diff --git "a/simple Regression.ipynb" "b/simple Regression.ipynb"
new file mode 100644--- /dev/null
+++ "b/simple Regression.ipynb"
@@ -0,0 +1,357 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IMPORTING LIBRARIES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IMPORTING DATASET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.1]\n",
+      " [ 1.3]\n",
+      " [ 1.5]\n",
+      " [ 2. ]\n",
+      " [ 2.2]\n",
+      " [ 2.9]\n",
+      " [ 3. ]\n",
+      " [ 3.2]\n",
+      " [ 3.2]\n",
+      " [ 3.7]\n",
+      " [ 3.9]\n",
+      " [ 4. ]\n",
+      " [ 4. ]\n",
+      " [ 4.1]\n",
+      " [ 4.5]\n",
+      " [ 4.9]\n",
+      " [ 5.1]\n",
+      " [ 5.3]\n",
+      " [ 5.9]\n",
+      " [ 6. ]\n",
+      " [ 6.8]\n",
+      " [ 7.1]\n",
+      " [ 7.9]\n",
+      " [ 8.2]\n",
+      " [ 8.7]\n",
+      " [ 9. ]\n",
+      " [ 9.5]\n",
+      " [ 9.6]\n",
+      " [10.3]\n",
+      " [10.5]]\n",
+      "[ 39343.  46205.  37731.  43525.  39891.  56642.  60150.  54445.  64445.\n",
+      "  57189.  63218.  55794.  56957.  57081.  61111.  67938.  66029.  83088.\n",
+      "  81363.  93940.  91738.  98273. 101302. 113812. 109431. 105582. 116969.\n",
+      " 112635. 122391. 121872.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset  = pd.read_csv('Salary_Data.csv')\n",
+    "x=dataset.iloc[:,:-1].values\n",
+    "y= dataset.iloc[:,-1].values\n",
+    "print(x)\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SPLITTIG TRAINING AND TEST DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TRAINING\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "#creating a instance for the linaer regression class\n",
+    "regressor = LinearRegression()\n",
+    "#fit the model this will train the model\n",
+    "regressor.fit(x_train,y_train)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PREDTICT THE RESULTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = regressor.predict(x_test)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "VISUALIZING THE TRAINGING SET RESULTS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'salary versue experience (training set)')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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GDhyIgQMHIjs7G08//TQmTJiADh064NixYzhz5gy+//579O7dW3rNpk2bHnjetWvXIi8vD2vWrJH9j9KSJmzA8I9wz549sWDBAkyZMgWrVq3Cm2++Wew/zoV5eXmhU6dOWLZsGaZPn44lS5YgJiZG1nwfFRUFIQQiIyNRs2ZNi2Ir6rnnnsNzzz2HCRMmYNGiRUhKSsJPP/2EN954Q/ofeE5Ojuw1Fy9elD03tsZVqFChVMleUVFRUdi8eTOaN2/+wERDr9fjtddeg4+PD5KTkzFx4kQkJiYiISHBpK4xWTISQuDs2bN46qmnpOsChi7Jh4m7ONb8/RQ+Z2pqKvLz84ttSbDkPppTu3ZtLFy40KTl82FaIbZv347r168jJSUFLVu2lMrT09MtPldZCAgIgIeHB86ePWtyzFxZcaKjoxEdHY2PP/4Ye/bsQfPmzTFv3jyMHz/eos/Bg+5xeno6XFxcrPZ5okfDMUKkGJ1OZ9KkHhAQgJCQEOTl5QH453+Chf/nJ4TAzJkzH3h+c6/VaDT47rvvLI711Vdfxc2bNzFgwADcunVLNgbkQV555RVcuXIF33zzDY4ePYpXXnlFdjwhIQGurq5mF4MUQuD69esPvMbNmzdNXmtsFTHey2rVqsHV1RU7duyQ1ZszZ47seUBAAFq3bo2vvvoKV69eNbnWX3/9VWIs3bp1g06nw7hx40yOFRQUyBKx6dOnY8+ePZg/fz7GjRuHZs2a4a233jI7duKHH35Abm6u9Hz58uW4evWqNAakUaNGiIqKwqeffopbt25ZHHdxrPH7Kapr1664du0avvjiC5NjxmtYch/Nadq0KYQQOHjwoKzcOK7tQa8vzNzfpfv375t8dpTi6uqK2NhYrFq1Sjbe8OzZs9iwYcMDX6/ValFQUCAri46OhouLi/T3x5LPgZeXV4ndhQcPHkTdunVlCSophy1CpJjc3FyEhYUhMTER9evXh7e3NzZv3oz9+/fjs88+A2D4X21UVBTee+89ZGRkwMfHBytWrChVk3+7du3g7u6Ozp07SwnM119/jYCAALNf8CVp2LAh6tWrh2XLluHJJ5+UpvaXxgsvvIBKlSrhvffeg6urK7p27So7HhUVhfHjx+ODDz7AhQsXEB8fj0qVKiE9PR0rV65E//798d5775V4je+//x5z5szBSy+9hKioKOTm5uLrr7+Gj48PXnjhBQCGAaEvv/wyZs+eDZVKhaioKKxbt87sWJMvv/wSLVq0QHR0NN58801Ur14dWVlZ2Lt3Ly5fviyt8WNOq1atMGDAAEyaNAlHjhxBu3btUKFCBaSlpWHZsmWYOXMmEhMTcerUKYwcORKvvfYaOnfuDMAwRbtBgwYYOHAgli5dKjtv5cqV0aJFC/Tt2xdZWVmYMWMGnnjiCbz55psAABcXF3zzzTfo0KED6tati759