00- Tutorials/Machine Learning for Beginners Tutorial and Assessment/Machine Learning for Beginners (run).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ad53740",
   "metadata": {},
   "source": [
    "## Machine Learning for Neuroscience, <br>Department of Brain Sciences, Faculty of Medicine, <br> Imperial College London\n",
    "### Contributors: Francesca Palermo, Nan Fletcher-Lloyd, Alex Capstick, Yu Chen\n",
    "**Winter 2022**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9246f7fe",
   "metadata": {},
   "source": [
    "# Machine Learning for Beginners"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b161ecd",
   "metadata": {},
   "source": [
    "This tutorial will provide an introduction to machine learning using the machine learning library for Python scikit-learn (https://scikit-learn.org/stable)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca16fa6e",
   "metadata": {},
   "source": [
    "scikit-learn features various machine learning algorithms and can also be used for dimensionality reduction (reducing the number of random variables to consider), model selection, and pre-processing of data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb7b588",
   "metadata": {},
   "source": [
    "## Import scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa77cbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn as sk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c97eaa",
   "metadata": {},
   "source": [
    "## Types of Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9546e5c",
   "metadata": {},
   "source": [
    "There are many types of machine learning. \n",
    "\n",
    "There are three main types of machine learning. These are supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning (decision making).\n",
    "\n",
    "Other types include semi-supervised and self-supervised learning (not discussed in this module).\n",
    "\n",
    "Deep learning and deep reinforcement learning are further types of machine learning that we will not discuss in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cebdae8",
   "metadata": {},
   "source": [
    "In this tutorial, we will focus on supervised and unsupervised learning examples."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c97eaa",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b6e7882",
   "metadata": {},
   "source": [
    "First, we must remember to import our other dependencies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c97eaa",
   "metadata": {},
   "source": [
    "### Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa77cbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b8253d",
   "metadata": {},
   "source": [
    "### Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9618181b",
   "metadata": {},
   "source": [
    "Supervised learning uses labelled datasets to train algorithms to identify the category to which an obejct belongs or predict outcomes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "331de788",
   "metadata": {},
   "source": [
    "#### Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b325901",
   "metadata": {},
   "source": [
    "Classification involves identifying a category to which an object belongs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed69c4c6",
   "metadata": {},
   "source": [
    "For this example, you are going to use the sci-kit learn dataset breast cancer.\n",
    "\n",
    "More on this dataset can be found here: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e10f0b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = datasets.load_breast_cancer(as_frame=True) # this loads the dataset as a dictionary\n",
    "features = data.data # this derives features as a dataframe (30 features by 569 instances)\n",
    "labels = data.target # this derives labels as a dataframe (569 instances)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae0f399",
   "metadata": {},
   "source": [
    "Let's just do a quick check to make sure everything has loaded as it should."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3255149",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.7119                0.2654          0.4601   \n",
       "1             0.2416                0.1860          0.2750   \n",
       "2             0.4504                0.2430          0.3613   \n",
       "3             0.6869                0.2575          0.6638   \n",
       "4             0.4000                0.1625          0.2364   \n",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     worst fractal dimension  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2dec9a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "564    0\n",
       "565    0\n",
       "566    0\n",
       "567    0\n",
       "568    1\n",
       "Name: target, Length: 569, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99497d36",
   "metadata": {},
   "source": [
    "*N.B. The labels are binary, with 0 indicating a negative diagnosis and 1 indicating a positive diagnosis of breast cancer, respectively.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4044c5ab",
   "metadata": {},
   "source": [
    "Here, we are going to focus on two features (mean area and mean smoothness), so the first thing we'll do is extract this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "079a9e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean area  mean smoothness\n",
       "0       1001.0          0.11840\n",
       "1       1326.0          0.08474\n",
       "2       1203.0          0.10960\n",
       "3        386.1          0.14250\n",
       "4       1297.0          0.10030\n",
       "..         ...              ...\n",
       "564     1479.0          0.11100\n",
       "565     1261.0          0.09780\n",
       "566      858.1          0.08455\n",
       "567     1265.0          0.11780\n",
       "568      181.0          0.05263\n",
       "\n",
       "[569 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_and_smoothness = features[['mean area', 'mean smoothness']]\n",
    "area_and_smoothness"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d5aa97",
   "metadata": {},
   "source": [
    "For this example, we are going to use logistic regression which, despite its name, is a linear classification model.\n",
    "\n",
    "Learn more about logistic regression here: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420abd6e",
   "metadata": {},
   "source": [
    "To use the Logistic Regression classifier, we must first import it using the following line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ad0e8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788e6f67",
   "metadata": {},
   "source": [
    "But before we starting training our model, we must first scale our feature data.\n",
    "\n",
    "Feature scaling is one of the most critical pre-processing steps in machine learning, with the most common techniques being standardization and normalization.\n",
    "\n",
    "Machine learning algorithms that calculate distance or assume normality are sensitive to relative scales of features, meaning that if the data is not scaled, features with a higher value range start dominating the model's decision-making process. Feature scaling is therefore needed to bring features with different ranges into comparable ranges.\n",
