examples/2.OOP_Multi_Feature_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [PREV](1.OOP_Single_Feature_Linear_Regression.ipynb) | [HOME](../README.md) | [NEXT](3.OOP_Polynomial_Liner_Regression.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OOP Multiple Feature Linear Regression Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook provides a basic implementation of Multiple Feature Linear Regression. The data set contains measurements on a single species of insect captured on two continents. We want to predict an insects wing size given the other measurements we have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1 - Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2 - Import Data And Visualise The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "continent",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "latitude",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "sex",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "wingsize",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "conversionMethod": "pd.DataFrame",
       "ref": "11222edc-b6ad-4d30-bea8-2d9c864670af",
       "rows": [
        [
         "0",
         "1",
         "35.5",
         "0",
         "901"
        ],
        [
         "1",
         "1",
         "37.0",
         "0",
         "896"
        ],
        [
         "2",
         "1",
         "38.6",
         "0",
         "906"
        ],
        [
         "3",
         "1",
         "40.7",
         "0",
         "907"
        ],
        [
         "4",
         "1",
         "40.9",
         "0",
         "898"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 5
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>continent</th>\n",
       "      <th>latitude</th>\n",
       "      <th>sex</th>\n",
       "      <th>wingsize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0</td>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>38.6</td>\n",
       "      <td>0</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>40.9</td>\n",
       "      <td>0</td>\n",
       "      <td>898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   continent  latitude  sex  wingsize\n",
       "0          1      35.5    0       901\n",
       "1          1      37.0    0       896\n",
       "2          1      38.6    0       906\n",
       "3          1      40.7    0       907\n",
       "4          1      40.9    0       898"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import data as a Pandas DataFrame\n",
    "insects = pd.read_csv('./data/insects.csv', sep='\\t')\n",
    "insects = pd.DataFrame({\n",
    "    'continent': insects['continent'],\n",
    "    'latitude': insects['latitude'],\n",
    "    'sex': insects['sex'],\n",
    "    'wingsize': insects['wingsize']\n",
    "})\n",
    "\n",
    "# Features variable\n",
    "X_insects = insects[['continent', 'latitude', 'sex']]\n",
    "# Target variable\n",
    "y_insects = insects['wingsize']\n",
    "\n",
    "insects.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data, colour coded by sex\n",
    "ax = plt.scatter(insects.latitude, insects.wingsize, c=insects['sex'])\n",
    "plt.xlabel(\"Wingsize\")\n",
    "plt.ylabel(\"Lattitude\")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Instantiate a LinearRegression Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "insects_regression = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "insects_regression.fit(X_insects, y_insects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Predict Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New insect is:\n",
      "   continent  latitude  sex    wingsize\n",
      "0          1        40    0  903.740233\n"
     ]
    }
   ],
   "source": [
    "# Predict the target for the whole dataset\n",
    "wing_size_predictions = insects_regression.predict(X_insects)\n",
    "\n",
    "#Predict the target for a new data point\n",
    "new_insect = pd.DataFrame({\n",
    "    'continent': [1],\n",
    "    'latitude': [40],\n",
    "    'sex': [0]\n",
    "})\n",
    "new_insect['wingsize'] = insects_regression.predict(new_insect)\n",
    "print(f\"New insect is:\\n{new_insect}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Plot the Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the predictions compared to the actual data\n",
    "fig, axs = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "axs[0].scatter(insects.latitude, insects.wingsize, c=insects['sex'],  s=40)\n",
    "axs[0].set_title(\"Actual Data\")\n",
    "axs[0].set_xlabel(\"Latitude\")\n",
    "axs[0].set_ylabel(\"Actual Wing Span\")\n",
    "\n",
    "\n",
    "axs[1].scatter(insects.latitude, wing_size_predictions, c=insects['sex'], s=40)\n",
    "axs[1].set_title(\"Predicted Data\")\n",
    "axs[1].set_xlabel(\"Latitude\")\n",
    "axs[1].set_ylabel(\"Predicted Wing Span\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6: Evaluate The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model coefficient :[ -4.12890383   1.79260932 -98.85714286]\n",
      "Model y intercept :836.1647645123378\n",
      "Model score :0.9585580994936529\n",
      "MAE: 7.750187049787379\n",
      "MSE: 110.55759329458347\n"
     ]
    }
   ],
   "source": [
    "#Get Evalutative Data from the model\n",
    "print(f\"Model coefficient :{insects_regression.coef_}\")\n",
    "print(f\"Model y intercept :{insects_regression.intercept_}\")\n",
    "print(f\"Model score :{insects_regression.score(X_insects,y_insects)}\")\n",
    "mae = mean_absolute_error(y_true=y_insects,y_pred=wing_size_predictions) \n",
    "mse = mean_squared_error(y_true=y_insects,y_pred=wing_size_predictions)\n",
    "print(\"MAE:\",mae)\n",
    "print(\"MSE:\",mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cost of this model is 55.27142\n",
      "    Target  Predicted result      Loss\n",
      "0      901        895.673491   28.4089\n",
      "1      896        898.362405    5.5696\n",
      "2      906        901.230580   22.7529\n",
      "3      907        904.995060    4.0000\n",
      "4      898        905.353582   54.0225\n",
      "5      893        908.042496  226.2016\n",
      "6      913        912.703280    0.0900\n",
      "7      915        915.929977    0.8649\n",
      "8      927        919.515195   55.9504\n",
      "9      924        921.307805    7.2361\n",
      "10     930        923.100414   47.6100\n",
      "11     905        901.415744   12.8164\n",
      "12     889        906.614311  310.1121\n",
      "13     915        910.199529   23.0400\n",
      "14     930        913.964009  257.2816\n",
      "15     895        917.728488  516.6529\n",
      "16     926        920.955185   25.4016\n",
      "17     944        923.106316  436.3921\n",
      "18     925        926.512274    2.2801\n",
      "19     920        929.559710   91.3936\n",
      "20     934        936.730147    7.4529\n",
      "21     797        796.816349    0.0324\n",
      "22     806        799.505263   42.1201\n",
      "23     812        802.373438   92.7369\n",
      "24     807        806.137917    0.7396\n",
      "25     818        806.496439  132.2500\n",
      "26     809        809.185353    0.0361\n",
      "27     810        813.846137   14.8225\n",
      "28     819        817.072834    3.7249\n",
      "29     800        820.658053  426.8356\n",
      "30     823        822.450662    0.3025\n",
      "31     814        824.243271  104.8576\n",
      "32     789        802.558601  183.8736\n",
      "33     803        807.757168   22.6576\n",
      "34     812        811.342386    0.4356\n",
      "35     820        815.106866   23.9121\n",
      "36     808        818.871346  118.1569\n",
      "37     815        822.098042   50.4100\n",
      "38     855        824.249174  945.5625\n",
      "39     842        827.655131  205.6356\n",
      "40     819        830.702567  136.8900\n",
      "41     839        837.873004    1.2769\n"
     ]
    }
   ],
   "source": [
    "#Optional: Manually Evaluate the loss and cost of the model\n",
    "model_loss = pd.DataFrame({\n",
    "    'Target': y_insects,\n",
    "    'Predicted result': wing_size_predictions,\n",
    "    'Loss': abs(insects_regression.predict(X_insects).round(2) - y_insects)**2\n",
    "})\n",
    "model_cost = 1 / (2 * model_loss.shape[0]) * model_loss['Loss'].sum()\n",
    "\n",
    "print(f\"The cost of this model is {model_cost:.5f}\")\n",
    "print(model_loss)"
   ]
  }
 ],
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