ML0101EN-Reg-Polynomial-Regression-Co2-V2.ipynb

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    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Polynomial Regression\n",
    "\n",
    "Course: Machine Learning with Python.¶\n",
    "\n",
    "Stephane Dedieu April May 2022 - Rev. Oct 2022. \n",
    "\n",
    "Estimated time needed: **15** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Use scikit-learn to implement Polynomial Regression\n",
    "*   Create a model, train it, test it and use the model\n"
   ]
  },
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   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"https://#download_data\">Downloading Data</a></li>\n",
    "        <li><a href=\"https://#polynomial_regression\">Polynomial regression</a></li>\n",
    "        <li><a href=\"https://#evaluation\">Evaluation</a></li>\n",
    "        <li><a href=\"https://#practice\">Practice</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>\n"
   ]
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   "source": [
    "### Importing Needed packages\n"
   ]
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline\n"
   ]
  },
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   "source": [
    "<h2 id=\"download_data\">Downloading Data</h2>\n",
    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
   ]
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     "text": [
      "--2022-10-11 14:49:32--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 72629 (71K) [text/csv]\n",
      "Saving to: 'FuelConsumption.csv'\n",
      "\n",
      "     0K .......... .......... .......... .......... .......... 70%  522K 0s\n",
      "    50K .......... ..........                                 100% 1.52M=0.1s\n",
      "\n",
      "2022-10-11 14:49:32 (649 KB/s) - 'FuelConsumption.csv' saved [72629/72629]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
   ]
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   "source": [
    "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](https://www.ibm.com/us-en/cloud/object-storage?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\n"
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   "source": [
    "## Understanding the Data\n",
    "\n",
    "### `FuelConsumption.csv`:\n",
    "\n",
    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\n",
    "\n",
    "*   **MODELYEAR** e.g. 2014\n",
    "*   **MAKE** e.g. Acura\n",
    "*   **MODEL** e.g. ILX\n",
    "*   **VEHICLE CLASS** e.g. SUV\n",
    "*   **ENGINE SIZE** e.g. 4.7\n",
    "*   **CYLINDERS** e.g 6\n",
    "*   **TRANSMISSION** e.g. A6\n",
    "*   **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
    "*   **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
    "*   **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
    "*   **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
   ]
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   "source": [
