00_The Elements of Python.ipynb

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   "source": [
    "<center>\n",
    "<div>\n",
    "    <img src=\"./src/logo.svg\" alt=\"\">\n",
    "</div>\n",
    "    <b>© Jesús López 2022</b>\n",
    "    <br>Follow him on <a href=\"https://twitter.com/jsulopzs\">Twitter</a> or <a href=\"https://linkedin.com/in/jsulopzs\"><b>LinkedIn</b></a>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f16fc8-759d-48ff-bd69-a12ee3243fd3",
   "metadata": {},
   "source": [
    "# The Elements of Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaa8f799-e986-4355-9828-ae5e9298435e",
   "metadata": {},
   "source": [
    "**Table of Contents**\n",
    "\n",
    "1. [Concepts in Jupyter Auto-Completion](#Concepts-in-Jupyter-Auto-completion)\n",
    "2. [\"Synonyms\"](#\"Synonyms\")\n",
    "3. [Most Used Objects & Libraries](#Most-Used-Objects-and-Libraries)"
   ]
  },
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   "source": [
    "Most common syntaxis:\n",
    "\n",
    "```python\n",
    "import module\n",
    "instance = module.function(parameter=object)\n",
    "```"
   ]
  },
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   "source": [
    "For example:"
   ]
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       "    }\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>Player</th>\n",
       "      <th>Matches</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Yellows</th>\n",
       "      <th>Reds</th>\n",
       "      <th>Yellows2</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CR7</td>\n",
       "      <td>923</td>\n",
       "      <td>689</td>\n",
       "      <td>231</td>\n",
       "      <td>112</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>75372</td>\n",
       "      <td>1985-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Messi</td>\n",
       "      <td>800</td>\n",
       "      <td>679</td>\n",
       "      <td>312</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65299</td>\n",
       "      <td>1987-06-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lewandowski</td>\n",
       "      <td>662</td>\n",
       "      <td>498</td>\n",
       "      <td>131</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65299</td>\n",
       "      <td>1988-08-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benzema</td>\n",
       "      <td>737</td>\n",
       "      <td>370</td>\n",
       "      <td>182</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52245</td>\n",
       "      <td>1987-12-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        Player  Matches  Goals  Assists  Yellows  Reds  Yellows2  Minutes  \\\n",
       "0          CR7      923    689      231      112     7         4    75372   \n",
       "1        Messi      800    679      312       82     1         0    65299   \n",
       "2  Lewandowski      662    498      131       68     0         1    65299   \n",
       "3      Benzema      737    370      182       15     0         0    52245   \n",
       "\n",
       "     Birthday  \n",
       "0  1985-02-05  \n",
       "1  1987-06-24  \n",
       "2  1988-08-21  \n",
       "3  1987-12-19  "
      ]
     },
     "execution_count": 34,
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   ],
   "source": [
    "import pandas\n",
    "\n",
    "df = pandas.read_excel(io='data/football_players.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765274b2-3c09-4f9f-b98d-c29864244eea",
   "metadata": {},
   "source": [
    "## Concepts in Jupyter Auto-completion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d8fbfa-3a1a-448b-b58a-7da12e52a851",
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   "source": [
    "The **Autocompletion Tool** will guide us throughout the code's development.\n",
    "\n",
    "We'd better know every single detail about it. The concepts placed to the right gives us practical information on how to use those elements.\n",
    "\n",
    "For example, the functions are always followed by parenthesis `()`. Therefore, you will need to place the parenthesis after you write the `function()`."
