{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 1: Introduction to ML and Sklearn\n",
    "\n",
    "[![View notebooks on Github](https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey)](https://github.com/amonroym99/uva-applied-ml/blob/main/docs/notebooks/1_sklearn.ipynb)\n",
    "\n",
    "**Author:** Alejandro Monroy\n",
    "\n",
    "In this course we will introduce some of the classical ML algorithms. More specifically, we will focus on:\n",
    "\n",
    "- **Supervised learning:** the model is trained on labeled data to learn to predict a certain output. In this course we will focus on the two classical problems:\n",
    "    - Regression: predicting a numerical **target** ($y$) given the input features ($X$).\n",
    "    - Classification: predicting a categorical **label** ($y$) given the input features ($X$).\n",
    "- **Unsupervised learning:** The model is trained trained on unlabeled data to find patterns/structures in the data. In this course we will see:\n",
    "    - Clustering: group a set of samples ($X$) in such a way that samples in the same group/cluster are more similar to each other than to those in other groups.\n",
    "    - Dimensionality reduction: reducing the number of features in $X$ while retaining as much information as possible.\n",
    "\n",
    "A priceless ally in our journey will be the scikit-learn library, which offers a variety of algorithms for multiple machine learning tasks. It is built on top of NumPy, SciPy, and matplotlib. Although we will code our own implementation of some machine learning models, we will also see how to quickly build ML pipelines using scikit-learn.\n",
    "\n",
    "In this tutorial we will introduce the basic functionalities of scikit-learn through a regression problem. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading the data\n",
    "\n",
    "We are going to use the `diabetes` dataset provided in the `datasets` module of the `sklearn` library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "diabetes = datasets.load_diabetes(as_frame=True, scaled=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like it worked! But, what is the `diabetes` variable exactly? A NumPy array? A Pandas datafame? Or maybe something else? Let's figure it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.utils._bunch.Bunch'>\n",
      "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename', 'data_module'])\n"
     ]
    }
   ],
   "source": [
    "print(type(diabetes))\n",
    "print(diabetes.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "❗️ **Note:** Always make sure that you know the data types of the variables you are working with. It will help you make sure that you use them correctly and prevent errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that the `load_diabetes` function returned a [Bunch](https://scikit-learn.org/1.5/modules/generated/sklearn.utils.Bunch.html) with some attributes such as `data`, `target` or `DESCR`. Let's check the description of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _diabetes_dataset:\n",
      "\n",
      "Diabetes dataset\n",
      "----------------\n",
      "\n",
      "Ten baseline variables, age, sex, body mass index, average blood\n",
      "pressure, and six blood serum measurements were obtained for each of n =\n",
      "442 diabetes patients, as well as the response of interest, a\n",
      "quantitative measure of disease progression one year after baseline.\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 442\n",
      "\n",
      ":Number of Attributes: First 10 columns are numeric predictive values\n",
      "\n",
      ":Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
      "\n",
      ":Attribute Information:\n",
      "    - age     age in years\n",
      "    - sex\n",
      "    - bmi     body mass index\n",
      "    - bp      average blood pressure\n",
      "    - s1      tc, total serum cholesterol\n",
      "    - s2      ldl, low-density lipoproteins\n",
      "    - s3      hdl, high-density lipoproteins\n",
      "    - s4      tch, total cholesterol / HDL\n",
      "    - s5      ltg, possibly log of serum triglycerides level\n",
      "    - s6      glu, blood sugar level\n",
      "\n",
      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1).\n",
      "\n",
      "Source URL:\n",
      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
      "\n",
      "For more information see:\n",
      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(diabetes.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice! Now we know what the dataset is about. We can see that we have 10 features (stored in `diabetes.data`) and one target (stored in `diabetes.target`). By convention, we usually store the features in a matrix $X$ and the target in a vector $y$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442, 10)\n",
      "(442,)\n"
     ]
    }
   ],
   "source": [
    "X = diabetes.data\n",
    "y = diabetes.target\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at some samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>93.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8598</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>87.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>103.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8918</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>93.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.6728</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>131.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8903</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>125.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.2905</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex   bmi     bp     s1     s2    s3   s4      s5    s6\n",