    "## Reading the data in\n"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MODELYEAR</th>\n",
       "      <th>MAKE</th>\n",
       "      <th>MODEL</th>\n",
       "      <th>VEHICLECLASS</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>TRANSMISSION</th>\n",
       "      <th>FUELTYPE</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
       "\n",
       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
       "0          AS5        Z                   9.9                  6.7   \n",
       "1           M6        Z                  11.2                  7.7   \n",
       "2          AV7        Z                   6.0                  5.8   \n",
       "3          AS6        Z                  12.7                  9.1   \n",
       "4          AS6        Z                  12.1                  8.7   \n",
       "\n",
       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
       "0                   8.5                        33           196  \n",
       "1                   9.6                        29           221  \n",
       "2                   5.9                        48           136  \n",
       "3                  11.1                        25           255  \n",
       "4                  10.6                        27           244  "
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     "execution_count": 3,
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   ],
   "source": [
    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
    "\n",
    "# take a look at the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select some features that we want to use for regression.\n"
   ]
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       "      <th>ENGINESIZE</th>\n",
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       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
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       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.4</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
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       "      <td>244</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>230</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
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       "      <td>232</td>\n",
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       "      <th>7</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244\n",
       "5         3.5          6                  10.0           230\n",
       "6         3.5          6                  10.1           232\n",
       "7         3.7          6                  11.1           255\n",