    "\n",
    "Feature scaling also allows for much faster model convergence.\n",
    "\n",
    "Learn more about scaling (when you need to and why, and how to do so) using the links below:\n",
    "\n",
    "https://towardsdatascience.com/all-about-feature-scaling-bcc0ad75cb35 \n",
    "\n",
    "https://towardsdatascience.com/feature-scaling-effectively-choose-input-variables-based-on-distributions-3032207c921f\n",
    "\n",
    "*N.B. scaling will feature in assessed labs later on during this module, so it would definitely be valuable to read through these two articles.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a938886d",
   "metadata": {},
   "source": [
    "In this scenario, we are going to use the sci-kit learn StandardScaler to standardize our data.\n",
    "\n",
    "Learn more about this scaler here: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n",
    "\n",
    "Another commonly used scaler is the MinMax scaler. The MinMax scaler normalizes data. Learn more here: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba8c9f1",
   "metadata": {},
   "source": [
    "First, let's import out scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "579021c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb590e45",
   "metadata": {},
   "source": [
    "Next, let's implement our standardization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89b04426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.984375</td>\n",
       "      <td>1.568466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.908708</td>\n",
       "      <td>-0.826962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.558884</td>\n",
       "      <td>0.942210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.764464</td>\n",
       "      <td>3.283553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.826229</td>\n",
       "      <td>0.280372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>2.343856</td>\n",
       "      <td>1.041842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>1.723842</td>\n",
       "      <td>0.102458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>0.577953</td>\n",
       "      <td>-0.840484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>1.735218</td>\n",
       "      <td>1.525767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>-1.347789</td>\n",
       "      <td>-3.112085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean area  mean smoothness\n",
       "0     0.984375         1.568466\n",
       "1     1.908708        -0.826962\n",
       "2     1.558884         0.942210\n",
       "3    -0.764464         3.283553\n",
       "4     1.826229         0.280372\n",
       "..         ...              ...\n",
       "564   2.343856         1.041842\n",
       "565   1.723842         0.102458\n",
       "566   0.577953        -0.840484\n",
       "567   1.735218         1.525767\n",
       "568  -1.347789        -3.112085\n",
       "\n",
       "[569 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standardized_data = pd.DataFrame(scaler.fit_transform(area_and_smoothness),\n",
    "                   columns=['mean area','mean smoothness'])\n",
    "standardized_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710d673c",
   "metadata": {},
   "source": [
    "Now we've completed this step, we can move onto training our model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6475a7",
   "metadata": {},
   "source": [
    "As you learned in today's lecture, whenever we are building a machine learning model that uses a supervised learning algorithm, it is important we have some way of evaluating the performance of that model.  However, learning the parameters of the prediction function and testing it on the same data would mean a model would just be repeating the labels of the samples it has just seen, deriving a perfect score, but failing to predict anything useful on as-of-yet unseen data. This situation is known as overfitting, and to avoid it the most common practice is to hold out part of the available data as a test set.\n",
    "\n",
    "The most simple way to do this is by using a technique called the train-test split on our data. \n",
    "\n",
    "You can read more about how and when to use the train-test split at the following links: \n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html\n",
    "\n",
    "https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c0f06d",
   "metadata": {},
   "source": [
    "So let's import out train-test split helper from sklearn and apply it to our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "116f7c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2dd6a4",
   "metadata": {},
   "source": [
    "In this case, we want to have a train_size of 80% and a test size of 20%. \n",
    "\n",
    "We also want to shuffle our data before splitting as, without this, we risk creating batch data not representative of the overall dataset.\n",
    "\n",
    "Finally, we use the random_state parameter to control the shuffling applied to the data before the split to ensure a reproducible output across multiple calls of the function. To apply this, pass any integer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "570d19fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(standardized_data, labels, test_size = 0.20, shuffle=True, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6fe23e",
   "metadata": {},
   "source": [
    "Check that the output of this function is what you might expect (number of instances in the training and test sets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3217a047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>-1.150124</td>\n",
       "      <td>0.728714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1.866047</td>\n",
       "      <td>1.262455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-1.120545</td>\n",
       "      <td>-1.362838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>-0.875668</td>\n",
       "      <td>0.014925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>-0.975496</td>\n",
       "      <td>1.212639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>-1.168611</td>\n",
       "      <td>0.104593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>-0.689379</td>\n",
       "      <td>1.269571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>-0.063392</td>\n",
       "      <td>-2.282296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>-0.157532</td>\n",
       "      <td>0.686015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>-0.557982</td>\n",
       "      <td>-1.155035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>455 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean area  mean smoothness\n",
       "68   -1.150124         0.728714\n",
       "181   1.866047         1.262455\n",
       "63   -1.120545        -1.362838\n",
       "248  -0.875668         0.014925\n",
       "60   -0.975496         1.212639\n",
       "..         ...              ...\n",
       "71   -1.168611         0.104593\n",
       "106  -0.689379         1.269571\n",
       "270  -0.063392        -2.282296\n",
       "435  -0.157532         0.686015\n",
       "102  -0.557982        -1.155035\n",
       "\n",
