Linear Models for Classification & Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91d3c8c4-0115-40f0-93e8-8c26155a7937",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Models for Classification & Regression space\n"
     ]
    }
   ],
   "source": [
    "print(\"Linear Models for Classification & Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5cc1e819-2e4c-49ff-99a5-677ae75eced3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e0b120b1-96cf-4f6a-8ce9-dd25bc0ee5d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "61961cb7-7c74-49b1-aec8-b33fad075967",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "725020c7-235c-41b0-8eef-de702d6e2a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.datasets import make_regression\n",
    "X,Y = make_regression(n_features=1, noise=10, n_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b421d93-7fcd-4caf-a8e8-a8428db97fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Feature - X')\n",
    "plt.ylabel('Target - Y')\n",
    "plt.scatter(X,Y,s=5)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e48888f-5d65-4c7e-b42b-cb785e1d10bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b0b76dc2-a70d-4b5e-b4c9-a01df89ee43f",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training model\n",
    "lr.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad0369d2-3b2f-4b8c-96d5-1481d35ba64f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([54.26916292])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lr.coef_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3de42f9e-72d9-4e13-b121-2ad0d699fd03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0710813623916447"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e2d1933f-d79f-4de1-8fbf-553dfb1f6497",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting using trained model\n",
    "pred = lr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ee9b8453-8330-4319-9b61-3d99d04771da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y,s=5, label='training')\n",
    "plt.scatter(X,pred,s=5, label='prediction')\n",
    "plt.xlabel('Feature - X')\n",
    "plt.ylabel('Target - Y')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#Blue dots represent maps to actual target data\n",
    "#Orange dots represent predicted data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36e80bb-d57d-43a0-a9f2-943fa2a6df14",
   "metadata": {},
   "source": [
    "# Regularized Regression Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5601b077-b81e-4c57-8f89-c0a46d285978",
   "metadata": {},
   "source": [
    "### Ridge Regression\n",
    "- Ridge Regression imposes penalty on size of coef.\n",
    "- Less impacted by outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d26d1768-74b9-4a1c-9422-942c02564deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f11e19e9-65e7-4e34-be66-5f8ca99476bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\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-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: 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-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: 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-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 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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding outliers to data\n",
    "ridge = Ridge(alpha=.1)\n",
    "lr = LinearRegression()\n",
    "ridge.fit([[0, 0], [0, 0], [1, 1]],  [0, .1, 1])\n",
    "lr.fit([[0, 0], [0, 0], [1, 1]],  [0, .1, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e2044294-c885-4b39-8c0a-6b9d650ec720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.44186047, 0.44186047])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cdafe6b5-b401-4aeb-abaf-5d1a11fe2dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.475, 0.475])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c6fc79e6-e2fb-42a3-a6c5-6d231f751e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers = Y[950:] - 600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "da7bf336-01e7-4304-9232-41cb8376e534",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "Y_Out = np.append(Y[:950],outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5cdbd819-85f5-49be-a336-c5dc55e3523e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y_Out,s=5)\n",
    "plt.show() #Always remember this to diplay the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9961652e-68d3-45f8-b551-bf96c0b53fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7a403188-1499-4983-aea1-e6d0256d4078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4\" 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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X,Y_Out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2f92a444-2405-4eb7-a324-24f3a82b1ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_Out = lr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "508a99d8-7131-4ca7-bb35-23492a4287d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y_Out,s=5,label='actual')\n",
    "plt.scatter(X,pred_Out,s=5,label='prediction with outliers')\n",
    "plt.scatter(X,pred,s=5,c='k', label='prediction without outlier')\n",
    "plt.legend()\n",
    "plt.title('Linear Regression')\n",
    "plt.show() #Always remember this to diplay the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "402d839d-689d-4b7e-b6b2-7dd74299a9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([49.60817069])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a304217f-ebb4-4daa-a2de-ccc4be086155",
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge(alpha=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fcb4019b-3553-4380-95a3-142976389b9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\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-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=1000)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=1000)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.fit(X,Y_Out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "66db8a55-b6ac-4f4c-9dca-9d12b9657396",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_ridge = ridge.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "aa8c416a-e96b-42d3-babc-563cf1159019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(X,Y_Out,s=5,label='actual')\n",
    "plt.scatter(X,pred_Out,s=5, c='r' ,label='LinearRegression with outliers')\n",
    "plt.scatter(X,pred_ridge,s=5,c='k', label='RidgeRegression with outlier')\n",
    "plt.legend()\n",
    "plt.title('Linear Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7ee37df4-900e-4a48-a4cc-321e46e2685a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.80818231])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c04b0ca-f2a9-4083-8ec0-ae05d1d408ae",
   "metadata": {},
   "source": [
    "### Effects of alpha using Ridge on Coeficients\n",
    "- Data generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "116aeb73-50e1-47a3-9d5e-feeea27e87ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y, w = make_regression(n_samples=10, n_features=10, coef=True,\n",
    "                          random_state=1, bias=3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8455b5de-155e-4dfa-b657-cf63079ef0e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([80.71051956, 10.74941291, 38.78606441, 13.64552257,  5.99176895,\n",
       "       86.35418546, 12.13434557,  4.45518785, 74.71216427, 55.6240234 ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e6b6657-8992-46bb-86b4-d5fefb5d09da",
   "metadata": {},
   "source": [
    "### Training Ridge for different values of alpha\n",
    "- Coefs calculated are appended to a list\n",
    "- Generate 20 alphas from 10^-6 to 10^6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4e241ea2-3266-454c-abd7-9c1945af23a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e-06, 1.14895100e-06, 1.32008840e-06, 1.51671689e-06,\n",
       "       1.74263339e-06, 2.00220037e-06, 2.30043012e-06, 2.64308149e-06,\n",
       "       3.03677112e-06, 3.48910121e-06, 4.00880633e-06, 4.60592204e-06,\n",
       "       5.29197874e-06, 6.08022426e-06, 6.98587975e-06, 8.02643352e-06,\n",
       "       9.22197882e-06, 1.05956018e-05, 1.21738273e-05, 1.39871310e-05])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphas = np.logspace(-6, 6, 200)\n",
    "alphas[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5716c039-a833-4251-9125-fda2f95517f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs = []\n",
    "for a in alphas:\n",
    "    ridge = Ridge(alpha=a, fit_intercept=False)\n",
    "    ridge.fit(X, y)\n",
    "    coefs.append(ridge.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f821c1-46a9-41f1-bcef-ebeee237001e",
   "metadata": {},
   "source": [
    "#### Plotting alphas & coefs\n",
    "- Different colors represents different coefs\n",
    "\n",
    "- As alpha tends toward zero the coefficients found by Ridge regression stabilize towards the randomly sampled vector w (similar to LinearRegression).\n",
    "- For big alpha (strong regularisation) the coefficients are smaller (eventually converging at 0) leading to a simpler and biased solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cc549888-09be-402f-b842-714bba59e225",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqto0ubm5qKpaZdNBRESEW3nT09P5/fffK5Wjbdu2AHU21LYm5a5KRR+sMWPGVCrjq6++iqqqrmamCoGBgW7PjUYjQI2CmHfeeYcnn3ySuXPnMmjQIAICArj22murnPrgZNOnT+f++++nZ8+e/Pzzz6xbt44NGzZw5ZVXup339ttv54svvuDIkSOMHj2akJAQevbsyaJFi06b//r16xk2bBjg7Ef2999/s2HDBp555pkaX1tdONNrW9u/8+lkZ2fX6L1c0zLW1XnqypnKm56ejqqqhIaGVvoMrFu3rsaf0VOvrab5ns1327lISkrisssuIzU1lbfffptVq1axYcMGV3+wU/9+Zzvyc9euXVx77bVcdtllfP755277fvnlF2688UYiIyOZOXMma9euZcOGDYwfP56ysrJaXU/F61bde8rhcJCbm+u2/Vy+uy406ePTALVu3drVoXnQoEHY7XY+++wzfvrpJ8aMGVPtcYGBgaxfv77S9lM7N1e8YU/tLF1V2qCgIAIDA5k/f36V5zzdMOOK/gEpKSnVpvH390ej0XDs2LFK+44ePeoqQ8V9hw4dKv36qVDxpX+ualLuqlSU89133612xERdfvl6enoyZcoUpkyZQnp6uqv2Z+TIkezdu7fa42bOnMnAgQP58MMP3bYXFhZWSnvXXXdx1113UVxczMqVK3n++ecZMWIE+/fvJzY2tsr8v/vuO/R6PX/88Ydbjdm5zkVkMpkq9UEDZ8Bb8dqfjdr+nU8nMDCwRu/lxnKesxUUFISiKKxatcr1j/BkVW2ryqk1cDXN90zfbSfX+lS8N099T9UkOJs7dy7FxcX88ssvbp+DrVu31uh6TiclJYUrr7ySmJgYfv75Z/R6vdv+mTNnEhcXx/fff++Wb1WfjZqqeN2qe09pNBq3qVQaO6nxaQSmTZuGv78/zz33nNvIkFMNGjSIwsJCfvvtN7fts2bNcnvesmVLwsLC+OGHH9y2JyUlsWbNGrdtI0aMIDs7G7vdTrdu3SrdWrZsWW15WrRoQXx8PF988UW1H0pPT0969uzJL7/84vZLweFwMHPmTKKiolwdEkeMGMHOnTuJj4+vsix1FfjUpNxV6du3L35+fuzevbvK8nXr1g2DwXDW5TEajWf8FRUaGsq4ceMYO3Ys+/btq1QtfTJFUSr989i+fftpm0c8PT256qqreOaZZ7BYLK4h59Xlr9Pp3JpwSktL+eabb057DWfSpEkTtm/f7rZt//79VY7Uqok+ffrg6+vLRx99dNrRZmfzS3bIkCHs3r2bzZs3u22fMWMGiqIwaNCgWpW1qvMsXbrUFeicfB6z2Vyrocp18Yt9xIgRqKpKampqle//9u3b12u+PXv2xGg08v3337sdv27dukrNhxVB0KnvqVO/P6tSEXCc/DlSVZVPP/30rK/tZPn5+Vx11VUoisK8efOq7IysKIprYs8KaWlplUZ1VZSvJn/Pli1bEhkZyaxZs9w+C8XFxfz888+ukV4XC6nxaQT8/f15+umneeKJJ5g1a5ZruO+p7rjjDt58803uuOMOXn75ZZo3b868efNYsGCBWzqNRsOUKVOYMGECY8aMYfz48eTl5TFlyhTCw8Pd2nhvvvlmvv32W66++moefvhhevTogV6vJyUlhWXLljFq1Ciuu+66asv+/vvvM3LkSHr16sUjjzxCTEwMSUlJLFiwgG+//RaAqVOnMnToUAYNGsTjjz+OwWDggw8+YOfOncyePdv1AX/xxRdZtGgRffr0YdKkSbRs2ZKysjISExOZN28eH330UZ01W9Sk3Kfy8vLi3Xff5c477yQnJ4cxY8YQEhJCZmYm27ZtIzMzs1ItS020b9+e5cuX8/vvvxMeHo63tzctW7akZ8+ejBgxgg4dOuDv78+ePXv45ptvzvglNWLECP773//y/PPPM2DAAPbt28eLL75IXFyca1oAcM6j4+HhQd++fQkPDyctLY2pU6fi6+tL9+7dq81/+PDhTJ8+nVtuuYX77ruP7OxsXn/99Rr/2q9ORX+HiRMnMnr0aI4cOcK0adNqPerLy8uLN954g3vuuYfLL7+ce++9l9DQUA4ePMi2bdt47733AFz/VF999VWuuuoqtFotHTp0qDKIfeSRR5gxYwbDhw/nxRdfJDY2lj///JMPPviA+++/v85GFT3//POuPm/PPfccAQEBfPvtt/z5559MmzbttCPCquPt7U1sbCy//vorQ4YMISAggKCgoLPqG9O3b1/uu+8+7rrrLjZu3Ej//v3x9PTk2LFjrF69mvbt23P//fefddlqmm9AQACPPvooU6dOxd/fn+uuu46UlJQqv9u6d+9Oy5Ytefzxx7HZbPj7+zNnzhxWr159xvIMHToUg8HA2LFjeeKJJygrK+PDDz+s1Bx0tm655RZ2797NJ598QnJyMsnJya59UVFRREVFMWLECH755RcmTpzImDFjSE5O5r///S/h4eGVmrmr++44lUajYdq0adx6662MGDGCCRMmUF5ezmuvvUZeXh7/+9//zum6GpwL1ataVFbdaBZVVdXS0lI1JiZGbd68uWsUSFUjAlJSUtTRo0erXl5eqre3tzp69Gh1zZo1bqO6KnzyySdqs2bNVIPBoLZo0UL94osv1FGjRlUaGWC1WtXXX39d7dixo2oymVQvLy+1VatW6oQJE9QDBw6c8brWrl2rXnXVVaqvr69qNBrV+Ph49ZFHHnFLs2rVKnXw4MGqp6en6uHhofbq1Uv9/fffK+WVmZmpTpo0SY2Li1P1er0aEBCgdu3aVX3mmWfUoqIiV7pzHdVVk3JXNypkxYoV6vDhw9WAgABVr9erkZGR6vDhw9Uff/zRlaZiZERmZqbbsVXluXXrVrVv376q2Wx2G5ny1FNPqd26dVP9/f1Vo9GoNm3aVH3kkUfUrKysStd9svLycvXxxx9XIyMjVZPJpHbp0kWdO3dupZErX3/9tTpo0CA1NDRUNRgMakREhHrjjTeq27dvP23+qqqqX3zxhdqyZUtXuaZOnap+/vnnVb5ep6ruc+BwONRp06apTZs2VU0mk9qtWzd16dKl1Y7qOvn1VtUTI6lO/RzMmzdPHTBggOrp6amazWa1TZs26quvvur2et1zzz1qcHCwqiiK2zVUNdLsyJEj6i233KIGBgaqer1ebdmypfraa6+5jZapKMtrr71W6fqpZmTVqXbs2KGOHDlS9fX1VQ0Gg9qxY8dK11aRX01Gdamqqi5evFjt3LmzajQaVcB1bWfzflVV59+/Z8+ers9zfHy8escdd6gbN2487fmrO8/Z5OtwONSXXnpJjYqKUg0Gg9qhQwf1jz/+UDt27Og2alFVVXX//v3qsGHDVB8fHzU4OFh96KGH1D///LNGo7p+//1313diZGSk+u9//1v966+/Kh07YMAAtW3btlVez6nv3djYWBWo8nbye+J///uf2qRJE9VoNKqtW7dWP/30U9drd7Lqvjuq+86bO3eu2rNnT9VkMqmenp7qkCFD1L///tstzdm+FxoiRVXPMKOYuGTk5eXRokULrr32Wj755JMLXRwhhKgTCQkJtGrViueff75GE3CKi5sEPpeotLQ0Xn75ZQYNGkRgYCBHjhzhzTffZO/evWzcuNE1UkoIIRqTbdu2MXv2bPr06YOPjw/79u1j2rRpFBQUsHPnzjodYCAaJ+njc4kyGo0kJiYyceJEcnJyXB0iP/roIwl6hBCNlqenJxs3buTzzz8nLy8PX19fBg4cyMsvvyxBjwCkxkcIIYQQlxAZzi6EEEKIS4YEPkIIIYS4ZEjgI4QQQohLhnRuPoXD4eDo0aN4e3vX2cKFQgghhKhfqqpSWFhIRESE22SVp5LA5xRHjx6ttOKxEEIIIRqH5OTk087iL4HPKSoW3UxOTq5ynRQhhBBCNDwFBQVER0efdvFskMCnkormLR8fHwl8hBBCiEbmTN1UpHOzEEIIIS4ZEvgIIYQQ4pIhgY8QQgghLhkS+AghhBDikiGBjxBCCCEuGRL4CCGEEOKSIYGPEEIIIS4ZEvgIIYQQ4pIhgY8QQgghLhkS+AghhBDikiGBjxBCCCEuGRL4CCGEEOKSIYuUnicLPnqbwuws4JQF1E56rFS1rbrF1o5vd9+vnLr7lG1KpaQKVZfFlX8V56yuXLW/LuWUhCfvV9BoNCjHbxqNBkWrdW1z36c9aZvWbb9Gq0Wn16PV69HqDWj1etdz3fHnWp0encH5WG80odXJx0MIIS428s1+nqTu20Pu0ZQLXQxxFnQGIwYPD4xmM3qTB0YPMwazGYOH8+bh7Y3Zxxezr5/z5uOH2dcXk6cXikYqU4UQoiGSwOc86X/LOCylJaiqWuV+t+0nPVZRKx5UndZtR1VpTsrL7bCqzlFNWqopm1p5f/XXUfncNX0tHA47qsOB6nDgcDhw2N2fuz+2u7ZVPFePH2O32bBZLNhtVuwWCzarFbvNetI2Kzab1VU+m6Ucm6Wckvy8KstZHY1Wi1dAID5BIfgEBeMTHIJPSCgBEdEERkZj8vI6q/yEEELUHQl8zpNm3Xtd6CKIGlBVFbvNhrW8DEtJCZbSEspLnfeWkhIsZaVYSpzbSgsKKCnIoyQ/n5KCfErz8ygrLsJht1OQmUFBZkaV5zD7+hEQEUVIk6aExjcntGkzAsIjpZZICCHOAwl8hDiJoijojvf/8fDyPuvj7TYrxXl5FGZnUZCVQWFWJgWZGeSlHyMnNYXC7ExK8vMoyc8jZc9O13EGDw/C4psT3bYjse07Edq0GRqtti4vTQghBKCo1bU3XKIKCgrw9fUlPz8fHx+fC10ccZGxlJWSezSVrOQjZCQcIu3QATISD2OzlLulM3iYiW7bgfiuPWjWo3etgjAhhLiU1PT/twQ+p5DAR5xvDrud7JQkUvfu5siOrSTv3k55cbFrv0arJbZ9J1r26U98t56YPKWPkBBCnEoCn1qSwEdcaA6HnYyEwyRu3cT+davJTEp07dPpDbTqN5AuV40kODbuwhVSCCEaGAl8akkCH9HQZKcms2/NKvatXUVOarJre3Sb9nS++hriu/ZAo5H+QEKIS5sEPrUkgY9oqFRVJXXfbrb89TsH1q9BdTgA8I+Ios8Nt9CyVz8ZGSaEuGRJ4FNLEviIxqAgK4NtC+exffF8yoqLAAiOjWPgHfcS067DBS6dEEKcfxL41FJ9BT4v/LaLjMIy13P3pSKqfOi2rIP79pqndc+7qiUtqs77tOmrOcE55emWvuplOrQaBa1GQaMoaDWgVRQ0GuXE/cmPFWf6k/frjqfRazUYtBoMOg1GnfPe9VirdXtu0mvRaqpZNqQBKC8pYfNfv7Lpj7mUlzg7RDfr3ptB4+7DJyj4ApdOCCHOn4su8LHZbLzwwgt8++23pKWlER4ezrhx4/i///s/NMer91VVZcqUKXzyySfk5ubSs2dP3n//fdq2bVvj89RX4DP4jeUcziw+c0LR4HjotXgadXgZK+6dN8/jN18PPYGeBgI8DQR4GVyPAz2NeBjOT9+b0sIC1vw4i22L5qE6HOhNHlx2y510Gnq1NH8JIS4JF13g8/LLL/Pmm2/y9ddf07ZtWzZu3Mhdd93FSy+9xMMPPwzAq6++yssvv8xXX31FixYteOmll1i5ciX79u3D27tm86DUV+Azd0sqBWVWwH05CLWaZR3c0rhtr26Zh5PTq6fZd+ZjqntHqNUsaVGTPE93THVLW5yc3qGqOFTnvd3hvFU8PrHtpP2qisOhYnM47+0npbXaVMrtDsqtdix2Bxbb8dvxx+U2B3ZH3XwsfEw6ovzNRPp7EOXv4Xzs53zcNNgTs6Fu5xDNSkpk4afvcWz/XgCi23bgqgcexTswqE7PI4QQDc1FF/iMGDGC0NBQPv/8c9e20aNHYzab+eabb1BVlYiICCZPnsyTTz4JQHl5OaGhobz66qtMmDChRueRPj4CwO5QsdgclFhsFJfbKSq3UWyxOe/LbRSVVTy2k19qJae4nOxiC9lFFnKKnTeL3XHacygKxASYaRHqTctQb1qH+9A5xo8IP49zKrvqcLB14Z+snPUVtvJyTJ5eDLv/YZp3731O+QohRENW0//fjWbJin79+vHRRx+xf/9+WrRowbZt21i9ejVvvfUWAAkJCaSlpTFs2DDXMUajkQEDBrBmzZpqA5/y8nLKy0/MmltQUFCv1yEaB61GwcOgxcOgJbAW8wWqqkpRuY1j+WWk5paSkltCSm4pKXmlpOSWkpxTQk6xhSPZJRzJLmHR7nTXsWE+JrrE+tElxp8+8UG0Dveutt9TVRSNhs5XjiS2Qxfmvfs66YcP8NvrL9Nj1Bj63ny7DH0XQlzSGk3g8+STT5Kfn0+rVq3QarXY7XZefvllxo4dC0BaWhoAoaGhbseFhoZy5MiRavOdOnUqU6ZMqb+Ci0uSoih4m/R4m/S0CK26mTWrqJz96YXsTytkX3ohO1ML2H2sgLSCMubtSGPejuPvaR8jA1uEMKhVMH2bBeFt0teoDAERkYz97zRWzfqKTX/+yvpffyI94RAjJj8psz8LIS5ZjSbw+f7775k5cyazZs2ibdu2bN26lcmTJxMREcGdd97pSnfqL2NVVU/7a/npp5/m0UcfdT0vKCggOjq67i9AiFMEeRkJ8jLSJ/5E/5sSi43tKflsTsplY2Iuaw9lk15Qzvcbk/l+YzIGrYbL24QwuksUA1oEo9OevuOyVqdn4B33EhbfggUfv8OR7Vv47rknuP7pF/AJCqnvSxRCiAan0fTxiY6O5qmnnuKBBx5wbXvppZeYOXMme/fu5fDhw8THx7N582Y6d+7sSjNq1Cj8/Pz4+uuva3Qe6eMjGpIyq531CTks35fJsn0ZJGSdGBkY5GVgVKdIxvaIplnImTvvpyccYs6rUyjOzcHTz5/R/3lRlr0QQlw0avr/u9GMcy0pKXENW6+g1WpxHJ+9Ni4ujrCwMBYtWuTab7FYWLFiBX369DmvZRWirpj0Wvq3COa5kW1Y+tgA/pzUj/F94wj0NJBVZOHz1QlcPn0ld325njUHs6od9QcQGhfPLS+9QVB0LMV5ufww5WnSDu4/j1cjhBAXXqOp8Rk3bhyLFy/m448/pm3btmzZsoX77ruP8ePH8+qrrwLO4exTp07lyy+/pHnz5rzyyissX768QQxnF6IuWe0OVuzL5PuNySzek+4a+t+jSQCThzZ3az47VVlxEb/87wWO7d+LwcOD6556gahWNZ/rSgghGqKLbjh7YWEhzz77LHPmzCEjI4OIiAjGjh3Lc889h8FgAE5MYPjxxx+7TWDYrl27Gp9HAh/R2CRmFfPF3wl8tyEZi81ZAzqwZTDPXN2a5tV0rLaUlTJ32n9J3rUdvcmDG/7vJcKbtzyfxRZCiDp10QU+54sEPqKxSssv48PlB/n2nyRsDhWtRmFcnyY8OrQFnsbK4xislnLm/G8Kybu2Y/T05MbnphLSpOkFKLkQQpw7CXxqSQIf0dglZBXzv7/2sGCXc26gSD8PXr6uHQNbVh7FZSkr5edXnufovt14+Phyy39fxy8s/HwXWQghztlF17lZCFEzcUGefHx7N766qztR/h6k5pUy7ssNPPfrTsqsdre0BpMH1z/1PCFN4iktyOfnqc9RUpB/gUouhBD1TwIfIS5SA1uGsPCR/ozr0wSAGWuPMPLd1RzMKHJLZzR7ct1Tz+MTHEJe2jHmvvoiVkt5FTkKIUTjJ4GPEBcxs0HHC9e05evxPQj2NnIgo4hr3/+b+TvT3NJ5+Qdw/dNTMHl5c+zgPhZ+9M5ph8YLIURjJYGPEJeAAS2C+XNSP3rEBVBUbuNfMzfx3tIDbsFNYGQ01zz6NBqtlr1/r2D93B8vYImFEKJ+SOAjxCUixNvEt/f05K6+TQB4feF+nv5lB9aTVpGPbtuBwXc5F/Rd/f03HNq0/kIUVQgh6o0EPkJcQvRaDc+PbMt/R7VFo8B3G5L51zeb3Do9dxx6NR2HDQdV5a/33yAvPe00OQohROMigY8Ql6Dbezfhk9u7YdRpWLI3g3tnbKTUciL4GXTnPYQ3b0l5cTG/vzkVm8VyAUsrhBB1R+bxOUV9zeOza/djlJUdq3Kfwkmrx7utJK9Unaba9DXPr9q8q01fH/m753o2+Vf3eiiKBhQtiuumQeHEcyq2KToUNCfSKDpQKp7r0CgGNBoDGo3xxL3W6LxXTjzXaoxoNGY0GgNKtdfWcK05lMU9X2+kxGKnd9NAvryrOya9FoCCrExmPvUwpYUFdBx6FZff88AZcqu9PKuNNXlF/J1bRLrFSqldxazV0MnHTHcfM919PRvl6yuEOH9kAsNaqq/AZ+26oZSUHK6z/ETDoih6dDovtFovdDpvdDovdMcfa3Ve6HU+GAxBlW46ne8F/4e+MTGHcV9uoKjcxuBWIXx0W1cMOmdlcOK2zfz8ynMAXPvEs8R37Vmn595TVMr7SRnMycjFfppvojaeJiY3CWNEsC8aCYCEEFWQwKeW6ivwyc5egc1e7Hzi9pKrJz06aXu1f5Yq0leTX/V5V51fzcpSTRq3LGuSvp7yV+2oqgNVtTtvOJ9T8dy1reJ26j4HqmrD4bDicJSfcrOccl+OqtqqLmMNKYoeozEUkykSD1MkJlMUHh5RmM3xeHrGo9PVbHHdc/XP4Wzu+GI95TYHIzqE887NndFonAHG8m8+Z9Mfc/Dw8eXO197D08//nM+XbbHx/MFUfkrPdW1rbjZymb838WYjHloN2RYbWwtLWJ5TSPHxDth9/Lz4qE0sIUb9OZdBCHFxkcCnlmTJCnE2HA4bDkcpNlsRNlshdnuR87H9+HOb87nVlovFko3FkuW62WwFZ8zfaAzD09wML6+W+Ph0xMenIyZTZL3UEi3bl8F9MzZitavc178p/7m6NQA2q5VZ/3mEzKRE4jp347onnz+n8/+akcvT+1PIsdpRgBHBfjwYG0JHb3OV6XOtNj5PyeLD5AyK7Q5CDTo+bduEHn5etS6DEOLiI4FPLUngI84Xh6MciyWbsvJjlJWmUlaWQmlZCqWlSRQXH8JiyajyOL0+AF+fTvj79yIgoB+eni3qLBD6dWsqD3+3FYCXrm3Hbb1iAchKPsLMpydjt1oZMv5+Ol0x/KzztjpUphxK5bOULABae5qY3iqGzj5VBzynOlhSxvgdiewvKcOkUfi+Yzw9JfgRQhwngU8tSeAjGgqrtYCSkoMUFR+gsHAXBQXbKCraW6l5zWAIISCgL0GBAwkKGoxWW7NAojrvLjnAG4v2o9UofHVXdy5rHgzA5r9+Y9lXn6DTG7jtf28TGBVd4zxzrDbG70hgXb6zuffh2FAeaxKKQXN2A0uLbXbu23WEJTkF+Og0/NKpGe2qqSkSQlxaJPCpJQl8RENmt5dTVLSbvPxN5OSsJi9vAw5HmWu/VmsmKOhywkJHEhDQD43GcNbnUFWVx3/czs+bU/Az6/n9wX5EB5hRHQ5+nvo8R7ZvIaRJPLe8/Dpa3Zn72qSWWbh52yEOlJTjpdXwXutYrgz2PetyVSi1Oxi77RDr8osJ0utY0K0Fkaazv04hxMVFAp9aksBHNCZ2ezn5+RvJzllFRsZ8ysqSXft0Oj8iIsYQHXUnJlPEWeVbZrVz08dr2ZaST9sIH36+vw8mvZai3By+/veDlBUW0OeGW+k9Zuxp8zlUUsaNWw+RWm4l3KhndsemtPL0qNW1nqzAZue6LQfYVVRGbz9PfurUDK2M9hLiklbT/98ygaEQjZhWayQgoC/Nmz1Fn97L6Nb1Z6Ki7sRgCMJmyyMp6TPWrB3Ijp2TyC/YVuN8TXotH97WlUBPA7uOFvDs3J2AczHTIceXtFj3y/dkpyRVm8ehkjKu33KQ1HIrzcxGfu/SvE6CHgAfnZbP2sZh1mpYm1fMe0eq7g8lhBCnksBHiIuEoij4+naiZYvn6Nd3DR07fIq/f29U1U5Gxp9s3Hg9GzfdRG7uPzXKL8LPg3fHdkajwI+bUvh1ayoALfv0p2mX7jjsNhZ+/C6qw1Hp2MMl5Yzecoh0i41Wnibmdm5OVB03R8WZjUxtHgXAtMRjbC4ortP8hRAXJwl8hLgIKYqWoKDBdOk8kx7d/yA87HoURU9+/kY2b7mFrVvvoqho/xnz6dMsiAcHNwfgmTk7ScouQVEUhtw9EYOHB0f372Hrwj/djjlWbuGGrQdJs1hp6Wnix07xBBl09XKdN4b5c22IH3YVntqXgl1a7oUQZyCBjxAXOW/v1rRp8xp9+6wgMvJWFEVHds5K1m8Ywf4DL2GzFZ72+EmDm9Et1p+ichuTvtuCze7AJyiYy8aOA2DV7BkUZDmbmvKtNm7ZdpjUcivxHkZ+6hRPsKH+JhtUFIX/No/ER6dhe1Eps45l19u5hBAXBwl8hLhEGI2htGr5Ir16LiQ4aCiqaic5+UvW/XMlWVnLqj1Op9Xw9tjO+Jh0bE3O4+OVzqVXOg69ishWbbCWlbL40/cptdkZtzOBPcVlhBh0zO7YtF6DngrBBj3/bhIOwNTDx8izntts2kKIi5sEPkJcYszmWDp0+IhOHb/CwyOW8vI0tm2/h927/43NVlTlMZF+HrxwTVsA3lq8n91HC1A0Gobe9xBanY5D2zYzbvUm1uYV463VMLtjPDEexvN2TeMig2hhNpFjtfN6Ytp5O68QovGRwEeIS1Rg4GX07PEnMdF3AxqOpf3C+g3XUFCwo8r013WOZGibUKx2lcd+3IbF5iAwMpqeo8eytO9wVqgGDAp82T6Otl51M3qrpvQaZ5MXwIzUbI6WWc7r+YUQjYcEPkJcwrRaD5o3/w9du8zGaAyntPQIGzfdQGrqd5XSKorCK9e1x9+sZ8+xAj5d5WzyWtfpMra06wWqgwnp++nnf34WVj1Vf38vevl6YlFV3k+S4e1CiKpJ4COEwM+vGz17/HG874+VvfueYe++53A4rG7pgr2NPDeyDQBvLznAu/uP8urxOXSG/D0PjzkzST988LyXH5yB2WNNwgCYeSybtHLrGY4QQlyKJPARQgCg1/vRvv0HNI17BFBITf2WbdvuqdTv59pOkVzWPIhSPz2vpKQD8FBMCLf4GlFVB0s+/7DKuX3Oh37+XvTw9aTcofKB1PoIIaoggY8QwkVRNMTFPUiHDh+j0XiQk7uaTZvHUl6ecVIahZuGxmPtFIiqKPTSG/lP03AG3H43Bg8Pjh3cx45lCy9Q+RUebRIKwIyjWWRbZISXEMKdBD5CiEqCg4bQtcss9PpAiop2s2nzTZSVHQXgQHEZ/z5yDLQKmswyklekUlRuw8s/gD433AbAqllfU1pYcEHKPsDfmw5eHpQ5VJnXRwhRiQQ+Qogq+fh0oFvXHzGZoiktTWLT5rEk5Cdy87ZD5NrsdPTyoEVKGdmF5by9+AAAna8cQXBME8qKClk1++sLUm5FUbg7KhiAr1KzsDlkNmchxAkS+AghqmU2x9K1yyw8PJqQVZbHjVt2u2Zl/rZjPC8Md3Z0/nJNIvvTC9FotQy5eyIAO5Ys4NjBfRek3KNC/AjU60gttzI/K/+ClEEI0TBJ4COEOC2TKYI2Hb9lunYKyWoEAeQzo7UfQQYdA1uGMLRNKHaHyn//2A1AZKs2tB0wBIBlX35yQTo6m7Qa7ogIBOCzlMzzfn4hRMMlgY8Q4rSsDpUHDpSwz9EEL4p5Un2erL13Y7U6+/A8O7wNeq3CqgNZrNzvDDL6jb0TvcnZ0Xn3quqXw6hPd0QGolNgXX4xu4pKL0gZhBANjwQ+QohqOVSVR/YmsSSnAA+NwpdtwmhqKKeoeB87dtyPw2EhJtDM7b2aADD1r704HCpe/gH0uv4mAFbN/hpLacl5L3u40cBVQX4AzDoqnZyFEE4S+AghquRQVZ7an8JP6bloFfi0XRyXhcbTqeMXaLVe5OatY8/ep1FVlQcHN8PbqGPPsQLmbk0FoMvVo/ALC6c4N4d/5vxwQa5hbHgAAL+k51J+geYWEkI0LBL4CCEqUVWVp/enMONoNgrwTqsYLg/0AcDbuzXt272LomhJS5tLQuJ7BHgauH9QPABvLNxPmdWOTq9n4B33ALDpz7nkpR0779cxIMCbcKOeXJudBVkXZni9EKJhkcBHCOFGPV7T83VF0NM6htFhAW5pAgP707LlfwFISHiLzMxFjO8bR5iPidS8UmasTQSgaZcexHbojN1mY/k3n5/nKwGtonDj8bLPljl9hBBI4COEOImqqjx9INUV9LzdOoYbTgl6KkRG3ERU1O0A7Nr9GDbLYR4d2gKA95cdIr/EiqIoDLrzPhSNhkMb15G4fcv5uhSXm4+Xf0VOoazaLoSQwEcI4WQ/XtPzVWoWCvBWqxhXbUl1mjd7Bj+/ntjtxezYcT+jOvrRItSL/FIrHyx3LlYaGBVN5ytGALD860+x287vMhJxZiO9fD1xAD+l557XcwshGh4JfIQQlNod3Lcr0VXT82araG4KP33QA6DR6Gnf7l2MxjBKShI4cOBZnryyJeCc1DA1zzmMvPeYW/Dw9iE7JYlti+bV56VUqSKAmyOBjxCXPAl8hLjE5Vht3Lj1EH9m5mNQFD5qG8vN4YE1Pt5gCKRd27dRFC3p6b/T0nspPeMCsNgcfLDMWetj8vKi703OZrE1P35LScH5nU356mBf9IrCnuIy9hbLnD5CXMok8BHiEnaktJyRmw6woaAYX52W7zvFMyrE/6zz8fPrRnz8vwE4cPC/PDxAB8APG5M5erzWp/2QYQTHxlFeXMyaH2bW3UXUpHx6HYMCvAH4NT3vvJ5bCNGwSOAjxCVqU34xwzcd4FBpOZFGPb91aU5vP69a5xcTfQ+BgQNwOCzoCv9Ln6Y+WO0qHy4/BIBGo2XwuAkAbF+8gMwjCXVyHTV1bagzoPs1Iw9VlYVLhbhUSeAjxCVGVVW+Ts3i2i0HybLaaOtl4s+uLWjpaTqnfBVFoXWr/6HT+VFYuIsJXVcB8P2GZI7lO2t9otq0o0XPvqiqg+XffH5eA5ArAn3w0CgcLi1nhyxhIcQlSwIfIS4hRTY7D+1J4sn9KVhVleHBvvzauTlhRn2d5G80htCqlXN+H3vBV4xolYPF7nDV+gBcdutdaHU6knZsJWHLxjo5b0146rQMDfIFYK40dwlxyZLAR4hLxJaCEoZu3MdP6blogP9rGs5nbZvgpdPW6XlCQ64mNPQaVNXO9U0/x6At57v1yaTllwHgFxpGl6tHAbD8m8/P6/D2a0P8APgtM1eau4S4REngI8RFrszu4JVDRxmxeT8JpRYijXrmdG7Gg7GhKIpSL+ds2eIFjMYwVFsyD3RdgMXu4KMVJ2p9el53Ix4+vuQeTWH74r/qpQxVGRTgg4dGQ0qZVZq7hLhESeAjxEVsdW4hQzfu452kDOwqjArxY0n3lvQ8h07MNaHX+9K69asAtPNbTNvAPcxan0R6gbPWx2j2pO+NtwKw5sdZlBUV1Wt5KnhoNQwOdI7u+ivz/A6pF0I0DBL4CHERSi6zcN+uRMZsPcSBknKCDTq+aNeEj9s2wU+vOy9lCAzo51rS4p72P4KjzK3Wp/3gKwiMiqGsqJB1v3x3XsoEcNXxfj7zsiTwEeJSJIGPEBeRLIuN5w+k0nfdHn7LyEMDjI8MYlWPVlwd7HfeyxPf9N8YjeH4GLK4Jv4vZq9PIqfYuV6WRqtl4O13A7Bl/h/kHks9L2W6PNAHnQL7iss4XFJ+Xs4phGg4JPAR4iKQWFrOU/tT6L52Fx+nZGJRVfr5ebGwWwteaRF13mp5TqXTedKy5RQArmiyjCBjsmvldoAmnbrSpFNXHHYbK7/98ryUyU+vo6+fs7lrXmbeeTmnEKLhkMBHiEZKVVU25xdzz84E+qzbw1epWZQ6VDp6e/Bdx6b82Cmedt7mC11MgoOGEBx8BRrFwR1tvmPGmsOUWuyu/QNvvxtFo+HghnUk79p+Xsp0ZbCzuesvae4S4pIjgY8QjczRMgvvHklnwPp9XL35AH9k5uMABgd483OneOZ3bcHAAJ96G7FVGy1aPIdW60m83xE6Bi7nh43Jrn2BUTF0GHIlAMtnfI7DYa8ml7pT0c9nU0EJ6eXWej+fEKLhaFSBT2pqKrfddhuBgYGYzWY6derEpk2bXPtVVeWFF14gIiICDw8PBg4cyK5duy5giYWoGyllFmakZjFmy0G6rt3Ny4ePsb+kDKNG4YYwf5Z1b8msjvH09fduUAFPBZMxjPj4xwG4vtnvfLduMza7w7W/z423YjR7kpF4iN0rl9V7ecKMejodrw1bklNQ7+cTQjQcjSbwyc3NpW/fvuj1ev766y92797NG2+8gZ+fnyvNtGnTmD59Ou+99x4bNmwgLCyMoUOHUlhYeOEKLkQtFNvtrMopZMrBVAas30u3tbt5Yn8Kq/OKUIFevp680TKa7X3a8m7rWFp7eVzoIp9RVOSteHm1x6wvY2DYt8zbmebaZ/bxpef1NwGw+rsZWMrqf46dywN9AFicJYGPEJcSRW0k05c+9dRT/P3336xatarK/aqqEhERweTJk3nyyScBKC8vJzQ0lFdffZUJEybU6DwFBQX4+vqSn5+Pj49PnZVfiOrYHCqJZeVsLyxlQ34xm/KL2VVciv2kT6YG6OrjyeWBPlwX6keMh/GClfdcFBbu5p/1o1AUBz8lPM4H4//lqqGyWa189dj95Ken0Wv0WNc8P/VlW2EJV2zcj6dWw+5+7TBqGs3vQCFEFWr6/7vRBD5t2rThiiuuICUlhRUrVhAZGcnEiRO59957ATh8+DDx8fFs3ryZzp07u44bNWoUfn5+fP3111XmW15eTnn5iSGtBQUFREdHS+Aj6pSqqmRb7SSXWUgqKyep1MK+4jL2FpdxoKSMckflj2GEUU9ffy+GBPgwIMAb/ws0Mquubd/1Apnp33C0KJTYVj/Tv0W4a9/+f/7m9+lT0RmMjH/rY7wDg+qtHA5VpfOaXaRbbHzfMZ4BAd71di4hRP2raeDTaL5JDx8+zIcffsijjz7Kf/7zH9avX8+kSZMwGo3ccccdpKU5q81DQ0PdjgsNDeXIkSPV5jt16lSmTJlSr2UXFy+bQyXXZiPbaiPLUvk+tcxKcpmF5DILpQ5Htfl4aDS09jLRzceTrr5muvl4EmkynMcrOX9at3iEo2lzifBKZ/XWj+nf4gXXvuY9+hDZqi2pe3exevbXXPXgY/VWDo2iMCTQh1nHcliUnS+BjxCXiEZT42MwGOjWrRtr1qxxbZs0aRIbNmxg7dq1rFmzhr59+3L06FHCw0/8grz33ntJTk5m/vz5VeYrNT7n18lvN/WUe+d+Ku13Plaxq85f6XbArjqfO++d2xwnb0PFoZ5I51BVbMfTWR0qZQ4HFoeKRT3pscNBuUOl/KTHJQ4HRTY7RXYHhTY7xXYHhXY7RTYHxXY7pVXU1JxOmEFPjIeBKJOBFmYjrTw9aOVlIsZkQNMAOyXXl50HviI9+b+UWD2IbfUHHWKbuPalHTrAt/95BIBbX55OWLMW9VaOvzLzuGtnIrEmA+t6tW6QHcOFEDVz0dX4hIeH06ZNG7dtrVu35ueffwYgLCwMgLS0NLfAJyMjo1It0MmMRiNGY/33lxixyblAJDj/iZ+q+n/4Jz8+KWioIn21x7mdrnLgcbr0J/Ku5rgalPtSEKDXEqjXOW8GHUHH7yOMBqJNzlukSS/9SI5r2+x29h/+Bl99Iht3/o8OsR+59oXFN6fNZYPYvWoZy2Z8xs1TXq23gKS/vzcGReFImYWDJeU09zTVy3mEEA1Howl8+vbty759+9y27d+/n9jYWADi4uIICwtj0aJFrj4+FouFFStW8Oqrr5738p4qz2Yn22q70MW4KOkU0CoKGhS0xx9rFdyeaxTQoqBVFAwaBaNGwajRYNQoGBTN8ecKBo37Yw+NBm+dBm+tFs/j915aDV66E/c+Wi06jdQUnA1F0RIb93/kpdxDtGkxiWlbaBJ2om9ev7F3sv+fNRzdt5sD//xNi1796qUcnjotffy8WJ5byJLsAgl8hLgENJrA55FHHqFPnz688sor3Hjjjaxfv55PPvmETz75BABFUZg8eTKvvPIKzZs3p3nz5rzyyiuYzWZuueWWC1x6mNG+KRb1RB8PBeWkx1T9WKlm+/FnNUl7Mvf0NTh/tfmfXdndy1DNsdVe6wmnBjBahUuqeehi07XFID7a3YvmPuvYtut5YkN/db0vvQOD6Dbyetb9PJuV335J0y490Bnqp8/TwABvlucWsiK3kH/FhNTLOYQQDUejCXy6d+/OnDlzePrpp3nxxReJi4vjrbfe4tZbTwx5feKJJygtLWXixInk5ubSs2dPFi5ciLf3he+02NTcOIcfC1GfYpv8m/LMm/HS7iLl6G9ER45y7etxzWh2Ll1AfkY6W+b/TvdrRtdLGQYEeMMhWJdXRLnDIc2RQlzkGk3n5vNF5vER4vyx2R089+3jDIn6FbsSypD+y9BqT/xI2LlsEQs+ehujpyd3v/MZHl51/yNGVVU6HR/W/lOnePr5X/gfSkKIs1fT/9/y00YIccHotBqaxt5DTpkvWjWdlJQZbvvbDBhMUHQs5cXFrJ/7Y72UQVEU11D25Tkyy7sQFzsJfIQQF9QNPZozL3EkAAcPv4/Vmufap9Fo6X/rXQBs+es3CjIz6qUMA47X8qyQwEeIi54EPkKIC8rXQ09M5PUkF0aAWkhC4vtu+5t06kp02w7YbTb+/v6beilD/+M1PjuKSsm0yGrtQlzMJPARQlxw4/o246cDzo7NySkzKC1Ncu1TFMVV67N79XLSEw7V+fmDDXraHV/odVVuUZ3nL4RoOCTwEUJccHFBnoQFD2BXVktQbRw89Lrb/rD45rTqOwBUlVWzvqqXMpzo5yOrtQtxMZPARwjRIIzvG8cP+6/FoSpkZPxJfsE2t/39br4djVbHke1bSNy2uc7PP/Ckfj4y2FWIi5cEPkKIBqF3fCAe5pasPdodgIMHproFIL4hYXS6YjgAK7/9EvU0i77WRndfTzw0CukWG3uLy+o0byFEwyGBjxCiQVAUhTv6NGHOweFYHXry8jeQlbXYLU2v62/C4GEm80gC