       "8         3.7          6                  11.6           267"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot Emission values with respect to Engine size:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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7iK6CdZ0+cnc3O+5r5tUT7fCH2zl8cDlf6xvgjz42yKPv72Tx+7o5cTgHI3lYuBMOrOSVll4+/M1h3npDuDw9duQxDrx0oCRPr7lhM7/ebxt4a+mD3euhtYc5sxs48FL1n6fpMp2uQldm7j23PD125DEefe5RLe+IKHmqiFcTIm4APo31b38Q+CXwCnAvGeo+2vPLPbHSVwOjUQ9TVWOQrhm/fXSuI3DT6NZdNWtW9C6qIosXlx5z8eLoGrNG2hpJo/tIKbVRKTVfKZXDGkD+gVLqvcADwDp7t3XAd+zvDwA3iUiDiFwKXAY8npQ+gG37tyV5eC0YjXqYqhqDTPf0677ROfDspjGJ1clXXAH79pXG7dtnxUfRmDWyrDGNdQqfAd4uIk8Db7d/o5R6Ergf2Ad8D/iwUkrj5MDJdOQ6kjy8FoxGPUxVjUFeuH4vfZ3rCLzKce1aGByE06etz7jTUcsrBL94J1P1WgclqH+OqFSlUlBKFZRSK+zvI0qpZUqpy+zPlx37bVJKLVRKLVJK/WvSuoZGh5I+RWyMRj0kqVGXBzEvjX4vXL+Xvs5/8rV+rXURVWM1VuHX9IrmgZGBtCX4YjTqISmNOj2IRdUY5KVfXrFAtIqslq+1TqJqrMYq/ETXKSQdzDqFbFDLGnXO0Y+j8d57rXMGWYcQZ2DYS2PUQWEvyo8X5rhn8/0YxsBi5eMYg3iuZHmucBGjUQ9JadQ5Rz+oRpHJwa+14uziWrcuvG2kt73NOs9FN3QjYv0uEmdQ2Isnn4TFi0vjFi+24v04m+/HaqzCr+lKobmxOW0JvhiNekhKo86ZPUE0ejntqfSSL+/i8rLtdPiwe/zb3gYPP2z/GLU0PvzwmYohzqBwJZ58svS/cJAKAc7u+zGK2ZOw1HSl0N7SnrYEX4xGPSSlUefMHt0ai60VN6upbnj925yoEACG2t3jM8TZfD9WYxV+TVcK2we2py3BF6NRD0lp1DmzR7fGYmslaFdWIOuwi2r3WuskjsagBhajErhSEJFzk7ZaWm2WL1yetgRfjEY91JpGZ2slaFfWggUBdjpYW+WYFFnW6FkpiMg0EVkjIv8iIkeBp4BhEXlSRP5eRC6rnsxkOJunrlWTWtaYhSmpRbxaK25dXG5cf717/LJljh9NA+7xGaKW70cdVGopPAIsxLJJ9DtKqYuVUnOBa4GfAp8RkfdWQWNiDB4bTFuCL0ajHqJq9FuYptPLWRCNapKJyDPxXgvcyru4vMYOvvtd9/iHHnJUAHMsjcuWWfFQPb8UQTmb78eq4DVXFZjhtS3MPkkGs04hG5ytGoPM59dpbC5OOW7YoFRdnXXuurrKPiHiaE7SL0WYPITVmDXS1kiUdQpKqVdhwn1meZjh3GeqcjbPZ64mZ6vGIK2A8893T+sVX4mgGstt31xxRTjTB3Gm0Xr5pSjvSlq2LNwAqE7zDWfr/Vgtggw0PwG8CAwAT9vfD4nIEyISwDh3dsnNyaUtwRejUQ9RNFbbeUwQjW4vT6/1AHfe6d7tFWcarZvGLVtg167SuF27Jne1VeqK02m+4Wy9H6tFkErhe8D1SqkLlFJNwLuwrJneAqTko0oP+aZ82hJ8MRr1EEVjkH/UL7/svo9XfCW8NDpfpnfeGe6YboPfcabRumn0alHdeusZ3RdcADff7D0g7zUdNtA02QAas0aWNQapFNqVUg8WfyildgJ/oJT6KdCQmLIqsPPgzrQl+GI06iGKxiD/qL0qjmnTwhubc9NYPrspKq+8Ypm3KGqCypZXi2YtiqG4etlNo9dK6JGRM7pHRuDEicmail1xOgerz9b7sWp4DTYUA7AT+O/AAjv8NfB9oA54wi99kiHuQHPv872x0lcDo1EPUTX6GZpzG4wuD0GNzblp9DK4FzdU0rRsmXuaZcvcNcbRURzcrnTOsJzN96MuiGkQbw0wH/hnO1xsx9UB79FbRVWX3qHetCX4YjTqIapGP18GQaZ7Bp2i6qYxqfGL8paDszXjZb7i4Yeh/YZerY5dii2tZ55x3+4VX4mz+X6sBr6VglLqJaXUXyil3miHv1BKvaiUOqGUinDJssPw2HDaEnwxGvWQpEZnxXH6tPs+lV7uxdlEH/pvw5NeuFEM6wXl1Kkz/fs33xywm2v28KSZQVHXIzi74nQO6tf6/RgbryYE0A20emw7F7gZWFsh/TlYPpZ/DjwJfNKO7wKeB3bb4XpHmo3AM8AB4B1exy4Gs04hGxiNZ2hsdO8GaWx0379kjv/s5yfN8ffqVkkiNDVZ56y4n0NjXZ21v5fvg/IwY4Z1DreuuKz4pagWaWskYvfRHcD/JSL7ReSbInKHiNwlIj8CHgVmA9+qkH4cuE4p9QZgCfBOEXmLve0flVJL7PBdABFZDNwEXAG8E7gjaVtLWZ4rXMRo1EOSGp2zg8bG3Pfxii+ZctnWPSm+UPA/f0tLMDMWfoyMWJ8VzVc4NBZnBu3f7727c4bT3XfDSy+5d8XptDZb6/djXKZ7bVBK7QbeIyKNQDvQDBwH9iulDvgd2K6Nio/CDDuoCkluBO5TSo1jrYN4Brga2FUhTSyyPC2siNGoh6Q0FmcHBTFN7UbJlMuR/KT4SlMyleNp2rLFGrd49lmrcnJLV1dnvZBVpacQy3xFiQ8FJw6NxW6jSscruv70o1hBFPNwySVWhRDF2mwt3486CDKmMKaUKiiltiql/jlIhVBEROpEZDdwFPi+Uuoxe9NHRGSP3fI4z467CHjOkfyIHZcYLbNbkjy8FoxGPQTVWL5a2G9ANaivAi9K+uNHWybFB52q+ZOfwJEj1gv69GlLu5NZs+Cee6xtTU3ux3TGP/TQmU6cDRvcNep07AL+g/pBOZvuxzTwbCnoQCl1ClgiInOAfxKRK4E7gU9htRo+BXwOa3zCzafUpP8gItIJdALMa5lHV6GLVZevojBYYOT4CJ1tnXT3ddM6t5XG+kZ2HdnF6itXs2NgB+OnxlnTuobNuzfT1txGT38PhcEC65esp6e/h4