       "[455 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3217a047",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>204</th>\n",
       "      <td>-0.491999</td>\n",
       "      <td>0.234114</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1.351264</td>\n",
       "      <td>-0.446227</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.267377</td>\n",
       "      <td>0.913744</td>\n",
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       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>-0.532101</td>\n",
       "      <td>0.643316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>-0.716683</td>\n",
       "      <td>0.247636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>0.031601</td>\n",
       "      <td>-0.708116</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.463051</td>\n",
       "      <td>-0.333074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>-0.707013</td>\n",
       "      <td>0.351537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>-0.088136</td>\n",
       "      <td>-1.005588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>2.173210</td>\n",
       "      <td>-0.118866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean area  mean smoothness\n",
       "204  -0.491999         0.234114\n",
       "70    1.351264        -0.446227\n",
       "131   0.267377         0.913744\n",
       "431  -0.532101         0.643316\n",
       "540  -0.716683         0.247636\n",
       "..         ...              ...\n",
       "486   0.031601        -0.708116\n",
       "75    0.463051        -0.333074\n",
       "249  -0.707013         0.351537\n",
       "238  -0.088136        -1.005588\n",
       "265   2.173210        -0.118866\n",
       "\n",
       "[114 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3217a047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68     1\n",
       "181    0\n",
       "63     1\n",
       "248    1\n",
       "60     1\n",
       "      ..\n",
       "71     1\n",
       "106    1\n",
       "270    1\n",
       "435    0\n",
       "102    1\n",
       "Name: target, Length: 455, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3217a047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204    1\n",
       "70     0\n",
       "131    0\n",
       "431    1\n",
       "540    1\n",
       "      ..\n",
       "486    1\n",
       "75     0\n",
       "249    1\n",
       "238    1\n",
       "265    0\n",
       "Name: target, Length: 114, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0e79f6",
   "metadata": {},
   "source": [
    "In this case, we then train the model with the following line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d9b073b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(random_state=42).fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c05b472",
   "metadata": {},
   "source": [
    "Next, we want to return the mean accuracy on the given test data and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfa9860d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9298245614035088"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = model.score(x_test, y_test) \n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61bf76c",
   "metadata": {},
   "source": [
    "Now, accuracy is not the only metric that can be used to evaluate model performance. \n",
    "\n",
    "Read more about the different metrics that can be used to evaluate model performance including how to calculate them and when to use them: https://towardsdatascience.com/performance-metrics-for-classification-machine-learning-problems-97e7e774a007"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b99a271d",
   "metadata": {},
   "source": [
    "For this, we must first use our model to predict the labels of the test feature data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4aebb4e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_labels = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f3dff2",
   "metadata": {},
   "source": [
    "We now want to use these predicted labels to derive a confusion matrix. A confusion matrix displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).\n",
    "\n",
    "TPs are test results that correctly indicate the presence of a condition or characteristic.\n",
    "\n",
    "TNs are test results that correctly indicate the absence of a condition or characteristic.\n",
    "\n",
    "FPs are test results that incorrectly indicate the presence of a condition or characteristic.\n",
    "\n",
    "FNs are test results that incorrectly indicate the absence of a condition or characteristic."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0c3909",
   "metadata": {},
   "source": [
    "Now let's import the confusion matrix helper function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3c2f677c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a8679f3",
   "metadata": {},
   "source": [
    "We can then print the confusion matrix using the following line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1d3bfc05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[38  5]\n",
      " [ 3 68]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, pred_labels)\n",
    "print(cm)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b30af09e",
   "metadata": {},
   "source": [
    "From this confusion matrix, we can then derive further metrics, including precision, recall, and the F1-score.\n",
    "\n",
    "Precision is the ratio of correctly classified positive instances to the total predicted positive classifications. \n",
    "\n",
    "Recall or sensitivity is the ratio of correctly classified positive instances to the total positive instances.\n",
    "\n",
    "Precision helps us understand how useful results are; however, recall helps us understand how complete the results are.\n",
    "\n",
    "The F1-score balances the two previous scores, being the harmonic mean of precision and recall.\n",
    "\n",
    "*N.B. accuracy is the ratio of correctly classified instances to the total instances.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab9b319",
   "metadata": {},
   "source": [
    "First, let's import our helper functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4da8e22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import mean\n",
    "from numpy import std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5a7164ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78fd3d16",
   "metadata": {},
   "source": [
    "Now, let's print all these scores.\n",
    "\n",
    "For precision, recall, and the F1-score, you should report the average and standard deviation of these scores. This is because the precision, recall, and F1-score is provided for each class in the dataset.\n",
    "\n",
    "However, particularly when working with multiclass data, it is important to understand the performance of the model for each class. For this, we print a classification report, which tells you the precision, recall, and F1-score for each class and support (weighted by number of instances in each class in comparison to total number of instances for each class, respectively)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "65f22a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d5d325a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "f1:\t\t 0.9444444444444444\n",