       "0  59.0  2.0  32.1  101.0  157.0   93.2  38.0  4.0  4.8598  87.0\n",
       "1  48.0  1.0  21.6   87.0  183.0  103.2  70.0  3.0  3.8918  69.0\n",
       "2  72.0  2.0  30.5   93.0  156.0   93.6  41.0  4.0  4.6728  85.0\n",
       "3  24.0  1.0  25.3   84.0  198.0  131.4  40.0  5.0  4.8903  89.0\n",
       "4  50.0  1.0  23.0  101.0  192.0  125.4  52.0  4.0  4.2905  80.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data exploration\n",
    "Analyzing the distribution of the data helps in understanding it better and identifying any potential anomalies.\n",
    "\n",
    "The `df.describe()` method gives us some basic statistics of each of the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.518100</td>\n",
       "      <td>1.468326</td>\n",
       "      <td>26.375792</td>\n",
       "      <td>94.647014</td>\n",
       "      <td>189.140271</td>\n",
       "      <td>115.439140</td>\n",
       "      <td>49.788462</td>\n",
       "      <td>4.070249</td>\n",
       "      <td>4.641411</td>\n",
       "      <td>91.260181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.109028</td>\n",
       "      <td>0.499561</td>\n",
       "      <td>4.418122</td>\n",
       "      <td>13.831283</td>\n",
       "      <td>34.608052</td>\n",
       "      <td>30.413081</td>\n",
       "      <td>12.934202</td>\n",
       "      <td>1.290450</td>\n",
       "      <td>0.522391</td>\n",
       "      <td>11.496335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.258100</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>164.250000</td>\n",
       "      <td>96.050000</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.276700</td>\n",
       "      <td>83.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.620050</td>\n",
       "      <td>91.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.275000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>209.750000</td>\n",
       "      <td>134.500000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.997200</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>242.400000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.090000</td>\n",
       "      <td>6.107000</td>\n",
       "      <td>124.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex         bmi          bp          s1          s2  \\\n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  442.000000   \n",
       "mean    48.518100    1.468326   26.375792   94.647014  189.140271  115.439140   \n",
       "std     13.109028    0.499561    4.418122   13.831283   34.608052   30.413081   \n",
       "min     19.000000    1.000000   18.000000   62.000000   97.000000   41.600000   \n",
       "25%     38.250000    1.000000   23.200000   84.000000  164.250000   96.050000   \n",
       "50%     50.000000    1.000000   25.700000   93.000000  186.000000  113.000000   \n",
       "75%     59.000000    2.000000   29.275000  105.000000  209.750000  134.500000   \n",
       "max     79.000000    2.000000   42.200000  133.000000  301.000000  242.400000   \n",
       "\n",
       "               s3          s4          s5          s6  \n",
       "count  442.000000  442.000000  442.000000  442.000000  \n",
       "mean    49.788462    4.070249    4.641411   91.260181  \n",
       "std     12.934202    1.290450    0.522391   11.496335  \n",
       "min     22.000000    2.000000    3.258100   58.000000  \n",
       "25%     40.250000    3.000000    4.276700   83.250000  \n",
       "50%     48.000000    4.000000    4.620050   91.000000  \n",
       "75%     57.750000    5.000000    4.997200   98.000000  \n",
       "max     99.000000    9.090000    6.107000  124.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now plot the histogram for each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a 2x5 grid for the histograms\n",
    "fig, axes = plt.subplots(2, 5, figsize=(12, 4))\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Plot histogram for each feature in a separate subplot\n",
    "for i, column in enumerate(X.columns):\n",
    "    axes[i].hist(X[column], bins=20)\n",
    "    axes[i].set_title(f'Histogram of {column}')\n",
    "    axes[i].set_xlabel(column)\n",
    "    axes[i].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histograms allow us to quickly see the range and type of distribution of each variable. For example, we observe that `bmi` and `s4` are skewed to the right, and that `sex` only takes two possible values (somehow, it is a categorical variable encoded as a numerical one!)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how the variables relate to each other by computing the correlation matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = X.corr()\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".3f\", annot_kws={\"size\": 10}, linewidths=0.5)\n",
    "\n",
    "plt.title('Correlation Matrix')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe some positive correlation between `s1`and `s2` and negative between `s3` and `s4`. We can also observe this if we plot their respective scatter plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a figure with 1 row and 2 columns\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "# Plot (s1, s2) on the first subplot\n",
    "axes[0].scatter(X['s1'], X['s2'])\n",
    "axes[0].set_xlabel(\"s1\")\n",
    "axes[0].set_ylabel(\"s2\")\n",
    "\n",
    "# Plot (s3, s4) on the second subplot\n",
    "axes[1].scatter(X['s3'], X['s4'])\n",
    "axes[1].set_xlabel(\"s3\")\n",