a6BlbkV7B171aWzl/K2Ikx+o/2TxyzaWYTHbkOtu3fRkeug6HRIQZGBia2Nzc2097SzvaB7SxfuJyBkQEGjw1ObM/NyZFvyrPz4E5W5lfSO9TL8NjwxPZ8U56W2S0UBgsTedq2fxs9q3oC5wmgb7ivqnn60eEfURgsBM5T2OukI0/dfd0T5eiVp0Pf6uTr+7uhNQcjeU4t3Mmd/7ySJ2f28taV7nk6fHwVXF2AWSPQ12l1r7zQCica4eJd0L8a8jtg+jiDxybn6dI3rueZmT1wsgHmHIJcAZ5bSvPrxugq9LP2Q518/clueKUJBjtg8TYY7ODU7CHkrQPQ18m0N3Vz+tfNMLcdFm1HHVzOyaYBZswd5NVdnbz2um6uel2Ov/pCnvd+dSevvXAl8rpe1LnDE5rrjuX54C0tdBUmX6e57+lmeUMrD/1LI6ev+TTTvvsV3vy+Hcy/0cpT3ds2c+pZ27hBSx/sXg+tPUyXBg68VP3naXR8lMJgITP3nlueugpd/M2yv9HyjoiSJ78Xd1UCcDvw8bK4HLBXnRlk3ujY9iCwtNIx4w407/nlnljpq4HRqIcgGovG2MpDcUA1TJryMG2ae/pp0xz7zd3jur/TUFyYUNTttpai0qBv2HJsaqo8cF1tzpb7MUmIuU4hEiJyod1CQERmAm8DnhIRp3PSdwN77e8PADeJSIOIXApchjV7KTEKg4UkD68Fo1EPQTRGMbUQ1AyD11TVkvhcwTXe6WkrDEVtbl1cr75qDSwrZXU7/eQnwY7pVo46TX3o4Gy5H9PCt/tIRPLAf8NazTyxv1LqOp+kzcA99gyiacD9SqkdIvINEVmC1TU0CHzQPt6TInI/sA84CXxYWd1PiTFyfCTJw2vBaNRDEI11dd4vebE7NxcvLnUgv2CBt5kHJ4G8ms3SW47FcQe/uf7FdQfgb9nUrRzr6qxKy+v81eZsuR9Tw6sJUQxY6ww2YM0EaisGv3TVCGadQjaYyhqjdM0sXnwmfRAzF+BtrsFrDYA1f89nf59QXOsQ1FRGpW6ySuVY6ZhpMJXvx2pBzO6jk0qpO5VSjyul+oohsVqqimR5rnCRLGsszs+/6IbuUIbfktDgZ3zOrRzLzVAXEbcpDw6cpqrLzVx4EWS9gXMNgBdz5rjHz5hROmNpw4Yz//qDuuMM0hWW5fuxiNEYjyCVwnYRuUVEmp2OdhJXVgVa57amLcGXrGp0Wu/khdZYvol1aFCqsn9kt3L0stU/bVq4/nvnVEovAo09vOB/rVevdo//sz87M+5w8mRpN1DQiitId09W70cnRmM8glQK67DGFB4F+uyQXWtOIWisb0xbgi9Z1VgyeHnC0hjVN7EWDTZeGtzKUacN/yKxTECf8L/WUZ3ROCuuknUHDoKsO8jq/ejEaIxHkMVrl7qE11VDXNLsOpLYYmltZFVjyeDlxbvc46upwSferRz9XuCLF7tvL493dmHNnOmeJtBCr4v9r7WOiuyaayytTqZNs+L9cCtHr0rGKz5psvrMOMm0Rq/BhmLAMk/xl1h2jr4FfASY4ZeuGiHuQPNTLz4VK301yKrGksHLpqcmvkcxYKZFgyM4NRT9IXDBU5Pm4wdxOF9u7M05yFw8fvlAc8nagwqDzEpZ6wTcyrHoZ6CcKGspopRbOXHLsZpk9ZlxkrZGKgw0B6kUvgrcA1xnh7uBr/qlq0aIWyl89iefjZW+GmRVY8nLcOlnFQR3JpOIBjs4NQTR6Jx9VFcX/kUWZGZPpXIp2dfWWGnmjo4XcElF5AheFZFfOeqoqHSS1WfGSdoaK1UKQcYU3qSUWqeU+oEd3g+8SXeLJQ3GT42nLcGXrGpcu9Zy0lJXB0wfp67O+h3VXk1UDZV8DZeMOUy3yrF8zMG5MKx8gBb8ZzcF6S6rNNZSsn5h+rh7vIM77rC6ZbxmGgUhiO9pJ37lmMTYTByy+sw4ybRGr9qiGIAngIWO368jZTecxRC3pXDoV4dipa8GWdVY8u9xzqFUWgpFHV7uMkv+EdsaK/0jdjt2pZaIUsHXAAT6F16lcgzb2vArx7Atj6TJ6jPjJG2NxOw+WgY8CxSAH2KtQn6rX7pqhLiVwu2P3B4rfTXIqsaSl2HH7YH6pXXj99I+91x3jeeeG+z4Qccs6uv9K4W6Om87QxNdWB23R+rCCkvYMQW/a10p3078/F3rIqvPjJO0NcaqFKz0NACvB94ANARJU40Qt1J44KkHYqWvBlnVWPLvMP+Aln+HYV8afi+3kniHRq/++nKCvOzuvdcyLhekteBWcZWktzXOmFE57+XnmzEjWH6KxBpTsDU68xC0nMrPK5JMxZDVZ8ZJ2horVQqeYwoicp39+Z+BPwJ+F1gI/JEdZ6hhwvZL+xFmIVoRnX59o3LbbZZxuXLq6qxxDrdpr87++FtvnZz+1VeteDfq6933r68PrjnstXOO3eAydhOEm2+2rqsTpax4Q7aoNND8h/bnSpewImFdVaFvOPvWOrKqscR0QoulMar7RPBeiLZunfcgb6iXW0sy5ehVAZ0+bQWvwdaiEb0Rp100h8YRD3tpbhVQpXg3Nm2aXInU11e+dsXFb7d/uW+SAxyvVdLO+BMn3Pfxio9DVp8ZJ5nW6NWEmArBDDSny8Tc9TmHYvcRe3VpeHW7FM9faUyhJL1jgFRn95FfF5bfdM2wGoP231fCrcvLr8uqiNv9GGTgWofuoGT5mSmStkZiDjTfCrwGyzPaV7FmIy33S1eNELdS2PRvm2Klrwa1ojHoLJ7ywdBK4xAlaa/dlEilEKpicjlGWI1BX66V1l9EWbxWxOta+633qGalUCvPTBwqVQpB1incrJT6DbAcmAu8H/hMEq2WatNQ15C2BF9qReOmTZNNL7hx+HCpD+Wf/MTbr29Jt8bJBtf4SusQgpi58Fsr4bXewDX+pH85zpjhH19u/bXoL6HobzrOWIzXtfZb77FsmfvxvOLjUCvPTGJ41