      "recall\t\t 0.9577464788732394\n",
      "precision\t 0.9315068493150684\n",
      "\n",
      "f1_avg:\t\t 0.9246031746031745\n",
      "recall_avg\t 0.9207337045528987\n",
      "precision_avg\t 0.9291680588038758\n",
      "\n",
      "f1_sd:\t\t 0.019841269841269882\n",
      "recall_sd\t 0.03701277432034061\n",
      "precision_sd\t 0.0023387905111927343\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.88      0.90        43\n",
      "           1       0.93      0.96      0.94        71\n",
      "\n",
      "    accuracy                           0.93       114\n",
      "   macro avg       0.93      0.92      0.92       114\n",
      "weighted avg       0.93      0.93      0.93       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f1 = f1_score(y_test, pred_labels)\n",
    "recall = recall_score(y_test, pred_labels)\n",
    "precision = precision_score(y_test, pred_labels)\n",
    "\n",
    "f1_avg = mean(f1_score(y_test, pred_labels, average=None))\n",
    "recall_avg = mean(recall_score(y_test, pred_labels, average=None))\n",
    "precision_avg = mean(precision_score(y_test, pred_labels, average=None))\n",
    "\n",
    "f1_sd = std(f1_score(y_test, pred_labels, average=None))\n",
    "recall_sd = std(recall_score(y_test, pred_labels, average=None))\n",
    "precision_sd = std(precision_score(y_test, pred_labels, average=None))\n",
    "\n",
    "print('\\nf1:\\t\\t',f1)\n",
    "print('recall\\t\\t',recall)\n",
    "print('precision\\t',precision)\n",
    "\n",
    "print('\\nf1_avg:\\t\\t',f1_avg)\n",
    "print('recall_avg\\t',recall_avg)\n",
    "print('precision_avg\\t',precision_avg)\n",
    "\n",
    "print('\\nf1_sd:\\t\\t',f1_sd)\n",
    "print('recall_sd\\t',recall_sd)\n",
    "print('precision_sd\\t',precision_sd)\n",
    "\n",
    "print('\\n',classification_report(y_test, pred_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e54d2496",
   "metadata": {},
   "source": [
    "Another metric that can be used to evaluate model performance is the area under the receiver operating characteristic curve (ROC-AUC), which plots the true positive rate (recall) against the false positive rate. The AUC ranges in value from 0 to 1. The closer the score to 1, the greater the number of correct predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b48a87ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c6e14cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9207337045528988\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_test, pred_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2411bed2",
   "metadata": {},
   "source": [
    "Now, even using a train-test split, there is still a risk of overfitting on the test set when tuning the parameters of a model until the estimator performs optimally. In this scenario, knowledge can leak into the model until th evaluation metrics no longer report on generalization performance. \n",
    "\n",
    "One way of solving this is hold yet another aprt of the available dataset out as a so-called validation set, on which an initial evaluation is done. However, the problem with partitioning the available data into three sets is that we drastically reduce the number of samples which can be used for learning the model (a particular issue for small datasets and imbalanced datasets). It also means that the results can depend on any random choice for the pair of train and validation sets.\n",
    "\n",
    "A solution to this and another way of evaluating model performance is to use cross-validation, which removes the need for a validation set.\n",
    "\n",
    "In the basic approach, otherwise known as k-fold cross-validation, the data is split into k folds. The model is trained using k - 1 of the folds as training data and validation on the remaining part of the data. This is repeated for each fold. The performance measures reported by k-fold cross-validation is then the average of the values computed in the loop.\n",
    "\n",
    "Learn more about cross-validation here: https://towardsdatascience.com/5-reasons-why-you-should-use-cross-validation-in-your-data-science-project-8163311a1e79 \n",
    "\n",
    "*N.B the article above includes more information on when it is particularly useful to use cross-validation and the different strategies for doing so.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0a96a4",
   "metadata": {},
   "source": [
    "For this, we must first import the cross_val_predict helper function. This allows us to fit a model to our data and generate cross-validated estimates for each input data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "944adf50",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e26670",
   "metadata": {},
   "source": [
    "We then reset the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9cd5831d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55042d85",
   "metadata": {},
   "source": [
    "And finally, we generate cross-validated estimates for each input data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c09af385",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_pred_labels = cross_val_predict(model, standardized_data, labels, cv=10) # Here we use a 10-fold cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab3df144",
   "metadata": {},
   "source": [
    "Now all we need to do is output our results as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8d5d325a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:\t 0.8980667838312829\n",
      "[[173  39]\n",
      " [ 19 338]]\n",
      "\n",
      "f1:\t\t 0.9209809264305178\n",
      "recall\t\t 0.9467787114845938\n",
      "precision\t 0.896551724137931\n",
      "\n",
      "f1_avg:\t\t 0.888708284997437\n",
      "recall_avg\t 0.8814082236668253\n",
      "precision_avg\t 0.8987966954022988\n",
      "\n",
      "f1_sd:\t\t 0.03227264143308067\n",
      "recall_sd\t 0.06537048781776861\n",
      "precision_sd\t 0.00224497126436779\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.82      0.86       212\n",
      "           1       0.90      0.95      0.92       357\n",
      "\n",
      "    accuracy                           0.90       569\n",
      "   macro avg       0.90      0.88      0.89       569\n",
      "weighted avg       0.90      0.90      0.90       569\n",
      "\n",
      "0.8814082236668251\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(labels, cv_pred_labels)\n",
    "\n",
    "cm = confusion_matrix(labels, cv_pred_labels)\n",
    "\n",
    "f1 = f1_score(labels, cv_pred_labels)\n",
    "recall = recall_score(labels, cv_pred_labels)\n",
    "precision = precision_score(labels, cv_pred_labels)\n",
    "\n",
    "f1_avg = mean(f1_score(labels, cv_pred_labels, average=None))\n",
    "recall_avg = mean(recall_score(labels, cv_pred_labels, average=None))\n",
    "precision_avg = mean(precision_score(labels, cv_pred_labels, average=None))\n",
    "\n",
    "f1_sd = std(f1_score(labels, cv_pred_labels, average=None))\n",
    "recall_sd = std(recall_score(labels, cv_pred_labels, average=None))\n",
    "precision_sd = std(precision_score(labels, cv_pred_labels, average=None))\n",
    "\n",
    "print('accuracy:\\t', accuracy)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "print('\\nf1:\\t\\t',f1)\n",
