examples/1.OOP_Single_Feature_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [HOME](../README.md) | [NEXT](2.OOP_Multi_Feature_Linear_Regression.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OOP Linear Regression Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook provides a basic implementation of Single Feature Linear Regression. The data set contains measurements on a single species of insect captured on two continents. We want to predict an male insects latitude given the wingspan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1 - Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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, Filter and Visualise The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": "f88ab82b-07d3-415c-9ec3-dc0b9be4e272",
       "rows": [
        [
         "21",
         "1",
         "35.5",
         "1",
         "797"
        ],
        [
         "22",
         "1",
         "37.0",
         "1",
         "806"
        ],
        [
         "23",
         "1",
         "38.6",
         "1",
         "812"
        ],
        [
         "24",
         "1",
         "40.7",
         "1",
         "807"
        ],
        [
         "25",
         "1",
         "40.9",
         "1",
         "818"
        ]
       ],
       "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>latitude</th>\n",
       "      <th>sex</th>\n",
       "      <th>wingsize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>35.5</td>\n",
       "      <td>1</td>\n",
       "      <td>797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>38.6</td>\n",
       "      <td>1</td>\n",
       "      <td>812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>40.7</td>\n",
       "      <td>1</td>\n",
       "      <td>807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>40.9</td>\n",
       "      <td>1</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    continent  latitude  sex  wingsize\n",
       "21          1      35.5    1       797\n",
       "22          1      37.0    1       806\n",
       "23          1      38.6    1       812\n",
       "24          1      40.7    1       807\n",
       "25          1      40.9    1       818"
      ]
     },
     "execution_count": 24,
     "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",
    "# Filter the data to only male insects\n",
    "insects = insects[insects.sex == 1]\n",
    "\n",
    "# Features variable\n",
    "X_insects = insects[['wingsize']]\n",
    "# Target variable\n",
    "y_insects = insects['latitude']\n",
    "\n",
    "insects.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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\n",
    "plt.scatter(X_insects, y_insects, label=\"Actual Data\", color='g')\n",
    "plt.xlabel(\"Wing size\")\n",
    "plt.ylabel(\"Latitude\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Instantiate a LinearRegression Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "insects_regression = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insects_regression.fit(X_insects, y_insects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Predict Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New insect is:\n",
      "   wingsize   latitude\n",
      "0       800  40.870569\n"
     ]
    }
   ],
   "source": [
    "# Predict the target for the whole dataset\n",
    "latitude_predictions = insects_regression.predict(X_insects)\n",
    "\n",
    "#Predict the target for a new data point\n",
    "new_insect = pd.DataFrame({\n",
    "    'wingsize': [800]\n",
    "})\n",
    "new_insect['latitude'] = insects_regression.predict(new_insect)\n",
    "print(f\"New insect is:\\n{new_insect}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6: Plot the Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 predictions compared to the actual data\n",
    "plt.scatter(X_insects, y_insects, label=\"Actual Data\", color='g')\n",
    "plt.scatter(X_insects, latitude_predictions, label=\"Predicted Data\", c='r')\n",
    "plt.xlabel(\"Wing size\")\n",
    "plt.ylabel(\"Latitude\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7: Evaluate The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model coefficient :[0.24706008]\n",
      "Model y intercept :-156.77749557179794\n",
      "Model score :0.43823454530425576\n",
      "MAE: 3.6235280772732184\n",
      "MSE: 17.429174492974532\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=latitude_predictions) \n",
    "mse = mean_squared_error(y_true=y_insects,y_pred=latitude_predictions)\n",
    "print(\"MAE:\",mae) \n",
    "print(\"MSE:\",mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cost of this model is 8.71629\n",
      "    Target  Predicted result     Loss\n",
      "21    35.5         40.129389  21.4369\n",
      "22    37.0         42.352930  28.6225\n",
      "23    38.6         43.835290  27.4576\n",
      "24    40.7         42.599990   3.6100\n",
      "25    40.9         45.317651  19.5364\n",
      "26    42.4         43.094110   0.4761\n",
      "27    45.0         43.341170   2.7556\n",
      "28    46.8         45.564711   1.5376\n",
      "29    48.8         40.870569  62.8849\n",
      "30    49.8         46.552951  10.5625\n",
      "31    50.8         44.329410  41.8609\n",
      "32    36.4         38.152908   3.0625\n",
      "33    39.3         41.611749   5.3361\n",
      "34    41.3         43.835290   6.4516\n",
      "35    43.4         45.811771   5.8081\n",
      "36    45.5         42.847050   7.0225\n",
      "37    47.3         44.576470   7.3984\n",
      "38    48.5         54.458874  35.5216\n",
      "39    50.4         51.247093   0.7225\n",
      "40    52.1         45.564711  42.7716\n",
      "41    56.1         50.505912  31.2481\n"
     ]
    }
   ],
   "source": [
    "#Optional: Manually Evaluate the loss and cost of the model\n",
    "model_loss = pd.DataFrame({\n",
    "    'Target': y_insects,\n",
    "    'Predicted result': latitude_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)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}