RbFAOyxP78AvNv+/jO/dNUIxsxFNtCh0asLIkjwmsLZ2OjYz2FCorHR2u42nbS+vrS14Wfmwo9QLQmHxkr/oP1mH5Wb2SiGoovPknJxhGK5VCLOtV62rPR8lcx/xKFWnpk4ELOl0CciO4HrgQdFZDZQwUjw1GHr3q1pS/AlyxqL/7J/70+3xvan4GflM0rasTHHj9atk+JvvXXyQOeJE6Uzf558svTV6fS65oWz9XHbbdZguVdLooTWYNf6xIlSTeV58HP9+dvfum8fG/P3SxHnfnzooVLdDz0U+VAVyfIzUyTTGr1qi2LAmqF0FTDH/n0+8PoA6c7B8rH8c+BJ4JOO9N8HnrY/z3Ok2Qg8AxwA3uF3jrgthe89/b1Y6atBVjWW/ANe+L1J/4DDErWVUOlfdckgr62x2Oftd86oBFkF7Zlvh8Y4GvzyFaQ8vTRn9X50YjT6Q8yWwlLggFLqmIi8F/gfwK8DpBsHrlNKvQFYArxTRN4CfAJ4WCl1GfCw/RsRWQzcBFwBvBO4w/bvnBhjJ8b8d0qZrGosmUJab2mM408hjj9fr7Ql00Hrx9zjNRPGx8Mk6vVca78pokHK2ktzVu9HJ0ZjPIJUCncCr4jIG4C/Bg4DX/dLZFdIxZzPsIMCbsSyuor9+cf29xuB+5RS40qpQ1gthqsD5iMS/Uf7kzy8FrKqscRZ/bx+9/gQBPI3EDJto9OPiUNjMb6pyT2dV3wQvAZrDx/275pxaqzELbeUDrYXB5CLWI3uyRTjOzoCncY1L1m9H50YjTHxakIUA7bxO+B/Ah9wxgVIWwfsBsaAv7XjjpXt8yv78/8B3uuI/xrwJ5WOH7f7KG3n2UHIqsaSrhmHw/mo5pLvvdd7gLR8MNQ59XHZsoAG8Rwai+Yc4szXd+KcjhkkOM9Rss2h0av7SMeagKjTf5XK7v3oxGj0hwrdR9MD1BujIrIReC/wB3aXjsfEuEkVzilgiYjMAf5JRK6ssLtbo3fSfx4R6QQ6Aea1zKOr0MWqy1dRGCwwcnyEzrZOuvu6aZ3bSmN9I7uO7GL1lavZMbCD8VPjrGldw+bdm2lrbqOnv4dFFyxi/ZL19PT30FDXwIr8Crbu3crS+UsZOzFG/9H+iWM2zWyiI9fBtv3b6Mh1MDQ6xMDIwMT25sZm2lva2T6wneULlzMwMsDgscGJ7bk5OfJNeXYe3MnK/Ep6h3oZHhue2J5vytMyu4XCYGEiT9v2b6NnVU/gPIG1WjLpPJ2a1Wk5mj+Wg0t+BL+5GA6s5FRLL10FK08fububH/1znpcOtfDaNxT4xA2rOPcK9+v04W/u4vR5qyG/wzLP3L8GlmyGIStPtPTB7vWMtfZw3uwGfvX4CurftJUf/mIpJ2eNwR/2c7ivk/X3dLPzWBMf/5MO1B9ug8EOmD1kad3WA23dqNFm+obaefqi7Wy8Yzn/730DjJwa5KJfdnL1hm5evSLHo88Fu077v72K+/sKcO0I9Nll8kKr5Vrz4l3QPzlPrw61seEf4emL+mDOemjtsaajzjkExy+A55ZC/RhdhcnX6ctPbYOcnaemgYlzfrm3mQ8MWdeJi5db2+YMntF0LAcjeeStO+HESmjrhdnDZ7aP5GG0BXIF2LeKGfkCV/3pCEOjpdfp0z/+NF9Z8ZVJ997oQBvf+Aa8OL2Pi15cz3/6YA9vbkvneRodH2V2w2zX5ynsOyKp5+mvdv4Vf7Psb7S8I6Lkye/F7fdv/3eAjwHX2r8vAf6LXzqX49wOfBxrELnZjmvGGq8Aa5B5o2P/B4GllY4Zt6XwxZ9+MVb6apBVjU1Njn+VV39x4ntTk7U91oBrzOBqEM+hMcwg7syZpceZObN0e6UWgt8q7SDlGKacdJSlnzFCt/tRV4tLF1l9ZpykrZG4VlKjBOBCzsxYmgn8CMtm0t8Dn7DjPwH8nf39CqyZSg3ApcAvgLpK54hbKez55Z5Y6atBVjWWvAzn7pn4Xuw+CrtqVmelUOweKol3aKxUKQTpCnJWDH4vab/tJea9HRq9zHsnXSn44XY/llRsjuBVsSVNVp8ZJ2lrrFQpVLKS+mP7c1REfuMIoyLyG/82CM3AIyKyB/h34PtKqR1Yq6HfLiJPA2+3f6OUehK4H9gHfA/4sLK6nxJj2/5tSR4+FsW57q+/aVvsNQDgPzgZlpIZPIu3TYpP04Kpq0G8xf7XunwlsBfHj5/57jWTJ+hsqpI1Aw6NXmsJ0sbtmfEy3ucVnzRZfq6LZFqjV20xFULclsIjhx6JlT4pSrpeco8oiLcGIAnH6iXHsjU6/22m1VLwXC3sorGcMIPFQcs2VEsigMa0Wwpuz0yc4yVBVp9rJ2lrJOY6BUTkPBF5vYhcVQwJ11VVYWh0KG0JrpTMdZ9taYyzBsBrxW+cVcQlzJ5cjiWmtW3imNb2w3e1sIvGcqKsX/DzmRxq2msAjUHwsrfkRxA7RG7PTBJTe+OQ1efaSZY1+lYKIvIpYA/wJeBzdvhswrqqwsDIQNoSXCnpYmkacI8PQeKO1Zsml6OfoTjduBnE89MYlZkzS39fcw3Mn2/lc/5863eRL3xhshG7GTOs+CgaN2zwj3erkItdh2BVXC0tpduXLQtmdsLtmfnCF9z9M7jmsQpk9bl2kmmNXk2IYsCaLVTvt18a4Wxdp1AycOeYux514M7PQFoUSo4VYH69H2G6boJ2U4QtxyA+HcrLLMgsq0rmvUuMxDk0VjIW52emuvycTU2TZwdF7Y70emaq5X85CFl9rp2krZGY3Ud7gTkJ1kup0d2nq/8kQdriayz/Z+sXHxoNGpMwPbFkieOHQ2NJvAPrP1BlTp8uHaQP4jEOvM175/PuGkviy/AzUw1nPKWdPm2t4C73zBa1O9LrmXGer2KLrQpMhec6yxqDVAqfBn4mIg+KyAPFkLSwatDc2Jy2BFdeftnxY7TZPT4E5S8tv/jQjMYvxxKTFJooFBw/HBpL4h