    "print('recall\\t\\t',recall)\n",
    "print('precision\\t',precision)\n",
    "\n",
    "print('\\nf1_avg:\\t\\t',f1_avg)\n",
    "print('recall_avg\\t',recall_avg)\n",
    "print('precision_avg\\t',precision_avg)\n",
    "\n",
    "print('\\nf1_sd:\\t\\t',f1_sd)\n",
    "print('recall_sd\\t',recall_sd)\n",
    "print('precision_sd\\t',precision_sd)\n",
    "\n",
    "print('\\n',classification_report(labels, cv_pred_labels))\n",
    "\n",
    "print(roc_auc_score(labels, cv_pred_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664cb551",
   "metadata": {},
   "source": [
    "And that's it! You have now learnt all about classification."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "331de788",
   "metadata": {},
   "source": [
    "#### Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b325901",
   "metadata": {},
   "source": [
    "Regression involves predicting a continuous-valued attribute associated with an object."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed69c4c6",
   "metadata": {},
   "source": [
    "For this example, you are going to use the sci-kit learn dataset diabetes.\n",
    "\n",
    "More on this dataset can be found here: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e10f0b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "data = datasets.load_diabetes(as_frame=True) # this loads the dataset as a dictionary\n",
    "features = data.data # this derives features as a dataframe (10 features by 442 instances)\n",
    "labels = data.target # this derives a continuous-valued attribute as a dataframe (442 instances)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae0f399",
   "metadata": {},
   "source": [
    "Let's just do a quick check to make sure everything has loaded as it should."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c3255149",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031988</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.019662</td>\n",
       "      <td>0.059744</td>\n",
       "      <td>-0.005697</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.007207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>-0.067642</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.079165</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.018114</td>\n",
       "      <td>0.044485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>0.017293</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.046883</td>\n",
       "      <td>0.015491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044529</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.073030</td>\n",
       "      <td>-0.081413</td>\n",
       "      <td>0.083740</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004222</td>\n",
       "      <td>0.003064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2    0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017293 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
       "\n",
       "           s4        s5        s6  \n",
       "0   -0.002592  0.019907 -0.017646  \n",
       "1   -0.039493 -0.068332 -0.092204  \n",
       "2   -0.002592  0.002861 -0.025930  \n",
       "3    0.034309  0.022688 -0.009362  \n",
       "4   -0.002592 -0.031988 -0.046641  \n",
       "..        ...       ...       ...  \n",
       "437 -0.002592  0.031193  0.007207  \n",
       "438  0.034309 -0.018114  0.044485  \n",
       "439 -0.011080 -0.046883  0.015491  \n",
       "440  0.026560  0.044529 -0.025930  \n",
       "441 -0.039493 -0.004222  0.003064  \n",
       "\n",
       "[442 rows x 10 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ddadf8df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      151.0\n",
       "1       75.0\n",
       "2      141.0\n",
       "3      206.0\n",
       "4      135.0\n",
       "       ...  \n",
       "437    178.0\n",
       "438    104.0\n",
       "439    132.0\n",
       "440    220.0\n",
       "441     57.0\n",
       "Name: target, Length: 442, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca51fc5",
   "metadata": {},
   "source": [
    "You might think the features dataframe looks a bit odd. This is because each of the 10 feature variables have already been mean centred and scaled by the product of the standard deviation and number of samples (i.e. the sum of squares of each column totals 1) to bring them to a common scale."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1d58ef",
   "metadata": {},
   "source": [
    "First, let's check the linear correlation between variables in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bb25bfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([features, labels], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d54713e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "\n",
    "# visualize the correlation matrix\n",
    "\n",
    "plt.subplots(figsize=(7,7))\n",
    "sns.heatmap(corr, cmap= 'RdPu', annot=True) # plots correlation matrix as a heatmap with values\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "16f0a8c5",
   "metadata": {},
   "source": [
    "This is the point at which multicolinear variables (indepdendent variables that are significantly correlated with one another) can be removed for feature selection, but we are just going to focus on demonstrating the methodology behing regression models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "078c98d9",
   "metadata": {},
   "source": [
    "For now, we are just going to use a simple train-test split to evaluate model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "39bdf523",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(features, labels, test_size = 0.20, shuffle=True, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c8c45c",
   "metadata": {},
   "source": [
    "For this example, we are going to use linear regression.\n",
    "\n",
    "Learn more about linear regression here: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28cd89c",
   "metadata": {},
   "source": [
    "Let's import it now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "549cb40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f880c6",
   "metadata": {},
   "source": [
    "Now we want to fit the model using all the feature variables and the dependent variable and use this model to predict values of the dependent variable based on the test feature variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e1af7b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b5550a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_y = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27846615",
   "metadata": {},
   "source": [
    "Finally, let's evaluate the model.\n",
    "\n",
    "To speed things up, we can import metrics from sklearn using the code in the next cell.\n",
    "\n",
    "Learn more about the different metrics (what they measure and when to use them) here: https://scikit-learn.org/stable/modules/model_evaluation.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c2779dd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics as mt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80631692",
   "metadata": {},
   "source": [
    "We are going to look at three measures:\n",
