Polynomial Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d36d2d73-9b7b-413a-a02a-e9f3337aecb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Temp\\ipykernel_4232\\750269417.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1386b4b8-8793-4922-8da7-f418105d98f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(7 * np.random.rand(100, 1) - 3) \n",
    "x1 = x.reshape(-1, 1) \n",
    "y = 13 * x*x + 2 * x + 7 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6be76ce8-f0cb-49b3-b1c3-d63e6315ca90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x13eb3ad7530>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4728c1a8-4525-4714-9ff5-e39c6404a0df",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "405ec7b7-e554-4c32-ba69-73c3d0213d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "085b9261-08c2-48ea-a2e1-e63f7a86baf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: 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-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 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-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5f6047ed-c439-445f-9d88-9037c2f04db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.7014377]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "18758f7c-98a2-47e7-a589-0b1348dc8f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([47.39686312])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9ac45265-f152-4e8a-9a43-24c876c9a41b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05755882635487064"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lr.predict(x_test)\n",
    "r2_score(y_test,y_pred) #that gave us pathetic result on the linear data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fde1fa7f-5d29-47c6-8662-7d24fcbac0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cOHGidZZGkqqqqrRt2zZlZGQoIyND8+fP1y233CKn06nKykr98Ic/1PDhwzVp0iRJ0oUXXqiCggLdc889Wrx4sZqbmzV79mzddtttnMQCAFhCgt2mvGH9jS7D1Gxer9ewO+HXr1+va665psPjM2bM0KJFizRt2jR9+OGHOnbsmLKysnT99dfriSee0KBBg1rH1tbWavbs2SoqKpLdbtctt9yiX/3qV+rXr1/QddTV1cnhcMjlcrGkBQCASQT789vQsBMrCDsAAJhPsD+/TbVBGQAAIFSEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmmuggUAAArcHu8XO4ZRYQdAACiaPXHB/TjleWqrW9ufSzTkaJ5U3JUkJtpYGXWxTIWAABRsmB1hb697EO/oCNJ1a4GzVpapuLyaoMqszbCDgAAUbD642q9uLGqy+e9kuYXVcjtifv7ucOOsAMAQIS5PV79eGV5wHHVrgaVVtVGoaL4QtgBACDCSqtqVVvfFNTYQ8cbIlxN/CHsAAAQYWsqaoIeOzA1JYKVxCfCDgAAEVRcXq3f/m13UGMz+iZqXHZGZAuKQ4QdAAAixO3x6pE/bg96/JNTc+m3EwGEHQAAImRT5VEdO9kceKCk+67K1uSRWRGuKD4RdgAAiJBXN+8OalzBRYM0d3JOZIuJY4QdAAAiwO3xasPOI0GNHT4wNcLVxDfCDgAAEVBaVav6RndQY/OG9Y9wNfGNsAMAQAQE2y+nb1KCJgwl7EQSYQcAgAgItl/OvVcN4wRWhBF2AACIgHHZGcp0pKi7GJPeJ1GzJw6PWk3xirADAEAEJNhtmjel5YRVV4HnqZsvZlYnCgg7AABESEFuphZNHyOnw39JK9ORosXTx6ggN9OgyuJLL6MLAADAygpyM3VdjlOlVbU6dLxBA1NTNC47gxmdKCLsAAAQYQl2G8fLDcQyFgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDT67AAAcJrcHi/NAk2AsAMAwGkoLq/W/KIKVbsaWh/LdKRo3pQcroGIMSxjAQAQouLyas1aWuYXdCSpxtWgWUvLVFxebVBl6AxhJ0LcHq9KKo9q5bb9Kqk8KrfHa3RJAIAwcHu8ml9Uoc6+q/sem19Uwff9GMIyVgQwtQkA1lVaVdthRqctr6RqV4NKq2q5DytGMLMTZkxtAoC1HTreddA5nXGIPMJOGDG1CQDWNzA1JazjEHmEnTAKZWoTAGA+bo9XHq9X6b0TuxxjU8vWhXHZGdErDN1iz04YMbUJANbV2X7M9nwdduZNyaHfTgwh7IQRU5sAYE2rP67Wt5eVBRzn5DBKTCLshNG47AxlOlJU42rodN+OTS3/EJjaBADzWP3xAc1+7cNux6T3SdTC28dowrD+zOjEoJD37MyYMUMbN26MRC2ml2C3ad6UHElfTGX6MLUJAOZTXF6tby/7UIHOlRw72Sy73cb39xgVcthxuVzKz8/XiBEj9LOf/Uz79++PRF2mVZCbqUXTx8jp8F+qcjpStGj6GKY2AcAkfCdsg8V+zNgV8jLWW2+9pcOHD+uVV17R7373O82bN0/5+fmaOXOmpk6dqsTErneox4uC3Exdl+PkcjgAMLFAJ2zbYz9m7Dqto+cDBgzQnDlz9NFHH2nz5s0aPny47rrrLmVlZenBBx/Uzp07w12n6STYbcob1l9TR5+lPNZwAcB0Qpmp4ah5bOtRn53q6mqtWbNGa9asUUJCgiZPnqzt27crJydHzzzzTLhqBAAg6kKZqWE/ZmwLOew0NzfrzTff1Fe+8hWdc845ev311/XAAw/owIED+t3vfqe3335bf/jDH/STn/wkEvUCABAVvhO23UUYu0164Q72Y8a6kPfsZGZmyuPx6Pbbb1dpaalGjx7dYcw111yj9PT0MJQHAIAxfCdsZy0tk03qtKXI87dfoskjCTqxzub1ekO6qOmVV17RrbfeqpQU62zEqqurk8PhkMvlUlpamtHlAABiSGedkzNpHhgTgv35HXLYsSKjwo7b4+XEFgCYAN+vY1OwP7/poGwQflMAAPPwnbCFOXHruQGKy6s1a2lZh/4NNa4GzVpapuLyaoMqAwDAegg7UebryNnZ2qHvsflFFXIH6k0OAACCQtiJskAdOb2Sql0NKq2qjV5RAABYGGEnyoLtyMkdKwAAhAdhJ8qC7cjJHSsAAIQHYSfKAnXktIk7VgAACCfCTpT5OnJK6hB4fB06b7tssFZ9fEAllUfZqAwAQA/RVFDGNBXsrM9Oep9ESdKxk82tj9F7BwCAztFBOQSx0EF595GTevbtv3c4ku6b/Vk0nYvmAABoK9if3yxjGcjXkfMrI7O0fMseeu8AABABhJ0YQO8dAAAix9Cws3HjRk2ZMkVZWVmy2Wx66623/J73er167LHHlJmZqd69eys/P187d+70G1NbW6s777xTaWlpSk9P18yZM3XixIkofhY9R+8dAAAix9CwU19fr1GjRmnhwoWdPv/zn/9cv/rVr7R48WJt3rxZffv21aRJk9TQ8MUP/TvvvFM7duzQmjVrtGrVKm3cuFH33ntvtD6FsAi2p86R440sZQEAEKKY2aBss9m0YsUKTZs2TVLLrE5WVpa+//3v6wc/+IEkyeVyadCgQXr55Zd122236ZNPPlFOTo62bNmiSy+9VJJUXFysyZMna9++fcrKyur0YzU2NqqxsbH1z3V1dRo8eHDUNyj7uD1eXfH0OtW4Gjrdt9MWp7MA4PS1PRgyMLWlp1mCvavOZ4h1pt+gXFVVpZqaGuXn57c+5nA4NH78eJWUlEiSSkpKlJ6e3hp0JCk/P192u12bN2/u8n0vWLBADoej9W3w4MGR+0SC0F3vnfa4GR0ATk9xebWueHqdbn9pk763fJtuf2mTrnh6Hd9P40DMhp2amhpJ0qBBg/weHzRoUOtzNTU1GjhwoN/zvXr1UkZGRuuYzsydO1cul6v1be/evWGuPnQFuZlaNH2MnI7ul7Q4nQUAoSsur9aspWUdDoPwC2R8iNmwE0nJyclKS0vze4sFBbmZeu/hiXr0xgu7HcfpLAAIntvj1fyiCtp7xLGYDTtOp1OSdPDgQb/HDx482Pqc0+nUoUOH/J4/deqUamtrW8eYTYLdpjNTk4May+ksAAiM9h6I2bCTnZ0tp9OptWvXtj5WV1enzZs3Ky8vT5KUl5enY8eOaevWra1j1q1bJ4/Ho/Hjx0e95nDhZnQACB/ae6CXkR/8xIkT2rVrV+ufq6qqtG3bNmVkZGjIkCF64IEH9OSTT2rEiBHKzs7Wo48+qqysrNYTWxdeeKEKCgp0zz33aPHixWpubtbs2bN12223dXkSywx8N6N3dTrLJsnJzegAEBR+gYShMzsffPCBLrnkEl1yySWSpDlz5uiSSy7RY489Jkn64Q9/qO985zu69957ddlll+nEiRMqLi5WSsoXfyFfffVVXXDBBbr22ms1efJkXXHFFfr1r39tyOcTLoFuRpekeVNyOC4JAEHw/QLZ1XdMm1raevALpHXFTJ8dIxl1EWggnd2MTp8dAAid7zSWJL8Zcy5bNjduPQ9BrIYdiQZYABAu/AJpPYSdEMRy2AEAhA+/QFpLsD+/Dd2gDABANCXYbcob1t/oMhBlMXv0HAAAIByY2TE5pmQBAOgeYcfE2GwHAEBgLGOZFJfaAQAQHMKOCXGpHQAAwSPsmBCX2gEAEDzCjglxqR0AwDAul2SyFn2EHRMK9rK6I8cbWcoCAPTMoUPSnDmSzdbylp4u2c0VH8xVLSQFvtTO54n/+0RXPL2OzcoAgODV1Eh9+34RbgYNkp55xuiqeoSwY0Ld3YreXrWrQfdzOgsA0JW6ui+Cjc0mZWZKJ092PvaGG6QPPmAZC9FRkJupRdPHyOkIbknrkT9uZ0kLACCdOiVddNEX4cbh6HpsTo60Zo3k8bQEnNWrpbFjo1drmBB2TKwgN1PvPTxRj954YcCxx0426/l1u6JQFQAg5kyf/kW4SUyUKiq6Hjt1qtTc3BJuduyQ8vNbXmdihB2TS7DbdGZqclBjl7xfxewOANNze7wqqTyqldv2q6TyKN/XOvPII/5LU6++2v34zz9vCTder/TWW1Iva12wYK3PJk4Fezrr2MlmlVbVcuMvANPimpwu/OIX0kMPBT++slIaOjRy9cQYZnYsYFx2htJ7JwY1tqaO3jsAzIlrctpYvdp/5iZQ0HnvvS9mbrzeuAo6EmHHEhLsNt19+blBjX1i1Y74+oYAwBLi/pqcLVv8w82NN3Y/fv58/3Bz+eXRqTNGEXYsYvbEEUrvE3h2p7a+Of5+AwJgenF3Tc7Bg/7hZty47sffeKN/uHnssejUaRKEHYtIsNv01M0XB+y7I7V8U/jRiu1qOuWJdFkAEBaWvyanqck/3Did3Y9PTv7iOLjXK61aFZ06TYqwYyG+3jsZfZMCjq2tb9aEBWuZ4QFgCsEexAh2XExoG26SgzhVe/LkF+GmocH0x8GjibBjMQW5mUH13ZGk2vomlrQAmEKga3JsajmVNS47I5plhaZtuAkmqOzZ47801bt35Gu0KMKOBTkdof2DsPSmPgCW0N01Ob4/z5uSowR7DM125OSEFm42bPAPN4MHR77GOEHYsaBgLwqVLLipD4BldXVNjtORokXTxxjfZ6d9I79PPul+/KOP+oebq66KTp1xiKaCFuT7DWjW0rKgX2PaTX0A4kpBbqauy3GqtKpWh443aGBqy9KVITM6v/+9dNttwY/PyJCOHo1cPegSYceifL8B/WjFdtXWNwccb6pNfQDiWoLdZkwn+J07pfPOC+01Jrsd3KpYxrKwgtxMbZqb3+3pLFNs6gMAI/zrX/7LUsEEnbbHwQk6MYOwY3FJvez62VdzZZOJNvUBgFHahps+fQKPP3bMP9xwHDwmEXbiQHeb+hbeMUaO3kncHgwgPoV6HHzrVv9w43BEvkb0GHt24kRnm/o+r2/UE//H7cEA4kjfvi3N+YL1wgvSrFmRqwdRwcxOHPFt6ps6+iy5/tWkwmUfcnswAGu7917/mZtAQaf9HVMEHUtgZicOBbo92KaWRoPX5TjZywPAXN54Q7r11tBew0Ziy2NmJw7F3e3BAKxr1y7/mZtggk7bmRuCTlxgZicOWf72YADW1dAQ+h1RHg+npOIcMztxyJK3BwOwrrYzN8EEnc8/5zg4/BB24pAlbg8GYF2hHgf/4AP/cJOeHvESYS6EnThkytuDAVhXenpo4eb55/3DzdixES8R5kbYiVPB3B7s9nhVUnmUhoMAwmv6dP9w43J1P76gwD/cFBZGp05YBhuU41h3twcXl1drfpF/w8GMvol6cmquJo/MMrBqAKazaJH07W+H9hpOSSGMCDtxrrPbg4vLqzVraVmHPjy19c369rIPdd++Y5o7OSd6RQIwl+3bpZEjQ3sN4QYRRNiBn+4aDvq8uLFKo84+Q5NHcqUEALV0Je7bN6SXnPvDIslma90nuKi8mmtqEDGEHfgJ1HDQ59GV5ZqUS4dlIG6FeJy74Edv6FN3x3YWdG1HNLBBGX6CbSR4tL6JDstAPAn1OPjata0biv97zWedBh0furYj0gg78BNKI8GaOjosA5YVariZM8f/xNTEiZJa9gA+8/bOoD4kXdsRKSxjwc+47Axl9E1UbX1zwLFPrNqh3ol21tkBKzjrLOnAgdBeE2BTsW8PYLDo2o5IYWYHfhLsNj05NTeosbX1zZq1tEzF5dURrgpA2D35pP/MTTBBJ8QLNIPdAyjRtR2RRdhBB5NHZum+q7KDHj+/qIKGg0Cs++AD/3Dz6KOBX9PD28FDWZaiazsiibCDTs2dnKMX7hij1JTuVzrZWAjEqPp6/3Bz2WWBX+N29yjctBfsstSD+eexHI6IIuygS5NHZuonN10U1Fg2FgIxoG246dcv8Pj9+/3DjT28PxICXTosSc60ZM2eODysHxdoj7CDbjkdvYMax8ZCwAChnph6803/cJMV2atfAl06bJP0+E0XsXyFiCPsoFuBfjOziY2FQNSEGm6+/nX/cHPzzZGvsZ1gLh0GIo2j5+iW7zezWUvLZJP8rpHwfatlYyEQISF2KZYUk3dMdXfpMBANzOwgIH4zA6LkvvtCm7mRenxiKlp8lw5PHX2W8ob1J+ggqpjZQVD4zQyIgA0bpC9/ObTXxHCgAWIVYQdB8/1mBuA01dVJDkdor3G7w35KKpzcHi+/BCHmEXYAIJJC3XdTWSkNHRqZWsKsuLxa84sq/LokZzpSNG9KDsvbiCmx++sCAJhRqCemnn/ef8+NiYLOrKVlHa6DqHE1cI0MYg4zOwDQE6HO3DidUrW5g4Dvgs/Odg951XJSc35Rha7LcbKkhZjAzA4AhCLUmRvJf+bG5EFHCnzBJ9fIINYQdhBxbo9XJZVHtXLbfpVUHuXSUJjLxImWPQ5+uoK9HoZrZBArWMZCRLGBEabzpz9JU6eG9hoLBpruBHs9DNfIIFYws4OIYQMjTKGuzn/mJpigc+qUpWduAuEaGZgNYQcREWgDo1ctGxhZ0oIh2oabYPrefPyxf7hJSIh8jTGk/VK0pG4v+PQ9z+ZkxAqWsRARgTYwSi0bGJ9ft1Pfyz8vSlUhboV6YuoHP5D+3/+LTC0mU1xercf/VKGaui/+PTvTUvT4TTlaNH1Mh2VqJ8vUiEGEHUREsBsTn3l7p853pvKNEeFlkQs0jVZcXq37l5Z1eLymrkH3Ly3T4ulj9N7DE+mgjJhH2EFEhLIx8UcrtmviBYOU1ItVVZwmwk3YuT1ePfLH7d2OmfvH7boux8k1Moh5Mf3T5fHHH5fNZvN7u+CCC1qfb2hoUGFhofr3769+/frplltu0cGDBw2sGD6+DYzBqK1v1oQFa9mwjOCdfz7HwSPsubU7dexkc7djPj/ZrE3/OBqlioDTF9NhR5IuuugiVVdXt7699957rc89+OCDKioq0uuvv64NGzbowIEDuvnmmw2sFj4JdlvrBsZg1NY3cUILXXvjDf9w8/e/B34N4ea0FZdX69m1O4Ma69uwDMSymF/G6tWrl5xOZ4fHXS6X/ud//kfLli3TxIkTJUlLlizRhRdeqE2bNmnChAldvs/GxkY1Nja2/rmuri78hUMFuZl6MP88PfN2ED+Y1HJCyzctzpp/nHO5pPT00F7T3Cz1ivlvaTHPd5IyeARJxL6Yn9nZuXOnsrKyNHToUN15553as2ePJGnr1q1qbm5Wfn5+69gLLrhAQ4YMUUlJSbfvc8GCBXI4HK1vgwcPjujnEM9mTxwuZ1py0OM/P9ms54L8jRIW03bmJpigs2mT/8wNQScsgjlJ2Vbe0DMjWA0QHjEddsaPH6+XX35ZxcXFWrRokaqqqnTllVfq+PHjqqmpUVJSktLbfVMcNGiQampqun2/c+fOlcvlan3bu3dvBD+L+JZgt+nxmy7qsvlYZ55du1Ortu2PWE2IEaHeMXX33f7hZvz4yNcYh0K54iG9T6ImsDkZJhDTvwrdcMMNrf89cuRIjR8/Xuecc47+8Ic/qHfv3qf9fpOTk5WcHPxsA3qmIDdTi6aP0Y9WbFdtffcbHn1mL9+m7Qdcmjs5+H0/iHGcmDKFUE5SPnXzxSw5wxRiemanvfT0dJ133nnatWuXnE6nmpqadOzYMb8xBw8e7HSPD4xVkJupTXPz1S85+M6zL26sUtFHByJYFSKqp7eDE3QMEegqCKmlS/ILd1xCfyyYhqnCzokTJ1RZWanMzEyNHTtWiYmJWrt2bevzn332mfbs2aO8vDwDq0RXknrZdc+Vw0J6zXeXf6jVHxN4TOGqqwg3FtD2JGVX/xcX3jFGk0dmRa8ooIdiOuz84Ac/0IYNG7R79269//77+upXv6qEhATdfvvtcjgcmjlzpubMmaN33nlHW7du1d133628vLxuT2LBWLMnDld6n8Sgx3u90reXfciR9FhUVOQfbt59N/BrCDem4Ft6drbrlZXpSNHi6WM0eSQzOjCXmN6zs2/fPt1+++06evSoBgwYoCuuuEKbNm3SgAEDJEnPPPOM7Ha7brnlFjU2NmrSpEl64YUXDK4a3Umw2/TUzRd32oK+O3RZjgF1dcFdmtlWU5OUGHy4RewoyM3UdTlOroKAJdi8Xn69qqurk8PhkMvlUlpamtHlxIXVH1ercFlZSB06zuiTqAU3X8w+gWgKdVNxaal02WWRqQUA2gn25ze/JsMQk0dm6rnbRof