r\n",
+    "\r\n",
+    "2 - pick the third attribute (bmi, presumably)\r\n",
+    "\r\n",
+    "without np.newaxis, we end up with 1 array of all the bmi's - not what we want.\r\n",
+    "\r\n",
+    "this way we get 442 arrays of 1 entry, the bmi.\r\n",
+    "\r\n",
+    "I have no idea why instructions said to overwrite original `X` so I saved it as `origX`, I want to check that the numbers match below"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "source": [
+    "print(X.ndim, X.shape)\r\n",
+    "print(X[0], X[1])\r\n",
+    "print(origX[0, 2], origX[1, 2])"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2 (442, 1)\n",
+      "[0.06169621] [-0.05147406]\n",
+      "0.0616962065186885 -0.0514740612388061\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Yay it matches! Maybe I really do understand what's going on here 😀"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Split into training and test data"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "source": [
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(296, 1) (296,) (146, 1) (146,)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Now we see if the model can detect a link between bmi and diabetes"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "source": [
+    "model = linear_model.LinearRegression()\r\n",
+    "model.fit(X_train, y_train)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 210
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We now have a trained model, based on 2/3 of the initial data.\r\n",
+    "\r\n",
+    "Time to see how accurate it is"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Just the inputs, no outputs, and this is data the model hasn't seen before"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "What does y_pred look like? Presumably the same 'shape' as y_test, which is the actual results"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "source": [
+    "print(y_pred.shape)\r\n",
+    "print(y_test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(146,)\n",
+      "(146,)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Let's eyeball the first 10 entries, just for fun"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "source": [
+    "print(y_pred[:10])\r\n",
+    "print(y_test[:10])"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[106.13314033 158.77362293 206.44802227 178.63795599 146.8550231\n",
+      " 141.88893983 306.76290422 180.6243893  140.89572318 108.11957364]\n",
+      "[138. 151. 242. 296. 141.  81. 242. 270.  60.  93.]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Let's plot it on a graph"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "source": [
+    "plt.scatter(X_test, y_test, color = 'black')\r\n",
+    "plt.plot(X_test, y_pred, color = 'blue', linewidth = 3)\r\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Are the actual predicted values directly on the line? My prediction: yes"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "source": [
+    "plt.scatter(X_test, y_test, color = 'black')\r\n",
+    "plt.scatter(X_test, y_pred, color = 'green')\r\n",
+    "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\r\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Note that there is one green dot for every black one, on the X axis. The value on the Y axis is the machine's prediction (i.e. on the line), based on bmi alone"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Presumably the point of test data is to see if the prediction matches the reality, but we didn't analyse that here"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Challenge question"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Let's use blood sugar level (attribute 10)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "source": [
+    "X = Xview[:, np.newaxis, 9]\r\n",
+    "print(X.shape)\r\n",
+    "print(X[0])"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(442, 1)\n",
+      "[0.06169621]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Split into test and train"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\r\n",
+    "print(X_train.shape, y_train.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(296, 1) (296,)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Train"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "source": [
+    "bloodSugarModel = linear_model.LinearRegression()\r\n",
+    "bloodSugarModel.fit(X_train, y_train)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 238
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Predict"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "source": [
+    "y_predict = bloodSugarModel.predict(X_test)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "source": [
+    "plt.scatter(X_test, y_test, color = 'black')\r\n",
+    "plt.scatter(X_test, y_pred, color = 'green')\r\n",
+    "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\r\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "I am ... not quite sure what happened here :)\r\n",
+    "\r\n",
+    "Does this mean the machine couldn't figure out any correlation? Surely it's still supposed to give a straight line, even if it's not tightly correlated?"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ah, typo 😊"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "source": [
+    "y_pred = bloodSugarModel.predict(X_test)\r\n",
+    "plt.scatter(X_test, y_test, color = 'black')\r\n",
+    "plt.scatter(X_test, y_pred, color = 'green')\r\n",
+    "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\r\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "That is more like it"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ok, this is pretty cool, but surely Excel could do this 20 years ago? I remember making a graph and telling Excel to 'Add trendline', and nobody called it machine learning back then..."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "And I'm still not sure why we are using test data, we didn't actually test it"
+   ],
+   "metadata": {}
+  }
+ ],
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