    "- the coefficient of determination or R² score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score)\n",
    "- the mean absolute error or MAE (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error)\n",
    "- the mean squared error or MSE (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f28de59",
   "metadata": {},
   "source": [
    "The R² score represents the proportion of the variance that has been explained by the independent variables of the model. It provides an indication of goodness of fit, with the best possible score being 1.0.\n",
    "\n",
    "The MAE computes a risk metric corresponding to the expected value of the absolute error loss.\n",
    "\n",
    "The MSE computes a risk metric corresponding to the expected value of the squared error or loss. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ea471697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R² score of the model is 0.45\n",
      "The Mean Absolute Error of model is 42.79\n",
      "The Mean Squared Error of the model is 2900.19\n"
     ]
    }
   ],
   "source": [
    "print(\"The R² score of the model is\", np.round(mt.r2_score(y_test,pred_y),2))\n",
    "print(\"The Mean Absolute Error of model is\", np.round(mt.mean_absolute_error(y_test,pred_y),2))\n",
    "print(\"The Mean Squared Error of the model is\" , np.round(mt.mean_squared_error(y_test,pred_y),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3268b8",
   "metadata": {},
   "source": [
    "Finally, we want to determine the coefficients and interecept of the regression equation as calculated by the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651dc4e6",
   "metadata": {},
   "source": [
    "Regression coefficients tell you two things:\n",
    "\n",
    "1) Whether there was a positive or negative correlation \n",
    "between the independent variable and the dependent variable.\n",
    "\n",
    "*If there is a positive correlation, as the value of the independent variable increases, the dependent variable also tends to increase. If there is a negative correlation, as the value of the independent variable increases, the dependent variable tends to decrease.*\n",
    "\n",
    "2) The size of the effect of the independent variable on the dependent variable.\n",
    "\n",
    "*The coefficient value signifies how much the mean of the dependent variable changes given a unit shift in the independent variable while all other variables are held constant.*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "03f76e21",
   "metadata": {},
   "source": [
    "The intercept represents the mean value of the dependent variable when all of the independent variables in the model are equal to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c050816c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age     37.904021\n",
       "sex   -241.964362\n",
       "bmi    542.428759\n",
       "bp     347.703844\n",
       "s1    -931.488846\n",
       "s2     518.062277\n",
       "s3     163.419983\n",
       "s4     275.317902\n",
       "s5     736.198859\n",
       "s6      48.670657\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef = pd.Series(model.coef_, index=x_train.columns) # derives the coefficient for each variable\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "28c4a99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151.34560453985995"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intercept = model.intercept_ # derives the model intercept\n",
    "intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7db4488",
   "metadata": {},
   "source": [
    "The regression equation as calculated by the model would be as follows:\n",
    "\n",
    "Diabetes Progression = intercept + coef(1) x age + coef(2) x sex + coef(3) x bmi + coef(4) x bp + coef(5) x s1 + coef(6) x s2 + coef(7) x s3 + coef(8) x s4 + coef(9) x s5 + coef(10) x s6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b8253d",
   "metadata": {},
   "source": [
    "### Unsupervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9618181b",
   "metadata": {},
   "source": [
    "Unsupervised learning uses machine learning algorithms to analzye and cluster unlabelled datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "331de788",
   "metadata": {},
   "source": [
    "#### Clustering"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9618181b",
   "metadata": {},
   "source": [
    "Clustering is the automatic grouping of similar objects into sets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed69c4c6",
   "metadata": {},
   "source": [
    "For this example, you are going to use the sci-kit learn Iris flower dataset containing a total of 150 samples across 3 classes (species of Iris) for which four features (sepal length, sepal width, petal length, and petal width) were measured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e10f0b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = datasets.load_iris(as_frame=True) # this loads the dataset as a dictionary\n",
    "features = data.data # this derives features as a dataframe (4 features by 150 instances)\n",
    "labels = data.target # this derives labels as a dataframe (150 instances)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f0479d",
   "metadata": {},
   "source": [
    "*N.B. We added two lines of code above to rename the columns to make our visualisations neater and easier to code later on.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f7cc06bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "256e925f",
   "metadata": {},
   "source": [
    "*N.B. We will also standardize the feature data. The feature variables are all measured in cm, meaning they share a common scale. In this case, standardizing the data may not have much effect, but it is still good practice.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "89b04426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal Length (cm)</th>\n",
       "      <th>Sepal Width (cm)</th>\n",
       "      <th>Petal Length (cm)</th>\n",
       "      <th>Petal Width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.019004</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.328414</td>\n",
       "      <td>-1.397064</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.098217</td>\n",
       "      <td>-1.283389</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.249201</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.038005</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.819596</td>\n",
       "      <td>1.448832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.553333</td>\n",
       "      <td>-1.282963</td>\n",
       "      <td>0.705921</td>\n",
       "      <td>0.922303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.795669</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.819596</td>\n",
       "      <td>1.053935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.432165</td>\n",
       "      <td>0.788808</td>\n",
       "      <td>0.933271</td>\n",
       "      <td>1.448832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.068662</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.762758</td>\n",
       "      <td>0.790671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sepal Length (cm)  Sepal Width (cm)  Petal Length (cm)  Petal Width (cm)\n",