0ms9PNut+uixHVqibir/1Lf9lKYIOgBgU08tYsLavjD5L2w+49OLGqpBe9whdlsOH4+BxpemUR6+U7NY/a0/qnIw+uivvXJaGERcIOzDU3Mk5yj0rXd9d/mHQP0OPnWzWpsqjunwEnVtDRriJWwtWV+ild6vkafO/86erP9E9V2bTzwqWR6SH4aaMytLC2y8J6TUl/zgSoWos5sorOQ4OLVhdoRc3+gcdSfJ4W/pZLVgdyl1YgPkQdhATJo/M0uLpY9Q36KaDLGF1atUq/3Dz3nuBX0O4sbSmUx699G73S8UvvVulplOeKFUERB9hBzGjIDdTi+8cG9TYPO7jaXH8uH+4mTIl8GsaGwk3ceSVkt0dZnTa83hbxgFWRdhBTPnS8DMDNh08o0+iJgyN47DTNtwE0yrh/ff9w01SUuRrRMz4Z+3JsI4DzIiwg5jiazrYnQXxdvlgqMfBp0/3DzdcnxLXzsnoE9ZxgBkRdhBzCnIztXj6GDnTOm9V37apoNvjVUnlUa3ctl8llUflDjRfbwY9vUDzlVciWx9M5a68cxXodwO7rWUcYFUcPUdMCqZVfXF5teYXVaja1dD6WKYjRfOm5JiryzLHwRFBLRfwZnfbz+qeK7PptwNL47oIcV2EGRWXV2vW0o7XTfhiw6J2M0Ax5ZprpPXrQ3sN/0wRgNvj7faXg8767Nhtos8OTC3Yn9+EHRF2zMbt8eqKp9f5zei0ZZPkdKTovYcnxsbentWrpRtvDO01/LNECIKd5aSDMqwm2J/fLGPBdEqrarsMOpLklVTtalBpVa0xR9Tr66V+/UJ7TWMjp6RwWrqa5axxNWjW0jK/Wc6kXnbNvHJo9IsEDEakh+kcOt510DmdcWHRdkNxMEHn3Xc5Do4ec3u8ml9U0SHoSGp9bH5RhTU27gM9QNiB6QxMTQk8KIRxpyXUE1O33+4fbq64InK1wfJ8pxCfWfP3oGc5gXjGMhZMZ1x2hjIdKapxNXT6G61vz8647IzwfVBOTCFGdLY/J5CoznICMYiZHZhOgt2meVNaTo+0jyC+P8+bktOzzck97XVD0EEE+PbnhBJ0pAjPcgImQNiBKRXkZmrR9DFyOvy/iTsdKad37Pyhhwg3iGnd7c/pik0tp7LCOssJmBDLWDCtYBoPttW2D0n2Pz/TyGnXhvYBCTQwUKBTiO2FbZYTsADCDkwtwW4L6nj5X8p2a9LYbIV0SxTHwRFDQt134zRjN3EgQgg7sK42y1GTghm/bZs0alSkqgFC0r4j8pn9koN63exrhuny4QO6neUE4g1hB9YR4ompp6+eocUTbo2tbsuApNUfH9CPV5artr659TFnWrLS+yTKdbK521OID153Pn+XgXYIOzCvEMNNZcZZuvaeFzs8Xu1q0Mt/q9KZqckB9/0AkbZgdUWnl3bW1DW2/rdN8gs87M8BukfYgXkMGSLt3RvSS1Z+uE/fW74t4Lgn/u+T1v825c3psITVH1d3ezu5JKX3SVRKrwTV1H2xh4f9OUD3CDuIXc89J333u6G9pt2JqYGVR0P+sJ3dKQREmtvj1Y9Xlgccd+xks16dOUZ2uy2oU4gACDuIJZ98IuXkhPaaAMfBA3Vb7vRdqmVZYH5Rha7LcfJDBFFRWlWr2vqmoMYeqW/U1NFnRbgiwDpoKgjjNDf7N/ILJug0NYXUyK+7bsvd4U4hRFsoR8vpiAyEhrCD6GobboLpYfPpp/7hJjEx5A/ZVbflYPxt1xGt3LZfJZVHuTkaYeW7zNP39+vMvsEdLc/om0hHZCBENq+XtrB1dXVyOBxyuVxKS0szuhxrCfUCzRdekGbNikgpbfuWHDne6LcpORhsXEY4uD1ePb9up5b8bbeO/avt0fIUNZxy69jJ5m5eLb1wxyWaPDIr0mUCphDsz2/27CC8Qg03+fnSmjWRqaWdtt2W3R6vfvNeVUh7eapdDbp/aZkezB+h2RNHsJcHISsur9Yjf9zeaaA5WBf47+J9V2UTdIDTwDIWeuZrX+vZBZpRCjrtne5eHkl65u2duvypdSourw5/YbAs343lXc3c+DbGn9EnUc40/yWt/n2T9MIdYzR3cogb+AFIYhlLEstYIXntNemOO0J7TQz/FSsur9b8ooqQLlhsiyUFBOL2eLWp8qgKl5X5LVt159VvjZfdxtFyIBCWsRAee/ZI55wT2mtiONy01/7m9J0Hj+v5dyqDfv3s1z7Uf3ulKaMIPOjodMP0kRMcLQfCiWUs+Dt1yn9ZKpigE+Jx8Fjj28szdfRZunz4gJBe6/FK33ntQ818uZQTW/DjW7Y6nVlDjpYD4UXYgX+4CeZod2Vlj4+DxypfE8JQFwzWfnpYt7+0SVc8zV6eeOf2ePW3nUf0yJvbg9783lamI4Wj5UCYEXbiUdtwE8ym4j/+0T/cDB0a+RoN0nbj8unwXTVB4IlPxeXVuuLpdbrzfzYHvT+nPS7zBMKPsBMPrroqtHDzrW/5h5uvfjXyNcaQ1iaEacE1eWvL95v8/KIKlrTiTE+WraSWCz4Xcx8bEBFsULai3/5Wmjkz+PFnnx3ybeJW59u4/Py6XXrm7b+H9Nq2V034+vrAeto2qTyzX7Ie/9OO01q2Su+TqLu/lK3ZE4czowNECGHHCnbulM47L7TXmHAjcbQl2G36Xv4IjRjYV4WvfRjylyyUu45gLqs/rtaPV5YHfXFnZ9J7J2rhnWM0YWh/Qg4QYYQdMzp+XAq1HxDh5rRNHpmlX/371FUo2p6oaTsLQN8Uc1uwukIvbqw67df7/q8/dcvFunz4meEpCkC3CDtm4PFICQmhvebUqdBfgy5NGZWl8v3HgvohZ5PkbHOiprNeK9yzZS5uj1eb/nFUr5TsVvGOgz16X07+3wNRR9iJVV/7mvTmm8GPd7lCn+1BSOZOztGos9P/vXzR+Ukb32/tvhM1vk2r7efVfKe2FrEhNeZ1d59VKFi2AoxD2IkVf/yjdMstwY//+9+lESMiVw86NXlkliblZqq0qlZrKmr01rYDfvs22v7W7vZ4Nb+ootNNq757kOYXVei6HCc//GJUcXm17l9a1qP3wbIVYDzCjlH275d+8hPp178Obvxf/iJdf31ka0JQfB2X84b11/93Y06Xe3FKq2q7PYbMqa3Y5vZ49fifKnr8fli2AoxH2ImWzz+X/uu/pJ/+NLjxy5dL3/hGZGtCj/mCT2eCPY3Fqa3YVFpVq5q60P/fZPRN0nO3XaIj9Y1sRgdiBGEnkvbvb1lq+te/Ao/9/e+lr3898jUhaoK934h7kIwR6ITc6YbQJ6fm6vIRLFcBsYSwE0kzZnQedK6+WnriCenKK6NfE6LGd89Wjauh03077U9tdYVj6+EXzAm50wmh912VrckjWa4CYg1hJ5K+/W1p7VopN7dl+WrKlOCua4Al+O7ZmrW0TDbJL/C0P7XVleLyaj3+px2qqWtsfcyZlqzHb7qIPSAhaBsYdx+p1zNv7+wwpv0JuXHZGXKmpQS1lJXRN1FPTs3V5JFZkSgfQA/ZvF66zdXV1cnhcMjlcimN49sIs9PtsxPoJBD3KAUnlG7Hvtm29x6e2No6INBprAfzR2j2xBHMtgEGCPbnN2FHhB1EXqhLUW6PV2OfXNNtb5f0Pona+uPr+CHbjdPtdvzaPRNaN5531WcnvU+inrr5YgInYKBgf36zjAVEQXentjqzqfJowCZ2x042a1PlUU0Y1j/u9/T4OhyXVB6V5FXe0DP1eX3jaV/r0HZzsu9S2Pbvf8IwmgMCZkHYAWJQyT+OBDVu6ebd+sEbH/ktkTnTUvSNywbL7fFIaglZVuza65st++uOGr22ZY8amj2tzz3/TmWP3nf7zckJdpsuH34mTQEBkyLsADEpuGDy5/KO9zTV1DXov9d+sQH3+Xd2tS65XJfjNOUsUPtlwM/rG/XE/33SbdPG0xHsCTkA5kLYAWJQ3rD+ev6dXWF7f8dONuv+pWVK75PotzxmhgtJO9vgHQnBnpADYD6EHSAGTRjav0MwCYf278933HrhHZfI0SfJb0/KZdkZ2vrPzyM2C+T2ePX+riN6s2yfTjad0mXn9teML52rpF721jFdXaQaCVzrAFgXp7HEaSzEpnBcQhksm01q/52gfW8gZ1qKrhjeX7162bSz5oRSEhM0dEBfPVxwoT7ad6w1KI0/t7/sCTYdOdH1dQnF5dWa84ePdLLJ3aGOe6/M1tzJOXJ7vLri6XURndF54NoRyh7Q11RLegC+wNHzEBB2EKtamgpW+DW2y3Sk6LbLhuiZt/9uYGXBa79UFkyIu++qbH35/EG6/aVNEavrvqtaQhUA8+LoOWABvmPP7TcVS9LyLXu6vIoilrTtTHxdjlPzVu4I+JqX3q3S+YPC84tH+xkquh0D8YewA8S4rnr0dHUVRazxqiVwzC+qUGpyog4ebwz0Enm80od7Pw/Lx194xxid0TfJdCfQAIQPYQcwqYLcTC2aPiYqJ5V6yiup2tUQdP8g32u6u0g1kL5JCfqvr49iwzEAwg5gZu2XuXYfqdeSv1Xp2L9O+Y3rm5yg+kZ3DMwCBT+jkt2/72nNXvVNStC3rszWd689jxkcAJIIO4DptV/mmj1xhDZVHv33LMoXHZTXVNR0mAWy21qWjKIlb1h/vf7B3oBLWXabdFdeyzH0zmavMh0pevTGHJ3RN0k1dQ2qPdGojL5Jcjp6s0wFoANOY4nTWIgfHTsRN+nbyyJ/vL3tbeJrKmqCOo3V9qRUqBepAogPHD0PAWEH8ayrW73DteTliySLpo/xO34eqM8OAARC2AkBYQfxrrNbw9t2UH67okarPq45rfDT1ZUUwXRQBoDuEHZCQNgBAms65dErJbu18/DxHndQBoBwIOyEgLADAID5BPvz2zLzxQsXLtS5556rlJQUjR8/XqWlpUaXBAAAYoAlws7vf/97zZkzR/PmzVNZWZlGjRqlSZMm6dChQ0aXBgAADGaJsPPLX/5S99xzj+6++27l5ORo8eLF6tOnj377298aXRoAADCY6cNOU1OTtm7dqvz8/NbH7Ha78vPzVVJS0ulrGhsbVVdX5/cGAACsyfRh58iRI3K73Ro0aJDf44MGDVJNTU2nr1mwYIEcDkfr2+DBg6NRKgAAMIDpw87pmDt3rlwuV+vb3r17jS4JAABEiOnvxjrzzDOVkJCggwcP+j1+8OBBOZ3OTl+TnJys5OTkaJQHAAAMZvqZnaSkJI0dO1Zr165tfczj8Wjt2rXKy8szsDIAABALTD+zI0lz5szRjBkzdOmll2rcuHF69tlnVV9fr7vvvtvo0gAAgMEsEXa+8Y1v6PDhw3rsscdUU1Oj0aNHq7i4uMOm5a74mkhzKgsAAPPw/dwOdBkE10VI2rdvHyeyAAAwqb179+rss8/u8nnCjlr2+Bw4cECpqamy2cJ3YWFdXZ0GDx6svXv3xuWdW/H++Ut8DSS+BvH++Ut8DeL985ci9zXwer06fvy4srKyZLd3vQ3ZEstYPWW327tNhD2VlpYWt3/BJT5/ia+BxNcg3j9/ia9BvH/+UmS+Bg6HI+AY05/GAgAA6A5hBwAAWBphJ4KSk5M1b968uG1gGO+fv8TXQOJrEO+fv8TXIN4/f8n4rwEblAEAgKUxswMAACyNsAMAACyNsAMAACyNsAMAACyNsBMlN910k4YMGaKUlBRlZmbqrrvu0oEDB4wuK2p2796tmTNnKjs7W71799awYcM0b948NTU1GV1a1Pz0pz/Vl770JfXp00fp6elGlxMVCxcu1LnnnquUlBSNHz9epaWlRpcUNRs3btSUKVOUlZUlm82mt956y+iSomrBggW67LLLlJqaqoEDB2ratGn67LPPjC4rqhYtWqSRI0e2NtLLy8vTn//8Z6PLMsxTTz0lm82mBx54IOofm7ATJddcc43+8Ic/6LPPPtObb76pyspKfe1rXzO6rKj59NNP5fF49OKLL2rHjh165plntHjxYv3oRz8yurSoaWpq0q233qpZs2YZXUpU/P73v9ecOXM0b948lZWVadSoUZo0aZIOHTpkdGlRUV9fr1GjRmnhwoVGl2KIDRs2qLCwUJs2bdKaNWvU3Nys66+/XvX19UaXFjVnn322nnrqKW3dulUffPCBJk6cqKlTp2rHjh1GlxZ1W7Zs0YsvvqiRI0caU4AXhli5cqXXZrN5m5qajC7FMD//+c+92dnZRpcRdUuWLPE6HA6jy4i4cePGeQsLC1v/7Ha7vVlZWd4FCxYYWJUxJHlXrFhhdBmGOnTokFeSd8OGDUaXYqgzzjjD+5vf/MboMqLq+PHj3hEjRnjXrFnjvfrqq73f+973ol4DMzsGqK2t1auvvqovfelLSkxMNLocw7hcLmVkZBhdBiKgqalJW7duVX5+futjdrtd+fn5KikpMbAyGMXlcklS3P6bd7vdWr58uerr65WXl2d0OVFVWFioG2+80e/7QbQRdqLo4YcfVt++fdW/f3/t2bNHK1euNLokw+zatUvPPfec7rvvPqNLQQQcOXJEbrdbgwYN8nt80KBBqqmpMagqGMXj8eiBBx7Q5ZdfrtzcXKPLiart27erX79+Sk5O1v33368VK1YoJyfH6LKiZvny5SorK9OCBQsMrYOw0wOPPPKIbDZbt2+ffvpp6/iHHnpIH374of76178qISFB//Ef/yGvyRtYh/o1kKT9+/eroKBAt956q+655x6DKg+P0/n8gXhTWFio8vJyLV++3OhSou7888/Xtm3btHnzZs2aNUszZsxQRUWF0WVFxd69e/W9731Pr776qlJSUgythesieuDw4cM6evRot2OGDh2qpKSkDo/v27dPgwcP1vvvv2/qKc1QvwYHDhzQl7/8ZU2YMEEvv/yy7HZz5+3T+Tvw8ssv64EHHtCxY8ciXJ1xmpqa1KdPH73xxhuaNm1a6+MzZszQsWPH4m5W02azacWKFX5fi3gxe/ZsrVy5Uhs3blR2drbR5RguPz9fw4YN04svvmh0KRH31ltv6atf/aoSEhJaH3O73bLZbLLb7WpsbPR7LpJ6ReWjWNSAAQM0YMCA03qtx+ORJDU2NoazpKgL5Wuwf/9+XXPNNRo7dqyWLFli+qAj9ezvgJUlJSVp7NixWrt2besPeI/Ho7Vr12r27NnGFoeo8Hq9+s53vqMVK1Zo/fr1BJ1/83g8pv++H6xrr71W27dv93vs7rvv1gUXXKCHH344akFHIuxExebNm7VlyxZdccUVOuOMM1RZWalHH31Uw4YNM/WsTij279+vL3/5yzrnnHP0i1/8QocPH259zul0GlhZ9OzZs0e1tbXas2eP3G63tm3bJkkaPny4+vXrZ2xxETBnzhzNmDFDl156qcaNG6dnn31W9fX1uvvuu40uLSpOnDihXbt2tf65qqpK27ZtU0ZGhoYMGWJgZdFRWFioZcuWaeXKlUpNTW3dq+VwONS7d2+Dq4uOuXPn6oYbbtCQIUN0/PhxLVu2TOvXr9df/vIXo0uLitTU1A57tHz7VqO+dyvq57/i0Mcff+y95pprvBkZGd7k5GTvueee673//vu9+/btM7q0qFmyZIlXUqdv8WLGjBmdfv7vvPOO0aVFzHPPPecdMmSINykpyTtu3Djvpk2bjC4pat55551O/3/PmDHD6NKioqt/70uWLDG6tKj5z//8T+8555zjTUpK8g4YMMB77bXXev/6178aXZahjDp6zp4dAABgaebfNAEAANANwg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4Ayzl8+LCcTqd+9rOftT72/vvvKykpSWvXrjWwMgBG4CJQAJa0evVqTZs2Te+//77OP/98jR49WlOnTtUvf/lLo0sDEGWEHQCWVVhYqLfffluXXnqptm/fri1btig5OdnosgBEGWEHgGX961//Um5urvbu3autW7fq4osvNrokAAZgzw4Ay6qsrNSBAwfk8Xi0e/duo8sBYBBmdgBYUlNTk8aNG6fRo0fr/PPP17PPPqvt27dr4MCBRpcGIMoIOwAs6aGHHtIbb7yhjz76SP369dPVV18th8OhVatWGV0agChjGQuA5axfv17PPvusXnnlFaWlpclut+uVV17Ru+++q0WLFhldHoAoY2YHAABYGjM7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0v5/eUUZF7hpZXAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_train,lr.predict(x_train),color=\"red\")\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2fa070ca-9250-4d4b-beb2-0865b1769db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now Applying polynomial on linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8743c746-d1bc-4dee-b428-9004be767212",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly=PolynomialFeatures(degree=2)\n",
    "x_train_trans=poly.fit_transform(x_train)\n",
    "x_test_trans=poly.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "26b8a483-72e6-419f-b86a-623acae139e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: [3.62909427]\n",
      "x_train_trans: [ 1.          3.62909427 13.17032524]\n"
     ]
    }
   ],
   "source": [
    "print(\"x_train:\",x_train[0])\n",
    "print(\"x_train_trans:\",x_train_trans[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "15785c2e-8f36-4df9-bd9c-fd16ee5248eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c45d9a1a-687c-44b4-9562-2d2409141d1b",
   "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/1.4/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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train_trans,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d4be81b1-2cd3-4df7-b073-8f752e231ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(x_test_trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2f39dd38-123f-4ebe-9029-9d76b299e2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d591bc8d-b1d0-4fad-a2ca-0d576ad000fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_train,lr.predict(x_train_trans),color=\"red\")\n",
    "plt.scatter(x,y,color=\"blue\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ada9aa2-136e-47ee-94ab-5470f42499ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
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