examples/3.CSV_course_specification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [PREVIOUS](2.graphical_course_specification.ipynb) | [HOME](../README.md) | [NEXT](4.SQL_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 using a CSV for input. This allows students to use larger data sets.\n",
    "\n",
    "This can also be viewed as a Python script in [3.CSV.py](\\py_scripts\\3.CSV.py.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1\n",
    "\n",
    "Load the four 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import frameworks\n",
    "import numpy as np\n",
    "import pandas as pd\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 [CSV](3.course_specifications_data.csv) file with [Pandas](https://pandas.pydata.org/)\n",
    "- Parse the columns as\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 feature and the target\n",
    "training_data = pd.read_csv(\"data/3.course_specifications_data.csv\", delimiter=',')\n",
    "x = np.array(training_data.iloc[:,1]).reshape(-1, 1)\n",
    "y = np.array(training_data.iloc[:,0])"
   ]
  },
  {
   "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",
    "    training_data.columns[0]: x.flatten(),  # Flatten x for easy display\n",
    "    training_data.columns[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": {
      "image/png": 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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 {training_data.columns[0]}')\n",
    "# Set the x-axis label\n",
    "plt.xlabel(f'Training {training_data.columns[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 {training_data.columns[0]}')\n",
    "plt.xlabel(f'Training {training_data.columns[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 {training_data.columns[0]}')\n",
    "plt.xlabel(f'Training {training_data.columns[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",
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