+yI0NBQZGRnYtm0bfHx8sHbt2gf/woqwxu+nqN69e+OHH37A0KFDsW/fPsTExOD27dvYvHkzBg4ciC5dupT6PhanRYsWqFKlCjZv3ox//etfsvfj5+eHefPmoVKlSvDy8kKTJk1KHC/VrFkz+Pv7o0+fPnj77behUqnw3//+165WcR8zZgz+97//oXnz5njrrbeg0+nwxRdfoF69eg/cTmTr1q0YPHgwXn75ZdSsWRMFBQX473//K/v7asnnoFGjRliyZAmGDh2KZ555Bt7e3tJnPD8/H7/++isGDhxYpveDLGCLqWlE5uTl5Ylhw4aJ+vXri0qVKgkvLy9Rv359MWfOHFm9kydPitjYWOHt7S0ee+wx8eabb0rTZwtPATY39XbNmjXiqaeeEh4eHiIiIkJMmTJFmrqcnp4u1atWrZro2LFjifFOnTpVABATJ060+L0mJSUJACI2NrbYOitWrBAtWrQQXl5ewsvLS9SuXVsMGjRInD59WqrTqlUrUbduXZPXHjp0SPTo0UNUrVpVqNVqERAQIDp16iQOHDggq/fXX3+Jrl27iooVKwp/f38xYMAAcfz4cbPTqc+dOyd69+4tgoKCRIUKFURoaKjo1KmTWL58eane8/z580WjRo2Ep6enqFSpkoiOjhbvv/++uHLliigoKBDPPPOMCAsLEzk5ObLXzZw5UwAQS5YsEUL8M31+8eLF4oMPPhABAQHC09NTdOzYUbZ8gtHhw4dFQkKCqFKlilCr1aJatWqiW7duYsuWLVId42flr7/+Mnm9uc+REI/2+yk6pVwIw3T0jz76SERGRooKFSqIoKAgkZiYKM6dO1fq+/ggb7/9tnjiiSdMylevXi3q1KkjTS03/u6Li18IIXbv3i2ee+454enpKUJCQsT7778vfvnlF5MlGYqbPj9t2jSTcwIQo0ePlp4XN33e3BIb1apVE3369JGVbdmyRTRs2FC4u7uLqKgo8c0334h3331XeHh4mH1PRufPnxevv/66iIqKEh4eHqJy5cqiTZs2YvPmzSZ1S/M5uHXrlujZs6fw8/MTAGT3Y8OGDQKASEtLKzEmsh3uNUZUSjNnzsQ777yDCxcumJ3FQmVj+/btaNOmDZYtW1ZiCwiZOn/+PGrXro0NGzagbdu2SoejiPj4eJw4ccJkjJlS4uPjoVKpsHLlSqVDob9xjBBRKQgh8O2336JVq1ZMgshhVK9eHf369cPkyZOVDsUmim67k5aWhvXr16N169bKBFTEqVOnsG7dOrPjvkg5HCNEVILbt29jzZo12LZtG44dO8b9gcjhmNtbr7yqXr06XnvtNVSvXh0XL17E3Llz4e7ujvfff1/p0AAYtjgpOiiblMdEiKgEf/31F3r27Ak/Pz98+OGHePHFF5UOiYiK0b59eyxevBiZmZlQq9Vo2rQpJk6cWOwiiEQAwDFCRERE5LQ4RoiIiIicFhMhIiIiclocI1QCvV6PK1euoFKlStwcj4iIyEEIIZCbm4uQkBC4uJTc5sNEqARXrlxBeHi40mEQERHRQ7h06RLCwsJKrMNEqASVKlUCYLiRPj4+CkdDREREpaHVahEeHi59j5eEiVAJjN1hPj4+TISIiIgcTGmGtXCwNBERETktJkJERETktJgIERERkdNiIkREREROi4kQEREROS0mQkREROS0mAgRERGR02IiRERERE6LiRARERE5LSZCRERE5LSYCBEREZHTYiJERERETouJEBEREdncjbs3EDUrCjN/m6loHEyEiIiIyKZWnlqJKlOr4PzN80j+JVnRWNwUvToRERE5lef/+zw2n98sPZ8aO1XBaJgIERERkQ1k385G4KeBsrLDAw6jQVADZQL6GxMhIiIiKlNLji9B9xXdpeduLm648+EdVHCtoGBUBhwjRERERGVCCIHm/9dclgSNaTUG+SPz7SIJAtgiRERERGXgau5VhEwPkZUdf+s46gbUVSgi89giRERERFb1w9EfZEmQj9oH+SPz7S4JAtgiRERERFYihMDT85/GkcwjUtmktpMwosUI5YJ6ACZCRERE9Mguay8j/PNwWdkfg/5ArcdqmX+BTgfs3AlcvQoEBwMxMYCrqw0ilWPXGBERET2Srw9+LUuCgryDUDCyoPgkKCUFiIgA2rQBevY0/IyIMJTbGBMhIiIieihCCNT6ohb6r+svlX0e9zmuvnsVri7FtO6kpACJicDly/LyjAxDuY2TIZUQQtj0ig5Eq9XC19cXGo0GPj4+SodDRERkN9JvpqP6rOqysnNvn0N1/+rFvAKG7rCICNMkyEilAsLCgPT0R+oms+T7my1CREREZJEv9n0hS4Kq+1eHbpSu5CQIMIwJKi4JAgAhgEuXDPVshIOliYiIqFT0Qo9qM6rhsvafZGbOC3Pw1jNvle4EV69at54VMBEiIiKiBzp74yxqzK4hK7uYfBFVfauW/iTBwdatZwXsGiMiIqISfbrnU1kSVC+gHvSj9JYlQYBhinxYmGEskDkqFRAebqhnIxYnQjt27EDnzp0REhIClUqFVatWScfy8/MxfPhwREdHw8vLCyEhIejduzeuXLkiO8eNGzeQlJQEHx8f+Pn5oV+/frh165aszu+//46YmBh4eHggPDwcU6dONYll2bJlqF27Njw8PBAdHY3169fLjgshMGrUKAQHB8PT0xOxsbFIS0uz9C0TERE5JZ1ehypTq2DYpmFS2bcvfotjbx2DqrhkpiSursDMmYY/F3298fmMGTZdT8jiROj27duoX78+vvzyS5Njd+7cwaFDhzBy5EgcOnQIKSkpOH36NF588UVZvaSkJJw4cQKbNm3CunXrsGPHDvTv/8/UO61Wi3bt2qFatWo4ePAgpk2bhjFjxmD+/PlSnT179qBHjx7o168fDh8+jPj4eMTHx+P48eNSnalTp2LWrFmYN28eUlNT4eXlhbi4ONy7d8/St01ERORUTv11Cm7j3HDj7g2pLGNoBl5v+PqjnTghAVi+HAgNlZeHhRnKExIe7fyWEo8AgFi5cmWJdfbt2ycAiIsXLwohhDh58qQAIPbv3y/V2bBhg1CpVCIjI0MIIcScOXOEv7+/yMvLk+oMHz5c1KpVS3rerVs30bFjR9m1mjRpIgYMGCCEEEKv14ugoCAxbdo06XhOTo5Qq9Vi8eLFpXp/Go1GABAajaZU9YmIiMqDcb+OExgD6fHs188KvV5v3YsUFAixbZsQixYZfhYUWO3Ulnx/l/kYIY1GA5VKBT8/PwDA3r174efnh8aNG0t1YmNj4eLigtTUVKlOy5Yt4e7uLtWJi4vD6dOncfPmTalObGys7FpxcXHYu3cvACA9PR2ZmZmyOr6+vmjSpIlUp6i8vDxotVrZg4iIyFnk6/LhOcETI7eNlMoWJixE6hupD9cVVhJXV6B1a6BHD8NPBbbXAMp4sPS9e/cwfPhw9OjRQ1rQKDMzEwEBAbJ6bm5uqFy5MjIzM6U6gYGBsjrG5w+qU/h44deZq1PUpEmT4OvrKz3Cw8PN1iMiIipvfs/6He7j3XGv4J/hI5nvZqJndE8Foyp7ZZYI5efno1u3bhBCYO7cuWV1Gav64IMPoNFopMelS5eUDomIiKjMfbz1Y9SfV1963jqiNcRogUDvwBJeVT6UyTpCxiTo4sWL2Lp1q2x566CgIGRnZ8vqFxQU4MaNGwgKCpLqZGVlyeoYnz+oTuHjxrLgQusRZGVloUGDBmbjVqvVUKvVlr5dIiIih3Rfdx/q8fLvvWUvL0NinUSFIrI9q7cIGZOgtLQ0bN68GVWqVJEdb9q0KXJycnDw4EGpbOvWrdDr9WjSpIlUZ8eOHcjPz5fqbNq0CbVq1YK/v79UZ8uWLbJzb9q0CU2bNgUAREZGIigoSFZHq9UiNTVVqkNEROSsDl45aJIE/TXsL6dKgoCHSIRu3bqFI0eO4MiRIwAMg5KPHDmCP//8E/n5+UhMTMSBAwewcOFC6HQ6ZGZmIjMzE/fv3wcAPPnkk2jfvj3efPNN7Nu3D7t378bgwYPRvXt3hISEAAB69uwJd3d39OvXDydOnMCSJUswc+ZMDB06VIpjyJAh2LhxIz777DP88ccfGDNmDA4cOIDBgwcDAFQqFZKTkzF+/HisWbMGx44dQ+/evRESEoL4+PhHvG1ERESO673/vYfGX/8zaanDEx0gRgs8VvExBaNSiKVT0rZt2yYAmDz69Okj0tPTzR4DILZt2yad4/r166JHjx7C29tb+Pj4iL59+4rc3FzZdY4ePSpatGgh1Gq1CA0NFZMnTzaJZenSpaJmzZrC3d1d1K1bV/z888+y43q9XowcOVIEBgYKtVot2rZtK06fPl3q98rp80REVJ7czb8rmxaPMRCr/1itdFhWZ8n3t0oIIRTJwByAVquFr68vNBqNbJwTERGRo/nt8m9o+q18aMiN92/A39NfoYjKjiXf39xrjIiIqJwbvH6wLAlKeDIBYrQol0mQpbj7PBERUTl1J/8OvCZ6yco2Jm1E3BNxCkVkf5gIERERlUM7L+5EywUtZWWaERr4qDnUozB2jREREZUz/Vb3kyVBvZ7qBTFaMAkygy1CRERE5cSNuzdQZap8/b6tvbeiTWQbhSKyf0yEiIiIyoFvDn2DN9e+KSvL/SAX3u7eCkXkGJgIEREROTj1eDXu6+5Lz4O9g3Hl3SsKRuQ4OEaIiIjIQWXfzoZqrEqWBC1KWMQkyAJsESIiInJAs1Nn4+2Nb8vKOCvMckyEiIiIHIxqrEr2vEblGjjznzMKRePY2DVGRETkIK7kXjFJglK6pTAJegRsESIiInIAU3ZNwYgtI2Rltz+8jYoVKioUUfnARIiIiByXTgfs3AlcvQoEBwMxMYCrq9JRWV3RVqBGwY1woP8BhaIpX9g1RkREjiklBYiIANq0AXr2NPyMiDCUlxMXcy6aJEHre65nEmRFTISIiMjxpKQAiYnA5cvy8owMQ3k5SIbGbB+DiJkRsrK7H91FhxodlAmonFIJIYTSQdgrrVYLX19faDQa+PhwOiIRkV3Q6QwtP0WTICOVCggLA9LTHbKbTAgBl0/k7RStqrXC9te2KxOQA7Lk+5stQkRE5Fh27iw+CQIAIYBLlwz1HMzZG2dNkqAtvbeUPgnS6YDt24HFiw0/dTprh1jucLA0ERE5lqtXrVvPTgz73zB8uvdTWVnex3lwd3Uv3QlSUoAhQ+RJYlgYMHMmkJBgxUjLFyZCRETkWIKDrVtPYea6wl6o8QJ+7vlz6U9iHDNVdLSLcczU8uVMhorBrjEiInIsMTGGlg6VyvxxlQoIDzfUs3Mn/zppkgTt6rvLsiRIpzO0BJkb8mssS05mN1kxmAgREZFjcXU1dPcApsmQ8fmMGXY/UHrQz4NQd05dWVn+yHw0r9rcshOV4zFTtsBEiIiIHE9CgqG7JzRUXh4WZvfdQHqhh2qsCnMOzJHKutXtBjFawM3lIUaslNMxU7bCMUJEROSYEhKALl0camXpo5lH0eCrBrKy/W/uR+OQxg9/0nI2ZsrWmAgREZHjcnUFWrdWOopS6bOqD344+oOsTDdKBxfVI3bOGMdMZWSYHydkXFfJAcZMKYFdY0RERGVIp9dBNVYlS4Jeb/A6xGjx6EkQUG7GTCmFiRAREVEZ2ZexD27j5J0vR/99FN92+da6F3LgMVNKY9cYERFRGUhcmogVp1bIyvSj9FAVN+3/UTngmCl7wESIiIjIigr0BagwroKsbPAzgzH7hdllf3EHGjNlL5gIERERFUens6iFZefFnWi5oKWs7NSgU6j9WO2yjpQeEhMhIiIicyzcu6vDwg7YeHajrKxMu8LIKjhYmoiIqCjj3l1FV2w27t2VkiIV3dfdh2qsSpYEDW8+HGK0YBLkAJgIERERFWbB3l1bzm+BerxaVuXsf85icuzkso+TrIJdY0RERIWVcu+uFrMaYLf2uOwQu8IcD1uEiIiICnvAnlz33ADVGMiSoLGtx7IrzEGxRYiIiKiwEvbk+rkG0ClJXnYx+SKq+lYt46CorDARIiIiKqyYvbsaDgCOFMmRxGgz44jIobBrjIiIqLAie3fluhu6wgonQVODXmUSVE4wESIiIirq7727RsR7w+dD+aErtb/BsAE/mH8dORx2jREREZmhOtYVqC8vEx8XcO+ucoYtQkRERIVk3cqCaqx89lfnmp0NXWFMgsodtggRERH9bdDPgzDnwBxZ2dn/nEVU5SiFIqKyxkSIiIgIMGkFAjgrzBmwa4yIiJzaJc0lkySo11O9mAQ5CbYIERGR0+qV0gsLjy2Ulf2Z/CfCfcMf/eQ6nWG7jqtXDYs0xsRwjJEdYiJEREROqUy7wlJSDBu3Ft6zLCzMsD5RQoJ1rkFWwa4xIiJyKudunDNJgt5q/JZ1k6DERNONWzMyDOUpKda5DlmFSgjBTtBiaLVa+Pr6QqPRwMfHR+lwiIjoEb24+EWsPbNWVpb5biYCvQOtcwGdDoiIKH73epXK0DKUns5usjJkyfc3u8aIiMgp2GRW2M6dxSdBgGHvskuXDPVat7butemhsGuMiIjKtZN/nTRJgkY0H1E2s8KuXrVuPSpzbBEiIqJyq+V3LbHzz52ysuvvX0dlz8plc8Hg4AfXsaQelTkmQkREVC4pskBiTIxhDFBGhqEbzCSov8cIxcSUbRxUauwaIyKicuXQ1UMmSdD4NuNts0Ciq6thijxgSHoKMz6fMYMDpe0IW4SIiKjceGruUziWfUxWphmhgY/ahjN/ExKA5cvNryM0YwbXEbIzTISIiMjhCSHg8olpJ4di22QkJABdunBlaQfARIiIiBza+rT16Lioo6xsRtwMDHluiEIR/c3VlVPkHQATISIicljmBkTf/vA2KlaoqEA05IiYCBERkcOxu64wclicNUZERA7lh6M/mCRBw5oNYxJED4UtQkRE5DDMdYXd+fAOPCt4KhANlQdMhIiISHk6XYkzrHR6HdzGmX5lsRWIHhW7xoiISFkpKYYd29u0AXr2NPyMiDCUA5izf45JEmSzBRKp3GOLEBERKSclBUhMNN2OIiMDSEyEykyyk/dxHtxd3W0UIJV3TISIiEgZOp1h9WUze3LlqwTcR5m+hK1AZG3sGiMiImXs3CnfguJvk1rAJAma1X4WkyAqE2wRIiIiZVy9alKkGmNaraDGj3BtklT28ZBTYosQEREpIzhY+uPtCuaTIDEGcA0JtVlI5HwsToR27NiBzp07IyQkBCqVCqtWrZIdF0Jg1KhRCA4OhqenJ2JjY5GWliarc+PGDSQlJcHHxwd+fn7o168fbt26Javz+++/IyYmBh4eHggPD8fUqVNNYlm2bBlq164NDw8PREdHY/369RbHQkREComJAcLC8NIrgPdH8kMLVgJirAoIDzfUIyojFidCt2/fRv369fHll1+aPT516lTMmjUL8+bNQ2pqKry8vBAXF4d79+5JdZKSknDixAls2rQJ69atw44dO9C/f3/puFarRbt27VCtWjUcPHgQ06ZNw5gxYzB//nypzp49e9CjRw/069cPhw8fRnx8POLj43H8+HGLYiEiIoW4ukL1xmWselJerB8D9Pn974UTZ8zgju1UtsQjACBWrlwpPdfr9SIoKEhMmzZNKsvJyRFqtVosXrxYCCHEyZMnBQCxf/9+qc6GDRuESqUSGRkZQggh5syZI/z9/UVeXp5UZ/jw4aJWrVrS827duomOHTvK4mnSpIkYMGBAqWMp6t69e0Kj0UiPS5cuCQBCo9FYemuIiKgEN+/eFBgDk4fA34/wcCFWrFA6THJQGo2m1N/fVh0jlJ6ejszMTMTGxkplvr6+aNKkCfbu3QsA2Lt3L/z8/NC4cWOpTmxsLFxcXJCamirVadmyJdzd/1knIi4uDqdPn8bNmzelOoWvY6xjvE5pYilq0qRJ8PX1lR7h4eGPcjuIiMiMFv/XAv5T/GVlX7afDdFqG7BoEbBtG5CeDiQkKBQhOROrzhrLzMwEAAQGBsrKAwMDpWOZmZkICAiQB+HmhsqVK8vqREZGmpzDeMzf3x+ZmZkPvM6DYinqgw8+wNChQ6XnWq2WyRARkRWZ2ytMP0oPlcq0nMgWOH2+ELVaDbVarXQYRERl5wF7epWVrFtZCPosyKScawOR0qzaNRYUZPiQZ2VlycqzsrKkY0FBQcjOzpYdLygowI0bN2R1zJ2j8DWKq1P4+INiISJyKg/Y06us1PqilkkS9ONLPzIJIrtg1UQoMjISQUFB2LJli1Sm1WqRmpqKpk2bAgCaNm2KnJwcHDx4UKqzdetW6PV6NGnSRKqzY8cO5OfnS3U2bdqEWrVqwd/fX6pT+DrGOsbrlCYWIiKnYdzTq+hKzn/v6VVWyZBqrApnrp+RlYnRAklPcYFEshOWjsTOzc0Vhw8fFocPHxYAxPTp08Xhw4fFxYsXhRBCTJ48Wfj5+YnVq1eL33//XXTp0kVERkaKu3fvSudo3769aNiwoUhNTRW7du0SNWrUED169JCO5+TkiMDAQPHqq6+K48ePi59++klUrFhRfPXVV1Kd3bt3Czc3N/Hpp5+KU6dOidGjR4sKFSqIY8eOSXVKE0tJLBl1TkRktwoKhAgL+2dGVtGHSmWYpVVQYLVLXsy5aH5WGJENWPL9bfGnctu2bQKAyaNPnz5CCMO09ZEjR4rAwEChVqtF27ZtxenTp2XnuH79uujRo4fw9vYWPj4+om/fviI3N1dW5+jRo6JFixZCrVaL0NBQMXnyZJNYli5dKmrWrCnc3d1F3bp1xc8//yw7XppYSsJEiIjKhW3bik+CCj+2bbPK5Xwn+ZokQGv+WGOVcxOVhiXf3yohzGz7SwAMXWm+vr7QaDTw8fFROhwiooezeLFhTNCDLFoE9OjxSJcyNyuMY4HI1iz5/uZeY0RE5V2hPb2sUs+MP679wSSIHBKnzxMRlXd/7+mFjAxDJ1hRKpXh+EPu6WUuAdraeyvaRLZ5qPMR2RJbhIiIyjtXV2DmTMOfiy5cqHq0Pb2KawViEkSOgokQEZEzSEgAli8HQkPl5WFhhnILt7M4fPUwu8KoXGDXGBGRs0hIALp0eeSVpc0lQPve2IdnQp+xVqRENsNEiIjImbi6Aq1bP/TL2QpE5Q27xoiI6IF2XtzJJIjKJbYIERFRicwlQCcGnkCdx+soEA2RdTERIiKiYrEViMo7do0REZGJ9WnrTZIgtauaSRCVO2wRIiIiGXOtQOffPo9I/0gFoiEqW0yEiIiKo9M98lRzR8OuMHI27BojIjInJQWIiADatDFsWNqmjeF5SorSkZWJJceXmCRBVX2rMgmico8tQkRERaWkAImJpvtyZWQYyh9iJWZ7Zq4V6MrQKwiu9PCbsBI5CrYIEREVptMBQ4aY35zUWJacbKhXDhTXFcYkiJwFEyEiosJ27gQuXy7+uBDApUuGeg7s64NfmyRBjUMasyuMnA67xoiICrt61br17JC5VqDr719HZc/KCkRDpCwmQkREhQWXskuotPXsiBACLp+YdgSwFYicGbvGiIgKi4kBwsIAlWmrCQBDeXi4oZ4DGfrLUJMk6IUaLzAJIqfHFiEiosJcXYGZMw2zw1Qq+aBpY3I0Y4ay6wlZuL6Rua6wnOE58PXwLcsoiRwCW4SIiIpKSDBMkQ8NlZeHhSk/dd6C9Y10el2xs8KYBBEZqIQwN0eUAECr1cLX1xcajQY+Pj5Kh0NEtmZvK0sXt76RsaWqUJLWK6UXFh5bKKvWLLwZdr++2xaREinKku9vJkIlYCJERHZDpzO0/BQ3tV+lMrRYpadDNd501MOdD+/As4Jn2cZIZCcs+f7mGCEiIkdQivWN8q5cgoeZJIgDoomKxzFCRESO4AHrFrV7FfAYKS97qfZLTIKIHoAtQkREjqCEdYtUY0zL7n98HxVcK5RdPETlBFuEiIgcgZn1jW65m0+CxGjBJIiolJgIERE5AuP6RgCgUqFaMlDpQ3mV/pWfZ1cYkYXYNUZE5Cj+Xt9IdayrySFdvWVw6ZqoQFBEjo2JEBGRg8i+nY1AM0mQ+LhA2fWNiBwYEyEiIgdgboXo1xu8jm+7fKtANETlBxMhIiI7Zy4J0o/SQ1XcxrBEVGocLE1EZKcu5lwsdq8wJkFE1sFEiIjIDqnGqhAxM0JWNqL5CM4KI7Iydo0REdmZ4lqBiMj62CJERGQnTv51kkkQkY2xRYiIyA6YS4CmPT8N7zV7T4FoiJwHEyEiIoWxFYhIOewaIyJSSOrlVCZBRApjixARkQLMJUALuixAnwZ9FIiGyHkxESIisjG2AhHZD3aNERHZyObzm5kEEdkZtggREdmAuQRo1Sur0KV2FwWiISIjJkJERGWMrUBE9otdY0REZWTZiWVMgojsHFuEiIjKgLkEaFufbWgd0dr2wRBRsZgIERFZGVuBiBwHu8aIiKxk/sH5TIKIHAxbhIiIrMBcAnSw/0E8Hfy0AtEQUWkxESIiekRsBSJyXOwaIyJ6SJN2TmISROTg2CJERPQQzCVApwefRs0qNRWIhogeFhMhIrI/Oh2wcydw9SoQHAzExACurkpHJWErEFH5wa4xIrIvKSlARATQpg3Qs6fhZ0SEoVxhQ38ZyiSIqJxhixAR2Y+UFCAxERBFEouMDEP58uVAQoIioZlLgC69cwlhPmEKRENE1sIWISKyDzodMGSIaRIE/FOWnGyoZ0NCiGJbgZgEETk+JkJEZB927gQuXy7+uBDApUuGejaSlJIEl09M/5lkVxhR+cGuMSKyD1evWrfeIzLXCnRt2DVUqVjFJtcnIttgIkRE9iE42Lr1HpJOr4PbONN/GtkKRFQ+sWuMiOxDTAwQFgaoTFtiABjKw8MN9cqCTod/zXzaJAny9/BnEkRUjjERIiL74OoKzJxp+HPRZMj4fMaMsllPKCUFqvFu2JZzWFacW+dH3Bh+w/rXIyK7wUSIiOxHQoJhinxoqLw8LKzMps7fW/4TVMe6mpSLsSp4v/KqXaxfRERlRyWEubmqBABarRa+vr7QaDTw8fFROhwi52GjlaU9xnsgT5cnK/POA3In/f1EpTIkYenpdrWyNRGVzJLvbw6WJiL74+oKtG5dppcwNyvs7njAo6BQQeEp+2UcDxEpg11jRORUNPc05hdIHFMkCSrMRlP2icj2rJ4I6XQ6jBw5EpGRkfD09ERUVBTGjRuHwj1wQgiMGjUKwcHB8PT0RGxsLNLS0mTnuXHjBpKSkuDj4wM/Pz/069cPt27dktX5/fffERMTAw8PD4SHh2Pq1Kkm8Sxbtgy1a9eGh4cHoqOjsX79emu/ZSJyEKqxKvhN8ZOV1ck2JEElKuMp+0SkHKsnQlOmTMHcuXPxxRdf4NSpU5gyZQqmTp2K2bNnS3WmTp2KWbNmYd68eUhNTYWXlxfi4uJw7949qU5SUhJOnDiBTZs2Yd26ddixYwf69+8vHddqtWjXrh2qVauGgwcPYtq0aRgzZgzmz58v1dmzZw969OiBfv364fDhw4iPj0d8fDyOHz9u7bdNRHbOXCtQwYd5OLFGwSn7RKQ8YWUdO3YUr7/+uqwsISFBJCUlCSGE0Ov1IigoSEybNk06npOTI9RqtVi8eLEQQoiTJ08KAGL//v1SnQ0bNgiVSiUyMjKEEELMmTNH+Pv7i7y8PKnO8OHDRa1ataTn3bp1Ex07dpTF0qRJEzFgwIBSvReNRiMACI1GU6r6RGR/MnMzBcbA5CFZsUIIlcrwMIwKMjyMZStWKBc8ET0US76/rd4i1KxZM2zZsgVnzpwBABw9ehS7du1Chw4dAADp6enIzMxEbGys9BpfX180adIEe/fuBQDs3bsXfn5+aNy4sVQnNjYWLi4uSE1Nleq0bNkS7u7uUp24uDicPn0aN2/elOoUvo6xjvE6ReXl5UGr1coeROS4VGNVCPosSFbWLqqdfIFEBabsE5H9sPqssREjRkCr1aJ27dpwdXWFTqfDhAkTkJSUBADIzMwEAAQGBspeFxgYKB3LzMxEQECAPFA3N1SuXFlWJzIy0uQcxmP+/v7IzMws8TpFTZo0CWPHjn2Yt01EdsZcV5h+lB4qc91gCQlAly42mbJPRPbF6onQ0qVLsXDhQixatAh169bFkSNHkJycjJCQEPTp08fal7OqDz74AEOHDpWea7VahIeHKxgREVnq/M3ziJoVZVL+wG0ybDBln4jsj9UToWHDhmHEiBHo3r07ACA6OhoXL17EpEmT0KdPHwQFGZqps7KyEFxoJkZWVhYaNGgAAAgKCkJ2drbsvAUFBbhx44b0+qCgIGRlZcnqGJ8/qI7xeFFqtRpqtfph3jYR2QFzrUC96/fG9/HfKxANETkCq48RunPnDlxc5Kd1dXWFXq8HAERGRiIoKAhbtmyRjmu1WqSmpqJp06YAgKZNmyInJwcHDx6U6mzduhV6vR5NmjSR6uzYsQP5+flSnU2bNqFWrVrw9/eX6hS+jrGO8TpEVH6YXRtotGASREQlsnoi1LlzZ0yYMAE///wzLly4gJUrV2L69Ol46aWXAAAqlQrJyckYP3481qxZg2PHjqF3794ICQlBfHw8AODJJ59E+/bt8eabb2Lfvn3YvXs3Bg8ejO7duyMkJAQA0LNnT7i7u6Nfv344ceIElixZgpkzZ8q6toYMGYKNGzfis88+wx9//IExY8bgwIEDGDx4sLXfNhEp5Pes34tNgoiIHsjaU9a0Wq0YMmSIqFq1qvDw8BDVq1cXH330kWyau16vFyNHjhSBgYFCrVaLtm3bitOnT8vOc/36ddGjRw/h7e0tfHx8RN++fUVubq6sztGjR0WLFi2EWq0WoaGhYvLkySbxLF26VNSsWVO4u7uLunXrip9//rnU74XT54nsm7lp8cM3DVc6LCJSmCXf39x0tQTcdJXIfrEViIiKY8n3N/caIyKHsuvPXUyCiMhquPs8ETkMcwnQZ+0+w9CmQ83UJiJ6MCZCROQQ2ApERGWBXWNEZNfWnVnHJIiIygxbhIjIbplLgBZ0WYA+Dex7lXoichxMhIjILrEViIhsgV1jRGRXvj/yPZMgIrIZtggRkd0wlwCt7bEWnWp2UiAaInIGTISIyC6wFYiIlMCuMSJS1PS905kEEZFi2CJERIoxlwDt7LsTLaq2UCAaInJGTISISBFsBSIie8CuMSKyqf5r+zMJIiK7wRYhIrIZcwnQ0X8fxVOBTykQDREREyEishG2AhGRPWLXGBGVqRcWvsAkiIjsFluEiKjMmEuATg8+jZpVaioQDRGRKSZCRGR1Qgi4fGLa4MxWICKyN0yEiMiqoudG43j2cZNyJkFEZI+YCBGR1ZjrCssYmoGQSiEKRENE9GBMhIjoken0OriNM/3nhK1ARGTvmAgR0SOp82UdnLp2yqScSRAROQImQkT00Mx1heUMz4Gvh68C0RARWY6JEBFZLK8gDx4TPEzK2QpERI6GiRARWcR7ojdu59+Wlald1bj38T2FIiIienhMhIio1Mx1hd396C483Exbh4iIHAETISJ6oNy8XPhM9jEpZ1cYETk6JkJEVCJzrUA1KtfAmf+cUSAaIiLrYiJERMUylwTlj8yHmwv/6SCi8oG7zxORiezb2cXuGM8kiIjKE/6LRkQy5hKgNhFtsLXPVgWiISIqW0yEiEhiLgnSj9JDpTItJyIqD9g1RkS4kHOh2K4wJkFEVJ6xRYjIyZlLgJKik/Bjwo8KRENEZFtMhIicWHGtQA9FpwN27gSuXgWCg4GYGMDV9REjJCIqW+waI3JCZ66fsW4SlJICREQAbdoAPXsafkZEGMqJiOwYEyEiJ6Maq0KtL2rJyib+a+KjJUGJicDly/LyjAxDOZMhIrJjKiEE18gvhlarha+vLzQaDXx8TLcXIHI0Vm0FAgzdYRERpkmQdEEVEBYGpKezm4yIbMaS72+2CBE5gUNXD1k/CQIMY4KKS4IAQAjg0iVDPSIiO8TB0kTlnLkEaF7HeRjQeMCjn/zqVevWIyKyMSZCROVYmbQCFRYcbN16REQ2xq4xonJo+4XtZZ8EAYYp8mFhhrFA5qhUQHi4oZ6RTgds3w4sXmz4qdNZNyYiIgswESIqZ1RjVWjzfRtZ2dLEpdZPggDDAOiZM/++cJFkyPh8xox/Bkpzmj0R2RkmQkTlSHGtQC/XfbnsLpqQACxfDoSGysvDwgzlCQmG55xmT0R2iNPnS8Dp8+QoVv+xGvFL4k3Ky6QVqDglrSzNafZEZEOWfH9zsDSRgzPXCrTp1U2IrR5r20BcXYHWrc0fs2SafXHnICIqA0yEiByYTQZEWwOn2RORneIYISLA4WYyLTiywHGSIIDT7InIbrFFiCglBRgyRN51ExZmmA1lHOhrR8wlQKlvpOLZ0GcViKaUjNPsMzIM3WBFGccIFZ5mT0RkA2wRIufmYDOZimsFsuskCLB8mj0RkY0wESLnpdMZWoLMtVAYy5KT7aKb7LM9nzlWV5g5pZ1mT0RkQ+waI+flIDOZzCVAJwaeQJ3H6ygQzSNKSAC6dCl+mj0RkY0xESLnZW8zmcysw6Mab/pX1KFagcwpaZo9EZGNMREi52VPM5mKDNj+oC0w2cy4YYdPgoiI7AwTIXJe9jKTyThg++8YVGNMq1wYcgHV/KqVbRxERE6Ig6XJednDTKZCA7YFzCdB4ttwVKsUVnYxEBE5MSZC5NyUnsn094Dtj/4FuIwxPSzG4J8B20REZHXsGiNScibT1atmW4GypgEBt+X1iIjI+pgIEQGKzGTSCz1cz/Q0KRdjzFTm1hNERGWCXWNECui/tj9cP5G3OEVnmUmCVCogPJxbTxARlRG2CBHZmLkFEnMnAt75KgCFZq/ZYsC2mbWLuLghETkTtggR2Ui+Lr/YbTK8F6+w/YDtlBQgIgJo0wbo2dPwMyLC7vZXIyIqSyohzC2gQgCg1Wrh6+sLjUYDHx8fpcMhB/bSkpew6o9VsrLnqz+P/736v38KbNk6U2TtIomxFYp7fxGRA7Pk+5uJUAmYCJE1mGsFuvfRPajd1ApEA0PCFRFR/D5rxoUk09PZTUZEDsmS7292jVH5ptMB27cDixcbftpwJ/m7+XeL7QpTLAkCLNtsloionCuTRCgjIwO9evVClSpV4OnpiejoaBw4cEA6LoTAqFGjEBwcDE9PT8TGxiItLU12jhs3biApKQk+Pj7w8/NDv379cOvWLVmd33//HTExMfDw8EB4eDimTp1qEsuyZctQu3ZteHh4IDo6GuvXry+Lt0z2SMExMM2+bYaKEyvKypKik+xjrzB722yWiEhBVk+Ebt68iebNm6NChQrYsGEDTp48ic8++wz+/v5SnalTp2LWrFmYN28eUlNT4eXlhbi4ONy7d0+qk5SUhBMnTmDTpk1Yt24dduzYgf79+0vHtVot2rVrh2rVquHgwYOYNm0axowZg/nz50t19uzZgx49eqBfv344fPgw4uPjER8fj+PHj1v7bZO9MY6BKdrykZFhKC/DZEg1VoW9l/fKygpGFuDHhB/L7JoWsafNZomIlCasbPjw4aJFixbFHtfr9SIoKEhMmzZNKsvJyRFqtVosXrxYCCHEyZMnBQCxf/9+qc6GDRuESqUSGRkZQggh5syZI/z9/UVeXp7s2rVq1ZKed+vWTXTs2FF2/SZNmogBAwaU6r1oNBoBQGg0mlLVJztRUCBEWJgQhk4e04dKJUR4uKGeFeXczREYA5OH3THeH5XKpveHiMhWLPn+tnqL0Jo1a9C4cWO8/PLLCAgIQMOGDfH1119Lx9PT05GZmYnY2FipzNfXF02aNMHevYb/Re/duxd+fn5o3LixVCc2NhYuLi5ITU2V6rRs2RLu7u5Snbi4OJw+fRo3b96U6hS+jrGO8TpF5eXlQavVyh7kgBQYAxM5MxJ+U/xkZclNku2jK6woe9hslojITlg9ETp//jzmzp2LGjVq4JdffsFbb72Ft99+G99//z0AIDMzEwAQGBgoe11gYKB0LDMzEwEBAbLjbm5uqFy5sqyOuXMUvkZxdYzHi5o0aRJ8fX2lR3h4uMXvn+yAjcfAqMaqcCHngqxMP0qPz9t/bpXzlwmlN5slIrITVl9ZWq/Xo3Hjxpg4cSIAoGHDhjh+/DjmzZuHPn36WPtyVvXBBx9g6NCh0nOtVstkyBHZaAxM9u1sBH4aaFJul61A5ii52SwRkZ2weiIUHByMOnXqyMqefPJJrFixAgAQFBQEAMjKykJwoS+irKwsNGjQQKqTnZ0tO0dBQQFu3LghvT4oKAhZWVmyOsbnD6pjPF6UWq2GWq3gtGayjpgYQ8tGRobpgoHAP+vkPML+XerxatzX3ZeVjWszDh+3/Pihz6kIBTabJSKyJ1bvGmvevDlOnz4tKztz5gyqVasGAIiMjERQUBC2bNkiHddqtUhNTUXTpk0BAE2bNkVOTg4OHjwo1dm6dSv0ej2aNGki1dmxYwfy8/OlOps2bUKtWrWkGWpNmzaVXcdYx3gdKqfKeAyMaqzKJAkSo4XjJUFERGT9WWP79u0Tbm5uYsKECSItLU0sXLhQVKxYUfz4449SncmTJws/Pz+xevVq8fvvv4suXbqIyMhIcffuXalO+/btRcOGDUVqaqrYtWuXqFGjhujRo4d0PCcnRwQGBopXX31VHD9+XPz000+iYsWK4quvvpLq7N69W7i5uYlPP/1UnDp1SowePVpUqFBBHDt2rFTvhbPGHNyKFaazx8LDDeUP4cLNC44xK4yIyMlZ8v1dJv+Kr127VtSrV0+o1WpRu3ZtMX/+fNlxvV4vRo4cKQIDA4VarRZt27YVp0+fltW5fv266NGjh/D29hY+Pj6ib9++Ijc3V1bn6NGjokWLFkKtVovQ0FAxefJkk1iWLl0qatasKdzd3UXdunXFzz//XOr3wUSoHCgoEGLbNiEWLTL8fMgp4eYSoC/3fWnVUImIyDos+f7mXmMl4F5jBJjfK8xhBkQTETkh7jVGZAWn/jrFJIiIqJyz+qwxovLAXAK0KGERekT3UCAaIiIqK0yEiIpgKxARkfNg1xjR3w5cOcAkiIjIybBFiAjmW4F+7vkzXqjxggLRFKLTceVnIqIyxESInJ7dtgKlpABDhsg3kA0LMywWyb3AiIisgl1j5LR2/bnLvpOgxER5EgQYtg1JTDQcJyKiR8Z1hErAdYTKL3MJ0N5+e/Fc2HMKRFOETgdERJgmQUbGvdLS09lNRkRkhiXf3+waI6djt61ARjt3Fp8EAYbNQi5dMtTjhqlERI+EXWPkNDakbSg+CdLpgO3bgcWLDT91OpvHJ7l61br1iIioWGwRIqdgLgE69tYx1AuoZ3+DkoODrVuPiIiKxRYhKveKawWSkiB7G5QcE2NIxFSmcQMwlIeHG+oREdEjYSJE5damc5tKHg+k0xlagszNFzCWJSfbvpvM1dXQGgWYJkPG5zNmcKA0EZEVMBGickk1VoV2P7aTlZ17+5x8ULQlg5JtLSEBWL4cCA2Vl4eFGcq5jhARkVVwjBCVO6WeFWbvg5ITEoAuXbiyNBFRGWIiROXGylMrkbBU3lISUzUGO/ruMP8CRxiU7OrKKfJERGWIiRCVC+ZagbLfy8bjXo8X/yLjoOSMDPPjhIwLF3JQMhFRucUxQuTQhBDFdoWVmAQBHJRMRERMhMhxfX/ke7h8Iv8Ix9eOt2yVaA5KJiJyauwaI4dkrhUoZ3gOfD18LT8ZByUTETktJkLkUIQQJq1AgBX2CuOgZCIip8SuMXIYs1JnmSRB/Rr2s68NU4mIyKGwRYgcgrmusNsf3kbFChUViIaIiMoLJkJk13R6HdzGmX5M2QpERETWwK4xslvjfh1nkgS91/Q9JkFERGQ1bBEiu2SuKyzv4zy4u7orEA0REZVXTITIrtzX3Yd6vNqknK1ARERUFtg1RnZj6C9DTZKg8W3GMwkiIqIywxYhsgvmusIKRhbA1YWLGhIRUdlhixAp6k7+nWL3CmMSREREZY2JECmm7+q+8JroJSv78oUv2RVGREQ2w64xUoS5ViD9KD1URXeBJyIiKkNsESKbyrmXU2xXGJMgIiKyNSZCZDMvLn4R/lP8ZWU/xP/ArjAiIlIMu8bIJtgVRkRE9ogtQlSmsm9nsyuMiIjsFhMhKjPN/685Aj8NlJWtemUVu8KIiMhusGuMykRxrUBERET2hC1CZFWXNJeYBBERkcNgixBZTc3ZNZF2I01WtvnVzWhbva1CEREREZWMiRBZBVuBiIjIETERslc6HbBzJ3D1KhAcDMTEAK72t/fWZe1lhH8eblLOJIiIiBwBEyF7lJICDBkCXL78T1lYGDBzJpCQoFxcRXRa1Ak/p/0sKzv676N4KvAphSIiIiKyDBMhe5OSAiQmAqJIi0pGhqF8+XK7SIbYFUZEROUBZ43ZE53O0BJUNAkC/ilLTjbUU8i5G+dMkqBm4c2YBBERkUNii5A92blT3h1WlBDApUuGeq1b2ywso+b/1xx7Lu2RlZ39z1lEVY6yeSxERETWwETInly9at16VsSuMCIiKo/YNWZPgoOtW88KzC2Q+EKNF5gEERFRucAWIXsSE2OYHZaRYX6ckEplOB4TY5NwkjcmY2bqTFnZpXcuIcwnzCbXJyIiKmtMhOyJq6thinxioiHpKZwMGXdqnzHDJusJsSuMiIicAbvG7E1CgmGKfGiovDwszCZT58/fPG+SBM1qP4tJEBERlUsqIcz1wRAAaLVa+Pr6QqPRwMfHx7YXV2Bl6TfWvIFvD38rK8t57zp89/9u9ytcExERGVny/c2uMXvl6mrTKfJmu8KiVwC169v9CtdEREQPi11jTu6Pa3+YJEHfdP7GkAQlJpqua2Rc4TolxYZREhERlQ22CDmxV5a/gqUnlsrKbn1wC16uHkCXiOJXuFapDCtcd+nCbjIiInJoTISckBACLp+YNgZKA6K3b7frFa6JiIishV1jTuZo5lGTJGhhwkL5rDA7XuGaiIjImtgi5EQ6LOyAjWc3ysrufnQXHm4e8op2uMI1ERFRWWAi5ATMdYV5VfDCrQ9vmX+Bna1wTUREVFbYNVbO7cvYZ5IErXxlZfFJEPDPCtfAPytaG9l4hWsiIqKyxESoHGv+f83R5JsmsrK8j/MQXzv+wS9WeIVrIiIiW2DXWDmkF3q4fiJvrQmtFIrLQ0uYCWZOQoJhiryNV7gmIiKyFSZC5cyOizvQakErWdnGpI2IeyLu4U5o4xWuiYiIbImJUDkSPTcax7OPy8ryR+bDzYW/ZiIiInP4DVkO6PQ6uI2T/yrrPF4HJwaeUCgiIiIix8BEyMGdu3EOT8x+Qla2vc92tIpoVcwryohOx7FERETkcMp81tjkyZOhUqmQnJwsld27dw+DBg1ClSpV4O3tja5duyIrK0v2uj///BMdO3ZExYoVERAQgGHDhqGgoEBWZ/v27Xj66aehVqvxxBNPYMGCBSbX//LLLxEREQEPDw80adIE+/btK4u3qYjpe6ebJEG6UTrbJ0EpKUBEBNCmDdCzp+FnRAQ3ZiUiIrtXponQ/v378dVXX+Gpp56Slb/zzjtYu3Ytli1bhl9//RVXrlxBQqHp2DqdDh07dsT9+/exZ88efP/991iwYAFGjRol1UlPT0fHjh3Rpk0bHDlyBMnJyXjjjTfwyy+/SHWWLFmCoUOHYvTo0Th06BDq16+PuLg4ZGdnl+XbfjCdzrCf1+LFhp86nWUv1+sQ+Gkg3v3fu1LZlNgpEKMFXFQ2XhEhJYW71BMRkeMSZSQ3N1fUqFFDbNq0SbRq1UoMGTJECCFETk6OqFChgli2bJlU99SpUwKA2Lt3rxBCiPXr1wsXFxeRmZkp1Zk7d67w8fEReXl5Qggh3n//fVG3bl3ZNV955RURFxcnPX/22WfFoEGDpOc6nU6EhISISZMmmY353r17QqPRSI9Lly4JAEKj0TzazShsxQohwsKEMKzZbHiEhRnKS+HUX6cExkD2uKS5ZL34LFFQYPpeCj9UKiHCww31iIiIbESj0ZT6+7vMmg8GDRqEjh07IjY2VlZ+8OBB5Ofny8pr166NqlWrYu/evQCAvXv3Ijo6GoGBgVKduLg4aLVanDhxQqpT9NxxcXHSOe7fv4+DBw/K6ri4uCA2NlaqU9SkSZPg6+srPcLDwx/hDpjxiK0nk3ZOwpNfPik9bxTcCPpReoT5hFk3ztLaubP0u9QTERHZoTIZLP3TTz/h0KFD2L9/v8mxzMxMuLu7w8/PT1YeGBiIzMxMqU7hJMh43HispDparRZ3797FzZs3odPpzNb5448/zMb9wQcfYOjQodJzrVZrvWRIpwOGDDG/d5cQhq0rkpMNCxgWGWRcoC+A32Q/3M6/LZX996X/otdTvawT28PiLvVEROTgrJ4IXbp0CUOGDMGmTZvg4eHx4BfYEbVaDbVaXTYnt6T1pNAChseyjuGpefIxVlffvYog76CyidMS3KWeiIgcnNW7xg4ePIjs7Gw8/fTTcHNzg5ubG3799VfMmjULbm5uCAwMxP3795GTkyN7XVZWFoKCDF/uQUFBJrPIjM8fVMfHxweenp547LHH4OrqaraO8Rw29RCtJ6O3jZYlQTFVY6AfpbePJAj4Z5f6ohuzGqlUQHg4d6knIiK7ZfVEqG3btjh27BiOHDkiPRo3boykpCTpzxUqVMCWLVuk15w+fRp//vknmjZtCgBo2rQpjh07JpvdtWnTJvj4+KBOnTpSncLnMNYxnsPd3R2NGjWS1dHr9diyZYtUx6YsaD25r7sPl7Eu+GTHJ1Lx0sSl2NF3B1RFk45HnIH2SLhLPREROTobDN6WzRoTQoh///vfomrVqmLr1q3iwIEDomnTpqJp06bS8YKCAlGvXj3Rrl07ceTIEbFx40bx+OOPiw8++ECqc/78eVGxYkUxbNgwcerUKfHll18KV1dXsXHjRqnOTz/9JNRqtViwYIE4efKk6N+/v/Dz85PNRiuJJaPOH8g4w0qlKnGG1aHL+01mhWXfyjZ/zkecgWY15uIID7d9HERERMKy729FEqG7d++KgQMHCn9/f1GxYkXx0ksviatXr8pec+HCBdGhQwfh6ekpHnvsMfHuu++K/Px8WZ1t27aJBg0aCHd3d1G9enXx3XffmVx79uzZomrVqsLd3V08++yz4rfffit13FZNhIQwJAYqlWky9HfZsC+6yBKg5394/sHnMpdQqVS2T0IKCoTYtk2IRYsMPzllnoiIFGLJ97dKCHPTmAgwzBrz9fWFRqOBj4+PdU6akmKYPVZo4HRetTB49JUPpF71yip0qd3F/Dl0OsPKzcUNvlapDGN30tPZLUVERE7Hku9v7jVmawkJhinyf+/Llep1E88dHiSrcv3966jsWbn4czzkDDQiIiKSs/F+DATA0ErTujXe9tsrS4K61OoCMVqUnAQBXL+HiIjIStgipJDHpz2Oa3euSc/X91yPDjU6lO7FXL+HiIjIKpgIKUCbp5UlQTnDc+Dr4Vv6ExjX78nIML9StXGMENfvISIiKhETIQX4qH2wNHEp7hXcw6v1X7X8BMb1exITDUlP4WSI6/cQERGVGscIKeTlui8/XBJklJAALF8OhIbKy8PCDOUJCY8WIBERkRNgi5AjKzIDDcHBhu4wtgQRERGVChMhR/f3DDQiIiKyHLvGiIiIyGkxESIiIiKnxUSIiIiInBYTISIiInJaTISIiIjIaTERIiIiIqfFRIiIiIicFhMhIiIiclpMhIiIiMhpMREiIiIip8VEiIiIiJwW9xorgRACAKDVahWOhIiIiErL+L1t/B4vCROhEuTm5gIAwsPDFY6EiIiILJWbmwtfX98S66hEadIlJ6XX63HlyhVUqlQJKpVK6XAUo9VqER4ejkuXLsHHx0fpcJwO77+yeP+VxfuvLEe9/0II5ObmIiQkBC4uJY8CYotQCVxcXBAWFqZ0GHbDx8fHof4ilDe8/8ri/VcW77+yHPH+P6glyIiDpYmIiMhpMREiIiIip8VEiB5IrVZj9OjRUKvVSofilHj/lcX7ryzef2U5w/3nYGkiIiJyWmwRIiIiIqfFRIiIiIicFhMhIiIiclpMhIiIiMhpMREiIiIip8VEiIo1adIkPPPMM6hUqRICAgIQHx+P06dPKx2WU5o8eTJUKhWSk5OVDsWpZGRkoFevXqhSpQo8PT0RHR2NAwcOKB1WuafT6TBy5EhERkbC09MTUVFRGDduXKk20KSHs2PHDnTu3BkhISFQqVRYtWqV7LgQAqNGjUJwcDA8PT0RGxuLtLQ0ZYK1MiZCVKxff/0VgwYNwm+//YZNmzYhPz8f7dq1w+3bt5UOzans378fX331FZ566imlQ3EqN2/eRPPmzVGhQgVs2LABJ0+exGeffQZ/f3+lQyv3pkyZgrlz5+KLL77AqVOnMGXKFEydOhWzZ89WOrRy6/bt26hfvz6+/PJLs8enTp2KWbNmYd68eUhNTYWXlxfi4uJw7949G0dqfVxHiErtr7/+QkBAAH799Ve0bNlS6XCcwq1bt/D0009jzpw5GD9+PBo0aIAZM2YoHZZTGDFiBHbv3o2dO3cqHYrT6dSpEwIDA/Htt99KZV27doWnpyd+/PFHBSNzDiqVCitXrkR8fDwAQ2tQSEgI3n33Xbz33nsAAI1Gg8DAQCxYsADdu3dXMNpHxxYhKjWNRgMAqFy5ssKROI9BgwahY8eOiI2NVToUp7NmzRo0btwYL7/8MgICAtCwYUN8/fXXSoflFJo1a4YtW7bgzJkzAICjR49i165d6NChg8KROaf09HRkZmbK/h3y9fVFkyZNsHfvXgUjsw7uPk+lotfrkZycjObNm6NevXpKh+MUfvrpJxw6dAj79+9XOhSndP78ecydOxdDhw7Fhx9+iP379+Ptt9+Gu7s7+vTpo3R45dqIESOg1WpRu3ZtuLq6QqfTYcKECUhKSlI6NKeUmZkJAAgMDJSVBwYGSsccGRMhKpVBgwbh+PHj2LVrl9KhOIVLly5hyJAh2LRpEzw8PJQOxynp9Xo0btwYEydOBAA0bNgQx48fx7x585gIlbGlS5di4cKFWLRoEerWrYsjR44gOTkZISEhvPdkdewaowcaPHgw1q1bh23btiEsLEzpcJzCwYMHkZ2djaeffhpubm5wc3PDr7/+ilmzZsHNzQ06nU7pEMu94OBg1KlTR1b25JNP4s8//1QoIucxbNgwjBgxAt27d0d0dDReffVVvPPOO5g0aZLSoTmloKAgAEBWVpasPCsrSzrmyJgIUbGEEBg8eDBWrlyJrVu3IjIyUumQnEbbtm1x7NgxHDlyRHo0btwYSUlJOHLkCFxdXZUOsdxr3ry5yXIRZ86cQbVq1RSKyHncuXMHLi7yrydXV1fo9XqFInJukZGRCAoKwpYtW6QyrVaL1NRUNG3aVMHIrINdY1SsQYMGYdGiRVi9ejUqVaok9QX7+vrC09NT4ejKt0qVKpmMxfLy8kKVKlU4RstG3nnnHTRr1gwTJ05Et27dsG/fPsyfPx/z589XOrRyr3PnzpgwYQKqVq2KunXr4vDhw5g+fTpef/11pUMrt27duoWzZ89Kz9PT03HkyBFUrlwZVatWRXJyMsaPH48aNWogMjISI0eOREhIiDSzzKEJomIAMPv47rvvlA7NKbVq1UoMGTJE6TCcytq1a0W9evWEWq0WtWvXFvPnz1c6JKeg1WrFkCFDRNWqVYWHh4eoXr26+Oijj0ReXp7SoZVb27ZtM/vvfZ8+fYQQQuj1ejFy5EgRGBgo1Gq1aNu2rTh9+rSyQVsJ1xEiIiIip8UxQkREROS0mAgRERGR02IiRERERE6LiRARERE5LSZCRERE5LSYCBEREZHTYiJERERETouJEBERETktJkJERETktJgIERERkdNiIkRERERO6/8ByRdBrOGa0lYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x_train,y_train,color = 'red')\n",
+    "plt.plot(x_train,regressor.predict(x_train),color = 'green')\n",
+    "plt.title('salary versue experience (training set)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "VISUALIZING THE TEST SET RESULTS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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_test,y_test,color = 'red')\n",
+    "plt.plot(x_test,regressor.predict(x_test),color = \"green\")\n",
+    "plt.title('salary vs experience (test set)')\n",
+    "plt.ylabel('salary')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9720725422361338\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_auc_score,r2_score\n",
+    "print(r2_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "poly_reg = PolynomialFeatures(degree = 5)\n",
+    "regressor1 = LinearRegression()\n",
+    "x_poly =poly_reg.fit_transform(x)\n",
+    "regressor1.fit(x_poly,y)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17029faf890>]"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "x_grid = np.arange(min(x),max(x),0.1)\n",
+    "x_grid = x_grid.reshape((len(x_grid),1))\n",
+    "\n",
+    "\n",
+    "plt.scatter(x,y,color = 'red')\n",
+    "plt.plot(x_grid,regressor1.predict(poly_reg.fit_transform(x_grid)),color = 'green')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "normal linear regression score 0.9720725422361338\n",
+      "polynomial linear regression score 0.9872124303653989\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"normal linear regression score\",r2_score(y_test,regressor.predict(x_test)))\n",
+    "print(\"polynomial linear regression score\",r2_score(y_test,regressor1.predict(poly_reg.fit_transform(x_test))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}