+9atrtPzm7Qaevl5AbAyV0Z3CXGxksBHCNFgXNc5EivBLEwcCMDBQ9NwOE6MsvLw9qHbyOsAWPPDTBx2e52ef6DM5yPERU8CHyFEg+Fl1DGmaxR/JV5Omd2bkpLDHD36g1uarlePwsPbh9xjR9m1Ykmdnr+ig/O6vCLK7HXblCaEaBgk8BFCNCi39Yql1ObBLweuAOBwwtvYbCeGmBs8zPS49gYA1v40G5vFUmfnbmk2EWbQU+pQ2ZBfXGf5CiEaDgl8hBANSrMQL/o1C2J5cl/KHBFYrdkkJX3mlqbjsKvxCgikMDuT7Yv/qrNzK4pC/wBnP5/l0s9HiIuSBD5CiAbnjt6x2FUtP+xzjuJKSv4CiyXLtV9vMNJ79FgA1s35AUtZaZ2de2CAc3FDWb5CiIuTBD5CiAZnSOtQIv08WJHcDpu2JXZ7MYmJH7qlaTvwcvxCwyktyGfLX7/X2bkv83fW+OwsKiXHaquzfIUQDYMEPkKIBkerUbi1Vwyg8Osh51IWKamzKC1NOZFGp6PPjbcCsOG3nykrqpulJoINelp6mgBYI8tXCHHRkcBHCNEg3dQtGoNOw7z9Ueg8eqCqFhIS3nZL06pPf4KiYykvKWbjH3Pq7Nz9js/nszpPAh8hLjYS+AghGqRALyMjOoQDsPLYaACOpc2hqGifK42i0bhqfbbM/63Oan36HW/u+ls6OAtx0ZHARwjRYN3aMxaAmZs98Q8YBqgcOjzdLU2zbr0IimmCpbSUTfN+rZPz9vbzQgEOlJSTXm49Y3ohROMhgY8QosHqEuNHqzBvym0OdhXeDGjIylpMfv6JRUoVjYZe198MwJa/fqOs+Nxrffz0Otp7eQDwtzR3CXFRkcBHCNFgKYrCrT1jAJixAcLDnU1eBw+97raAaYuefQiMiqG8pJgt8+tmhFcfae4S4qIkgY8QokEb1TkSD72WAxlFFOjuQKMxkJf3Dzk5q1xpnLU+NwGw+c9fKS8pOefz9vN3Ll+xWkZ2CXFRkcBHCNGg+Zj0XNMxAoDvNpUTFXk7AIcOvY6qnlhPq0XvfvhHRFFWXMTWBX+c83l7+XqiVeBImYXksrpbFkMIcWFJ4COEaPCcc/rAvJ1p+AbfjVbrRWHRLjIy5rnSaDRaV63Pxj/mYCk9t1ofL52WTt5mQJq7hLiYSOAjhGjwOkT50S7SB4vNwa/bi4mNuQeAQ4en43CcGHXVqk9//MMjKCsqZOvCedVlV2PS3CXExUcCHyFEo3BLD+fQ9tnrk4iKugu9PoDS0iMcPfajK41Gq6XndSdqfazlZed0zoqJDNfkFbl1phZCNF4S+AghGoVrOkXgadByOKuYDUllxDV5EICEhHex208sUtq630B8gkMpLchn1/Il53TObr6eGBSFo+VWEkqln48QFwMJfIQQjYKXUce1nSMBmPVPEpGRN2MyRWGxZJCcMsOVTqPV0m3EtQBs/HMODru91uf00Gro6uvs57Na+vkIcVGQwEcI0WjccnxOnwW70sgpgaZxkwE4cuQjrNZ8V7p2g4Zi8vYhPz2N/f/8fU7n7Ovn7OcjExkKcXGQwEcI0Wi0jfClY7QfVrvKjxtTCAu7Bk/P5thsBRxJ+tSVTm800fmKEYBz5fZz6Z9TsW7X6lzp5yPExUACHyFEo1Ixk/Ps9Umoqob4po8BkJLyNRZLlitdpyuGozMYyUg4RNLObbU+XxcfMx4ahWyrjb3F59ZZWghx4UngI4RoVEZ2iMDbpCMpp4TVB7MICrocH+8O2O0lJB752JXO7ONL+8HDAGetT20ZNBp6+B5fvkKau4Ro9CTwEUI0Kh4GLdef1MlZURSaNn0UgNTUmZSVHXOl7Tr8WhSNhiPbt5CReLjW5+x7vLlrjcznI0SjJ4GPEKLRuaWnc06fRXvSySgoIyCgH36+3XE4LCQe+cCVzjcklJa9LwPOrdan7/H5fNbmFeGQfj5CNGoS+AghGp2WYd50jfXH7lD5aXOKW63P0aM/UFqa5Erb/Rrniu771q4iPyOtVufr4G3GU6sh12Znd1HpmQ8QQjRYEvgIIRqlm7pFA/DjxhRUVcXfvwcBAZehqjYSEt51pQtp0pTYDp1RHQ42zfu1VufSaxR6+noC0s9HiMZOAh8hRKM0vEM4ngYtCVnFrE/IASD+eK3PsbS5FBcfdKXtNuI6AHYtX0x5Se0WL+17fN2uv6WfjxCNmgQ+QohGydOoY0SHCAC+35gMgI9PB4KCLgccHE5425U2tkNnAiKjsZSWsmv5olqdr6Kfz7r8IuzSz0eIRksCHyFEo3Vjd2dz17wdxygoc67S7qz1UcjImEdh4W4AFEWhy1XXALBl/h84HGe/jEV7bw+8tRoKbA52FEo/HyEaKwl8hBCNVpcYP5qFeFFmdfD7tqMAeHm1JDRkOACHE95ypW3TfxAmTy/y0o+RsGXjWZ9Lqyj08pP5fIRo7CTwEUI0WoqiuDo5/7Ah2bU9Lu5hQENW1hLy87cCzmUs2g+5AoDNtezkXNHc9bcsWCpEoyWBjxCiUbuuSyQ6jcK2lHz2phUA4OnZlPDw6wE4fHi6K22nK4ajaDQk7dxOZlLiWZ+rYiLDf/KLsTqkn48QjZEEPkKIRi3Iy8jlrUMB+GFDimt7XJOHUBQ9Obl/k5u7DgCfoBCad+8NwJa/fjvrc7X18sBPp6XY7mB7Ye1GhwkhLiwJfIQQjd5Nxzs5z9mSQrnN2XHZwyOKiIibADh0eLprZfXOVzs7Oe9ZtZySgvyzOo9GUeh9vLlrjfTzEaJRksBHCNHo9W8RTJiPidwSK4t3Z7i2xzWZiEZjJD9/Ezk5KwGIbNmGkLh4bFYLO5YsOOtzVTR3yXw+QjROEvgIIRo9rUZhTNco4MScPgBGYyhRkbcBJ2p9Th7avnXhn9httrM6V0UH53/yi7E4HHVRfCHEeSSBjxDionDj8dFdqw5kkpp3Yp6d2NgJaLVmCgt3kpm5EICWffpj9vWjKCebQ5v+OavztPQ0EaDXUupwsLVA+vkI0dhI4COEuCjEBJrp3TQQVYWfNp7o5GwwBBIdNQ6AhMR3UFUHOr2e9oOHAbBt4Z9ndR6NotBH5vMRotGSwEcIcdGo6OT846ZkHCcNN4+JuRut1ouior1kZjmXrOgw5EoUxTm0PTs1ucr8qiPrdgnReEngI4S4aFzZLgxvk46U3FLWHMp2bdfr/YiOugOAhIR3UVUHPsEhxHXpBsD2RX+d1Xkq+vlsLCimXPr5CNGoSOAjhLhomPRaRnVyX7i0QkzM+OO1PnvIyloMQKdhzqUtdq1YgrWsrMbnaW42EmzQUeZQ2ZQv/XyEaEwk8BFCXFRu6hYDwIJdaeSXWF3b9Xp/oqNuB+BwwruoqkqTDp3xDQ2jvKSYvWtW1vgcils/H1m+QojGRAIfIcRFpV2kD63CvLHYHPy+/ajbPmdfH0+KinaTlbUYRaOh4+VXAc6h7RWTHNbEiXW7pJ+PEI1Jow18pk6diqIoTJ482bVNVVVeeOEFIiIi8PDwYODAgezatevCFVIIcd4pyok5fX7alOK2T6/3J8qtr49K24GXo9XryUg4RNqh/TU+T8VEhpsLSii1Sz8fIRqLRhn4bNiwgU8++YQOHTq4bZ82bRrTp0/nvffeY8OGDYSFhTF06FAKC6UqWohLyahOkWg1CluT8ziU6V4jExM93jmvT9EusrKWYPbxpWWvfgBsWzivxudo6mEkzKDHoqpsKiiu0/ILIeqP7kIX4GwVFRVx66238umnn/LSSy+5tquqyltvvcUzzzzD9dc7V2X++uuvCQ0NZdasWUyYMOFCFRmAuxfcTXKhe2dLBcX9ueL+/FRnSn+2+yvlf6b8zra8Z5lfTY4513OcNr0CGjRoFA1ajdZ5r1S+VxSl8vZT0us1egxaAwaNAb1Wj0FjcD7XGtz2GbQGTDoTnnpPPHWeeOg98NR7YtKazvj6iuoFexsZ2CKYJXsz+HlTCk9c2cq1z2AIICrqDo4c+YiExHcIChpCx2FXs3vVMvatWcWA2+/Gw9vnjOdQFIW+/l78nJ7L37lF9Ds+xF0I0bA1usDngQceYPjw4Vx++eVugU9CQgJpaWkMGzbMtc1oNDJgwADWrFlTbeBTXl5OeXm563lBQUG9lDujJINjxcfqJW9x8dEoGsw6M2a9GU+9J94Gb/yN/vib/PE3+uNn8nPeG/0I8Qwh3DMcf6O/BEsnGd01iiV7M/hlcyqPDWuJVnPitYmJvpuUlBkUFu4iK3sp4c0HE9ykKZmJh9m1fDHdRl5fo3P09Tse+MhEhkI0Go0q8Pnuu+/YvHkzGzZsqLQvLS0NgNDQULftoaGhHDlypNo8p06dypQpU+q2oFWYPnA6FrvF9VzFvRPlqZ0qK+0/Q/pTnXX+Z9hfKf9zLG+V+aunPj2/ZXaoDlRVxa7acaiOqu8dDhw4nM8dVaezOWzYHDYsDgsW+/Gbw4LVbnXbZnVYsdgtlNnLKLYWU2ItocRW4ipLkbWIImvN/6EatUbCPcMJ8wwj3DOccM9wmvg2oalvU5r4NsGoNdY4r4vBkNYh+HroSSsoY82hLC5rHuzaZzAEEBV5O0eSPiYh4R2CAgfTaejVLPr0PbYvXUjXEdfVKIis6OezpaCEYrsdT6223q5HCFE3Gk3gk5yczMMPP8zChQsxmUzVpjv1y6piUcLqPP300zz66KOu5wUFBURHR597gU/R3L95necpLj4O1UGprZQSa4kzGLI57wssBeSV5ZFbnktuWS555XnkluWSU5ZDRkkGmaWZlNvLSSxIJLEgsVK+CgpR3lE082tG28C2tAtqR9vAtviZ/M77NZ4vRp2WazpG8M26I/y0KcUt8AHnCK/klBkUFu4kO3sZrfr2Z/mMz8g9mkLqnl1EtWl3xnPEmAxEGvWkllvZkF/MwIAzN5EJIS6sRhP4bNq0iYyMDLp27eraZrfbWblyJe+99x779u0DnDU/4eHhrjQZGRmVaoFOZjQaMRovrV/CouHSKBpnfx+9J8EEn/mA4yx2C+kl6aQVp3Gs+BhHi45ytOgoCfkJHM4/TIGlgOTCZJILk1mWvMx1XKRXJF1Du9IrvBc9w3sSYg6pj8u6YMZ0jeKbdUdYsCuNgjIrPia9a59zDa/bOZL0CYcT3qF7t0G06tufHUsXsn3J/BoFPhX9fH5Ic/bzkcBHiIavTgKfgoICli5dSsuWLWndunVdZFnJkCFD2LFjh9u2u+66i1atWvHkk0/StGlTwsLCWLRoEZ07dwbAYrGwYsUKXn311XopkxANhUFrINo7mmjvyrWVqqqSXZZNQn4Ce3P2sjNrJ7uyd3Gk4AipRamkFqXy26HfAIjzjaNPRB+GxAyhS0gXtJrG3XTTIcqXZiFeHMwoYt72Y9zcI8Ztf0zMPSSnfENh4Q6ys5fTfsgV7Fi6kP3//M2guybg4XXmDst9/bydgY/08xGiUahV4HPjjTfSv39/HnzwQUpLS+nWrRuJiYmoqsp3333H6NGj67qceHt7066d+y8wT09PAgMDXdsnT57MK6+8QvPmzWnevDmvvPIKZrOZW265pc7LI0RjoSgKQR5BBHkE0T2su2t7fnk+O7N28k/aP6w/tp7d2btJyE8gIT+Bb/d8S4ApgCExQxgaO5TuYd3RaRpNBbGLoiiM7hLFq/P38vPmlEqBj8EQSFTkLSQlf07ikQ/p2uV7gmPjyDySwJ5Vy+hy1TVnPEef4/18thWWUGSz46Vr3MGiEBe7Ws3js3LlSi677DIA5syZg6qq5OXl8c4777iNtDrfnnjiCSZPnszEiRPp1q0bqampLFy4EG9vGWYqxKl8jb70jezLo10f5bsR37Hq5lW8NfAtRsWPwsfgQ05ZDj/u/5H7Ft3HFT9fwQdbPyCtOO1CF/usXdc5Eo0CGxJzScyqPN9OTMzdKIqB/PxN5OVvpP2QKwDYsWRBjWZyjjYZiDEZsKuwLl/m8xGioVPUs5mj/TgPDw/2799PdHQ0d9xxBxEREfzvf/8jKSmJNm3aUFTUeKt8CwoK8PX1JT8/Hx8faa8Xlyarw8qGYxtYeGQhS5KWkFeeBzj7IPWP7M+NLW+kX2S/RjN8/o4v1rNyfyaTBjfj0WEtK+3fs/cZjh79jsDAAbRq9g4f/+tObJZyxv73NSJanLn5/pG9Scw+lsPE6BCeaxZRH5cghDiDmv7/rlWNT3R0NGvXrqW4uJj58+e75s7Jzc097YgrIUTjoNfo6RPZhxf6vMCSG5bw6mWv0i20Gw7VwfKU5UxcMpEbfr+BhYkLcagNf7mG0V0iAfh5cyoOR+XferEx9wEasrNXYHUcoUWvvgBsX7KgRvn3lQVLhWg0ahX4TJ48mVtvvZWoqCgiIiIYOHAg4GwCa9++fV2WTwhxgRm0Bq5uejVfXvklv177K7e1vg2zzsy+3H08tuIxrvv1Ov44/Ac2h+1CF7VaV7QNw9uoIzWvlHUJ2ZX2m82xhIYOByDxyEd0GHIlAPvWrqK8pOSM+VfM57OjsJQCm70OSy6EqGu1CnwmTpzIunXr+OKLL1i9ejUajTObpk2b8vLLL9dpAYUQDUdT36Y82eNJFoxewIQOE/DWe3M4/zBPr3qaG36/gTVH11zoIlbJpNcyoqNzmoufN6VWmSY29l8AZGT8hV+0iYDIaGzl5ez9e/kZ8w83GmjqYcQBrJPRXUI0aLUKfF588UVat27Nddddh5eXl2v74MGDWbx4cZ0VTgjRMPmZ/Hiw84MsGLOAhzo/hK/Rl4N5B5mwaAIPLXmIIwXVz5Z+oVSs2P7XzmMUl1eunfL2akVQ4GBAJSnpUzoc7+S8fXENm7uO1/qsypXmLiEasloFPlOmTKmyA3NJScl5Wf5BCNEweBu8ua/Dffx53Z/c2vpWtIqW5SnLufbXa3lj4xuUWM/cTHS+dInxJy7IkxKLnb92Vj06LbaJs9bnWNoc4nu1RavTkZF4iPTDB8+Y/2XHFyldkSM1PkI0ZLUKfKpbBmLbtm0EBAScc6GEEI2Lr9GXp3o8xS/X/EK/yH7YHDa+2vUV1/92Pf8c++dCFw9wzulzfWdnJ+efNiVXmcbPtyt+fj1QVSsZOT/QvGdFJ+f5Z8y/n78XCrC/pIy0cmudlVsIUbfOKvDx9/cnICAARVFo0aIFAQEBrpuvry9Dhw7lxhtvrK+yCiEauKZ+Tfnw8g95f8j7hHuGk1qUyj0L7+HFtS9SZLnwNSHXH2/uWnc4h5TcqmujmsTeD0Bq6mzaDOwNwN6/V2ApKz1t3gF6HR28PQBYKc1dQjRYZzUV61tvvYWqqowfP54pU6bg6+vr2mcwGGjSpAm9e/eu80IKIRqX/lH9mTNqDm9uepPv933Pj/t/ZFXqKqb0mUKfiD4XrFyRfh70bhrI2sPZzNmcykNDKi8eHBBwGd5ebSks2oXDawN+YeHkpR1j35pVtB887LT5D/D3ZlthKStzCrkxTGq/hWiIajWB4YoVK+jTpw96vf7MiRsZmcBQiLq1IW0Dz/39HClFKQCMbzeeBzs/iF5zYb4/ftqUwuM/biMuyJOljw2ostk+PeMvdu58EJ3OF1PuY6yeNZvwZi255eU3Tpv36txCxmw9RIhBx7Y+bRvNBI9CXAzqdQLDAQMGoNVq2b9/P6tXr2blypVuNyGEqNA9rDs/X/MzN7W8CYAvdn7BuL/GkVKYckHKc1W7MDz0WhKyitmclFdlmpDgYZjNcdhs+QS0ykaj1XLs4D4ykxJPm3d3X088NBoyLDb2FpfVfeGFEOesVoHPunXraNasGa1bt6Z///4MHDjQdRs0aFBdl1EI0ciZ9Wb+r9f/MX3gdLz13mzP2s6Nv9/IwsSF570snkYdV7ULA+DnzVUHX4qiJTZmAgBpmbOJ794VcK7fdTpGjYZefp4ArMiRfj5CNES1Cnz+9a9/0a1bN3bu3ElOTg65ubmuW05OTl2XUQhxkRgaO5Qfr/mRDsEdKLQW8tiKx3h1/avnfdbn0cc7Of+x7Shl1qpnWg4LG4XRGIbFkkGTPmYAdq9aitVSftq8+1cMa5cOzkI0SLUKfA4cOMArr7xC69at8fPzw9fX1+0mhBDVifSK5Ksrv2J8u/EAzNwzk/sW3UdO2fn70dSraSDhviYKymws2ZNRZRqNxkB09DgAitWF+ASHUF5czIF1f5827wEBzsBnXV4R5Y6Gv46ZEJeaWgU+PXv25ODBM0/oJYQQVdFr9DzS9RHeHPgmZp2ZDWkbuPmPm9mdvfu8nF+rUbiuc8XCpdX3NYqMuBmt1ouSkkO0HhoLnHnh0taeJoINOkodKhvyi+uu0EKIOlHjwGf79u2u20MPPcRjjz3GV199xaZNm9z2bd++vT7LK4S4iFweezmzhs8i1ieWY8XHuOOvO/jj8B/n5dzXd3E2d63Yn0lmYdXNVzqdN5ERzk7ZxvDdKBoNqXt3kZ1a9QSI4JwosaK5a6X08xGiwanxcHaNRoOiKFSXvGKfoijY7Y13dWIZzi7E+VdgKeDpVU+zMsU5KnRChwk80OmBeh8OPur9v9mWnMf/DW/NPZc1rTJNWdlR1qwdiKraKd49kgOrDtJ1+LUMvOOeavP9/lgOD+9NoqO3Bwu6tayv4gshTlLnw9kTEhI4fPgwCQkJVd4q9h0+fLhOLkAIcenwMfjw7uB3uae9M5j4ePvHPLnyScrtp+9IfK7GdKlo7qp6xXYAkymCkJDhAIR0dPZD2rVyKTZr9ctS9A9wLli6vbCUXOv57bgthDi9Ggc+sbGxNb4JIcTZ0igaHu7yMC/2eRGdouOvxL+4e8HdZJdm19s5R3aMQK9V2HOsgN1HC6pNFxNzNwAl1nX4RXlTVljAwfVrqk0fbjTQwmxCBVbnXvilOoQQJ5zVkhUVfvvttyq3K4qCyWSiWbNmxMXFnVPBhBCXpuuaX0ekVySTl09mW+Y2bp13K+8PeZ94v/g6P5ef2cCQVqHM35XGL5tTaBPRpsp0Pt7t8PfrRW7eOpoN1rNxhrOTc6u+A6rNe0CAF/tLyliZW8jIEL86L7sQonZqtWRFdf19Tu7n069fP+bOnYu/v3+dFfZ8kD4+QjQMCfkJPLDkAZILk/HWe/PGwDfoHVH3awEu2p3OvTM2EuRlZN3Tg9Fpq64Iz8paxrbt96DRmNn2eQx2i8Jdb35MQERk1flm5XP7jgRiTAb+6dValq8Qop7V65IVixYtonv37ixatIj8/Hzy8/NZtGgRPXr04I8//mDlypVkZ2fz+OOP1/oChBCXtjjfOL69+ls6h3Sm0FrI/Yvv55cDv9T5eQa2DCbA00BWUTmrDmRVmy4wcACens1xOEpoPsQ5O/OOpdUPbe/j54VeUUgqs3C4tH77Kgkhaq5Wgc/DDz/M9OnTGTJkCN7e3nh7ezNkyBBef/11/v3vf9O3b1/eeustFi1aVNflFUJcQvxN/nw27DOGNx2OXbXz/Jrn+XDbh9WOLq0NvVbDNR0jAPjpNHP6KIqGmGjnpIuesQmgUdm1fHG1nZw9dVrX8hWLs6vvPySEOL9qFfgcOnSoymokHx8f16iu5s2bk5VV/a8nIYSoCYPWwNR+U7m3/b0AfLD1A15c92KdLnMx5vgSFot2p5NfUv1ordDQURgMQdjVHMI6OCgtLODghrXVpr880Pk9uUQCHyEajFoFPl27duXf//43mZmZrm2ZmZk88cQTdO/eHXAuaxEVFVU3pRRCXNIURWFSl0k80/MZFBR+2v8Tjy5/lDJb3ayA3jbCh5ah3lhsDv7ccazadFqtkaioOwAI71oIqOxYMr/a9EOOBz5r84opsjXe+c2EuJjUKvD5/PPPSUhIICoqimbNmtG8eXOioqJITEzks88+A6CoqIhnn322TgsrhLi03dzqZqYPnI5BY2BZ8jLuXXgv+eX555yvoihc3+XMS1gAREXegkZjQtUdwyuylKSd28k9VvU8QPEeRpp4GLCqKqtk0VIhGoRaBT4tW7Zkz549/Prrr0yaNIkHH3yQ3377jV27dtGiRQsArr32Wm6//fY6LawQQlweezmfDPsEb4M3WzO3cvtft3O06Og553tt50g0Cmw6kktiVvVrbOn1/kSE3wBAbD9ns9iOpQurTKsoiqu5S/r5CNEw1CrwAecH+sorr2TSpEk8/PDDXHHFFWg0tc5OCCFqrGtoV2ZcOYNQcygJ+QncPu929uXsO6c8Q31M9GseDMAvZ6j1iY6+C1DQ+x3F6FfOzuWLsduq7hs0JKCin09hnXbKFkLUTo0nMHznnXe47777MJlMvPPOO6dNO2nSpHMumBBCnE4z/2bMvHom9y++n4N5Bxk3fxzvDH6H7mHda53n6C6RrNyfyc+bU5l8eQs0mqrn3jGbYwkOupzMrEVEdCsmYXE+Bzf8Q8ve/Sql7e3nhYdGQ5rFyq6iUtp5m2tdPiHEuavxBIZxcXFs3LiRwMDA087KrChKo16vSyYwFKJxyS/PZ9LSSWzO2Ixeo2fqZVO5oskVtcqrzGqn+0uLKSy38d19vejVNLDatLl5G9i8+WZQdeyYEUdk827c8H8vVZn2ju2HWZhdwNNx4TzcJLRWZRNCnF69LFIaGBjoeny6xUqFEOJ88TX68smwT7g85nKsDiv/XvFvftr/U63yMum1XN0+HICfN52+ucvPtxve3u1AsRHUJo+kHVvJS6t6RJj08xGi4TinTjkWi4V9+/Zhs8nqw0KIC8eoNfL6gNcZ02IMKipT1k7hi51f1Cqv0cfn9Jm34xglluq/2xRFISbauXhpaMdCFI2j2pmcBx8PfDYVFJMjq7ULcUHVKvApKSnh7rvvxmw207ZtW5KSkgBn357//e9/dVpAIYSoCa1Gy3O9nuPuds5g5M1Nb/LWprfOukNx9yb+RAd4UGyxs3BX+mnThoRchdEYhsZQhl+zgmo7OUeZDLT2NOEAlufIsHYhLqRaBT5PP/0027ZtY/ny5ZhMJtf2yy+/nO+//77OCieEEGdDURQmd53M5C6TAfh85+f8d91/sTtqPnmgoihc39lZ63OmOX00Gj1Rkc5pO8I651OSn8uhjf9UmVZmcRaiYahV4DN37lzee+89+vXr57bicJs2bTh06FCdFU4IIWrj7vZ383zv51FQ+HH/jzy16ims9uqXojjV6C7OwGf1wSyO5ZeeNm1k5M1oNB4Y/Urwiihh26J5VaarmMV5WU4BdhnWLsQFU6vAJzMzk5CQkErbi4uL3QIhIYS4UMa0GMO0AdPQaXTMT5zPpGWTKLWdPoipEBNopkeTAFQV5m45/eSIer0f4eGjAQjpkEPSzu1kpyRXStfNxxNfnZYcq50tBSVnf0FCiDpRq8Cne/fu/Pnnn67nFcHOp59+Su/eveumZEIIcY6ubHIl7w5+F5PWxOrU1Tyw5AFKrDULOk5ewuJM/YRioscBCj6xRRh9y9m2uHKtj06jMDDAG4BF0twlxAVTq8Bn6tSpPPPMM9x///3YbDbefvtthg4dyldffcXLL79c12UUQoha6xfZj0+GfYKn3pMNaRu4f/H9FFurX5KiwtUdwjHqNBzMKGJ7yunXAzOb4wgKGgxAcPscdi1fgqWscu3SFUG+APyVee7riwkhaqdWgU+fPn1Ys2YNJSUlxMfHs3DhQkJDQ1m7di1du3at6zIKIcQ56RzSmU+Hfoq33pvNGZuZsGgChZbTj67yMekZ1jYMOPMSFgAx0eMBCGhZgN1RwN7VKyqlGRLgjU6B/SVlHCqpm5XlhRBnp1aBz6233sratWt55pln2LlzJ7t372bmzJm0b9++rssnhBB1on1wez694lN8DD5sy9zGhEUTKLCcvslp9PHmrt+2HcVic5w2rZ9fT7y82qDROQhsncfWBX9UaiLz1evo6+ds7pqfJc1dQlwItQp8vLy8eOONN2jdujURERGMHTuWjz76iL1799Z1+YQQos60DWzLZ8M+w8/ox46sHdy78F7yy6tvdurXLIgQbyO5JVaW7s04bd7OCQ3vAiC4fS5ZKQkc3benUrorgyuau/JqfyFCiFqrVeDz8ccfs3fvXlJTU5k+fTq+vr68/fbbtG3blvDw8LouoxBC1JnWga35bNhn+Bv92Z29m3sW3kNeWV6VaXVaDdd2dtb61KS5KzR0BAZDCHqzDb/4ArYu/LNSmiuDKmZxLiG9vOZD7IUQdeOclqzw9vbG398ff39//Pz80Ol0hIWF1VXZhBCiXrQMaMkXV3xBgCmAvTl7Gb9wPDllOVWmrZjTZ9m+DHKKLafNV6MxEBV1GwDB7bPZv241xXm5bmnCjQY6e5tRgQVZ0slZiPOtVoHPk08+Sa9evQgKCuL//u//sFgsPP3006Snp7Nly5a6LqMQQtS5Zv7N+PKKLwnyCOJA7gH+tehfVfb5aRnmTbtIH6x2lblbUs+Yb2TEWDQaE+bgcjxCCti5bFGlNFdVNHdJ4CPEeVerwOe1114jISGB559/nhkzZvDGG29wzTXX4OfnV8fFE0KI+tPUr6mr5mdPzp5qh7rf0DUagO83JJ9xTh+DIYDwsOsA54SG2xb9hcPuvmTG1ccDn1W5heTKoqVCnFe1Cny2bNnCM888w/r16+nfvz9hYWHcdNNNfPjhh+zZU7kznxBCNFRxvnF8MvQTfAw+bM/czkNLH6LM5j7U/NpOkRh1GvalF7I1Oe+MeUYf7+TsE1tEuTWVgxvWuu1vZjbR2tOETYX5UusjxHlVq8CnY8eOTJo0iV9++YXMzEwWLFiA2Wxm0qRJtGvXrq7LKIQQ9aplQEs+Hvqxa5LDycsnY7Gf6M/ja9YzvL1z4MZ36ysvR3EqT894AgMHoijOCQ03zfutUpqRIX4A/J6RVyfXIISomVp3bt6yZQtvvvkmo0aNYtCgQXzzzTd07NiRRx99tC7LJ4QQ50W7oHZ8MOQDPHQe/J36N0+ufNJtVfebe8QA8Pv2oxSVn7l5qmJCw8CWeaQn7CDt4H63/SOD/QBYlVtEnjR3CXHe1Crw8ff3p0ePHnz77bc0b96cGTNmkJOTw8aNG3nttdfquoxCCHFedAntwtuD3kav0bM4aTEv//Oyq09P9yb+xAd7UmKx89vW0y9cCuDv3wcvz5Zo9CqBrfPYNO9Xt/3NPU208jRhVVVp7hLiPKpV4PPNN9+QnZ3Nxo0bef311xkxYgQ+Pj51XTYhhDjvekf05tX+r6Kg8OP+H/lo20eAc4LCm7s7a32