       "0            -0.900681          1.019004          -1.340227         -1.315444\n",
       "1            -1.143017         -0.131979          -1.340227         -1.315444\n",
       "2            -1.385353          0.328414          -1.397064         -1.315444\n",
       "3            -1.506521          0.098217          -1.283389         -1.315444\n",
       "4            -1.021849          1.249201          -1.340227         -1.315444\n",
       "..                 ...               ...                ...               ...\n",
       "145           1.038005         -0.131979           0.819596          1.448832\n",
       "146           0.553333         -1.282963           0.705921          0.922303\n",
       "147           0.795669         -0.131979           0.819596          1.053935\n",
       "148           0.432165          0.788808           0.933271          1.448832\n",
       "149           0.068662         -0.131979           0.762758          0.790671\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standardized_data = pd.DataFrame(scaler.fit_transform(features), columns = ['Sepal Length (cm)', 'Sepal Width (cm)', 'Petal Length (cm)', 'Petal Width (cm)'])\n",
    "standardized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "28947590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "145    2\n",
       "146    2\n",
       "147    2\n",
       "148    2\n",
       "149    2\n",
       "Name: target, Length: 150, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c89f4d4",
   "metadata": {},
   "source": [
    "Now, before we look at clustering this dataset, we want to understand the relationship between each variable and every other variable within the dataset. To do this, we use the function sns.pairplot()."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b735327f",
   "metadata": {},
   "source": [
    "First, we need to concatenate the data (features and labels) into one dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "333d1a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal Length (cm)</th>\n",
       "      <th>Sepal Width (cm)</th>\n",
       "      <th>Petal Length (cm)</th>\n",
       "      <th>Petal Width (cm)</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.019004</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.328414</td>\n",
       "      <td>-1.397064</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.098217</td>\n",
       "      <td>-1.283389</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.249201</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.038005</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.819596</td>\n",
       "      <td>1.448832</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.553333</td>\n",
       "      <td>-1.282963</td>\n",
       "      <td>0.705921</td>\n",
       "      <td>0.922303</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.795669</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.819596</td>\n",
       "      <td>1.053935</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.432165</td>\n",
       "      <td>0.788808</td>\n",
       "      <td>0.933271</td>\n",
       "      <td>1.448832</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.068662</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.762758</td>\n",
       "      <td>0.790671</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sepal Length (cm)  Sepal Width (cm)  Petal Length (cm)  Petal Width (cm)  \\\n",
       "0            -0.900681          1.019004          -1.340227         -1.315444   \n",
       "1            -1.143017         -0.131979          -1.340227         -1.315444   \n",
       "2            -1.385353          0.328414          -1.397064         -1.315444   \n",
       "3            -1.506521          0.098217          -1.283389         -1.315444   \n",
       "4            -1.021849          1.249201          -1.340227         -1.315444   \n",
       "..                 ...               ...                ...               ...   \n",
       "145           1.038005         -0.131979           0.819596          1.448832   \n",
       "146           0.553333         -1.282963           0.705921          0.922303   \n",
       "147           0.795669         -0.131979           0.819596          1.053935   \n",
       "148           0.432165          0.788808           0.933271          1.448832   \n",
       "149           0.068662         -0.131979           0.762758          0.790671   \n",
       "\n",
       "     Class  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "..     ...  \n",
       "145      2  \n",
       "146      2  \n",
       "147      2  \n",
       "148      2  \n",
       "149      2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.concat([standardized_data, labels], axis=1)\n",
    "iris.rename(columns = {'target':'Class'}, inplace=True)\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4219a37",
   "metadata": {},
   "source": [
    "Next, we plot the pairplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "70bcab9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 906.375x864 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "g = sns.pairplot(data=iris, hue=\"Class\", size=3)\n",
    "sns.move_legend(g, \"lower center\", bbox_to_anchor=(0.5, -0.035), ncol=3, title='Class', frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d5aa97",
   "metadata": {},
   "source": [
    "For this example, we are going to use K-Means Clustering.\n",
    "\n",
    "This is a distance-based algorithm.\n",
    "\n",
    "In K-Means Clustering, k (any random whole number) data points can be assigned as a centroid. All other points are then assigned to the cluster of the closest centroid based on distance. At this point, the centroids of the clusters are recomputed.\n",
    "\n",
    "This exercise is performed in a loop to find updated cluster centers and allocate each observation.\n",
    "\n",
    "Learn more about K-Means Clustering and other types of clustering methods here: https://scikit-learn.org/stable/modules/clustering.html#k-means\n",
    "\n",
    "Learn more about how to use K-Means Clustering function here: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f99f6ae",
   "metadata": {},
   "source": [
    "Now, let's import KMeans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cc1d4c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b9e6f2e",
   "metadata": {},
   "source": [
    "Next, we want to find the optimum number of clusters for k-means classification. The KMeans algorithm clusters data by minimizing what is known as the inertia (the within-cluster sum-of-squares). \n",
    "\n",
    "To find the optimum number of clusters, we must first calculate the within-cluster sum-of-squares. We do this using only the features. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c90196ea",
   "metadata": {},
   "source": [
    "For this to work, our feature data must be in array form. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ac182aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_array = standardized_data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "96cef20a",
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "\n",
    "for k in range(1, 11): # sets k in the range of 1 - 11\n",