   ]
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  {
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   "id": "aad0f447-27c8-4d41-9589-6637e05d15cf",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"src/python-elements.png\" width=\"40%\"/>\n",
    "</div>"
   ]
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    "This are the key concepts you need to know about `Python` ↓\n",
    "\n",
    "1. `module`: where the code of functions are stored\n",
    "2. `.` the **dot notation** to access the functions inside the library\n",
    "3. `function()` to execute several lines of code with one `word()`\n",
    "4. `(parameter=object)` to **pass** the object the function will work with\n",
    "5. `object`: **data structure** to store information\n",
    "    1. `instance = Class()` to create the object\n",
    "    2. `instance.` once the object is created"
   ]
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    "## \"Synonyms\"\n",
    "\n",
    "People use a variety of names for things that *practically* mean the same thing.\n",
    "\n",
    "The following is a list of the most common synonims you can hear in:\n",
    "\n",
    "### Python\n",
    "\n",
    "- module ~ library\n",
    "- instance ~ class ~ object\n",
    "- parameter ~ argument"
   ]
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   "source": [
    "### Pandas Library for Data Engineering\n",
    "\n",
    "- rows ~ index\n",
    "- columns ~ Series ~ variables\n",
    "- values ~ cells\n",
    "- labels: name of an element of the index or the columns\n",
    "- groups: unique values of a Series ~ unique categories within a column\n",
    "- categorical Series:\n",
    "    - object\n",
    "    - string\n",
    "    - category\n",
    "- numerical Series:\n",
    "    - int64\n",
    "    - float64"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac8294ba-185e-4b0e-b6ab-61633cbd9654",
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   "source": [
    "### Machine Learning\n",
    "\n",
    "- y ~ target ~ class ~ label ~ dependent variables\n",
    "- X ~ explanatory ~ features ~ independent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a0dff92-4588-4908-a66c-f6d255f1f70e",
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   "source": [
    "## Most Used Objects and Libraries\n",
    "\n",
    "### Pandas\n",
    "\n",
    "Python Data Analysis Library\n",
    "\n",
    "> Operations with Data Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1eb1c5eb-d9b3-44bf-86ef-48622832894b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "efcf8e82-e606-4753-a94f-de22f9db94bf",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Matches</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Yellows</th>\n",
       "      <th>Reds</th>\n",
       "      <th>Yellows2</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CR7</td>\n",
       "      <td>923</td>\n",
       "      <td>689</td>\n",
       "      <td>231</td>\n",
       "      <td>112</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>75372</td>\n",
       "      <td>1985-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Messi</td>\n",
       "      <td>800</td>\n",
       "      <td>679</td>\n",
       "      <td>312</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65299</td>\n",
       "      <td>1987-06-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lewandowski</td>\n",
       "      <td>662</td>\n",
       "      <td>498</td>\n",
       "      <td>131</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65299</td>\n",
       "      <td>1988-08-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benzema</td>\n",
       "      <td>737</td>\n",
       "      <td>370</td>\n",
       "      <td>182</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52245</td>\n",
       "      <td>1987-12-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "        Player  Matches  Goals  Assists  Yellows  Reds  Yellows2  Minutes  \\\n",
       "0          CR7      923    689      231      112     7         4    75372   \n",
       "1        Messi      800    679      312       82     1         0    65299   \n",
       "2  Lewandowski      662    498      131       68     0         1    65299   \n",
       "3      Benzema      737    370      182       15     0         0    52245   \n",
       "\n",
       "     Birthday  \n",
       "0  1985-02-05  \n",
       "1  1987-06-24  \n",
       "2  1988-08-21  \n",
       "3  1987-12-19  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
    "pd.read_excel(io='football_players.xlsx')"
   ]
  },
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   "cell_type": "markdown",
   "id": "ec1cce2f-2963-4172-a333-6e47a3595c30",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"src/DataFrame.jpg\" width=\"45%\"/>\n",
    "<img src=\"src/Series.jpg\" width=\"45%\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "### Numpy\n",
    "\n",
    "Numerical Python\n",
    "\n",
    "> Mathematical operations with matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8406055a-0ceb-4349-b7b8-7f1eea1bdc56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17020c8e-a314-490a-95d0-4456b17fa559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.37536374, 27.99266518, 26.47762281],\n",
       "       [10.14353734, 18.01501075, 17.37491764],\n",
       "       [23.23999379, 28.86813342, 19.34095473],\n",
       "       [21.54749908, 31.38530142, 15.12413468],\n",
       "       [22.82096036, 25.4638213 , 22.55451483]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(loc=23, scale=7, size=(5,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14060803-862b-44cd-96db-1f7160af9578",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "\n",