+25B0xnwURSH6eK1PULsc9v+zisKcLLc017iauyTwEeJ8qVXgI4GOEOJiNjR2KM/0fAaAD7Z9wA/7fgCcK7brtQrbU/LZdfTMwUpY2EgMhiAMXjZ8YnPZusB9QsOK5q6VMrpLiPPmnCYwFEKIi9VNrW5iQocJALz8z8ssSVpCoJeRYW2ck7R+v+HMnZw1GiORkc4JDUM65LB98V9Yy0+MGGvuaaLN8eauP2XFdiHOCwl8hBCiGg90eoDRzUfjUB08seIJtmRs4eYezjl95mxJpdRiP0MOEBU5Fo1iwBxShtYrg90rl7ntvz7UH4Cf06ueOVoIUbck8BFCiGooisL/9fo/BkYPxOKw8PDSh4kJKSU6wIPCMhvzdhw7Yx4GQxBhYdcCENwhh83zfkV1nFjp/brjgc/avGJSy06/JIYQ4tw1msBn6tSpdO/eHW9vb0JCQrj22mvZt2+fWxpVVXnhhReIiIjAw8ODgQMHsmvXrgtUYiHExUCn0TGt/zTaBrYltzyXB5c+wLVdnMFKTTo5w4kJDX2bFFJUdJjE7SeW9ok0Gejl6wnAnPTcKo8XQtSdRhP4rFixggceeIB169axaNEibDYbw4YNo7j4xPTy06ZNY/r06bz33nts2LCBsLAwhg4dSmFh4QUsuRCisfPQefDu4HcJNYeSWJDI1vJ30GrsbEjMZc+xyut7ncrLqwUBAZc5JzRsl8PG33922z86zBlI/SKBjxD1rtEEPvPnz2fcuHG0bduWjh078uWXX5KUlMSmTZsAZ23PW2+9xTPPPMP1119Pu3bt+PrrrykpKWHWrFnV5lteXk5BQYHbTQghThVsDub9Ie9j1pnZmrWRuFYLAJUZaxNrdHzFhIYBLfNJ2b+FYwdP1FiPCPZDryjsLi5jT1FpPZReCFGh0QQ+p8rPd46ACAgIACAhIYG0tDSGDRvmSmM0GhkwYABr1qypNp+pU6fi6+vrukVHR9dvwYUQjVbLgJa8NuA1NIqGdHUlhsAVzNmSSn6J9YzHBgRchqdnc7QGB4Gt81g/9yfXPn+9jiGB3gD8mCa1PkLUp0YZ+KiqyqOPPkq/fv1ca4OlpaUBEBoa6pY2NDTUta8qTz/9NPn5+a5bcvKZh6gKIS5d/aP682T3JwEwBi/AatzFDxvP/L2hKAox0XcDzvW7Dm36m+yUE8fdFOb8EfdTeg42x+lXgBdC1F6jDHwefPBBtm/fzuzZsyvtUxTF7bmqqpW2ncxoNOLj4+N2E0KI07ml9S3c2OJGUFQ8Ir7jyw3/YK9BsBIWdg1GYxgGTxv+LfLZ8NuJvj6XB/oSqNeRYbGxNEea3IWoL40u8HnooYf47bffWLZsGVFRUa7tYWHOScVOrd3JyMioVAskhBDn6qkeT9ExuDOKtpw870/5a9fhMx6j0RhdfX1CO2Wz5++lFGRlAKDXKIw5PrT9+zSZ00eI+tJoAh9VVXnwwQf55ZdfWLp0KXFxcW774+LiCAsLY9GiRa5tFouFFStW0KdPn/NdXCHERU6v1fPWoOl4aALQGjOZuvF5HKrjjMdFRNyMTueH0deKT0wem/6Y69p3c7izuWthVgHZFlnCQoj60GgCnwceeICZM2cya9YsvL29SUtLIy0tjdJS5wgIRVGYPHkyr7zyCnPmzGHnzp2MGzcOs9nMLbfccoFLL4S4GAV5BDG17xuoDh0Fmm28subtMx6j03kSHXUHACGds9i+dD4lBc7BGq29POjg7YFVVWVouxD1pNEEPh9++CH5+fkMHDiQ8PBw1+377793pXniiSeYPHkyEydOpFu3bqSmprJw4UK8vb0vYMmFEBezIU27Ea8ZB8D3B79gyZElZzwmOvoONBoPzEHleITksGX+H659Y8MDAfjmaDaqKp2chahriiqfLDcFBQX4+vqSn58vHZ2FEDWy6kAm9/zxLIaAvzHrzHx79bc082922mP2H3iJ5OQvKTpqJmVpW+557wuMZjMFNjsd/95FqcPB3M7N6OXndZ6uQojGrab/vxtNjY8QQjRU/ZoFEem4AVtxU0psJTy87GHyy0+/2npM9N0oih6viBI0Xhls/utXAHx0WkYf7+T8dWpWvZddiEuNBD5CCHGOFEXhrr7NKEu9FcUeQFJhEk+uehK7o/rV202mcNfipaGdstn4+xxKi5zL69we6Wzu+iMzn0zLmSdHFELUnAQ+QghRB8Z0icLf5E/RkdvQK0b+Tv2bD7d9eNpjYmPuAxR8mxShNeew6Y85AHT0NtPJ24xVVfn+mAxtF6IuSeAjhBB1wMOg5fZesTjKI/ArGQvAx9s/Znny8mqP8fRsSmjIcADCumWyed5vrhFedx6v9fn6aDZ26YopRJ2RwEcI0eCoqtooRzTd0TsWo07D4cRWDA6/HoCnVz3NkYIj1R4TFzcJ0ODbpAiddy7rf3Wu4TUqxJ8AvZbkMgsLsk7fX0gIUXO6C10AIcSlR7U7sB4rxnqsGFtuGfa8cud9bjmOUhuq1Q4OQKug6DVovQ1ovQ3oQszoIzwxxvigCzWfdjmaCyHQy8gN3aKYuS6J3NQr6ByWwJaMLUxeNplvr/4Ws95c6RhPz3jCwq4hLW0uYd0y2bbgT7oNvxavgEBujwji7SPpfJKcydXBfuf/goS4CMlw9lPIcHYh6p69wIIlqYDypEIsSQVYU4tQrWee5fh0tD4GTK0CMHcJwRDr02CCoCPZxQx6fTkOFb65rxXPbbyHrNIsrmxyJdP6T6uynCUliaz7Zxiqamf/3FhadLqBIePv51i5he5rd2NTYUG3FnT0rhw4CSGcavr/W2p8hBB1yl5QROnOJMoP52LNsGLPA9WirZxQY0djtqL11aELMGOICUIfHYTWU4/GoAWNgupQUS12HIUWbHnlWNNLsKYWYTlSgL3AQvH6NIrXp6EL9sCrXySeXUNRdBe2BT820JNRnSKZsyWVb//OY/oV0xk/fzzzE+fTPqg9d7S9o9IxZnMTwsKu59ixHwnrmsX2BQvoPnI04cEhjArx5+f0XD5JzuT9NrEX4IqEuLhIjc8ppMZHiOqpqoqjoADr0aNYjx7FknIUa0omtkwb9lIjaALReEWgaPWnHOfAUXAUe85h7LmHceQcxlGUDrh//Wi8vDC2aIFHx454dO6EZ+/eaKuYeV21OihPyKdkWyalOzJRLc7aI62vAZ8hsZi7haJoLlwN0IH0Qoa+uRKAhY/0Z2PO70xdPxWtouXTYZ/SPax7pWNKS1NYu24IqmrjwK+xNGl5DVdOnMy2whKu2LgfnQLre7UhwmQ435cjRKNQ0//fEvicQgIfcSlTVRV7drYrsLGmpmJNPXrieUY+iiEYrV8MGv8maP1i0Xj4V87HUoxafgzVnoOiyUdjKkPraQRFg2q3gc2GarPjKCvDnp2NLSMD67FjcOrXkU6HuUsXfIYPx+eqK9FW8Zl0lNso3pBO4coUHAUWAPRRXviPaoYh+sItV3P/zE38tTONaztF8OZNnXhm9TP8fvh3AkwBfD/ie8I8wyods3fv/5F6dDaFqWYO/dmE2//3NiFNmnLdlgOszSvmvqhgXmweeQGuRoiGTwKfWpLAR1ysVJsNW3Y2trQ0rGnp2NLTsaanYUs7fp+egS09HdViAUWLxjMYjXe48+YXg9avCRpzQFU5o5gs6IP1GOL88GgbiSEm8Kz73DjKy7EmJVG6axel27ZRsu4fLAkJrv2KwYDPVVcRMP4uTC1bVi6F1UHRumMULD6CWm4HBbwHReMzJAZFe/6bv3am5jPi3dVoFFj4yAAiA7TcPu929uXuo0NQB7688ksMWvfam7Kyo6xZOwRVtXDw9xgCAvsy5pn/siK3kJu3HcZDo2FD7zYEGaSXghCnksCnliTwEQ2Zw2LBUVyMo7jk+P2Jm70gH3tePvbcXNfNlpeLPTcPe14ejoIC98y0BhQPfzQeASgeAWi8QtB4h6P1DkPxDEZRquiXA+gCTRiivdFHemOI9EIf5eXsk1MPLMnJFC5cSP7cXyk/cMC13bP/ZYQ88gim1q0rHWMvtJD352FKt2YCztqfwFtaowsw1UsZT+feGRtZtDud4R3Cef+WLiQXJnPzHzdTYCnghhY38Fzv5yods2//C6SkfENJhgf758Ry/VNTaNKpK1ds2s/2wlIeiQ3lyabh5/1ahGjoJPCppfoKfMq+eQK1MPPEBvWkX8OKwom+Die2q2rFvooNuB/j9qc7Nb/qjnEvl1rFed1UOuak81b7i/7U7cqJOVlOV7ZTqVXsq3SMAorGdVPRgEZzfN/xe0V7UpqTt1ds04JWBxo9aHTOm1YPGq1rm6poAS2oKqrdAarDee+wo9rt4HA47+0OVMcZ7i0W583qvHeUW05sO/lmteCwWFAtVtSSEuwlJWA9w/IFWgOK3gPF4I1i9EYxeKEYvY4/90Lx8EPrHYJi8kfRepw2K8WoRRfsgT7YOYTcEOmFPsILjen81zaoqkrZjh3kfPUVBfMXgMPZp8fnmpGE/vvf6IKDKx1Tsj2T3DkHUUttaMw6Asa2wtS8crNcfdpzrICr31mFqsK8SZfRJsKH1amrmbh4IioqL/Z5keuaX+d2THl5JmvXDcZuLyFxUSSa8rbcMe1d5ucWcffORHx0Gjb1bou3rn6CTSEaKwl8aqm+Ap9DvdpgyZOX+vyoQRNLlUFbDY9TTgqc0KCcHFSdFIQpJz0+eX+l7RotiuZ44KXVoRwPthRtRSCmR9FW3BtAZ0IxmlEMnih6E4rOBFojKHpn3mfzShm0aP2MaP2M6AJM6EPM6EKcwY7Gx9BghoifzJKURObb71Dw558AaLy9CXnsMfxuvAFF4379trxysmfuxppSBAr4XROPV++I81reh2Zv4fdtR7m8dSif3dkNgI+3fcx7W9/DoDEw46oZtA1q63bM4YR3SUh4C0uhkT3fNWHAbRPofPU1DFi/lwMl5TwZF8YjTSr3ERLiUiaBTy3VV+CT/PB3oPM7aUsV/1Bq9E+mquNqkKZG+dTyuBrlffb5nO0/cXGcAhpPPRpPvXNo+Ek3rbcerZ8Jra8RnZ8RxaRtkMFNTZTu2EnalCmU7dwJgGffvkT8b2ql2h/V6iB3zgFKNmcAx/v9DIs9b9d9KLOIodNX4FDhl4l96BLjj0N18PDSh1mespxwz3C+H/E9/qYTtVF2ewlr1g7GYskkdU0o+QejGP/Wxywsh/t3H8FHp2F9rzb46aWvjxAVJPCppfoKfNLe2Igts7TO8hMNjQqK86YoKs5phx0oqh2wOe9VGygOFByA3bkfh3M/VhTFioIV9DoUkxnFwxPFyw/FJxi8/FH0WhSdBo1Ji2LUojFqUYw6573pxHNFr7mgQ7nPJ9VuJ/fbb8mY/iZqWRnagAAiXn0Vr8v6uadTVQqXJlOwyLl0hLlbKP7XNz9vr9MTP23jh40pdI3156d/9UZRFAothYz9cyxHCo7QM7wnH13+ETrNiUAmNfU79u57BodVz65ZTWjd+yqGTpjEkA372FNcxqSYEP4Tf35rr4RoyCTwqaX6Cnxs2aWojhq81Kf8Cq3ya7lGlS41abapybaa1N7UtEJHqUGamuRTVbqa1CDVIp8qXyPF2cqlKKBRnN2MapK53QbFmVCQCvkpzltBKuQnOx/nHIayatZkMvpCREeI7Apx/SG6FxhkFt8K5QcPkvrY45Tv2wcaDSGPPUrA+PGV/i7F69PInXMAVDB3CcF/TIvzEvyk5Zcx6PXllFrtfHhrF65q7+ycfCD3ALfOu5VSWynj243nka6PuI5xOGxs2DiKoqK9ZO3yI2V1ODe/+Bo7AiIYtzMBD42G9b1bE2zQV3daIS4pEvjUkozqEheMqjoDo8x9kLUfMvfC0a2Qth1sZe5ptQaI7glxA6DFMAjrUMPI7uLlKC8n/aWXyfvxRwB8Ro4k/OWX0Bjch4yXbMsk5/u94ABz5xD8bzg/wc/0Rft5Z8kBYgPNLHpkAIbjM0zPT5jPv1f+25lm4HSGxg51HZOb+w+bt9yCqsL+n+PwNLfk1qlvMXJbAlsKSxgfGcQrLaLqvexCNAYS+NSSBD6iwbHbIHMPpG6GpHWQsMJZU3Qy/zhoey20GQXhnS7ZIEhVVfK++460l18Bmw1zr15EvfcuWi8vt3QlOzLJmb0PHCqevcPxuya+3vv8FJfbGPDacrKKyvm/4a2557Kmrn2vbXiNGbtnYNaZmT18Nk39TuzbsXMSGRl/UpLhzf45kfQbOw5L/yu5YdshdAos696K5p7nf6i+EA2NBD61JIGPaPBUFbIPQcJyOLQMDi52rxHyj4Oud0Ln28Ez6IIV80IqXrOGlAcfwlFSgrF1a2I+/QRdkPtrUbItg5zv9oEKPsNi8RkcU+/l+m59Ek/9sgMvo46ljw0gxMcZsNgcNu5bdB8b0jYQ5xvHrKtn4WVwBmtlZUdZu24YDkcpR5aFU5gQwp2vv8+kjFIWZhdweaAPMzs0Pd1phbgk1PT/twybEaKxURQIagbd74Gbv4V/H4IxXzpre3QekJsAi1+A6a3h53sh6Z/KS0Fc5Dz79CFmxgy0gYGU79nDkTvHYc3IcEtj7hiC3whnwFCw8AhF64/Ve7lu7BZNx2g/isptTP1rr2u7TqPjtf6vEWIOISE/gWf/ftY195XJFEFc3EMARPfLRtWWsOCjt3muaTg6BRZnF7Asu6DK8wkhKpMan1NIjY9o1CzFsGsObPgcjm4+sT2iCwx6BpoNuaSawSyJiRwZdxe2tDQMTZoQ8/XX6END3NLkL0ikcFkyKBB4a2s82lVfS6Y6VDKSCklPyCcjsZCC7FJKC61Yy+3oDBoMJh2+wR74h5kJi/clorkfOr37RIPbU/IY9f7fqCp8f18vejYNPLEvczvj5o/D6rDyYKcHmdBxAgAOh5UNG6+lqGgvuQcDOLIklEHj7uPXZl35OCWT5mYjS7q3xKCR37Li0iVNXbUkgY+4aKRugg1fwM6fTjSFRfWAQU9D00GXTABkSU7myJ13Yjt6DEOzeJrMnInWz8+1X1VV8n45SPGGNNApBI9vj7Gpr1semUmF7Pn7KIe2ZlKSb6nxubV6DdGtA2jdJ5zY9oFoj68Z9p85O5j1TxLNQ7z4Y1I/jCfNwvzj/h95ce2LALx62atc3fRqAPLzt7Bx0w2AysE/YijLDGDU1LcYnphPttXGf5qGMyk2tJavkhCNnwQ+tSSBj7joFGXC32/Bhs9OBEAxfeDKVyCi8wUt2vliSUnlyK23YktPx6NjR2K+/AKN+cR0AKpdJfvbPZTtzkZj1hHyQCe0ASYSt2exZVESxw6emGbA4KEjvJkvoU188As1Y/Y2oDdpsVkdlJfYyEsrITu1iJS9ORSfFCSZfQ10HBJNu/6RlDgcXD59BVlFFiYNbsajw9wXXZ22YRrf7P4GvUbPZ8M+o0toFwD27XuBlNRvsJeb2fVtFKGxbdA98DST9qVg0iis6NGKWA9jPb+aQjRMEvjUkgQ+4qJVmAar34KNX4C93LlcRvd7YPD/gcn3jIc3duUHDpB42+048vPx7NeP6A/eRzlpqLtqtZPx8Xbn8hZ+RjagcDSxEACNRiG+awgte4YR1cofre7MTUqqqpKdWsz+f9LYu+4YpYXOddaMnjq6XtmEo8FaHvhuKzqNwm8P9qNNxInvG7vDzmMrHmNJ0hJ8jb58e/W3xPrEYrMVs379CErLksg7GETikmB6XHcj77Tqw6rcIgYFeDOrQ9NGOxu3EOdCAp9aksBHXPQKjsLCZ51NYABeoXDFK9Bu9EXf/FW6dStH7hqPWlqKz9VXEfHaayjaE81MhSmFZH24Db1dJd3qYJNVpf2gaDoMisLTr/Y1KXabg/3r09g0/wj5Gc4Z3H2CPdgepPDjsWzaRvgw94G+6LUnAqpSWynj549nZ/ZOYrxj+Pbqb/Ez+ZGbu57NW24BVA7/FUVBsg89n3qJW/Oh3KHyZqtoxoYHVlMSIS5eEvjUkgQ+4pJxeDn8+RhkH3Q+jx8C134A3hf34pdFq1aTPHEiWK343zKW0GefBWDXqqOs+eUgZqudfl46dIqCsXsowaNb1Nm5HQ6VvWuP8c+vhykpcDaDHTA5WGAo567B8TxxZSu39FmlWdz6560cLT5K55DOfDL0E0w6E/sPvExy8heoNg92zYrCZAqh+JEXmXY0Dy+thqXdWxIjTV7iEiPD2YUQp9d0INy/xtnUpTPBoSXwYR/Yv+BCl6xeeV3Wj8hX/weKQu6s2SR/9A2/vb2VFbP2YS2zY4rxwXRlHADlG9IpXp9WZ+fWaBTa9I3g1hd70XloDIoCzcs03FVoYsmiBNYczHJLH+QRxAeXf4C33pstGVt4dPmjWO1W4ps+hpdnSxRdKfFXZVOUm034dx/T3cdMkd3Bw3uTcMhvWiGqJIGPEJcynRH6/xsmrIKw9lCSDbNuhHlPgLXszMc3Uj5XX03oU0+SGdSBvzYGkLI3F51eQ78bmzP6310JGxSNz9BYAHLnHqT8cDVrqNWSwaSjz+hmjH6yGwERnniqCtcUG5n7/jaOpRe7pY33i+fdIe9i0ppYlbqKp1Y9BYqedu3eQaPxwCM4h/BuBRzdvYPb9q/HrNWwNq+Y95Myqjm7EJc2CXyEEBDcAu5ZAr0mOp+v/xg+HQxZBy9sueqJzWpnp7EXO9pNwKY341OUxLVjA+k4ONq1bpf34Gg8OgaDQyX7293Ycus+EAxt4sONT3en4xUxOIDYUoXvXlpP0p4ct3RdQ7vy1qC30Gl0LDyykBfWvoCHuSktWzzvzKfrMbwii0mZ+x0TVWeH7P8lHGNtXlGdl1mIxk4CHyGEk84IV06FW38Cz2DI2AWfDYZDSy90yepUXnoJP0/bxI7lzvXO4u276bLpdQr+M8ltdmdFUfAf3Rx9pBeOYhvZX+/GYbHXeXm0eg39rmtGj/vakKN1YLCq/Pb2Vtb9egiH3eFK1zeyL6/1fw2NomHuwblM2zCNsLDRhIeNBlSaXZ2JwduC9pPXGO6hwa7Cv3Ylkmmx1nmZhWjMJPARQrhrPhT+9bdz9fey/2/vzuOjru79j7++s2Ym22RfyMK+bxIREFCooIhsAopYEVB7S73+2tv29v7a2l9te+9t773a295rVXCpUMWCIFgUlYqyI4ggCARZAiEJ2ddJJpntO9/fHwOBsCWGCTPJfJ6Px5iZ73rmyEzeOd/zPacO3pwLe5d1iWkvju8tZfVv91FZ2EBElJFpTw1j8u8fJaJHNt7SUoqe/Ed8TU3N2+tMehIWDEQXZcRT6qBmzQk66n6QUSNSGbiwL1+ZvCjA/g/Psv73X2KvulieSdmT+Nex/wrAymMr+eOXf6Rv398QEz0UdE30m1UNuBny+u/pY9ZT5vbynSP5uH2+q59UiDAkwUcIcaXoFFj4Hgx7GDQVPvwXeO8H4G37qMWhxO308snyXDa/novXpdKtr42HfnEb2YMT0MfEkLn0JfQ2G84jRyj+6c/QLgkKBpuZhEcGgF6h6XAl9Z8Wdlg559yWRdqkbmywunEpGqWn63j73/eRd+BiS9SMXjN4etTTALx+5HV+t+9ZBg3+EyZTInprDX1nVKPWVTL1/RVE6RT21Dn4yfGiDgtsQnQ2EnyEEFdnMPtvb7/73wAFDqyAN2f7W4E6kcqiBtb87gu+3lOKosBt03sw459uaTEujykri4w/PQ9GI/WbNlH5wostjmHuHkvcrN4A2D8+S9ORlndfBdLT9w0gZXA8K6JclJs0XI1ePnr5CFtXfo3n/KW2h/o/xC/H/BIFhbdPvM2v9/0vAwe/iE4XQURSBb3ursOUd4wH93yADlhdWs2fpLOzEICM43OFjhrH56233qK2tvaq6641yuo3XR7IY4Xiua+1TlEUFEVBp9O1+rO1dXq9HoPBgMFguObzC69NJhNmsxmz2YzBYLhmmbuEE3+HtY+Bu95/99cj6yAqufX9gkjTNI5sO8eutadQvT4iY01MfnwQ3frGXXOf2nfWUfK0vzWl23//npipU1uu35BHw+5iFJOO5CeHY0yN7JCyN7i8PLD0M44X25mmWOhX618el2pl8uODSMqMBuCD0x/w9M6n8WpeJmRO4GeDpnLs6PcBH9W53SjYEc2pCTNY338UAP/dP5OHZXBD0UXJAIbt1FHB5/nnn6eqqipgxxOhRafTNYcgs9ncHIqsVitWq5XIyMgrHtHR0Z0rMJUcgjfngKMC4nvCgvUQ1z3YpbqqRrubT/9yjLNH/J+57MEJ3LVwAJZoUyt7Qtl//hfVr7+OYjaT/eYbWIYMaV6nqRqVrx/BdaoWfZyZ5KduQR9p7JD3UFrnZM5LuzlX28TYqEjushtosrvRGRTGzOrVfAfatsJt/Hjbj3GpLm5LvY2f9b+ds3m/BaDiUDfO7Ylh3z0PsbXHYHTA0kHdmZFs65AyCxFMEnzaqaOCz86PDuJ0uIDzfUSbGy781a8o1+k7emkjx9U2amWWAY2L+1xrUw0N5fK1Sst9L32qXLGuRSGvcaKrv8Fr/gPUrnWcK4+gaf6Hr/m5r3mZ/5+4hq/Fskuec+k2KqpPxee77Keq4r3wWvXiVVW8Hg8eb/vvmFEUhejoaGw2Gzabjbi4uObnycnJREZ2TGvCDanKgzdmQW0BRKXCgnWQMijYpWrhzFeVbHnjGE31HvQGHWPu78XQiRnNt6m3RlNVip78Rxq2bcOQlET3tWswplyc9dzX6KHshYOoVU7MPWNJfHwwir5jeg2crXLw4LLPKLO7GJoUzWOmGM4d9d/qnjkgjrsWDSQy1sy+0n089clTNHobyY7J5l8H3kn1uaUAVBzKoGhPFDvue5S9mf0wKLB0YHemSfgRXYwEn3bqqOCz8pk91JY1Bux4IjRoaGiKiqZ40XTqxeeKCnoVzaCiGTxoOv9D1blR8T80rn+nTYTJQnxcIkmJSaR3S6NHr+4kJScGfwJKe4m/r095rn9y02+/A5kjg1smwN3kZfe6UxzdUQxAQrcoJj82kIRuUd/4WGpDA2fnz8d18hQRgwaR/eYb6CyW5vWeMgflLxxCc6tEjk5r7v/TEfIqGpi3bA+VDS56Jlr5zZAeHH0/H6/HR0SkkW892p8ew5I4VnWMH2z5ASWOEiKNkfxu0B34qt4BoPrrNPK32/j43gV8ldUPHfDHAVk8mBrfYeUW4maT4NNOHdbis/YkjXXn74i5rMq1a764fNPLV17n5RXHufb/5uv+C7hk5RWbaVfdrNXCXbmpdrWn1ytKqwe9/nGuLJvPp6H5NDQNfOr5liCfdnG5z7/fhXW+C8t8F7fz+bTrNF9dfkrNH4T0TlS9E5/e1fxc1Tfh0zuv2tql8xmJwEaUMZ6k2HSSElOIjo8g0mYmymb2/4yLwGjWX7lzIDXVwFvzoHAvmKLhkXcga1THnvMaNE3j1P5ydq65+BkbPimT0TN7oTe2vyXGXVRE/gMPotbUED1lCt3++/couksmEc2touqNXNDAdn9vokal3fB7uZbTFQ0seO1zztU2kRYbwQvThnByQz6Vhf4BCgeOT+f22b1poI4fbf0RB8oPoKDwdP/RJDo+AaChIJWTf49j88QHOdh7KAC/7p3OP2QkBT9MCxEAEnzaSSYpFe11IRCpHh+q14fq0c7/PP/6kudejw+PS8XjUnE7vXicKh6nitvlf97U5KKhqZb6ploaPbU4tTo8xnpQWn5cFdWIyRWP2RWP0R2HTvP3GbLGmrAlW4lNtrT8mWTBYApQKHI7/OEnfweYovwDH2aPCcyx26i2vJHtq05QmOu//BOTZGHit/uR0T8wLRmNX3zB2cWPgcdD4j/+I0n/56kW6+1bCrBvOgs6hcTFg4joc+2O0zequLaJBa/tJa/CQaRJzx/mDiPyRAMHN/tvr4+0mbnjob5kDrHxn/v+k9XHVwOwMLMPt3AEUHHVxHN8QyKbb53J/sGjAXg0PYF/75OBsY2XAoUIVRJ82kmCjwhFmk/DUe+ksOAcBWcLKTpXQHF5Iarqbd5GQYfZnYDJkYzJFYdyldEqFAVsKVYSukWR0C2KxIwoEjKiiIozt++vfncj/HUenNkOxkh4ZC1k334jb7VNXE1evvz7WQ5+XIjq9aEzKOTck82IKdkYjIFt7ap95x1Knv4FcOWdXpqmUb36OE0HK1BMepK+OxRTOy6ttVWNw833Vu5nz+lqFAV+OKkvs7olsH3lceoq/AMd9hqRxPh5ffmgbAO/3ftbvD4vo2OjeMhWD74mfO5oTryXwI70u9g25l40ReF2WxRLB2aTbO6YjtpC3AwSfNpJgo/oLLxeLwUFBZw8eZITJ060uGswIsJCVkpvUqN7odqN1JY3UVfeiKvRe9VjmSwGEjOiSM6OJik7muSsGGKTLG3rEOxuhFXz4fRWf/j59tvQfVyA3mVLriYvR7ef48Dfz+Jy+N9L5oA47nioH7YUa4ecE1q508vr89/plVeHLspI8veGYUiwXOdoN8aj+vjVhqOs3FsAwNjeCTx3/xDyd5Ty5d8L0HwaZquBkff1QD/Yzs92/5R8ez4pBh8/TDcSodWDpufc7iQ+qx/NxskP4TYYSTIZeGlgNuPiojus7EJ0JAk+7STBR3RGmqZRWlrKoUOHOHz4MA7HxRm++/fvz9ixY8nIyKDR7qaqqIHKcw1UnWugqqiBmpJGf/+ky5gi9CRmng9CrYUhTxOsetg/r5fR6r/s1X1swN5fbXkjR3cUc3THOTxO/yB+calWRs3sSc/hHd9HpdU7vZxeKpZ9hafEgT4hguTvDUMf1fqt8zdi7f4i/t+7R2jyqMRZjfxqxiBuT4hh65tfU37WP1FpbLKFnJmZrHW9ztsn38aiaCxKVuhn8t9o0Vgax759/Vk3bgGV8SkowHcykvhpzzSsHXSnmhAdRYJPO0nwEZ2dqqrk5eXxxRdfcOLEieblWVlZjBs3jj59+rQICqrXR02pg4qCeirO1lNeUE9lUQOq58q7zlqEoaxokrMvCUMe5/nw84m/5WfB+hvq8Nxod3PmUAUn95Vx7kRt8/K4tEhG3JNF39tS0d3EfimX3ull7teP7L+sQB8be3G93U35SwdRa1wYu0WR9A9D0Jk7dpymU+UNfP+vX5JbYgfgrv7J/PK+gTR8XcveDadpqvcPt5Dex4Ztgov/yv8NRQ2FjI/yMsvmRa9oaKqRM3tTWBP/CF8N8N+d18Ni4j/6ZnJnvLT+iM5Dgk87SfARXUlFRQW7d+/m0KFD+M7PP5WVlcXkyZPJzMy85n4+1Ud1SSMVBfZWw5AxQk/ShTDUzUz8wV8TXfIBJrMeZeG7kHFrq+XUNI36aidVRQ2UnKrj3Mlays/aL94lp0DWwHiG3JlB9uCENo/JE2juoiLy589HrajEcsstZL32KjrrxUtsnopGKpYewufwYu4VS8LCQegC1Zn8Gjyqj6Vb8/jfT0/iUTVMBh3fGd+Dx0dlc2JrMQc/KWz+/9ZtQCwlfY/wevULROPgoXg3Pc3+da6aSD45Ppa/DXmIhih/oLsvKZZf9kon22K+5vmFCBUSfNpJgo/oiux2O3v27OHzzz/H6/X3jRk2bBiTJ08mKqptnXF9qo+a0kbKz7YehgCMSiNRhhqis7Ixx8VjtBjQ6xRQFFTVh8ep4mr00FDjor7a2XwJ61LJ2dH0vCWJvrelEh0f0f4KCCDn8eOcXfAoPrudyPHjyXzhTyimi5e13IX1VLxyGM2tYu5tI3HhQJQAd7i+mpNl9fz6vVx2nvLPI2azGllyZy9m90vhqw8LOLGvDO38Jc2E7lZO9NjN2qY/c3u0l/tiPVjPX9kqL0pilfNhPu81Fk2nxwB8Oz2BH3ZPJVU6P4sQJsGnnST4iK6srq6OrVu38uWXXwJgNpuZPHkyOTk57eonczEM1VNx1k55QT11FU04G775iNY6vUJcaiTJ3aPp1sdGt35xRMWFRti5XOOBLyl4/HG0piZipt5L+rPPougvhhtXfh2Vfz6C5vZh7mMjYcHADm/5AX/L2d9zy/jPj77mdIW/n1dMhIF5IzOZ0y+V0s8rOLa7GJ/X/7VvSdBzKvULdlvXMCGxhtujvOjP/zM4VtqPv+ofJS+5PwBGNB5Ijed7WSn0iQzN/y8ivEnwaScJPiIcnDt3jvfff5+SkhIAevTowcyZM7HZbAE5vset4iirpn7Nz3GUVeDSJ+Ie9h18liQAFJ2CKUKPyWIgKs4/6GJskgW9ofN0qG3YsZPCJ58EjwfbvHmk/uqZFuHRdaaOytf94ceUFU3iokHorDenxcSr+vjbwWKe//Qk+VX+jsyKAnf1T2H+kHTMpx3k7ixubmVTdNCQXsqh+DUMycglJ9LLhauJBxuGsVabz5mYPs3HHxll5pGMFKYlxxKp7/hAJ0RbSPBpJwk+Ilz4fD727t3LJ598gtfrxWw2M336dAYPHhy4kzjt8Mb9cO4LsCbAwvchZWDgjh9k9g8/5NyPfgyahu2heaT+8pctRnd2FdipfP0oWpMXQ4qVxMWDMNhuXmuJz6ex9UQ5y3efZfuJiubliVEmpvRLYZQxAtdxO2Vn7M3rNKOXstTPsPX+OwMSSjGffzvHff141zWXw5bhaIp/oVXzMTPZxv3pSYy2RWLSdZ7gKroeCT7tJMFHhJuqqirWr19PUVERACNGjODee+/FaAxQ60RTrX9i0+IvITIJFm2EpH6BOXYIqH1nHSW/+AVoGrGzZpH27//W4rKXp9RBxWtH8NW70UUZSVgwEHP2zf9uyatoYMXufDYcKqa28eKlyMQoE5O7xdPPoaAWOHDaL7lMaXLg6/0hCVn7SInxj45dRTw7fBPYqk2iwnDxln6LpjLWamRaVjp3JcaSZJL+QOLmkuDTThJ8RDhSVZWtW7eyY8cOALp168a8efMC9xlorIa/zIDSwxCVAos+gMSOm9jzZqt7fyPF//f/gqoS9a1v0e25Z1vc7eWtdVK1IhdPiQMMCrYZvYgcmRqUObI8qo/deVV88FUJm3JLW4QgNMiJtjLSEEFslRdf/cV1xqhSontuJzLjCyKjavChcJwB7GACX2o52HW2FudJV90MtxgZl57M7cnx9LFGoJc5wUQHkuDTThJ8RDjLy8tj7dq1NDU1ERkZyfz588nIyAjMwRurYcV0KDsC0Wn+lp+EXoE5dgiwf/wxxT/+ZzS3m4ghQ8h86UUMiYnN630ulerVx3Hm+kfYtgxNJG52H3QRHTvWz/V4VB+fn6lm56lKdudVcbioluaxLDVI8Cn08OkZoDOR1AT6852ijZHlRKUfJjLtK6xJx0Hv4ww9+ZJb+ZJbyVd6XnEuo89Lpuqij8nA4NgohqUk08cWQ7cIo1wiEwER1sHnxRdf5Nlnn6WkpIRBgwbxxz/+kfHjx7dpXwk+ItxVV1ezatUqysvLMRgMzJ07l/79+wfm4I5KWD4NKo5BTAYs3ghx3QNz7BDQeOAARU/+I2ptLYaUFLr98Q9Yb7mleb3m02jYcY66Tfng09DHmrDN7I1lYELwCn0Ju9PD56er2Xe2miPn6jhcVIfdeX6aEw2SVYV0VUeKqiNV1ZGoKuj1HiLizmJJyMOScApL4ilcEXCSvpygPyfpRx59cClX79ukaD7iPHaS1UaSdZBqjiTdGk1apJWM6CgyYmPoFhVJpF4ns8iL6wrb4LN69WoWLFjAiy++yNixY1m2bBmvvvoqubm5ZGVltbq/BB8hwOVysXbtWk6ePAnAfffdx8iRIwNz8IZyWH4fVJ6A2Cx/+LG1/tnsLNz5+RR+70ncZ86AwUDKT/6ZuAULruj0XL3qOGq1EwDL4ARi7+3RoXN8tYemaRRUN3LknJ1T5Q3kVTRwurKBMxUOHG4VgwZJqkK8qiPBpxCvQoLmI8HUgDW2BHPsOUwxBRhjCqmJMVJsTKeQLArJpphulJOMR2nb4Ig6zYvF14RFbcKiOrH4nFh8biJ8Hsz4MANmnUKEosOi1xGh1xGp12M16LEaTFiMZiKMZqwmC9YIC1ZTJJEmKxazlQiDEYNeh1mvw6jXY9Lp0UsrVKcTtsFn1KhRjBgxgpdeeql52YABA5g1axa/+93vrtje5XLhcrmaX9vtdjIzMyX4iLCnqiobN27kwIEDAEyaNIlx4wI0+Wh9qT/8VJ3yt/gs2gixAbqkFgLUBgclv/gF9R99BID1tttI++2/Y7rksqHPrWLfXEDDziLwATqFyFGpRN+RgSFExy+6QNM0KhpclNY5KalzUlLbRInd6X9d66Si3omrwQONXqJVHdE+jRg8JBrqSYqoxmatJspSi8lchyPSQ7XVSJUlCrvJQp0hmlps1BJ3/mHDqXTcBLTXomg+9KgoaCj40KGdf37ZQ9PQ4bvmOgWtxb4tztHitXbJ8stda90lyy851OXnufY5r778mue45lG/uf9TVcO8h54I4BHDNPi43W6sVitr1qzh/vvvb17+gx/8gIMHD7Jt27Yr9vnVr37Fr3/96yuWS/ARwv8LbsuWLWzfvh2AO+64g4kTJwbmkoO9GF6fCjVnIL6nP/zEpN/4cUOEpmnU/PWvlD/7HFpTE4rFQsJ3niBh8WJ0lostO+4SB3UfnsF1osa/QAeWQYlE3paKuZctaNNzXI3P5cJTWoWnrAZvRT1qbQOqvQlfgwtfoxufU0Vza+BV0DAARkAPigFFZwS9CUV/9bu9NDTceKnXNdBoqsVtqMdlbMBraMBhclIfoeIw+XAYdTTqDTQZDDTpTTTpTXh0Brw6Ax6dAY9ixK0Y8SgmXIoZNybcigkV/WUPAyp6vBhQleD1swpXvz27jccW/SCgxwzL4FNcXEy3bt3YtWsXt99+e/Py3/72t6xYsYLjx49fsY+0+AjRup07d7J582YAJkyYwIQJEwJz4NpCWD4Vagv8LT8L3oX4HoE5dohwFxRQ8vQvaNy3DwBDaioJ//AdbLNno4u42LLjzKulfkshrlO1zct00SYsgxKI6GPD3NOGzhL4X9CapqG5VbxVDtz5JbgLy/GW16HWOVEdHjQXaKoBlAgUY9Q1g0tbePBSpzRSqzRSpzhw0IhDcdKguHDo3HiVG/t1pNPAqIHh/HMdoEdp8VOngU7R0Ck+FMXfWqPpfGg6DZ9Oh4oOnw68Ov35Jg4NTQFNUfw/ARTOP1fQdKBp/vUoPjSdgg8FFA2fokPj/L7gX875w16WZ/3rtPP/Pe/SDTV/OOTSLS47hta8oEXzzyXLL1974XgXD6cpV9sO/wiYAXT/4LHkjL4joMdsa/DpkjH38r9GNU275l+oZrMZs1km4BPiesaNG4der2fTpk1s3boVnU7HHXcE4EvLlukf1PAvM/0tP3+eAo++C8kDbvzYIcKUlUXWX1ZQ/+GHlD/3ezzFxZT95l+p/NML2B58gNgZMzH37EFELxsRvWy4Sxw49hTTdLgSX70bx54SHHv8I2wbEiIwpkdhSLCgjzejsxjRRehRzHp0Zj2KXoem+tC8GprqA68PX5MXX6MXtcGNt7IOb2U9al0TPocHzQ2aZvK3xjRTANvFV6YrL3FoniY0jx1FbQBfI4rmQKc1oOBAp2tApzTQaHBRYdRTbrRQboqlSmelHhOtsUSYiY6KxGqNxBoVjcViwWq1YrVasVgsREREYDKZMJlMmM3m5udGoxGDoUv+ShMB1qVafNpzqety0rlZiGu7tOUnoB2e60v9IzyX54IlDh55B7rlBObYIcTndFK7Zi3Vy5fjOXeuebl5wACixo0jcsxoIgYNQh8bi+b14TxVi/N4Na6TtXgrmzq0bJrHieauB8WFYlDR6V3olBoMvnIM3jMYKMQYUYPJVI1O5764ozkWLa471ZG9OO3rxunGSArrfDQ4rz5fm9VqJSkpifj4eGJjY5sfMTExxMTEYDK1Ho6EuJqwvNQF/s7NOTk5vPjii83LBg4cyMyZM6/auflyEnyEuL4tW7awbds2FEVh3rx5gbvVvbEaVj7gn97CFAXz/wo9AtsUHio0r5f6zZupW/8uDTt3gtpyZnpDehqmzCyMaWno4+LQRUaCLgJfowGfy4jmNqJ5jWge0FQFNB1gBEWP5vOCzwM+1f/c04jmbvA/PI0oZgV9jBlDXBSGZBumjERM2amYLLXoK/eh5O+Ewr3gdbYstM4IKYMgfTgkDcAb34fTTVEcyy/l9OnT1NXVtdhcURSSk5NJT08nPT2dlJQUEhMTsVpvfkdlER7CNvhcuJ196dKljBkzhpdffplXXnmFo0ePkp2d3er+EnyEuD5N03jvvfc4cOAABoOBhQsXkpmZGZiDuxpg1cNwZhvoTTDrJRgyNzDHDlHe6mocO3fi2LWLxi/2t2gJag9dbCymjAyMWZmYMjIxZmZgyszEmJmJMTUV5cLlINUD+Tvh6/fh6w+gvrjlgaJSoPt4yBwF3UZAymBUnZH8/HyOHDnCsWPHcDovhiOdTkdmZiY9e/ake/fupKWlSeuNuKnCNviAfwDD//qv/6KkpITBgwfzhz/8oc39EST4CNE6VVVZtWoVJ0+exGq18vjjj5OQEKBB+DxOWPcEHHvP/3rCz+HOfwl458pQpdrtuE6cwFNcjKekFLWuDp/DgebxoJiMKEYTitGIYjSij7NhSEzCkJiAITERQ1IS+tjYax9c0/ytOQffgtx3wXlJK40pCnpO8D963AmJfZrr3OFwsH//fvbt20d9fX3zLpGRkQwcOJC+ffuSnZ0tQUcEVVgHnxshwUeItnG5XCxfvpySkhLi4uJ4/PHHiYqKCszBfT7Y/EvY/bz/9dB5MON5MMiNCO1SWwAH/wqH/urvRH5BZBL0mwr9p/kvKxpbjh9UWlrK3r17OXz4MF6vfwRni8XCwIEDGTRoEN27d0cnA/2JECHBp50k+AjRdg0NDbz22mvU1NSQkZHBokWLAntnzRevw8Yfg6ZC1hiYtxIiQ2N6h5CnaZC/A/Yug+MfgObzLzdGwqBZMGw+ZN8OOv0Vu5aUlLBlyxZOnDjRvCwtLY3Ro0czaNAguXtKhCQJPu0kwUeIb6ayspJXX30Vp9NJTk4O06dPD+wJ8rbA2wvBVeef3+uB5ZAZoLvJuiJPE3y12h94ynMvLu8+Hm55BAZMB1PkVXetra1l8+bNHDlyBPB3UB44cCCjRo0iMzNT5soSIU2CTztJ8BHimzt58iQrV64EYPr06eTkBPhW9PKv/Z2eq/NAZ4DJ/wqjvxc2/X7axGmHL16Dz14AR4V/mTEShj0Et/0DJF/77juv18uOHTvYtWtX8yWtIUOGcOedd5J4yQzzQoQyCT7tJMFHiPbZvn07n376KXq9nsWLF5OREeC5t5x22PB//J1ywd9yMfMFiLhOZ95w0FgNe5f6Hxc6K8dmweglMPzbYLFdd/ezZ8/y3nvvUVlZCUB2djZTpkwhLS2tgwsuRGBJ8GknCT5CtI+maaxevZqvv/6a6Ohovvvd7waus/PFk8Dnr8Cmn/vHqonr4b/lPXtMYM/TGTjrYPefYM+L4G7wL0voA+N/BEMegFamlvB6vXz66afs3r0bgKioKKZMmcKgQYPkkpbolCT4tJMEHyHaz+l08uqrr1JZWUlWVhYLFy5Er7+y8+wNK9oPaxZBXQGg+C97fev/gSkMBsdzO/ytO7v+F5y1/mUpQ+COH8OAGVftrHy5yspK1q5dS2lpKQC33HILd999N5ZLJk8VorOR4NNOEnyEuDGVlZW8/PLLuN1uxo4dy+TJkzvmRE21sOlpOPim/3V8T/+lr+zbr7tbp+Vxwhd/hp3/fbEPT1J/mPhzf+BpYyvNsWPHWL9+ffMUPzNmzAjc6NtCBJEEn3aS4CPEjcvNzeXtt98G4JFHHqF3794dd7KTH8OG758feViBWxfDxF90ndvevW5/uNv+HNjPj+oc190/sOOQuW1q4QHw+Xxs2bKFHTt2AP6+PHPnziU6OrqDCi7EzSXBp50k+AgRGO+//z5ffPEFkZGRLFmypGN/wTrr/P1+vjzf+hMRC3f+FG77Tqt9XUKW6vXflr7tP/wDEALEdPOPYj3829/ofXk8Ht59912OHj0KwJgxY5g0aVLHXIYUIkgk+LSTBB8hAsPj8fDKK69QXl5Oz549eeSRRzp+lN8zO+Cjn0HZYf/rhD5wz79Dn7s7z63vPhWOrPMHnqpT/mWRyf5OyzmLrxhduTVNTU289dZbFBYWotPpmDFjBsOHDw98uYUIMgk+7STBR4jAqaioYNmyZXi9XiZNmsS4ceM6/qQ+FQ78BT79N2j036JNt1vhjp9A33tCNwD5fHBsA2z9HVR87V9mTYCx/wQjn2hXx+2GhgbeeOMNysrKiIiIYN68efTo0SOw5RYiREjwaScJPkIE1oEDB9iwYQOKovDYY48Fbib31jjrYPuz/tvfvednEU8d4g9A/adDqMwx5XHC4bf9Aw9eCDwRsXD792HUd8HcvkuEdXV1rFixgurqaqKioliwYAEpKSkBLLgQoUWCTztJ8BEisDRN45133uHIkSPYbDaWLFlCRMQ3u1xzQxrK/ZOd7nsNPA7/srjuMGKhv69MdJDCQGO1v0yfvwyOcv8yc4z/1vzRT7Y68OD12O12li9fTnV1NbGxsTz66KMkJHSRzt5CXIMEn3aS4CNE4DmdTpYuXUptbS233HILM2fOvPmFaKyGPS/557BynR/hWGeAfvf6Q1DPCR3fEVr1wKlP4KtVcPzDiy1RMd38gWfEozc8EnVDQwOvv/46VVVV2Gw2Fi1ahM1mu/GyCxHiJPi0kwQfITpGfn4+y5cvB2DevHkMGDAgOAVxO+Doeti/HIr2XVxujoHed0G/qdB7EljjA3M+1es/T+67cHjtxX5HAKlD/Ze0Bs0KSOhyOp28/vrrlJWVERMTw+LFi4mLi7vh4wrRGUjwaScJPkJ0nI8//phdu3ZhtVp58sknAz+lxTdVdhT2r4Cj6y4OCgig6CF9OGTcBt1yIHkAJPRu/Y4qTYOGMqg4Duf2+wNP/q6LLUwAkUkweC4MmwdpwwPW2drj8fDmm29y9uxZIiMjeeyxx+TylggrEnzaSYKPEB3H6/XyyiuvUFZWRt++fZk/f35ozAvl8/mDyokP4fhHUH70Khsp/tASk+a/HGWMBEXnnzPM7fAHp/pScNmv3NUS729NGvIA9PpWwC+p+Xw+1q5dS25uLiaTicWLF8skoyLsSPBpJwk+QnSssrIyXn75ZVRVZfr06eTk5AS7SFeqLYSCPVD0OZR8BRXHLs583hpFB7ZsSBsGGSMha4y/9aiNIyy3x+bNm9m5cyd6vZ5HHnlEblkXYamtv78NN7FMQghBSkoKd911F3//+9/56KOP6N69e+hdkrFl+h9DH/C/1jRwVPqnxbCX+GdDdztA8/lbb4wW/yCDUSkQlw0G800r6pdffsnOnTsBmDFjhoQeIVohwUcIcdONHj2aEydOkJ+fz/r161m8eHFoT5+gKBCV5H+kDQt2aZoVFhby3nvvAXDHHXcwbFjolE2IUBUiI3gJIcKJTqdj1qxZmM1mioqK2LVrV7CL1Ok0NDTw9ttv4/P5GDBgABMmTAh2kYToFCT4CCGCwmazMXXqVAC2bt3KuXPnglyizkNVVdauXUt9fT2JiYnMmjWr4+dBE6KLkE+KECJohg4dysCBA/H5fKxfvx6PxxPsInUKn376Kfn5+ZhMJubNm4fZfPP6FAnR2UnwEUIEjaIoTJs2jaioKCorK9m8eXOwixTycnNzmy8Nzpw5k6SkpCCXSIjORYKPECKorFZr8xQWe/fu5fTp00EuUeiqrKzk3XffBWDMmDEMGjQouAUSohOS4COECLo+ffo0j+fz7rvv0tTUFOQShR6v18uaNWtwu91kZWUxadKkYBdJiE5Jgo8QIiTcfffdxMXFYbfb+fDDD4NdnJDz6aefUlZWhtVq5YEHHgjt2/+FCGESfIQQIcFsNjN79mwUReGrr74iNzc32EUKGadPn2b37t2Af5DC6OjoIJdIiM5Lgo8QImRkZmYybtw4AN577z3q6+uDXKLga2xsZP369QCMGDGC/v37B7lEQnRuEnyEECHlzjvvJDU1laamJjZs2EA4TyeoaRrvv/8+9fX1xMfHM2XKlGAXSYhOT4KPECKkGAwGZs+ejV6v5+TJk+zfvz/YRQqaQ4cOkZubi06nY86cOZhMpmAXSYhOT4KPECLkJCcnc9dddwGwadMmqqqqglyim6+mpoYPPvgAgAkTJtCtW7cgl0iIrkGCjxAiJI0ePZru3bvj8XhYt24dqqoGu0g3jaqqrFu3rvnW9Qv9noQQN06CjxAiJF2YyDQiIoJz586xZcuWYBfpptm5cyeFhYWYzWbuv/9+mYdLiACST5MQImTZbDZmzJgB+MNAOIzqXFRUxNatWwGYOnUqcXFxwS2QEF2MBB8hREgbOHAgI0aMAGDdunU4HI4gl6jjuFwu1q1bh6ZpDB48mKFDhwa7SEJ0ORJ8hBAhb8qUKSQmJtLQ0MDf/va3LnuL+6ZNm6iuriYmJob77rsPRVGCXSQhuhwJPkKIkGcymZgzZw56vZ4TJ06wb9++YBcp4I4dO8aBAwcAmD17NhaLJcglEqJrkuAjhOgU0tLSmifm3LRpE8XFxUEuUeDY7XY2bNgAwNixY+nevXtwCyREFybBRwjRaYwePZp+/fqhqipvv/02jY2NwS7SDfP5fPztb3+jqamJ1NRUJk6cGOwiCdGlSfARQnQaiqIwa9Ys4uLiqK2tZd26dfh8vmAX64Z8/vnn5OXlYTAYmDNnDgaDIdhFEqJLk+AjhOhULBYLDz74IAaDgVOnTrFjx45gF6ndSkpK+PjjjwG4++67SUpKCnKJhOj6JPgIITqdtLQ0pk2bBsCWLVs4depUkEv0zblcLtauXYuqqvTt25eRI0cGu0hChAUJPkKITmn48OHk5OQA8M4771BbWxvcAn1DGzdupKqqipiYGGbNmiW3rgtxk0jwEUJ0WlOmTCE9PZ2mpibeeustnE5nsIvUJgcPHuSrr75CURTmzJmD1WoNdpGECBsSfIQQnZbRaGTevHlERUVRXl7OmjVrQn4y04qKCjZu3AjAxIkTyc7ODnKJhAgvEnyEEJ1abGwsDz/8MEajkby8PDZu3BiyIzt7PB7WrFmDx+OhR48eMuu6EEEgwUcI0emlp6czd+5cFEXhwIED7Nq1K9hFuoKmaWzcuJHy8nIiIyOZPXu2zLouRBDIp04I0SX069ePKVOmALB582aOHj0a5BK19Pnnn3Pw4EEURWH27NlER0cHu0hChCUJPkKILmPUqFGMGjUK8M/knpeXF+QS+Z05c4aPPvoIgMmTJ9OrV68gl0iI8CXBRwjRpdxzzz0MGDAAVVVZtWoVZ8+eDWp5ysvLWb16NZqmMXToUMaMGRPU8ggR7iT4CCG6FJ1Ox5w5c+jduzcej4eVK1cGLfzY7XZWrlyJ0+kkIyOD6dOny3g9QgRZpwg++fn5PP744/To0QOLxUKvXr145plncLvdLbYrKChg+vTpREZGkpiYyPe///0rthFCdH0Gg4F58+bRo0cP3G43b7755k2/7NXY2MjKlSupq6sjPj6e+fPnYzQab2oZhBBX6hTB5+uvv8bn87Fs2TKOHj3KH/7wB5YuXcrPf/7z5m1UVeW+++7D4XCwc+dOVq1axTvvvMOPf/zjIJZcCBEsRqOR+fPnN7f8vPXWWzetw3NjYyNvvPEGZWVlREZG8sgjjxAZGXlTzi2EuD5FC9UBL1rx7LPP8tJLL3H69GkAPvzwQ6ZNm0ZhYSHp6ekArFq1ikWLFlFeXk5MTMxVj+NyuXC5XM2v7XY7mZmZ1NXVXXMfIUTn4fV6eeeddzh27BgAd911F+PGjeuwS04Oh4M333yTkpISrFYrixYtIjk5uUPOJYS4yG63Exsb2+rv707R4nM1F5qPL/jss88YPHhwc+gBfydHl8vF/v37r3mc3/3ud8TGxjY/MjMzO7TcQoiby2AwMHfu3Oa7vT755BPWrVvX4g+eQKmoqODVV1+V0CNECOuUwScvL4/nn3+eJUuWNC8rLS0lJSWlxXZxcXGYTCZKS0uveayf/exn1NXVNT8KCws7rNxCiODQ6/Xce++9TJ06FUVROHz4MMuWLaO4uDhg5zhx4gSvvfYaNTU1xMXFsXjxYgk9QoSgoAafX/3qVyiKct3HF1980WKf4uJipkyZwgMPPMATTzzRYt3Vmq41Tbtuk7bZbCYmJqbFQwjRNd12220sWrSImJgYqqureeWVV9i0adMNtf643W7ef//95klSMzMzeeKJJ0hKSgpgyYUQgWII5smfeuopHnrooetu07179+bnxcXFTJw4kTFjxvDyyy+32C41NZW9e/e2WFZTU4PH47miJUgIEb6ys7NZsmQJ77//Prm5uXz22WccOXKEsWPHMmLECEwmU5uOo6oqhw4dYuvWrdjtdsA/gOKkSZPk7i0hQlin6dx87tw5Jk6cSE5ODm+++SZ6vb7F+gudm4uKikhLSwNg9erVLFy48Lqdmy/X1s5RQojO7+TJk3zwwQfU1NQAYLFYGDJkCAMGDCArK+uK7xmfz0dlZSVHjx7l0KFD1NbWAhATE8PMmTNlRGYhgqitv787RfApLi7mzjvvJCsri7/85S8tvoxSU1MB/19fw4cPJyUlhWeffZbq6moWLVrErFmzeP7559t8Lgk+QoQXj8fDoUOH2LVrV3MAAv9AiPHx8c3fA263m/Ly8hZjg1ksFsaPH8/IkSOllUeIIOtSwWf58uUsXrz4qusuLX5BQQFPPvkkn376KRaLhYcffpjnnnsOs9nc5nNJ8BEiPKmqyqlTpzh27BjHjx+nqanpqtvp9Xp69uzJkCFD6N+/f5svjQkhOlaXCj43kwQfIYTP58Nut1NRUUFjYyOKoqDX60lKSiIhIeGKS2BCiOBr6+/voHZuFkKIUKTT6bDZbNhstmAXRQgRYJ1yHB8hhBBCiPaQ4COEEEKIsCHBRwghhBBhQ4KPEEIIIcKGBB8hhBBChA0JPkIIIYQIGxJ8hBBCCBE2JPgIIYQQImxI8BFCCCFE2JDgI4QQQoiwIcFHCCGEEGFDgo8QQgghwoYEHyGEEEKEDZmd/TKapgH+6e2FEEII0Tlc+L194ff4tUjwuUx9fT0AmZmZQS6JEEIIIb6p+vp6YmNjr7le0VqLRmHG5/NRXFxMdHQ0iqIEuzgdzm63k5mZSWFhITExMcEuTkiTumo7qau2k7pqO6mrbybc6kvTNOrr60lPT0enu3ZPHmnxuYxOpyMjIyPYxbjpYmJiwuKDEQhSV20nddV2UldtJ3X1zYRTfV2vpecC6dwshBBCiLAhwUcIIYQQYUOCT5gzm80888wzmM3mYBcl5EldtZ3UVdtJXbWd1NU3I/V1ddK5WQghhBBhQ1p8hBBCCBE2JPgIIYQQImxI8BFCCCFE2JDgI4QQQoiwIcFHCCGEEGFDgo9oM4PBwPDhwxk+fDhPPPFEsIsTsurr6xk5ciTDhw9nyJAhvPLKK8EuUki7//77iYuLY+7cucEuSkiS+mkb+dx9M+H8fS63s4s2S0xMpLKyMtjFCHmqquJyubBarTQ2NjJ48GD27dtHQkJCsIsWkrZs2UJDQwMrVqxg7dq1wS5OyJH6aRv53H0z4fx9Li0+QgSYXq/HarUC4HQ6UVUV+fvi2iZOnEh0dHSwixGypH7aRj53oq0k+HQR27dvZ/r06aSnp6MoCu++++4V27z44ov06NGDiIgIcnJy2LFjxzc6h91uJycnh3HjxrFt27YAlfzmuxl1VVtby7Bhw8jIyOBf/uVfSExMDFDpb66bUVddmdRf2wWirrrK5641gairrvJ93h4SfLoIh8PBsGHD+NOf/nTV9atXr+af/umfePrpp/nyyy8ZP3489957LwUFBc3b5OTkMHjw4CsexcXFAOTn57N//36WLl3Ko48+it1uvynvLdBuRl3ZbDYOHTrEmTNneOuttygrK7sp7y3QbkZddWWBqL9wEYi66iqfu9YEoq66yvd5u2iiywG09evXt1h22223aUuWLGmxrH///tpPf/rTdp1jypQp2r59+9pbxJBxM+pqyZIl2ttvv93eIoaMjqyrLVu2aHPmzLnRIoa0G6m/cKifSwXi31pX+dy1JhB11VW+z9tKWnzCgNvtZv/+/dx9990tlt99993s3r27TceoqanB5XIBUFRURG5uLj179gx4WYMtEHVVVlbW/NeT3W5n+/bt9OvXL+BlDbZA1FU4k/pru7bUVbh87lrTlroKl+/zazEEuwCi41VWVqKqKikpKS2Wp6SkUFpa2qZjHDt2jO9+97vodDoUReF//ud/iI+P74jiBlUg6qqoqIjHH38cTdPQNI2nnnqKoUOHdkRxgyoQdQVwzz33cODAARwOBxkZGaxfv56RI0cGurghp631F671c6m21FW4fO5a05a6Cpfv82uR4BNGFEVp8VrTtCuWXcvtt9/O4cOHO6JYIelG6ionJ4eDBw92QKlC043UFcCmTZsCXaROpbX6C/f6udT16ircPnetuV5dhdv3+eXkUlcYSExMRK/XX/FXeHl5+RV/FYQ7qau2k7q6MVJ/bSd11XZSV62T4BMGTCYTOTk5fPzxxy2Wf/zxx9x+++1BKlVokrpqO6mrGyP113ZSV20nddU6udTVRTQ0NHDq1Knm12fOnOHgwYPEx8eTlZXFj370IxYsWMCtt97KmDFjePnllykoKGDJkiVBLHVwSF21ndTVjZH6azupq7aTurpBwbqdTATWli1bNOCKx8KFC5u3eeGFF7Ts7GzNZDJpI0aM0LZt2xa8AgeR1FXbSV3dGKm/tpO6ajupqxsjc3UJIYQQImxIHx8hhBBChA0JPkIIIYQIGxJ8hBBCCBE2JPgIIYQQImxI8BFCCCFE2JDgI4QQQoiwIcFHCCGEEGFDgo8QQgghwoYEHyGEEEKEDQk+QohOLz8/H0VROHjwYJv3Wb58OTabrcPKJIQITRJ8hBBCCBE2JPgIIYQQImxI8BFCdAofffQR48aNw2azkZCQwLRp08jLy7vqtlu3bkVRFDZu3MiwYcOIiIhg1KhRHD58+IptN23axIABA4iKimLKlCmUlJQ0r9u3bx+TJ08mMTGR2NhY7rzzTg4cONBh71EI0fEk+AghOgWHw8GPfvQj9u3bxyeffIJOp+P+++/H5/Ndc5+f/OQnPPfcc+zbt4/k5GRmzJiBx+NpXt/Y2Mhzzz3HG2+8wfbt2ykoKOCf//mfm9fX19ezcOFCduzYwZ49e+jTpw9Tp06lvr6+Q9+rEKLjGIJdACGEaIs5c+a0eP3aa6+RnJxMbm4uUVFRV93nmWeeYfLkyQCsWLGCjIwM1q9fz4MPPgiAx+Nh6dKl9OrVC4CnnnqK3/zmN837f+tb32pxvGXLlhEXF8e2bduYNm1awN6bEOLmkRYfIUSnkJeXx8MPP0zPnj2JiYmhR48eABQUFFxznzFjxjQ/j4+Pp1+/fhw7dqx5mdVqbQ49AGlpaZSXlze/Li8vZ8mSJfTt25fY2FhiY2NpaGi47jmFEKFNWnyEEJ3C9OnTyczM5JVXXiE9PR2fz8fgwYNxu93f6DiKojQ/NxqNV6zTNK359aJFi6ioqOCPf/wj2dnZmM1mxowZ843PKYQIHRJ8hBAhr6qqimPHjrFs2TLGjx8PwM6dO1vdb8+ePWRlZQFQU1PDiRMn6N+/f5vPu2PHDl588UWmTp0KQGFhIZWVle14B0KIUCHBRwgR8uLi4khISODll18mLS2NgoICfvrTn7a6329+8xsSEhJISUnh6aefJjExkVmzZrX5vL179+aNN97g1ltvxW6385Of/ASLxXID70QIEWzSx0cIEfJ0Oh2rVq1i//79DB48mB/+8Ic8++yzre73H//xH/zgBz8gJyeHkpISNmzYgMlkavN5//znP1NTU8Mtt9zCggUL+P73v09ycvKNvBUhRJAp2qUXtIUQogvYunUrEydOpKamRqalEEK0IC0+QgghhAgbEnyEEEIIETbkUpcQQgghwoa0+AghhBAibEjwEUIIIUTYkOAjhBBCiLAhwUcIIYQQYUOCjxBCCCHChgQfIYQQQoQNCT5CCCGECBsSfIQQQggRNv4//9sc3jVTLMMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('weights')\n",
