examples/5.model_test_and_validate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [PREVIOUS](4.SQL_course_specification.ipynb) | [HOME](../README.md) | [NEXT](6.export_import_course_specification.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation\n",
    "\n",
    "This Jupyter Notebook is a demonstration of evaluating a model. We will be comparing the prediction with some test data to evaluate the effectiveness of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1\n",
    "\n",
    "Load the required dependencies.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Step 2\n",
    "Open and parse the CSV file and store the data as variables array data objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "Use the [Scikit-learn](https://scikit-learn.org/stable/) library to instantiate a `LinearRegression` Machine Learning model class and store it in the `my_model` variable as an object, passing a range of parameters to configure the model. Then use the `fit` method from the `LinearRegression` class to fit the model to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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: #000;\n",
       "  --sklearn-color-text-muted: #666;\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: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
       "  text-align: center;\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\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/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></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the model\n",
    "my_model = LinearRegression()\n",
    "# Fit the model to the data\n",
    "my_model.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Step 4\n",
    "Open and parse the test data CSV file and store the data as variable array data objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data = pd.read_csv('data/6.model_test_and_validate_data.csv', delimiter=',')\n",
    "x_test = np.array(testing_data.iloc[:,1]).reshape(-1, 1)\n",
    "y_test = np.array(testing_data.iloc[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5\n",
    "\n",
    "Plot the test data features and targets to the linear regression model to visually evaluate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_test, y_test, 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 6\n",
    "\n",
    "Manually calculate and evaluate the 'loss' and 'cost' of the model using the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Feature  Target  Predicted result  Loss\n",
      "0      1.8     1.0               0.8   0.2\n",
      "1      4.1     3.3               3.1   0.2\n",
      "2      6.0     4.6               5.0  -0.4\n",
      "3      8.5     7.0               7.5  -0.5\n",
      "4     10.2     8.1               9.2  -1.1\n",
      "5     11.7    11.2              10.7   0.5\n",
      "6     14.1    11.0              13.1  -2.1\n",
      "7     16.0    15.0              15.0   0.0\n",
      "The cost or average loss of this model is -0.4\n"
     ]
    }
   ],
   "source": [
    "table = pd.DataFrame({\n",
    "    testing_data.columns[0]: x_test.flatten(),  # Flatten x for easy display\n",
    "    testing_data.columns[1]: y_test,\n",
    "    'Predicted result':my_model.predict(x_test),\n",
    "    'Loss' : y_test - my_model.predict(x_test).round(2)\n",
    "})\n",
    "print(table)\n",
    "cost = table['Loss'].sum() / table.shape[0]\n",
    "print(f\"The cost or average loss of this model is {cost}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loss** is a measure of the difference of a single example to its target value while the  \n",
    "**Cost** is a measure of the losses over the training set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7\n",
    "\n",
    "Use the 'score' method of the `LinearRegression` class to return a score based evaluation of the models accuracy or performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data score: 1.0\n",
      "Testing data score: 0.9578584680625496\n"
     ]
    }
   ],
   "source": [
    "train_score = my_model.score(x, y)\n",
    "test_score = my_model.score(x_test, y_test)\n",
    "print(f'Training data score: {train_score}')\n",
    "print(f'Testing data score: {test_score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpreting the score:\n",
    "| Score | Interpretation |\n",
    "| --- | --- |\n",
    "| Score = 1 | Perfect fit. The model explains all the variability in the target variable. |\n",
    "| Score = 0 | The model does not explain any variability in the target variable. |\n",
    "| 0 < Score < 1 |  - The model explains a proportion of the variability in the target variable with a higher Score indicating a better fit. |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 8\n",
    "\n",
    "Calculate the Mean Square Error (MSE), which measures how much the predicted result deviates from the actual, because of the squared term more weight is given to larger errors. This metric is very helpful when evaluating models with outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared error for the training data is: 0.00000\n",
      "The Mean Squared error for the training data is: 0.63750\n"
     ]
    }
   ],
   "source": [
    "train_mse = mean_squared_error(y, y_pred)\n",
    "test_mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"The Mean Squared error for the training data is: {\"%.5f\" % train_mse}\")\n",
    "print(f\"The Mean Squared error for the testing data is: {\"%.5f\" % test_mse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whilst a low MSE is desirable, interpretating MSE is less straight forward than 'Score'. It can be helpful when comparing performance of different models against the same dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Feature  Target  Predicted result  Loss\n",
      "0      1.8     1.0               0.8   0.2\n",
      "1      4.1     3.3               3.1   0.2\n",
      "2      6.0     4.6               5.0  -0.4\n",
      "3      8.5     7.0               7.5  -0.5\n",
      "4     10.2     8.1               9.2  -1.1\n",
      "5     11.7    11.2              10.7   0.5\n",
      "6     14.1    11.0              13.1  -2.1\n",
      "7     16.0    15.0              15.0   0.0\n",
      "The cost or average loss of this model is -0.4\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 9\n",
    "\n",
    "Evaluate the X axis intercept and gradient or coefficient of your model by using the getter methods to return the intercept and coefficient from my_model() object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Axis intercept: -1.0000000000000036\n",
      "Coefficient: [1.]\n"
     ]
    }
   ],
   "source": [
    "print(f'X Axis intercept: {my_model.intercept_}')\n",
    "print(f'Coefficient: {my_model.coef_}')"
   ]
  }
 ],
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