0EnTHkHIvx6tI7dcp6afv5mi4Z13ForDTeE3Ymmc6ZYFl9ZpwYjfEIUincA/wt1tTRzznClKe9pT1tCa6UTKkcanePj3q8APGhGYpfjklMwSxpfTg0erVKgtpfcqYPUoaV/pWH1ejnQMcNndff65mp5Kyo2mT1uXaSZY1BKoWXlFJfVEo9opT6YTEkrqwKbB/YnrYEV0ocqy+yNPo5Vvc7nt9MoFgP9aL45Rik66YSIla44oozcSX//B0avVoE5TOJvHBudytbN7wMBYbVGGUmmc6ZYG7PzJYtVoV6+HCw1lHSZPW5dpJpjV6DDcUA/ANWF9JSLL8KVwFX+aWrRog70PyTZ38SK31SlJgNuPgnCuKbDag0EBjFTEHJAKyt0W+gObA/5Zih6B2tZA2BQ2Ol9RnOQVyvgefy9M58eWnyMhQYVmPUNQG6BoLdnpk4Pp+TIKvPtZO0NRLHzAXwiEv4gV+6aoS4lcLdP7s7VvqkKHnIltyd+EMWxUxByb4OjV4vpzgO7qOEIhMv+SV3e87Uce5b6Zh+6f3yUem8QTV6zdKKap02LG7PjFdlWDQ3Um2y+lw7SVtjpUrBt/tIKfVWl3BdEq2WajN4bDBtCa6UDADOGXSP10hsMwUOjU6cXVLr1sVwPhOD4kyd2/9x0HOmTpE77/TeppT3TB8n554bLj6sxkWLwsXrxu2ZSXzMKiRZfa6dZFqjV20BfN7x/daybZu90lUznK3rFEqMpDnmrnsZSYtLlH+3Jfu5rFNwaxlUOkdSLYUiQa511K4ZJ3H+NQfRmHZLwU1jWIu4SZPV59pJ2hqJ2FL4A8f3dWXbXq+zYkqLrM4Vdhpcc85dL4nXSGwzBS7rFNzm77sRxw2nF4sXT46r1rW2/jMFj4czLaqLbuj2HeRPfHW6D27lWO3V635k9bl2kmWNlSoF8fh+1pCbk0tbgiunTzt+HMu5x2sktpkCh8YiQd1y6n6ZLV4MTz45OT7ItfbzbZwEW7bA+99vl9exHIcPW7+9Koa4Vlnj4lWOWVq8ltXn2kmWNVaqFKbZhvCaHN/PF5HzsdxsTnnyTRWWjWaFkWgay6eY3nKL95TTtWvhrrtK/+nddVeIB9tFY9CXlC6jacWOi/IKoVgO778h7/sv/EMfChevg1tvdaw2tsvx1VeteDe81lLE8XEdhqnwzBiNMfHqVwIGsRzdHHIJv/BKV80Qd0zh9kduj5U+KUr6iztuD923HaQ/f9q0eH2+fhqD9v0XZzjFHUPwmmo7UQ62Rr++7iB2hQKXS4BxiSjXOq7GOHg9M1myfZTV59pJ2hpJw/NaNULcSqH3+d5Y6ZOi5EXR3Bu6UgjqmD3OwLWfRi8jfF4vy7iVQjHU1595IZWUg0NjkvPnY1UKEa51tXF7ZrI20JzV59pJ2horVQqB/CmcrfQO9aYtwZWSLpWWXvf4CgTtz9dmWqJlcjmGGf+YpvEuPHHiTNdLyRReh8Ykvb+FHbSPe62rjdsz42UUMOnpxl5k9bl2kmWNiVUKInKxiDwiIvtF5EkRudWO7xKR50Vktx2ud6TZKCLPiMgBEXlHUtqKDI8NJ32KSJQM/M62NKZpn96X2fHKUSlNOmyK6ytK5sk7NCY5fz7soP1Uu9Zuz0yarlfdyOpz7STTGr2aEHEDlo/mq+zvs4EBYDHQBXzcZf/FwM+BBuBS4CBQV+kcZ+s6BaXO9NEy+/nQfbRBu1qcvgHC9gmXHMtlnUIQ3wRJhmKeJro1bI3V6NYIW5ZxrnW1cXtmsmbmIsvPdZG0NZJG95FSalgp9YT9fRTYD1xUIcmNwH1KqXGl1CHgGeDqpPRBtucKT6DBV4EXH/yg9RnboJmLRt3//qPgnD9PW3fV5s+HnZ5Z3P/2B7pTn87ph9szU23Xq35Mhec6yxo9KwURaRWRn4rIcyLSLSLnObY9HuYkIpID3gg8Zkd9RET2iMhdjuNeBDznSHaEypVIbLI6LWzLFnjf++yxgZE8hw9bv3VZnSz3Ixy7T9hlSmpW+sSLL9wtX8xn/oWr835MypS1m8asLV7L6nPtJMsap1fYdidWV89PgT8DfiwiNyilDgIzKqQrQUQagW3AR5VSvxGRO4FPAcr+/BxwM+4L5Cb93xSRTqATYF7LPLoKXay6fBWFwQIjx0fobOuku6+b1rmtNNY3suvILlZfuZodAzsYPzXOmtY1bN69mbbmNgaPDdJV6GL9kvX09PfQUNfAivwKtu7dytL5Sxk7MUb/0f6JYzbNbKIj18G2/dvoyHUwNDrEwMjAxPbmxmbaW9rZPrCd5QuXMzAywOCxwYntuTk58k15dh7cycr8SnqHehkeG57Ynm/K0zK7hf9ydwF14SrIFaClDwY7UG3drP/bVi54c+U8AdDRB7vXQ2sPnGyAgRXQuhWeW8q3HjiTp66ClafD/9EBHdtgsMNyHt80AH2dHL60m6/0uueJ2Z1WC+FYDmaMQUcXHFgJLb10FYY5fW4ntHZbFcZoi5WXfXaeZo1An53+hVY40QgX74L+1ZDfAdPHoX8NLNkMQ3aeWrzzRP0YzOs/c8xXmuh/ofQ67Ty4k45ch9brVBgsRL73APqG+0ruvQvPvZCuQlfse089s5zP/H8DnLh0EF62ruPNX8wxcDyP/G68PO08uJO25rbJeWpv40sPlubpc4+m8zxdOfdKugpdiV0nHXnaeXAnuTm51O69inj1KwG7y36/FXgaeAvwhFe6sjQzgAeBj3lszwF77e8bgY2ObQ8CSysd36xTCJDepa+9nCh9wn4aszCm4CSr19qJLo1J9vHXUjkmSdoaiTimICLyWkfl8QiwCvgGsMCvshERAb4G7FdK/YMj3umH7t1YPqABHgBuEpEGEbkUuAwI1U0VllWXr0ry8HrYl7zG2H3CLhrPPz++Lp1MhWutS2OSs4FqqRyTJMsaK1UKfwtc7oxQSu0BlgHfDnDsa4D3AdeVTT/9OxHpF5E9