    "plt.title('Ridge coefficients as a function of the regularization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd05a57-4799-46bd-ac74-97abb613828a",
   "metadata": {},
   "source": [
    "### Lasso\n",
    "- Linear model that predict's sparse coefs\n",
    "- Reduces the regressors predicting target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b70f4e9d-fea9-4d27-b7e5-45a945d03fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9339a855-182d-46f3-b25a-887d53cd9fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\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-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 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-9 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-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 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-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 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-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 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-9 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-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 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-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 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-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.1)</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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso(alpha=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso(alpha=.1)\n",
    "lasso.fit([[0, 0], [0, 0], [1, 1]],  [0, .1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "96a02353-a455-4dca-962f-ebac51e48226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 0. ])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lasso.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd585c1-4514-46e4-bf6e-4c54d1fffc0a",
   "metadata": {},
   "source": [
    "\n",
    "### Elastic Net\n",
    "- Elastic-net is useful when there are multiple features which are correlated with one another. Lasso is likely to pick one of these at random, while elastic-net is likely to pick both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c11c8414-bdc1-46a8-8858-f2c2beb04eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2fd4762c-baee-487b-b9c3-7be3783fc993",
   "metadata": {},
   "outputs": [],
   "source": [
    "en = ElasticNet(alpha=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "116e38e6-0849-490b-8645-937ebba52796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {\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-10 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 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-10 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-10 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-10 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-10 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-10 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-10 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 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-10 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-10 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-10 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-10 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-10 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-10 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 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-10 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 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-10 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-10 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-10 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-10 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-10 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-10 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-10 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-10 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-10 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-10 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-10 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-10 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-10 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-10 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 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-10 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-10 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-10 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-10 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-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ElasticNet(alpha=0.1)</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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "ElasticNet(alpha=0.1)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en.fit([[0, 0], [0, 0], [1, 1]],  [0, .1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9734e930-df9f-4725-a123-20d4631418d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32589556, 0.32579954])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0bf7d6-f18c-435a-93de-6b512abf45a3",
   "metadata": {},
   "source": [
    "## Classification\n",
    "### 1. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ff9a305d-db4b-4fcc-a2d1-6cebdf2e1b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4509380f-3c2f-4056-b396-911034f67fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_blobs(n_features=2, n_samples=1000, cluster_std=2,centers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b424affe-53af-4621-a399-08583283b86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y,s=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6e2776f0-1f53-47cb-b5ec-18730a878970",
   "metadata": {},
   "outputs": [],
   "source": [
    "h = .02\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ce6d2254-8a03-4268-a86a-75e806e85fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {\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-11 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 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-11 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-11 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-11 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 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-11 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 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-11 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-11 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-11 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-11 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ffc0da6c-9765-465a-8418-dc8936039632",
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = lr.predict(np.c_[xx.ravel(), yy.ravel()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ec5c36f5-9efd-484b-b492-f13b4f06a166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:,0],X[:,1],c=y,s=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34388969-2dbc-4802-96da-6633c64c27b7",
   "metadata": {},
   "source": [
    "## 2. Online Learning Models\n",
    "- Useful where number of samples is very large ( Scale of 10^5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a5ea7340-ac3b-48f7-8975-cf25d68c785a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification, make_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1eeb7e92-c65a-43f6-94f1-b422f069c50d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification, make_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "41398a95-9f0f-46b9-b56f-91330bb2b843",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f47d46db-ded1-4de4-9df4-c55c9ee98710",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX,testX, trainY,testY = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "20c5bae1-7f9e-4efe-8e40-3bba007d5ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1c19694c-eb51-4bc0-81f8-cfcc269dd9b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sgd = SGDClassifier(n_iter=10)\n",
    "#The SGDClassifier in Scikit-learn no longer accepts the n_iter argument.\n",
    "\n",
    "sgd = SGDClassifier(max_iter=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "3f17de6c-12cc-4ff4-8668-3f11a0a645b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.616"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd.partial_fit(trainX[:1500],trainY[:1500], classes=[0,1])\n",
    "sgd.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0dff4c80-7a38-46f6-8b07-a938e24668ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sgd.partial_fit(trainX[1500:5000],trainY[1500:5000])\n",
    "#sgd.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "dbd9ac42-51ac-453d-bac5-c014eb1a5250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(750, 2)\n",
      "(750,)\n"
     ]
    }
   ],
   "source": [
    "print(trainX.shape)\n",
    "print(trainY.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c380daf8-877f-4f2e-aebb-c6b0eb9ff368",
   "metadata": {},
   "source": [
    "## 3. Robust Regression\n",
    "- Robust regression is interested in fitting a regression model in the presence of corrupt data: either outliers, or error in the model.\n",
    "- Three techniques supported by scikit - RANSAC, Theil Sen and HuberRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "56c0d1b9-d44a-46e9-b66e-c56a11001a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 1000\n",
    "n_outliers = 50\n",
    "X, y, coef = make_regression(n_samples=n_samples, n_features=1,\n",
    "                                      n_informative=1, noise=10,\n",
    "                                      coef=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9bb2be6f-e311-4978-9637-96e1c0db7880",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add outlier data\n",
    "np.random.seed(0)\n",
    "X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1))\n",
    "y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5599ea60-ae10-4a88-af51-4f331b579400",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression,RANSACRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "8ee654fa-f88d-40df-ad17-5eab3f5a1ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {\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-12 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 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-12 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-12 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-12 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 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-12 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 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-12 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-12 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-12 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-12 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RANSACRegressor()</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-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RANSACRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RANSACRegressor.html\">?<span>Documentation for RANSACRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RANSACRegressor()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RANSACRegressor()"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X, y)\n",
