examples/4.SQL_course_specification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [PREVIOUS](3.CSV_course_specification.ipynb) | [HOME](../README.md) | [NEXT](5.export_import_course_specification.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV Course Specifications\n",
    "\n",
    "This Jupyter Notebook is a demonstration of the course specifications linear regression algorithm that imports the training data from a SQLite3 database. This allows students to use larger or dynamic datasets.\n",
    "\n",
    "This can also be viewed as a Python script in [4.SQL.py](\\py_scripts\\4.SQL.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1\n",
    "\n",
    "Load the five required dependencies:\n",
    "- [numpy](https://numpy.org/) a comprehensive library of mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.\n",
    "- [Scikit-learn](https://scikit-learn.org/stable/) a user friendly library of simple and efficient tools for predictive data analysis.\n",
    "- [Matplotlib](https://matplotlib.org) a comprehensive library for creating static, animated, and interactive visualizations in Python. A customised stylesheet for the visualisations is also applied.\n",
    "- [Pandas](https://pandas.pydata.org/) is library for data analysis and manipulation.\n",
    "- [SQLite3](https://docs.python.org/3/library/sqlite3.html) a library for interfacing with a SQLite3 serverless SQL database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import frameworks\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sqlite3 as sql\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Step 2\n",
    "- Open the [SQLite3](4.SQLite3.db) database file\n",
    "- Migrate the data to\n",
    "    - Training features into the `x` variable as a 2D array object.\n",
    "    - Example targets into the `y` variable as a 1D array object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the data for the feature and target\n",
    "con = sql.connect(\"data/4.SQlite3.db\")\n",
    "cur = con.cursor()\n",
    "col_0 = \"feature\"\n",
    "col_1 = \"target\"\n",
    "x = np.array(cur.execute(f\"SELECT {col_0} FROM data\").fetchall())\n",
    "y = np.array(cur.execute(f\"SELECT {col_1} FROM data\").fetchall()).flatten()\n",
    "con.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3\n",
    "\n",
    "Apply your Python skills and query the data set about how many training examples there are and inspect a sample of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples is: 8\n",
      "   feature  target\n",
      "0        2       1\n",
      "1        4       3\n",
      "2        6       5\n",
      "3        8       7\n",
      "4       10       9\n"
     ]
    }
   ],
   "source": [
    "m = len(x)\n",
    "print(f\"Number of training examples is: {m}\")\n",
    "table = pd.DataFrame({\n",
    "    col_0: x.flatten(),  # Flatten x for easy display\n",
    "    col_1: y\n",
    "})\n",
    "print(table.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4\n",
    "\n",
    "Plot the feature `x` and target `y` data on a graph using the column headings as the graph labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data points\n",
    "plt.scatter(x, y, marker='x', c='r')\n",
    "# Set the title\n",
    "plt.title(\"NESA Course Specifications Data\")\n",
    "# Set the y-axis label\n",
    "plt.ylabel(f'Training {col_0}')\n",
    "# Set the x-axis label\n",
    "plt.xlabel(f'Training {col_1}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5\n",
    "\n",
    "Use the [Scikit-learn](https://scikit-learn.org/stable/) library to instantiate a Machine Learning model class and store it in the `my_model` variable as an object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model\n",
    "my_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6\n",
    "\n",
    "Use the [sklearn](https://scikit-learn.org/stable/) library to fit the model to the training data which will provide a line of best fit with the lowest cost based on a [sklearn](https://scikit-learn.org/stable/) algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model to the data\n",
    "my_model.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7\n",
    "\n",
    "Plot the features, targets and model (linear regression)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = my_model.predict(x)\n",
    "plt.plot(x, y_pred)\n",
    "plt.scatter(x, y, marker='x', c='r')\n",
    "plt.title(\"NESA Course Specifications Data\")\n",
    "plt.ylabel(f'Training {col_0}')\n",
    "plt.xlabel(f'Training {col_1}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 8\n",
    "\n",
    "Use the model for predictions and plot them on the visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict = np.array([4]).reshape(1, -1)\n",
    "y_prediction = my_model.predict(predict)\n",
    "\n",
    "y_pred = my_model.predict(x)\n",
    "plt.plot(x, y_pred)\n",
    "plt.scatter(x, y, marker='x', c='r')\n",
    "plt.scatter(predict, y_prediction, marker='D', c='r', zorder=10, s=100)\n",
    "plt.text(y_prediction, predict, f\"Target {y_prediction[0]} is prediction from {predict[0,0]} input\")\n",
    "plt.title(\"NESA Course Specifications Data\")\n",
    "plt.ylabel(f'Training {col_0}')\n",
    "plt.xlabel(f'Training {col_1}')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 9\n",
    "\n",
    "Change the training data in in the [CSV file](course_specifications_data.csv), reload the data in 'Step 2', retrain the model in 'Step 6', evaluate the model in 'Step 7' and make predictions in 'Step 8'."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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