WK2P/2of+0ngfmAf8D3gw0qpRM18FQYLSR5eD7mC1sO5+fWN3SccQuO0acnaEvJiKlxrXRqTNGVdS+WYJFnW6DmmoJTqKX63xwWUUuq3SqlngT/3O7BS6se4jxN8t0KaTUDV5iyMHA/qMCBFZunXWPTrC/7+Abw45xz4j/+wfzg0nnOO9fnyy+7plLJm5SRZMbgNck+Fa61L46ZN1uwx5+QBXbOBaqkckyTTGr36laxuJzYAzwIjwMvAYeCWSmmqGc7WdQp+awD8WLw4WL970Qb/vfdONkvhZxup5BwOjUVXmH792kmNJXi5Ec3qtXaiU2NStohqrRyTIm2NRBlTEJH/AawEOpRSTUqp87G6e95lb5vyJDVXWOt0wAjrFPbtC7Zf0Wz1Bz842SzF6dNn1jH4nsOhsRh//fW44hUfh8bGM91ed9/t3u2V5XnhRXRqTMqUda2VY1JkWWOlKanvA96glCp2EqCU+oWIvAdr5fH/Slpc0rTObdV+zOJCsGLTvbgQDCI+mC+Uapw2zeob3rRJ34PuZQMpsG2kFyaX43c9Ogm94uNw/Li/raUkrrVujEY9GI3xqLii2VkhOOKOAwm5e6kujfWN2o+p3TjYiVKNSkVYceyBtn79E5PLsZr2cII46kniWuvGaNSD0RiPSpXCERFZVh4pItcBGbbmFJxdR3ZpP6b2l+HF7hp1WKFUk5YGRsRFYzWduQdx6JPEtdaN0agHozEelbqP/hL4joj8GOjDWl38JqyppjdWQVvirL5ytfZjXnKJu+nqyC/Dfm+NaVmhnISLRr8ZMHV1+lxxBvE6lsS11o3RqAejMR6eLQVlrRu4Evg3rJXHr7O/X2lvm/LsGNih/ZjaB1jz3hqTNAEdCheNfmsfFi2Kfrpiy6DchlMlkrjWujEa9WA0xsOzpSAivwvMU0rdVRZ/rYgMKcsG0pRm/NS49mPef793fKQ1AdPdNeq0Qini3pUUeMzBQ+Patd6D4QcOBDx2GfX1MB7hsiVxrXVjNOrBaIxHpTGFzwOjLvHH7W1TnjWta7Qfc8RjTYpXvC/9pRqTsEIZ22F9f/hyjNp1dO210dIlca11YzTqwWiMR6VKIacssxYlKKV6sbqTpjybd29OW4I/SzaX/NQ97xysFsyGDdG6Zdw0BiHI4LAbhUK0dFPhWhuNejAa41GpUjinwraZuoWkQSAzsglwyy2W/SE3O0STGKqOxjvugJMnrW6kkydDdnVF0BhkcNiNqC2MtK51GIxGPRiN8ahUKfy7iEyycSQiH8CajWRwwasfvhh/yy2W3aHiy61oh6hixZBB/PLpR3nrJChRWxgGgyEYlSqFjwLvF5GCiHzODj/Ecrhza1XUJUzfcLS6rZIZC6+5/8X4bo/V7V7xtFSn/g3VegEud9rPdWgsiffB2TppaQmWJmoLI+q1riZGox6Mxph4GUUqBix7R39hh+v89q9miGsQ79CvDoVOU+IM3g5OZ/B+jl+COIYpiZ9zKLRBvLDOaDZscN+2YYP3OUqc6Dg0ioQuUqWUtwE9p/G+Snr8iHKtq43RqAej0R8iOtkpVhqPKKW+ZIcfJFtFVZee/h7/ncqIa8bCq/vDs1ukNbzGsIRuvVDWInJojLpK2m3Bn/OYocc5yohyrauN0agHozEevpXC2UxDXUPoNF4vr0ovNScdHeHiORleY1Bm2tMFvAZvAw/qatAYurIMSZRrXW2MRj0YjfGo6UphRX5F6DRxX167d4eLZyC8xqAcP259RslTo9Oel0NjY0Q7X7ErJh+iXOtqYzTqwWiMR2KVgohcLCKPiMh+EXlSRG61488Xke+LyNP253mONBtF5BkROSAi70hKW5Gte7eGThP35RV6cVtreI1h8Rq8rTSo++UvWwPSwITG6dOt+Cgs8PD67RUflijXutoYjXowGuORZEvhJPBXSqnLgbcAHxaRxcAngIeVUpcBD9u/sbfdBFwBvBO4Q0QSnYC4dP7S0GmSfnlN4rnwGufMCbd/lMVra9fC5s12vo8sZcEC63fURXVJO+WJcq2rjdGoB6MxHolVCkqpYaXUE/b3UWA/cBGWhdV77N3uAf7Y/n4jcJ9SalwpdQh4Brg6KX0AYyfGQqeppkcxAOrDazx2LPxpoixeK3r3+tZ3xmKvsk7aKU+Ua11tjEY9GI3xqMqYgojkgDcCj2EZ2RsGq+IA5tq7XQQ850h2xI5LjP6j/aHTeL2kurutdQvamRdeY7WJUo7lJO2UR4fGpDEa9WA0xqOSPwUtiEgjsA34qFLqN+K95NVtw6QJjiLSCXQCzGuZR1ehi1WXr6IwWGDk+AidbZ1093XTOreVxvpGdh3ZxeorV7NjYAfjp8ZZ07qGzbs309bcRm5Ojq5CF+uXrKenv4eGugZW5Fewde9Wls5fytiJMfqP9k8cs2lmE4ePd0DHNhjsgNlD0DQAfZ2cauuG0WYYaodF2+HgcmvbnEHo64S2bjbvzsHFeVi4Ew6shJZemD08sb2nP0/L7BboKMC+VZArQP2odZ62bnihFbmskddesYvbblzNtEWT8wRARx/sXm9NFT3ZYA0Et261uqLqx6yKxj7nlx5roiPXwbb92+jIdTA0OsTAyMBEnpsbm2lvaWf7wHaWL1zOwMgAg8cGJ7bn5uR4Y/Mb6Sp0sTK/kt6hXobHhie255usPBUGCxWv02tu2MWvf7zaMsM9fdwysrdkMxecbGP7AWuxT5jrVJ6n0fFRhkaHAucp35Rn58GdsfJU6d6DyXm67tLr6Cp0Bc5T2OukI0+j46MceOlA4DyFvU468nTDohvoKnQldp105Gl0fJRHn3s0tXuvIl4LGHQEYAbwIPAxR9wBoNn+3gwcsL9vBDY69nsQWFrp+HEXr93+yO2h09TVhVscFnvxWsftrvs6F8yVE1ZTXKKUYzl+iwLjokNj0hiNejAa/SHO4rWoiNUk+BqwXyn1D45NDwDr7O/rgO844m