    "axes[1].set_ylabel(\"s4\")\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "📝 **Task for you:** Plot the scatter of all variables against all others (you can do that easily with `sns.pairplot(X)`) and check that the correlations in the matrix above align with what you observe in the plots!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data preprocessing\n",
    "Now we will prepare the data. In this tutorial we will only normalize the data and split it into a training and test set, but other datasets might need additional steps, such as handling missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Data normalization\n",
    "In both the descriptives summary and the histograms we observed that each feature is distributed across a different range of values. To enhance the performance of our models, we will normalize the features so that they all take values in a similar range. We will use the following types of normalization available in sklearn:\n",
    "\n",
    "- **Min-Max Scaling**: Transforms features by scaling each feature to a given range, usually $[0, 1]$.\n",
    "  $$ \\mathbf{x}_{\\text{scaled}} = \\frac{\\mathbf{x} - x_{\\text{min}}}{x_{\\text{max}} - x_{\\text{min}}}.$$\n",
    "- **Standardization**: Centers the data by subtracting the mean and scales it by dividing by the standard deviation, resulting in a distribution with a mean of 0 and a standard deviation of 1.\n",
    "  $$\\mathbf{x}_{\\text{standardized}} = \\frac{\\mathbf{x} - \\mu}{\\sigma},$$\n",
    "  where $\\mu$ is the mean and $\\sigma$ is the standard deviation of the feature.\n",
    "- **Normalization**: Scales individual samples to have unit norm (i.e., the sum of the squares of the values is $1$).\n",
    "  $$\\mathbf{x}_{\\text{normalized}} = \\frac{\\mathbf{x}}{\\|\\mathbf{x}\\|},$$\n",
    "  where $\\|\\mathbf{x}\\|$ is the Euclidean norm of the feature vector.\n",
    "\n",
    "The following code cell shows how to standarize the data with sklearn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.80050009,  1.06548848,  1.29708846, ..., -0.05449919,\n",
       "         0.41853093, -0.37098854],\n",
       "       [-0.03956713, -0.93853666, -1.08218016, ..., -0.83030083,\n",
       "        -1.43658851, -1.93847913],\n",
       "       [ 1.79330681,  1.06548848,  0.93453324, ..., -0.05449919,\n",
       "         0.06015558, -0.54515416],\n",
       "       ...,\n",
       "       [ 0.87686984,  1.06548848, -0.33441002, ..., -0.23293356,\n",
       "        -0.98564884,  0.32567395],\n",
       "       [-0.9560041 , -0.93853666,  0.82123474, ...,  0.55838411,\n",
       "         0.93616291, -0.54515416],\n",
       "       [-0.9560041 , -0.93853666, -1.53537419, ..., -0.83030083,\n",
       "        -0.08875225,  0.06442552]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_normalized = scaler.fit_transform(X)\n",
    "\n",
    "X_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Train-test split\n",
    "In supervised learning, we use part of the data to train the model and the other part to evaluate it. We can easily make this division with the `train_test_split` function from sklearn:\n",
    "\n",
    "Recall that our initial dataset had 442 samples. Let's create a training set with 80% of the samples and a test set with the other 20%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (353, 10)\n",
      "Shape of X_test: (89, 10)\n",
      "Shape of y_train: (353,)\n",
      "Shape of y_test: (89,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_normalized, y, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Shape of X_train:\", X_train.shape)\n",
    "print(\"Shape of X_test:\", X_test.shape)\n",
    "print(\"Shape of y_train:\", y_train.shape)\n",
    "print(\"Shape of y_test:\", y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Food for thought:** the `train_test_split` function randomly chooses the samples to be included in each split. To always get the same result, we set the argument `random_state=42` (or any other seed), so that the random numbers that are generated inside the function are always the same (and therefore so are the splits). Reproducibility is very important in AI: make sure that your results are reproducible by setting a seed every time there is randomness involved!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Our first ML model\n",
    "To keep things easy for our first model, we will train a regressor that outputs the mean of the targets in the training set. The following code cell includes the standard steps to train a supervised sklearn model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391, 153.73654391, 153.73654391, 153.73654391,\n",
       "       153.73654391])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "# Create instance (object) of the DummyRegressor class\n",
    "dummy_regr = DummyRegressor(strategy=\"mean\")\n",
    "\n",
    "# Train the model using the training data\n",
    "dummy_regr.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions using the test data\n",
    "dummy_regr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Food for thought:** You might be wondering what is the point in \"training\" a model that always outputs the mean of the targets in the training set. We don't really need ML or sklearn for that. And you are totally right! This is why this model is called _dummy_. We are working with it just to become familiar with the sklearn framework. Don't worry, we will dedicate the rest of the course to learning about less _dummy_ models 😉."
   ]
  }
 ],
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