    "ransac = RANSACRegressor()\n",
    "ransac.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f2500753-4afd-4069-834f-3b1f1cd6ec5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cUvKN2FjM81D/yFKs2XEMn5x14dqZNr92biIjY5ghIooQ7ShJTeV8vPHAYlVBrtaSYgf+ffVCPPDrZpzoGsRlUVKt/lvoSEehIx2d/ZfQ2X9JPt7cOQBAvQifchXhtBQLfvfPSyL3ZoliiGGGiChCtKMkTR19qK0Hth/s0A0yhY50vPHAYtQ1tOFIh3/hsADg7oX5fjtjK0OLb1qLWxZQMmGYISKKEO0oiShBd1NJnzu/MBOrX2vy6z7yjcYow0mg0MItCygZMcwQEU2QsiZGbzE6beBo6vBf6den0JGOo6cG0NjRr3u/9vkZWojGMMwQEU2QXueQMmBoA0dtPXCozT/Q+KaPth/s8DsuQb21AQMMkT+uM0NENEF6nUN6fGu/NHX0YXGxAwV2K/Kz0pCfZUWm1YIlc7Kx9ubZuCpN/fnSZp0m/z3Y8xMlu4iGmf379+OOO+5AXl4eBEHAO++8o7pfkiQ8+eSTyMvLg9Vqxa233oqPPvpIdc7w8DA2bNiAnJwcTJ8+HV/72tfQ3d0dycsmIhqXxyuquowA/d2qgbERnINtfWjs6EfXgBvdg0PoHnTD6fbgcHsfHvh1s2odmHy7Ff+0pFBecE8I8vxEyS6iYebixYv4whe+gJdeekn3/p/97Gf4xS9+gZdeeglHjx5Fbm4ubrvtNly4cEE+p6amBjt37sSbb76JAwcO4NNPP8VXv/pVeL3eSF46EVFQdQ1taFSsBZNvt6Kpow/P7/4bnt91UrUCb7DNJH20tTJF2dOxsXI+airn45a5OaipnM/OJKIABCnY1qtT+UKCgJ07d+Kuu+4CMDoqk5eXh5qaGjz22GMARkdhZsyYgZ/+9KdYt24dnE4nrr76arz++uv41re+BQDo6elBQUEB/vSnP+H2228P6bVdLhcyMzPhdDphs3HVSyKaHI9XxPKf71Ot8xJI+ZxsLCpy4MW9+uvK6BEA1FTOZ30MJb1Qf3/HrGamo6MD586dw8qVK+VjqampWLZsGQ4dOgQAaG5uxuXLl1Xn5OXlYcGCBfI5eoaHh+FyuVR/iIimSl1DW0hBBsDoSr6ChIdXzEWhIx0FdiuWFDuQn5Wme77FJODhFXMhipJqdIeIAotZN9O5c+cAADNmzFAdnzFjBk6fPi2fk5KSArvd7neO7/F6tmzZgqeeemqKr5iIaFRTh3qrAVuaBReGPAFHXuoa2pCbmSbXxHQPuPHw8tFRl3/b34Zhz1hYKStywCSY8MKewF1SRKQW824mQVDvJytJkt8xrfHO2bx5M5xOp/ynq6trSq6ViAiA3w7W1820oaZyPrIU3UdKHlFSFfdKGN1+wGQSVEEm327F9qobQ+6SIqJRMQszubm5AOA3wnL+/Hl5tCY3NxcjIyMYGBgIeI6e1NRU2Gw21R8ioqli0nyWMpsEVFeU+O1EHegjl68zSRtSirKnIy3FgrIiB7uYiMIQszBTXFyM3Nxc7N69Wz42MjKCffv24aabbgIAlJaWYtq0aapzzp49iw8//FA+h4goGnxrxdz36hHVyIxvm4LlP9+n2unaYhKQoVk3Jt9uVXUmBQot1RUl7GIiCkNEa2Y+/fRTtLa2yrc7Ojpw4sQJOBwOFBYWoqamBs888wzmzZuHefPm4ZlnnkF6ejruvfdeAEBmZibWrl2LRx99FNnZ2XA4HPj+97+Pz33uc6isrIzkpRNRkhhvSwLf/X9s7kKXYqpocZEd0yxmeEVJFWLkx4kSXEMe2NIsMAkCrp1pw/aqG5GWMvZjd93SYjS29+GTsy5cO9OGdUuLAXCrAqJwRTTMHDt2DBUVFfLtTZs2AQDuv/9+7NixAz/4wQ/gdrvx4IMPYmBgAIsXL8auXbuQkZEhP+b555+HxWLBN7/5TbjdbqxYsQI7duyA2WyO5KUTUZIYb0sC5f1KJ7qdKCty4MygG8GMeET89f9+Wfe+bfs70Ng+ur1BY3sftu3vYIghmoCIhplbb70VwZaxEQQBTz75JJ588smA56SlpWHr1q3YunVrBK6QiJJdsGJbj1fEH5u7dLuUhj0iDlwJP8GkTgv8wYuFvkRTI+bdTEREsRSs2LauoU01tRSIOchP0n9aPHtCr01EoeOu2USU1HzFtcqaGR+9kRKzCci1peHM4JB8TLumnflKQpmZZcW6ZcUTem0iCl3UtjOIJW5nQESh0BYDi6KE2r0tqnMKHenwiqIqzARTPicbv/vnJZG4XKKEF/fbGRARxRtfse+B1l68UH8SoiRi0ewsKNfo7Oy/FHKQAYD3Owe4HQFRhDHMEFFCUa4HE+6+Rk0dfaqC3Ff+pwNNpwcxmfHrIc/oaA8RRQ5rZogooQRqtVZOIZUW2gFBQvPpQdXaMtptCpRbDehJs5hwfUEWuvov4Ywz8GgNu5SIIothhogSil67s8crYvVrTfLidsqW6gOtvWhs74PZJKB7ILSdsH1uLHLgjQcW4x//vTFgmGGXElHkMcwQUUIpK3LgYGsvJIwFibqGNt1Ven2C3ReMxyvC4xWxqDgbB9vUz5FpnYbPzcpklxJRFDDMEFFC0Wt3rtp+NCKv1djRj7qGNlRXlOBwWy8aO8amk6rKi/DIyvkReV0iUmOYIaKEot3XyOMV4dUUwywpdmDxHAeaTw/i6Kn+cWtjgjl6qh8W8zy88cBivz2eiCg6GGaIKKFpp5iUQaasyIGOv1/AGefwhJ/fVw/DzSGJYodhhogSht4O2NpOoh7nEF7c0woJo8W/yjVkACDVYgpppMaWZsGam4o5AkMUBxhmiCghBOpY0hYES5Kk2jhSu4ZM2rTQwsyam4pZE0MUJxhmiCgh6HUsHT3Vjx1ryiBKIt4+fgauIQ/OXwg+pRTqAnnNnQMTvVQimmJcAZiIEoLewnRlRQ5YzCaYBBO6Btxwui+PO+riGvLIf19S7MA0nZ+SXDuGKL4wzBBRQigrckBZ/lI+Jxvrlhajtr4F2w92TOg5Pznngsmk/jFpNgmoqZwv18oMjXjwf15pxPVP7cL/eaURQyMevaciogjiNBMRJQS99WWUWxuEQlv863T7B5O8zDRV19KaHcfk6a3D7X1Ys+MYd8kmijKGGSJKCHqt0cqtDcYjALgmIxVdA+6g53194SzV7U/OuoLeJqLIY5ghIsMIZbNIpbIih2ofpkAKHelYdf0sNLb3+oUZAcAsuxWfDnlw7UwbvrdM3Yp97UybqvD42pm2ib9BIpoQhhkiMgzltJEypCh3x/bxeEWIooRM6zQ43ZdVz1Ngt+LrN+SjuXNANSV15JS6Q8mWZoEgCOi+EnAa2/uwbX+H6nW2V92INTuO4ZOzLlw704btVTdO9dsmonEwzBCRYQSaNvLtjq1U19CGF/e26J7/9YWzAAno7L+Ezv5LECURx075t1orO5sCvU5aioU1MkQxxjBDRIahXABP7z6lQMEn327F28fPqKaTave0ItUyfnMnW7KJ4hPDDBEZRnVFCURRwo7Dp1RTR/l2K9YtLVbV1Gg3l8y0WnBtrk21s7XSeOvPFDrScffCfG5fQBSHGGaIyDAsZhNMJgEuTQ1M94Ab2/Z3wOMRsfW9Vvm4CYAvorjcHvQ4h8J6vfysNBTlXBWwwJiI4gPDDBEZSqDpo/842OFX6Ksca5Ewui9TOAoc0/HGA4vDvkYiii5+zCCiuOTxiqitb8F9rx5BbX0LPN7RaKJd6ddHG2T09F8cxuIie8D7tXUzJr0XIqK4w5EZIopL2jbsPxzrQu+nw0ixmLCoyI4e59C4C9xpXRwR8fFZF/LtVrnd2ic/y4ruQfWxRcXZk30bRBQFDDNEFDPKgl1fXQowGmS2H+xQTSf5gsaQR8SRUwNImeC48oVhLy4M+4cgQTMKU+hIZ7EvkUEwzBBRzChHXw629qKxvQ9nBt3o7L807mNHgjcfhU0QBAgYra0RANy9MJ8Fv0QGwTBDRDGjLOaVANW2ANG26oY8mAST3ygREcU/hhkiiplgi+BFSoHdCtfQZdWO2JlWCzYsn8eRGCKDYpghopjxjX74FrlTjsyYTYLfwnehSrOYMKRYBC/NYsI1tjSsun4WNqyYi617W1C7Z2w9mqqbihhkiAyMYYaIYsZiNsmbNvqKgZs6+vDBGaffvkjhuMaWhq7+S/KIz5BHRFf/JZhMAixmEzYsn8cpJaIEwjBDRDGl7GgqLbTDK0qTCjICxupfth/swOCV9WeUm0QqQxQRGR/DDBHFlHY9mclaMidbVf/ie24A8IoSPF6RU0pECYb/o4kopgJtTxCqNM2qveYrU0nAaE3OkjljC98dbu9DXUPbJF6NiOIRwwwRRUSg7Qi0Am1PEKrUaWM/xoQrz+djMZtg1uxJ4JtqIqLEwWkmIooI7YJ4AFR1Kr5amSPtvZiVZcXfPx3GiEcMeZRGEABJgtxiXehIx90L8/2KeZXt39qwQ0SJgWGGiCJCuyDe0VP98HhFbN3bgp3v98DpvhzS5pCB2NIsqrViCh3pukW9yvZvdi4RJSZOMxFRRCinjwQApbOzsPq1JtTuaUVn/6VJBRkBwHUzM1XTU77iXiW9vZ9Y/EuUeDgyQ0QRoR0RETWL4k1UqlnA+ltL8L1lJViz45j8nL7iXuXozHhTXUSUGBhmiChiPB4RH5xx4tipfqRYpmZEZNgrwSSYkJZi8Svufet4t2oURm+qi4gSD8MMEUVEXUMbtr43tmWAcnuByfKFEu3eTp39l9DZf0kehWHxL1FyYJghoknTq02J5CiIL5Qop7J8QQYYG4XZsaZMvp/Fv0SJi2GGiCbF4xWx+rUmuXblQGsvRFGa8CaRwdjSLLhupg1NHX2orR8NM74amNr6Frk+xjcKw20LiJIDwwwRTUpdQ5tfYe/OE2fQdWWUZCqNeEQ0doyO+BxqG31NX1hhCzZR8mKYIaJJ0ZtOcrpHJrVFAQCkmAWkTTOrNp1U1t1oC3o5CkOUvLjgAhFNSunsLL9jysXsJmrEO7p7dvmcbGRZp/ndz4JeIvJhmCGiCfN4RRxp1y/0taVZkGqZzK5Loz4568JnczNUxwrsVtRUzudUEsVMqHuPUXRwmomIJmzr3ha5hkVLOT00GYPuy36v8fUb8jmlRDE11QsycrXqyWGYIaKQ6P2w3fl+T0yupblzICavS+Qz1QsycrXqyWGYISKVQJ8QlT9sD7T2orG9D+ddQ1G/PtbK0FSY7EjIVC/IyNWqJ4dhhohU9EKLSQA+7HGpOpSmYp+lUC0pdmDxHAeaTw+y7ZqmxGRHQqZ6KQCuVj05DDNEpKL8hAhMfWgRAHmfpuFxtjgodKTj7oX5rB9IMPFQHzLZkZCpXgrAF4aaOvogSlAtDBnsaxMPX8t4wDBDRDKPV4zIyr1KS+Zk4/W1i1BbfxJbG9p0z2GISWzxUB+it69XbX1LzL7nfOGoth7y10a7MKSeSH0tjRaSGGaISFbX0IbGKRqJEQDdhfMOt/dh9WtNuFGzPk1+lhWzs9OxqDg77n9w0uTEQ32IbyTkrePd8r5eL9SfBDD5MDCZIBDu1yZSX8t4CJzhYJghIpl2iknJlmbBxREPQl1OI9j4zuH2PpwZdKuOFeVMxxsPLA7tySks8fYpO1b1IYE2RNVuUDpZkwkC4X5tIvW1jIfAGQ7DhJlf/vKXePbZZ3H27Fn8wz/8A1544QV88YtfjPVlESWU0kI7Dlz54QsAtjQzstJTkZuRgqbTg1P+er7RGxY8Rla8fcqO1T5ael+HSISByQSBcL82kfpaGq0g2RBh5ve//z1qamrwy1/+EjfffDO2bduGL3/5y/j4449RWFgY68sjSgger4gjHdopptEVfP/fGdeUv96q62fBZBK4MWQUxNun7IkUzypHVUoL7YAgqbrbQhlp0vs67FhTBmC04NYrSvhjcxfeOt6NVdfPwoYVcyc0gqWtx/GKEjxeMaTnCvdrE6k9yUINSfEy6idIkhTZar8psHjxYixcuBAvv/yyfOzaa6/FXXfdhS1btoz7eJfLhczMTDidTthstkheKpFhPb/rJGr3tkTkufPtVhTarZAgwCSAdTFRVlvfIo9ICABqKufHdf2DHuV7UArl/fh+4frqY/QeV1vfguev1Mz4lF8pVg/3+9TjFbH6tSZVJ+AjBvyahyLS31uh/v6O+5GZkZERNDc344c//KHq+MqVK3Ho0CHdxwwPD2N4eFi+7XJN/adKIiMK9ilq54kzU/56AoCHl8+b8CdcmhrRnNbxfY/5WoynKrwGqucKZaRJOb0EqLvllM+vdbi9D3UNbWH/craYTTCb1PuSvXW8W/U1iJcRjcmKl1G/uA8zvb298Hq9mDFjhur4jBkzcO7cOd3HbNmyBU899VQ0Lo/IULQ1A43tfTCbBJQVOTAVg7QZqRZcGB7bk2nJnGw8snK+vCmf0X9wG1WkpiL0aIMDEFqL8Xi/3LVTN0rjTeNog1ChI93vWsqKHKp6MeVjJ0L7fJ39l1TBKJQ6pskGnok8PtzHxEttTdyHGR9BUKdcSZL8jvls3rwZmzZtkm+7XC4UFBRE9PqI4pnHK2Lr3hZs29eu+hTlGwY/2NqLxcUOdA2MdRhNTzHh4kh4OwFfGPbAlmbBpREvcjPT8O+rFwKIvwJUihy9EZRwR0/0vkeUo0ulhXYc6eiTNyAdbwQllF+41RUlECUROw6dgtPtUT12IqorSlTTWr5rV/59vBGN8b4m4wWPify/C/cxsSrm1or7MJOTkwOz2ew3CnP+/Hm/0Rqf1NRUpKamRuPyiAyhrqENtXtaA94vATCbBJTPyZYDTrhBxse3W/aZATdeO3gaGyvnxc1QNEWe3ghKKJ/Yx/se0Y4u3ffqEb/HBxLKL1yL2YRHbvsMNiyf5xcQJmpWllVVo6P8GoQSsMb7mowXPJo6+lSP/4+D7QCCryoc7v/VaI76Bb2OWF/AeFJSUlBaWordu3dj1apV8vHdu3fjzjvvjOGVERlHk1+XEpBpnQan+7J8W5SAT85OXX2Z8gdhvAxFU+Rpl+VX1swEE+h7JNDoQzjfU+H8wp3sL2e9YmNgdMpV+TUIJWCN1xU1XvDQLubtdHvGXRjQqP9X4z7MAMCmTZuwevVq3HjjjSgvL8crr7yCzs5OrF+/PtaXRmQIejsU3LeoAMe7nPjkrAtXpVnQ2N7nNz0wWaVXVvmNl6FoiryxMBBeIAj0PRJo9CES31Oq9u/ZWYAkoLlzIKx6Fb2aIWB05FP5+FBCU3VFCRrb++TRUu102njBw6RTiTHeaItR/68aIsx861vfQl9fH3784x/j7NmzWLBgAf70pz9h9uzZsb40IkPQ+6H2+pFOeUpoUDFCM6Wk0ReOl6Foir1AIy2BvkcCjT5E4ntKu2O8Tzj1Kno1QxMd4dDrilIGkfGCx6LibBxq6wtrys+o/1cNEWYA4MEHH8SDDz4Y68sgMqRFxdk42KaeavIFmYlKtZjG3fW6uXNgUq9BiSfcAtNoTnuM1/6tDDBeUVIV0AOj4UK7UateG7hWsELeYO9/vOAR6pRfIrSJGybMEFF4tCumLil2yN0fU2G8IGOk+XaKnnALTCM9nRSoDkfJ970caArJ9z7qGqDaqDXURfeCBbzJvP9Qp/wSoduQYYYoQW3d2yJ3MB1o7UX1sjkQBAFNVz5VRlIon0YpOYU70hLqtEc4owuh1OHo1cxUbT8asK6stNDuN7KjrZMJJFjAi8a0z2S6DeNlVIdhhsjgAq24+vZx9Yq+bzR1weW+PKEi32kmAR5RCvmxBXar4T7ZUXREqsA0nNGFidbhBFu4D4I04SmxWHcQTeb142VUh2GGyOACrbiaYlF/OrowNLEgAwCXRQmLi+w4ckpdA1Ngt2J29nSc6ruIbsWCexEe+KEYmYpP4ZEaaQhndGGiv7yVQayz/5Kq9br59KC8aWW4QS3WHUSTef14WUOKYYbI4AKtuKqtaQk1YNjSLLrFwZ+cu+B37BulBdhYOQ//+O+NqjCj1z1Fxhcvn8L1hBNQJvrLWxnEtBsslhU5JhzUIjmVFEoAnczrx3pUyYdhhsjggg59T6GLw+qAU2C3yr8ElC2gwpXblHji5VO4nnACylQsjCeKEgoc6QCAVTfkxW19WKQDaKxHlXwYZogMzrenzNvHz8A1NLo30qws64Q7lwK1bFvMJngVoz359nT5E168/ECjyIqXT+F6or2Z5ot7W+Svg0kwxW0rc6QDaLysS8MwQ2QggYp9AcibRDrdlyFJQEaqGReGvRN6nUJHOiRJUm08efVVKegeHJJvdw9cwn2vHpHDSzz8QKPIYmgdFc8jVFrxHECnEsMMkYEEKvb1DXf7dA+6MVECILdVK4NT98Al1XldA250DbjjrnaCIidePoXHmpECQrIEUIYZIgMJVOzrDGE7AluaBReGPEFra1ItJqxfNkcuEtxYOQ+19VAFqMIrwcnXyRHvn0yJppqRAkKyBFCGGSIDCVTsG0qY+XTIg7LZdvQ43bgw7IUtzQKn+7KqRmbYI2Ln+z0wCSasvXk2vvv6cb8AVehIR1mRw6+TgyhZJEtAMBKGGSID0dtrpWvArVrvIhARQNPpsXVinO7LyLT6/wjo7L+EF+pP4g/NXap2a2AsuBjpkykRJT5BkqSEX97K5XIhMzMTTqcTNpst1pdDNCV8xcB/bO5SFepOFZOgXpvGYhKwYfk8Q25CR0TGFOrvb47MEMUJj1fE1j2t2HlidBuCVTfkYcPyebrBweMVsfq1JnnX3kjQLrJXVuTg0DoRxSWGGaI4UdfQhtq9LfLt2j2tMAn6c/N1DW0RDTI+mVYLBAi4dqYN26tujPjrERFNBMMMUZzQ6whSHvON3Lz9fjd6nEN+505EqkXAsEd/plkA8J2b53A0hojiHie+ieKEXkdQZ/8l1Na3YGjEg9WvNaF2bwu6BtzwTtFOjl+Ylal7PMs6DTWV81nYS0SGwAJgojihrJkZvDSiapmelZWGM4NTMxqjlGWdhkFNW7cAoKZyPkdkiCjmQv39zZEZohjzeEXU1regavtRmEwC9j66DFnpKapzIhFkAODamTYoN7gudKRzRIaIDIc1M0Qx4mutfut4t7xOzIHWXhxu68WZwfHXjZkMW5oFa2+Zg3VLi7Ftf4dqvRi2XROR0TDMEEWZXohRmuhu1+H43KxMeRqJ00lEZHQMM0RRprdZZDhSzAI8XgniBB8vYGynbSKiRMDxZKIo09ssEgAyrdOQn2X1Oy5obo+EEWRmZaWpbhfYrayJIaKEwzBDFGVlRQ6/gAIAC/JsEHTumGbWOzuwWVlWFNitKHSk+4Wjry+chY2VY6sK+4qP73v1CGrrW+DxTnS8h4godjjNRBRlvlERZc2Mb+pHb48lQRCAAJNStjSLqoUbAM4Mjj2Htian+fSg6rZyyutgay8A1tAQkfEwzBBFmcVsQnVFCURJxM73ewAAq66fheqKEng8Ira+16o63yNKKLBb4RryICPVDKfbgwvDHggC8KkmyIxHuzCfcspLgv4qxERE8Y5hhmgK+TqVxmt1rmtow4t7WiHhSk2MIGHrnlb8qvGU37leUULXgBsCgKtSzbgwPBpgJCnQeI2+Qke6X61MWZEDB1t75evQW4WYiCjeMcwQTSHltM2B1l68dbwbdy/M9ws1TR19qhGRt4+f0Z1iMpsEeesCCZNbPO/uhfl+wcoXbpThi4jIaBhmiKaQtlOps/8SXqg/CUBdi6LdWklb9+JjUYSZUOXbrejWBCO9URlgdMqLNTJEZHTsZiKaQqWFdr9jEkaLfZUdQyZNg1JGqln3+YY9o91FqRYT8u3+bds++VlpuGVuDh6pnI/6R5aifM7YOjIC9EdliIgSBUdmiKaIxyviSEef7n2d/ZfQ2X8JB1p78cv3WpFqGQsWAoB8ezq6g0whDXtEdA+4kZFqxoVhr9/9giCoanReX7vIr3aHiChRMcwQTZG6hraQtiIY9ojyiEuaxYQbCu0ItZT3wrAX+VlWdA+qp5G6Btyq6SxOHxFRMmGYIZoiE2lrHvKIONzeB1ta6P8V//7psO5xtlYTUbLiJDrRJPlW0dXbNNJHb5sCpUAFwOEs/svWaiJKVgwzRJPka8f2hZn8rDS/kZZVN+RhcZF/cfB48nRC0NVXpahu29IsuLkkm3suEVHS4jQT0SQp14wBRkdZtCMtvz58GtM1HUuBNykYMyvLqlp/pnxONhYVOfDi3hZ5obu1t8xhfQwRJTWGGaJJCmXNGOeQB07N8VBKfs0mAY9UzvfrSjKZBHYqERFdwTBDNEnaNWOmUteAG4uKgR1rylTrxHAkhohoDGtmiCZpUXG237FM6zTcMjcnrC4lPb4VhOsa2ib1PEREiYxhhmiSqitKsKRY3UVUddNsvPHAYghTMGrDlmsiouAYZogmyWI2YceaMiwpdiDNYkKm1QKPV8TP/7+/YuiyOOnnZ8s1EVFwrJkhmgLb9nfIq/8OeUTUvdc+qecrsFvx9Rvy0dw5wCJfIqJxMMwQTYLHK6KuoQ3/cXBy4cWWZpG7oAQA3ygtYJEvEVGIGGaIgvCFFV8b9Lqlxdi2vwNNHX3wihI+PusKuHpvKDKt01B102x8b1kJtu3vYLs1EdEEMMwQ6fCFmLeOd8sr+x5s7UVjex8a2/tC3BZyfE73ZTR1DKD5dDPKihx+LdhERDQ+hhkiHb4tCpShRQLwyVnXlAUZn8PtfQCAA629ALiGDBFRuBhmiHQcPdWvG1rcl70Rf10t7VRXdUUJR2+IiBQYZoh0lBU5cLC11y/QDHsm32rtYzYBXs3T6bVgK0eJDnL0hojID8MMkcLQiAdrdhzDx2edmGW3otBuRffgkFw3M1UEAIuKslX1N+VzsnULf5WjRFxAj4jIH8MMkcKaHcfkGhan24MCezruXpiP5+tPTsnzFzrSUehIV3VGjTd9pBwl4gJ6RET+GGYo6QSrQfnkrEt17idnXXh97SKIooQdh0/B6b48qde+e2G+aooolOki32gN27aJiPQxzFDSCVSD4vGKuCrNgkFFYLnqykaRj6ycj2On+3GwrS+s1yqwWyFc2aBp1fWzJhRELGYTa2SIiIJgmKGkE6gGZeveFnQPuFXndg+4sXVPK0wmAR/2qEdtgNFF7yRJCrpw3t0L89mBREQUQQwzlFQ8XhFecaxHSVmDsvP9Ht3HvP1+N7oH3H6dTbY0y7jTTl0Dbrxwpd6GoytERJHBj4qUVOoa2uQCXwBYEqCDSKnHOaS75kymdVpIr8kOJCKiyGKYoaTh8Yp463i36tiZQTeqth9FbX0L7vx8nu7jlCM5SqKkPl5gt6J8TrbfeexAIiKKLE4zUdKoa2jzWy+ms/8SOvsv4WBrLx6qKEG+3epXN+NjMQnwKILNmcEh1f1fvyEfG1bMRV1DG5o6+iBKgEkAFhWPP/pDREQTF9GRmaeffho33XQT0tPTkZWVpXtOZ2cn7rjjDkyfPh05OTl4+OGHMTIyojrngw8+wLJly2C1WjFr1iz8+Mc/hiRN9Q45lOiaOtSdSKmWsW9/CcC7/+8szgQIMr7RFSHI8zd3DsidR7/57hL87p+X4DffXYKNlfNY/EtEFEERHZkZGRnBPffcg/Lycrz22mt+93u9XnzlK1/B1VdfjQMHDqCvrw/3338/JEnC1q1bAQAulwu33XYbKioqcPToUZw8eRJVVVWYPn06Hn300UhePhmU3joyAHBaMypzdUYqzlwp7PWFFP3aGAu+c/Mc1SJ3XlFS1d4Ao9NRHq/I4EJEFGURDTNPPfUUAGDHjh269+/atQsff/wxurq6kJc3Wq/w85//HFVVVXj66adhs9nwm9/8BkNDQ9ixYwdSU1OxYMECnDx5Er/4xS+wadMmeQ0PIh+9dWQA+E0fCQAeXj4Px06PhpOPz/q3XgPAd26eg+qKElVAWre0GC/va8OOQ6fgdI+2ZR9u70NdQxu7loiIoiymHyEPHz6MBQsWyEEGAG6//XYMDw+jublZPmfZsmVITU1VndPT04NTp07pPu/w8DBcLpfqDyUPvXVk9LqJugbc2HniDLyihMaOft21YjKt0yCKErbubcEL9SdxoLUXL9SfxLb9HXjkts9gQV6m6nztVBYREUVeTMPMuXPnMGPGDNUxu92OlJQUnDt3LuA5vtu+c7S2bNmCzMxM+U9BQUEErp7ilbK2RQBQOjsLHu321Fd09l9CY0fgtmmn+zJq97bg7eNndBfa0zY6BWh8IiKiCAo7zDz55JMQBCHon2PHjoX8fHrTRJIkqY5rz/EV/waaYtq8eTOcTqf8p6urK+Trofjl8YqorW/Bfa8eQW19S8CAUl1RgprK+bhlbg5qKucDkhA0sIRCuzheaaEdwGi3kpL2NhERRV7YNTMPPfQQvv3tbwc9p6ioKKTnys3NxZEjR1THBgYGcPnyZXn0JTc3128E5vz58wDgN2Ljk5qaqpqWosQQaE8lLe1eRve9esTvHLNJCLh+TEiE0ccuKs7GobY+uYh4UbH/OjNERBRZYYeZnJwc5OTkTMmLl5eX4+mnn8bZs2cxc+ZMAKNFwampqSgtLZXPefzxxzEyMoKUlBT5nLy8vJBDEyWGQHsqjaesyIEDikJgIPBCeIFkWqepamqaTw8C4I7WRETxIKLdTJ2dnejv70dnZye8Xi9OnDgBAJg7dy6uuuoqrFy5Etdddx1Wr16NZ599Fv39/fj+97+P7373u7DZbACAe++9F0899RSqqqrw+OOPo6WlBc888wyeeOIJdjIlmbIiBw629sqjIKWzs1Bb34Kjp/pHp30ECc2nB1WhwreA3ZJiB84MuiEIAiRJQleA9WQAYEmxA2aTAFECBEiQIKB7YKytW7miL3e0JiKKPUGK4OpzVVVV+NWvfuV3vKGhAbfeeiuA0cDz4IMPYu/evbBarbj33nvx3HPPqaaJPvjgA1RXV6OpqQl2ux3r168PK8y4XC5kZmbC6XTKIYmMR7t+jChKeHFvi9/aMAIwWisDyNNSvmMbK+fh+V0nUbu3RfWYNIsJ19jSsOr6WdiwYq68VkxtfYv8HABQ6EjnLthERFES6u/viIaZeMEwk5jue/WI3/SRzy1zcyBJEg62jbVKCwAWFztQOjsLde+1q85/5ErQGe81bi7JxqLibNW0EkMNEVFkhPr7m3szkWHp1cIAY9NAjZoVeiUAjR39+OScet2hQke6bq2Lxyv61daIEkIqQiYioujhR0oyrOqKEhQ60lXHsqzTUFM5H+uWFqvqXJQuDnvlvwsA7l6Y7ze64vGKWP1ak2rLgvI52TAJmFARMhERRQ7DDBmWxWzC3QvzVQvkrbm5GBsr5+Hl99oDFvkqd75eMkd/R+u6hja/vZfMJgGLirNVr+crBCYiotjhNBMZmrY1et3SYtTWt2Db/jbd8wUBUFaJmU2Cbs2L3oiLskuKrdhERPGDYYYMTdsare0+0lIGGeXIirZTqrTQrqrHybdb5X2XRot+WSdDRBQvGGbIcLTBQ9lRpFxYLxhlizXgv7rwwyvm4pHK+Th6anRH7cb2PnQPuHHoSncUi36JiOIHwwzFNY9XxNY9rdh54gwAYNUNeaO7WDeMTiMdaO2Fx+uFxWxGU0cfTvf7F/3a0ix+O2IXOtJVgUS7unDz6UG88cBiAKPt2Sz6JSKKXwwzFNfqGtpUC9zV7mlFqkVd4/Lrxk643JcDjsgIwmgnkrKgV1u4q11dWHl/sPuIiCj2GGYorumNggx71LtlBwsyAHDdzEy8vnaR39SUUrDCXhb9EhHFN64ATHGttr4Fz9efnPDjM63TcGTzcqSlMLcTERlNqL+/uc4MxbXqihJsXD4PhY50pFmCf7umWkx+U1DXzbThgV83o7a+BR6vGOCRRERkZByZIcMYr+1aqcBuRb49HY3tfX4bTRIRkTFwZIYMyeMVUVvfgvtePYLa+hYMjXjk26Io4eEVc/22MCh0pCPTOk11TBAEmE0Cu5CIiJIACwkormjXe2ls75NHVw629qKmcj72PrrMr5h3+c/3wem+rHoudiERESUHhhmKmGCL2wWiXe/l/c4B1e23jneP1tFopotW3ZCH2j2tqtvsQiIiSg4MMxQx2lEWYPyVc8uKHKptBIY0bdid/Zew+rUmvL52kSoYbVg+DybB5BecWCNDRJT4GGYoYrSjLKHUrFRXlOA/Dnb4TRkpHW7vQ11DmyqoMLgQESUvhhmKmEA1K8rpp9JCOyBIaD49KI+oZFqnBQ0zAIt5iYhoDMMMhS3UWhhtzcq6pcWorW/BW8e70XllDyXllNLB1l6IooS8zDT5/kBYzEtERD4MMxS2UGthtFM/460TIwHYeeKMbpApsFvx9Rvy0dw5wGJeIiJSYZihsE2kFkb7uEAGLw37HRMAfKO0gDUxRESki4vmUdjKihwQrvw9nPVblI8DRhe7W1KsfqxryKu6XehIR03lfFRXlPgtqMftCYiICODIDE3ARNdvUT6udHYWIAn41eFTAc8vsFux6oY8HD3Vj7oGQBQlvLi3JaxWbyIiSnwMMxS2ibZBKx9XW9+CF/YErp8RAOTb0/HinlY5vBQ40rk9ARER+WGYoZgIVj+TZjHhhkI7AEkVXiRJgnDl79yegIiIfBhmKCq07dylhXZ5DRqtIY+IxvY+zLJbVcclCXh4xVzVmjREREQMMxQV2nbuh1fMRU3lfBw91Q+vKOFwe5/qfAnAhSH1wnndg26YBBPeeGBx9C6ciIjiHsMMTZrHK2LrnlbsPHEGwOgmjxuWz1MtpKdt524+PSiHEt+ojXIxPQHAdTMz/UIO62SIiEiLrdk0aXUNbajd24LO/kvo7L+E2j2tqGtoU52jbcv2ipLcWu0rDN776DI8Ujkft8zNQU3lfGyvuhHlc7Llx7BOhoiI9HBkhiZNb7REe6y6ogSN7X3ySMvh9j7c9+oR9DiHAIyN5mi7pF5fu0g16iNKIjxeUXf7BCIiSk4MMzQpHq8Ir+hfxqsdQbGYTTCbBNWxxo6xwFO7pxUmwb/l22I2wWQS0NV/CRKAFwOcR0REyYsfb2lS6hraVHUttjQzlhQ70NTR57dKr3aqSStQPcxEt08gIqLkwJEZmhRtsBAEkzzicqhtNOT4RlGUKwDrdTAFqocpK3LIbdysmyEiIi2GGZoUZdAAAKd7rJ1aO4qiXAFYrwMq0LoxE90+gYiIkgPDDE2KMmj4upmUAo2iWMwmPLJyPh5ZOX/c15jo9glERJQcGGYoLNqVfKsrSlT7LT1ff1I+t3xONkdRiIgo4hhmKCzalXwB/ZoYX9BhCzUREUUawwyFJVhnEaeDiIgoFvixmcKit5Lvfa8e8WvDngoer4ja+paIPT8RESUGjsyQbh1MoOkhbXt1Y3uf7pTTVAg2pUVEROTDMENhhQblVNJ9rx6J6GJ2XCyPiIhCwWkmmlBo0G5jEInF7JRTWlwsj4iIAuHIDE1ohV3tNgZLItCGzcXyiIgoFAwzNKHQoB29MZuEKW/DZncUERGFgtNMSS6c4l8lTgEREVG84MhMkptoxxCngIiIKF4wzCQ5bfFvU0cfauvHX8WXU0BERBQvGGaSnHbX6w97XDjYNlrYy7VdiIjICBhmkpxveuit493o7L8Ep/uyfB/XdiEiIiNgAXASCLYtgG+6qNCR7vc4FvYSEZERcGQmCYxX5KtdAA8ACh3puHthPgt7iYgo7jHMJIHxVvjVLoBXPicbr69dpCr8nWgLNxERUaQxzCSB8Vb4DWUBPG76SERE8YphJgmMtyZMKNsZcNNHIiKKVwwzSWC8NWFCWQBvIvs3ERERRQPDTJLSr4GZXOAhIiKKBYaZJBVuDQxX/CUiongVsXaUU6dOYe3atSguLobVakVJSQl+9KMfYWRkRHVeZ2cn7rjjDkyfPh05OTl4+OGH/c754IMPsGzZMlitVsyaNQs//vGPIUnqVmIKD2tgiIgoUURsZOavf/0rRFHEtm3bMHfuXHz44Yf47ne/i4sXL+K5554DAHi9XnzlK1/B1VdfjQMHDqCvrw/3338/JEnC1q1bAQAulwu33XYbKioqcPToUZw8eRJVVVWYPn06Hn300UhdfsJjDQwRESUKQYriEMezzz6Ll19+Ge3t7QCAP//5z/jqV7+Krq4u5OXlAQDefPNNVFVV4fz587DZbHj55ZexefNm/O///i9SU1MBAD/5yU+wdetWdHd3QxCEcV/X5XIhMzMTTqcTNpstcm/QQLhuDBERxbtQf39HtWbG6XTC4RgbATh8+DAWLFggBxkAuP322zE8PIzm5mZUVFTg8OHDWLZsmRxkfOds3rwZp06dQnFxsd/rDA8PY3h4WL7tcrki9I6MIVBwYQ0MERElgqiFmba2NmzduhU///nP5WPnzp3DjBkzVOfZ7XakpKTg3Llz8jlFRUWqc3yPOXfunG6Y2bJlC5566qkpfgfGpS32bWzvg9kkcESGiIgSQti/xZ588kkIghD0z7Fjx1SP6enpwZe+9CXcc889eOCBB1T36U0TSZKkOq49xzczFmiKafPmzXA6nfKfrq6ucN9mQtEW+x5u78OB1l68UH8SdQ1tsbw0IiKiSQt7ZOahhx7Ct7/97aDnKEdSenp6UFFRgfLycrzyyiuq83Jzc3HkyBHVsYGBAVy+fFkefcnNzZVHaXzOnz8PAH6jOj6pqamqaalkpyz2VWIXExERJYKww0xOTg5ycnJCOvfMmTOoqKhAaWkptm/fDpNJPRBUXl6Op59+GmfPnsXMmTMBALt27UJqaipKS0vlcx5//HGMjIwgJSVFPicvL89v+on0KRe884qSvKkku5iIiCgRRKybqaenB8uWLUNhYSF+/etfw2w2y/fl5uYCGG3Nvv766zFjxgw8++yz6O/vR1VVFe666y65NdvpdOIzn/kMli9fjscffxwtLS2oqqrCE088EXJrNruZxrCLiYiIjCLU398RCzM7duzAmjVrdO9TvmRnZycefPBB7N27F1arFffeey+ee+451TTRBx98gOrqajQ1NcFut2P9+vV44oknQmrLBhIvzDCQEBFRMoh5mIkniRZmautb5O4kAUBN5Xy2WRMRUcIJ9fc3P84bELciICIiGsMwY0BlRQ74JthYxEtERMmOu2ZHWTj1LoHOVXYn+Y4TERElK4aZKNOuxgsgYL1LoHO5FQEREdEYTjNFWTj1LqyNISIiGh/DTJSFU+/C2hgiIqLxcZopysKpd2FtDBER0fi4zgwRERHFpVB/f3NkJsZ8HUtNHX0QJcAkAIuKs+VRGK70S0REFBzDTIwpO5Z8DrX1yX/XdjNVV5Qw4BARESkwzMSYsmPJR9m5pO1mqmvwDzhs0yYiomTGj/QxpuxY8vF1Lul1M7Fdm4iISI0jMzHmq40JVDMDqLuZ6hpGR2R8m0yyXZuIiJIdu5kMJpztEIiIiIyM3UwJilsZEBERqfEjPRERERkawwwREREZGsMMERERGRrDDBERERkawwwREREZGsMMERERGRrDDBERERkawwwREREZGsMMERERGRrDDBERERkawwwREREZGsMMERERGRrDDBERERkawwwREREZGsMMERERGRrDDBERERkawwwREREZGsMMERERGZol1hdgZB6viLqGNhw91Y+yIgeqK0pgMTMfEhERRRPDzCTUNbThhfqTkAAcbO0FAGysnBfbiyIiIkoyHEaYhKOn+iFd+bt05TYRERFFF8PMJJQVOSBc+btw5TYRERFFF6eZJqG6ogQAVDUzREREFF0MM5NgMZtYI0NERBRjnGYiIiIiQ2OYISIiIkNjmCEiIiJDY5ghIiIiQ2OYISIiIkNjmCEiIiJDY5ghIiIiQ2OYISIiIkNjmCEiIiJDY5ghIiIiQ2OYISIiIkNjmCEiIiJDS4qNJiVJAgC4XK4YXwkRERGFyvd72/d7PJCkCDMXLlwAABQUFMT4SoiIiChcFy5cQGZmZsD7BWm8uJMARFFET08PMjIyIAhCRF7D5XKhoKAAXV1dsNlsEXmNeMX3zvfO954ckvV9A3zvsXrvkiThwoULyMvLg8kUuDImKUZmTCYT8vPzo/JaNpst6b7Rffje+d6TTbK+92R93wDfeyzee7ARGR8WABMREZGhMcwQERGRoTHMTJHU1FT86Ec/QmpqaqwvJer43vnek02yvvdkfd8A33u8v/ekKAAmIiKixMWRGSIiIjI0hhkiIiIyNIYZIiIiMjSGGSIiIjI0hpkI+drXvobCwkKkpaVh5syZWL16NXp6emJ9WRF16tQprF27FsXFxbBarSgpKcGPfvQjjIyMxPrSouLpp5/GTTfdhPT0dGRlZcX6ciLql7/8JYqLi5GWlobS0lL8z//8T6wvKSr279+PO+64A3l5eRAEAe+8806sLykqtmzZgrKyMmRkZOCaa67BXXfdhb/97W+xvqyoePnll/H5z39eXjCuvLwcf/7zn2N9WVG3ZcsWCIKAmpqaWF+KLoaZCKmoqMB//ud/4m9/+xveeusttLW14Rvf+EasLyui/vrXv0IURWzbtg0fffQRnn/+efzbv/0bHn/88VhfWlSMjIzgnnvuwfe+971YX0pE/f73v0dNTQ3+9V//Fe+//z6++MUv4stf/jI6OztjfWkRd/HiRXzhC1/ASy+9FOtLiap9+/ahuroajY2N2L17NzweD1auXImLFy/G+tIiLj8/Hz/5yU9w7NgxHDt2DMuXL8edd96Jjz76KNaXFjVHjx7FK6+8gs9//vOxvpTAJIqKd999VxIEQRoZGYn1pUTVz372M6m4uDjWlxFV27dvlzIzM2N9GRGzaNEiaf369apjn/3sZ6Uf/vCHMbqi2AAg7dy5M9aXERPnz5+XAEj79u2L9aXEhN1ul1599dVYX0ZUXLhwQZo3b560e/duadmyZdLGjRtjfUm6ODITBf39/fjNb36Dm266CdOmTYv15USV0+mEw+GI9WXQFBkZGUFzczNWrlypOr5y5UocOnQoRldF0eZ0OgEg6f5ve71evPnmm7h48SLKy8tjfTlRUV1dja985SuorKyM9aUExTATQY899himT5+O7OxsdHZ24t133431JUVVW1sbtm7divXr18f6UmiK9Pb2wuv1YsaMGarjM2bMwLlz52J0VRRNkiRh06ZNuOWWW7BgwYJYX05UfPDBB7jqqquQmpqK9evXY+fOnbjuuutifVkR9+abb+L48ePYsmVLrC9lXAwzYXjyySchCELQP8eOHZPP/5d/+Re8//772LVrF8xmM/7pn/4JkgEXXA73fQNAT08PvvSlL+Gee+7BAw88EKMrn7yJvPdkIAiC6rYkSX7HKDE99NBD+Mtf/oLf/e53sb6UqPnMZz6DEydOoLGxEd/73vdw//334+OPP471ZUVUV1cXNm7ciDfeeANpaWmxvpxxcTuDMPT29qK3tzfoOUVFRbr/8N3d3SgoKMChQ4cMNzwZ7vvu6elBRUUFFi9ejB07dsBkMm5mnsi/+Y4dO1BTU4PBwcEIX130jYyMID09HX/4wx+watUq+fjGjRtx4sQJ7Nu3L4ZXF12CIGDnzp246667Yn0pUbNhwwa888472L9/P4qLi2N9OTFTWVmJkpISbNu2LdaXEjHvvPMOVq1aBbPZLB/zer0QBAEmkwnDw8Oq+2LNEusLMJKcnBzk5ORM6LG+zDg8PDyVlxQV4bzvM2fOoKKiAqWlpdi+fbuhgwwwuX/zRJSSkoLS0lLs3r1bFWZ2796NO++8M4ZXRpEkSRI2bNiAnTt34r333kvqIAOMfj2M+LM8HCtWrMAHH3ygOrZmzRp89rOfxWOPPRZXQQZgmImIpqYmNDU14ZZbboHdbkd7ezueeOIJlJSUGG5UJhw9PT249dZbUVhYiOeeew5///vf5ftyc3NjeGXR0dnZif7+fnR2dsLr9eLEiRMAgLlz5+Kqq66K7cVNoU2bNmH16tW48cYbUV5ejldeeQWdnZ1JURv16aeforW1Vb7d0dGBEydOwOFwoLCwMIZXFlnV1dX47W9/i3fffRcZGRlyfVRmZiasVmuMry6yHn/8cXz5y19GQUEBLly4gDfffBPvvfce/vu//zvWlxZRGRkZfjVRvhrQuKyVil0jVeL6y1/+IlVUVEgOh0NKTU2VioqKpPXr10vd3d2xvrSI2r59uwRA908yuP/++3Xfe0NDQ6wvbcrV1dVJs2fPllJSUqSFCxcmTYtuQ0OD7r/x/fffH+tLi6hA/6+3b98e60uLuO985zvy9/rVV18trVixQtq1a1esLysm4rk1mzUzREREZGjGLmggIiKipMcwQ0RERIbGMENERESGxjBDREREhsYwQ0RERIbGMENERESGxjBDREREhsYwQ0RERIbGMENERESGxjBDREREhsYwQ0RERIbGMENERESG9v8DNMKLDYLWvTsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,y,s=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "cd3fba11-89a2-43f2-82b5-065bbc6cc39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ransac_pred = ransac.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "f7788f1b-e1bf-470b-b298-527f927fc744",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pred = lr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "9d6f72be-661e-4c5f-a0c0-83d480584cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,y,s=5, label='data')\n",
    "plt.scatter(X,ransac_pred,s=5,label='ransac')\n",
    "plt.scatter(X,lr_pred,s=5, label='linear-regression')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e95653f-0b49-4f86-97d7-74a1e64b535f",
   "metadata": {},
   "source": [
    "#### Comparisions RANSAC, Theil Sen, HuberRegressor\n",
    "- HuberRegressor should be faster than RANSAC\n",
    "- Theil Sen and RANSAC are unlikely to be as robust as HuberRegressor for the default parameters.\n",
    "- RANSAC will deal better with large outliers in the y direction\n",
    "- RANSAC is faster than Theil Sen and scales much better with the number of samples\n",
    "- RANSAC is a good default option"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b83d61-39e5-45d1-a999-b8e0d8d3e308",
   "metadata": {},
   "source": [
    "## 4. Polynomial Regression\n",
    "- Sometimes relationship between variables & target is of higher polynomial degree\n",
    "- Transformer can be used to convert data to higher degree\n",
    "- Linear models can predict coef of these higher degree polynomials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "edcef216-e245-400f-8a96-363cdbe2fb27",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "3eabe8ee-5c02-4ca0-b282-8ad9a5d8519e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_circles(n_samples=1000, noise=.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "7080c098-e987-4653-a3b2-5ef82f7b595c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y,s=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "43a40521-6cd3-480d-8245-d49c568b24c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "62197fa1-bc67-4cbe-868e-9a9e218e4ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pol = PolynomialFeatures(degree=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2527adf6-599f-4f34-9bd1-2c826bf067d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tf = pol.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "28f8ef2c-66a6-4b7a-85bf-18092353db9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "47475c7f-2be5-4cd5-a261-c25a6ccd9ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX,testX,trainY,testY = train_test_split(X_tf,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "71f89a5a-0714-4f53-b507-fdca0c27a5d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {\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-13 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 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-13 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-13 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-13 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-13 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-13 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-13 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-13 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-13 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-13 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-13 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-13 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-13 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-13 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 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-13 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 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-13 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-13 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-13 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-13 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-13 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-13 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-13 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-13 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-13 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-13 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-13 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-13 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-13 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-13 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 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-13 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-13 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-13 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-13 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-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "29746970-1f2d-48ce-b9dd-9db092e34f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.996"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "9a0e5edc-57b1-45eb-b115-e7425819992d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.09235074e-03, -4.92250876e-02,  4.73121093e-02,\n",
       "        -9.49307413e+00, -1.28632009e-01, -9.36360238e+00]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6386842b-216b-4f80-8f6c-450796e6d1f1",
   "metadata": {},
   "source": [
    "## 5. Bias Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23812f2a-5038-4056-a831-c6452944e73b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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