    "    kmeans = KMeans(n_clusters = k, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 42) \n",
    "    kmeans.fit(feat_array)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "# max_iter is the number of iterations of the k-means algorithm in a single run (number of times the centroid is recomputed)\n",
    "# the n_init is the number of times the k-means algorithm is run with different centroid seeds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a750fe96",
   "metadata": {},
   "source": [
    "We then use something known as the elbow method to determine the optimal number of clusters for k-means clustering. \n",
    "\n",
    "Here, we know that there should be three clusters, but otherwise we pick the 'elbow' of the curve as the optimum number of clusters, i.e. where adding another cluster does not allow for much better modelling of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d31b0654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(range(1, 11), wcss, color='indigo')\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS') #within cluster sum of squares\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb07f1d",
   "metadata": {},
   "source": [
    "Now we've found the optimum number of clusters, we can implement K-Means clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "71ac54fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(feat_array) # predict the cluster index of each sample"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b80b48f0",
   "metadata": {},
   "source": [
    "We can then visualise the clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ad0a5b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdc130831f0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(figsize=(7,5))\n",
    "\n",
    "# visualising the clusters \n",
    "plt.scatter(feat_array[y_kmeans == 0, 0], feat_array[y_kmeans == 0, 1], s = 100, c = 'indigo', label = 'Cluster 0')\n",
    "plt.scatter(feat_array[y_kmeans == 1, 0], feat_array[y_kmeans == 1, 1], s = 100, c = 'purple', label = 'Cluster 1')\n",
    "plt.scatter(feat_array[y_kmeans == 2, 0], feat_array[y_kmeans == 2, 1], s = 100, c = 'pink', label = 'Cluster 2')\n",
    "\n",
    "# plotting the centroids of the clusters\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], s = 100, c = 'red', label = 'Centroids')\n",
    "\n",
    "# plots legend outside of the grid, bottom centre of the figure\n",
    "plt.legend(ncol = 4, bbox_to_anchor=(0.9, -0.1))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5a30277d",
   "metadata": {},
   "source": [
    "From this point, there are two avenues we can explore.\n",
    "\n",
    "Improving our model by running a dimensionality algorithm such as Principal Component Analysis (PCA) prior to applying the clustering algorithm. Such an approach reduces the number of dimensions by deriving a select number of feature variables, meaning we can visualize the dataset in fewer dimensions, leading to an interpretation to determine if different groups are well-separated.\n",
    "\n",
    "However, as the newly created feature variables aren't directly comparable to the original feature variables, while running a PCA can help us, it doesn't help to communicate our findings to others.\n",
    "\n",
    "It is very important for us to be able to describe our results, so the remainder of this tutorial will focus on explaining our algorithm. \n",
    "\n",
    "But if you'd like to learn more about PCA, this article provides a good overview (with examples): https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60 "
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    "For these next visualisations, we need to install and import plotly, an open-source plotting library in python.\n",
    "\n",
    "Learn more here: https://plotly.com"
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   "cell_type": "code",
   "execution_count": 56,
   "id": "190b4ecf",
   "metadata": {},
   "outputs": [
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     "text": [
      "Requirement already satisfied: plotly in /Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages (5.11.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /Users/nanfletcher-lloyd/opt/anaconda3/lib/python3.8/site-packages (from plotly) (6.2.0)\n",
      "Requirement already satisfied: six>=1.9.0 in /Users/nanfletcher-lloyd/opt/anaconda3/lib/python3.8/site-packages (from tenacity>=6.2.0->plotly) (1.15.0)\n"
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  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e4eae06e",
   "metadata": {},
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    "import plotly as plotly\n",
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   "source": [
    "Now, we want to visualise the mean of each feature variable by cluster."
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   "cell_type": "code",
   "execution_count": 58,
   "id": "e79f05f8",
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      "/Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  trace_data = trace_data.append(trace_data.iloc[0])\n",
      "/Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  trace_data = trace_data.append(trace_data.iloc[0])\n",
      "/Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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    "clusters['Cluster'] = kmeans.labels_ # assigns cluster labels to features\n",
    "polar = clusters.groupby(\"Cluster\").mean().reset_index() # finds the mean of each feature variable by cluster\n",
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   "source": [
    "pie = clusters.groupby('Cluster').size().reset_index()\n",
    "pie.columns=['Cluster','Value']\n",
    "fig = px.pie(pie, values='Value', names='Cluster', color='Cluster', category_orders={\"Cluster\": [\"0\", \"1\", \"2\"]}, height=400, width=700, color_discrete_sequence=['darkviolet','deeppink','orchid'], template=\"plotly_dark\")\n",
    "fig.update_layout(legend=dict(\n",
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    "fig.show()\n"
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    "From these plots, we can understand the characteristics of each cluster of irises. \n",
    "\n",
    "For example, if you look at Cluster 1, you can understand that the petal width, petal length, and sepal length of these irises were much smaller than in the other two clusters but that they're sepal width was a lot larger.\n",
    "\n",
    "Clusters 0 and 2 have a similar shape, with Cluster 0 having consistently larger measurements than Cluster 2."
   ]
  },
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    "Finally, you can apply names to your clusters based on these characteristics.\n",
    "\n",
    "- Cluster 0 is \"the common irises\"/\"the large irises\"\n",
    "- Cluster 1 is \"the wide sepal irises\"\n",
    "- Cluster 2 is \"the small irises\"/\"the common irises\""
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "And that's it! You've learnt the basics of clustering."
   ]
  },
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   "source": [
    "Now you've finished this tutorial, follow the instructions and complete the assessment."
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