    "Mathematical Plotting Library\n",
    "\n",
    "> Bar, Points, Area, Donut Charts ~ Plots ~ Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f6eb1e8-2b7e-446d-96b2-d986a2d62df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e5df05c-7503-4a22-9210-5e0af0811cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x157fb5e50>"
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     "execution_count": 17,
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      "image/png": 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Am5I8DvwPcEMNLlPPmKuB1wNfHpyjBPhD4BA0Ny/jjKWVebkU+ECSi+iH2Ueq6s4kvwnNzcs4Y2llXp5kO+fER+klqVGtnUKRJA0Y4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalR/w+M996faUYlJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=[1,2,3,4], y=[1,4,9,16], c='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4c2a03-e289-4275-9a6f-f85f1898c646",
   "metadata": {},
   "source": [
    "### Plotly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423d0a5f-ef6b-4df1-9472-748dfe3852cd",
   "metadata": {},
   "source": [
    "Python Graphing Library\n",
    "\n",
    "> **Interactive** Bar, Points, Area, Donut Charts ~ Plots ~ Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "402118fb-77e9-4fbf-b655-5e230d435f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "41e1d165-403c-4ce3-97c7-59002a684d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plotly.com"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "x=%{x}<br>y=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "low",
          "high",
          "medium"
         ],
         "xaxis": "x",
         "y": [
          1,
          7,
          5
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "autosize": true,
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
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              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
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            "baxis": {
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             "linecolor": "white",
             "minorgridcolor": "white",
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            "type": "carpet"
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          "choropleth": [
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             "ticks": ""
            },
            "type": "choropleth"
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          ],
          "contour": [
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            "colorscale": [
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            "type": "contour"
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            "type": "heatmap"
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          "heatmapgl": [
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          "mesh3d": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.bar(x=['low', 'high', 'medium'], y=[1,7,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c7aed8-628b-43ce-9dc3-3b7bae5e0442",
   "metadata": {},
   "source": [
    "### Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b080bfa-21f0-4f2e-9bed-b1320feac829",
   "metadata": {},
   "source": [
    "Seaborn is a Python data visualization library based on matplotlib. \n",
    "\n",
    "> It provides a high-level interface for drawing attractive and informative statistical graphics like: Bar, Points, Area, Donut Charts ~ Plots ~ Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "86afeb2b-da64-4525-bef7-b87d1f51f766",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2f742ff3-6d0f-43d3-9983-44870dbe962f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=['low', 'high', 'medium'], y=[1,7,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c69bcf9-5a33-4759-b4f6-7302b8196070",
   "metadata": {},
   "source": [
    "### Sci-kit Learn\n",
    "\n",
    "Science Kit for Machine Learning\n",
    "\n",
    "> Calculating mathematical equations to predict the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "704e7c03-89f7-463d-b62d-f7b5610a3eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d8635f85-20f3-4028-8fa3-66025c75bf1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_intercept': True,\n",
       " 'normalize': 'deprecated',\n",
       " 'copy_X': True,\n",
       " 'n_jobs': None,\n",
       " 'positive': False,\n",
       " 'n_features_in_': 1,\n",
       " 'coef_': array([1.]),\n",
       " '_residues': 3.869427575646595e-34,\n",
       " 'rank_': 1,\n",
       " 'singular_': array([2.23606798]),\n",
       " 'intercept_': 2.0}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X=[[1],[2],[3],[4]], y=[3,4,5,6])\n",
    "model.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4e2e90-23cd-4bd7-abc6-be355aded456",
   "metadata": {},
   "source": [
    "$$\n",
    "y = a + b \\cdot X \\\\\n",
    "y = intercept\\_ + coef\\_ \\cdot X \\\\\n",
    "y = 2 + 1 \\cdot X\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f53a4433-f477-4df0-aa80-ddecc5ae1e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X=[[3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23225cc-2bc1-44ef-b7ae-6b49fe32da4a",
   "metadata": {},
   "source": [
    "$$\n",
    "y = 2 + 1 \\cdot (X=3) = 5\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5163c686-0bbc-424c-a031-88e102b2b82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=[[1],[2],[3],[4]], y=[3,4,5,6])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a98011f-b7ae-483a-b5b5-75ddb0128a9e",
   "metadata": {},
   "source": [
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a>."
   ]
  }
 ],
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