8SkQYRuRS4DHg8KX0ATTObQqfRNUUyMK+4a9ThjjMuRXMfn/xE0yRzH2FZuxbWrSsd7F63Tp812CjXutoYjXowGmPiVVvEDcDvY3X/7AF22+F6oAlr1tHT9uf5jjS3AQexWhPv8jtH3JbCnl/uCZ0maishckth7h7P/b1MSpx7bjA9UU1SKFX2z97WGOef/b33lpnOsPXpailEudbVxmjUg9HoD2m0FJRSP1ZKiVLq9UqpJXb4rlJqRCm1TCl1mf35siPNJqXUQqXUIqXUvyalrci2/duSPkV8Fntr9HLH+Za3BDv0rFkR9NiUmPuwNcZpvXzgA5NNZChlxetgKlxro1EPRmM8anpFc0euI20J/gx2uEZXcsf5g4AWqn7722iSoGxWkENj1NlCXi42o7jedGMqXGujUQ9GYzxqulIYGh1KW4I/s0s1BnHHWf6POwlKWikOjV6tl7SZCtfaaNSD0RiPxKekZpmBkYG0JfjTVKrx9OmUdJSxaZNlBuOVV5jQWKn1kjZT4VobjXowGmPiNdgwFULcgebnf/N86DRVH2ie/XzoKaTlA7ZJTUm9917bD8Ls59WCBfEGhZctc9e3bFk8jUWiXOtqYzTqwWj0hzQGmqcC3X0VHAZkhbbwGoN2HzXFnBVXNHNx+wPdsc1cPPQQLFtWGrdsmRWvg6lwrY1GPRiN8ajpSqG5sTltCf6MhtcY1Djfe94T+tCu6CrHfL50nUI+r+WwwNS41kajHozGeNR0pdDe0p62BH+GwmvctCnYdNOvfz2CHhd0lOMtt8Cdd55ZHHjqlPXbz1d0UKbCtTYa9WA0xqOmK4XtA9vTluDPovAa1661ZictWGDNVvIizpRUJzrKMYpL0DBMhWttNOrBaIxHTVcKyxcuT1uCPwejaSz291djtpKOckza89pUuNZGox6MxnjUdKWQxrSwc8/1jy8ZcHVMSS0fiA2KV2uhUisiDJmeXmdjNOrBaNRDljXWdKUweGywaucqekML8oJ+5hlnwkH3+AxRzXKMitGoB6NRD1nWKKoay18Tor29XfX29kZOPzQ6ZPkvCEHUf9cLFljdOZXSFy9FyT6zh2C0ZdI+YQhyzjhEKcdycjk4fHhyfLHc4qJDY9IYjXowGv0RkT6llOtod023FKo5VziyB7EI6xSqjY5ydJsxpXOFdJbnhRcxGvVgNMajpiuF3Jyca/wtt8D06dY/7OnT9UyLPP/8iAmP5eKfPGG8yjEM5TOm/Ow7hUWHxqQxGvVgNMajpm0f5Zsmr44qzpcvUpwvD8Gc2WtnJP4KrmXL4OGH3eN14FaOUVi7Vl8lUI4ujUliNOrBaIxHTbcUdh7cOSnOb758VNMQL7/sv48rCydrDEvSJiTcyjFrGI16MBr1kGWNNV0prMyvnBTnN18+qmmIyCalD0zWGIWHHio1NaerQgD3cswaRqMejEY9ZFljkj6a7xKRoyKy1xHXJSLPi8huO1zv2LZRRJ4RkQMi8o6kdMEZ38LtN/ZO8i1ctL1TTjH+u98Nf74ZM84MmAaZklpf79jQ0usenyF6h6LPAKsWRqMejEY9ZFljki2FzcA7XeL/UTnccwKIyGLgJuAKO80dIuLxeo7Hli2WH4DDh4HGYQ4ftn4XK4bOTvd0xfgos4icL3yvKaDO+LvucqSZPTxxjLvuCn/uajA8Npy2BF+MRj0YjXrItEYvm9o6ApAD9jp+dwEfd9lvI7DR8ftBYKnf8aP4U1iwwN1XwYIFZ/bZsEGpujorvq7O+u2a3hHq6iw/BsV05aF4fK/0zvMrpddXQdKkbRs+CEajHoxGPaStkYz5U/iIiOyxu5fOs+MuAp5z7HPEjtNOyT99xxoAZ/w118D8+da/8/nzrd9FvObT33OPZWfIy9ZQ8fhB5+Pr9FWQNFmec13EaNSD0aiHLGus9pTUO4FPAcr+/BxwM+DW0+7a0SIinUAnwLyWeXQVulh1+SoKgwVGjo/Q2dZJd183rXNbaaxvZNeRXay+cjU7BnYwfmqcc35nDccXbYahNqgfg44u2L2e6Vf18LlHGzh9YAX/59atnJy+FH5vjMPz+vmzj3WybaSbt765iY63dfCOT2/j0Z4OXnhliNe+boAb53fy4W92897PNzOtpR112XbLkF3TgGWmoq+T11zXzebdOfJ/kOcdn97JrntW8stpvbz2omH+17s7efqibnr687TMbqEwWOCcX6zi775Z4Fev6eerG4a4ekM3a9/mnqc1rWvYvHszbc1tAPQN9zFncD2f/HYPx0YaaBldwZv/bCsfvH4pYyfG6D/aP1FOTTOb6Mh1sG3/NjpyHQyNDjEwMjCxvbmxmfaWdrYPbGf5wuUMjAwweGxwYntxvnVXoYuV+ZX0DvUyPDY8sT3fdCZPYa6TW57WL1lPT38PDXUNrMivYOverSydHyxP/S/0MzQ6FDhP+aY8Ow/urGqeGusb6Sp0Bc5T2OukI0/9L/Rz4KUDiV0nHXm6cNaFdBW6MnPvueWp/4V+Hn3u0dTuvYp4NSF0BMq6j7y2UcXuo2nTHN02uUcmvk+bZm336x4q78q5916lZs1yT1MMs2aF6/4pOaatMcwx7r1Xqfr6Ug319cl1QT1y6JFkDqwRo1EPRqMe0tZIVrqPRMTpbujdQHFm0gPATSLSICKXApcBjyehoaR7J1eYFO81kHzqlPV6LR+Yvu0223l9GXV10VfmlhzT1vjKK1Z8EG69FU6cKI07ccKKT4LCYCGZA2vEaNSD0aiHTGv0qi3iBmArMAy8ijVG8AHgG0A/sAerImh27H8bcBA4ALwryDmitBRKBoLn7ilpCSjl3VLwGhgWcd8uElraBCXHdGgMekw/7eWD53HZ88s9+g6WEEajHoxGPaStkTRaCkqp1UqpZqXUDKXUfKXU15RS71NKtSqlXq+UukEpNezYf5NSaqFSapFS6l+T0lUy5dTRUijGX389gSi2KLwWpUVerEaZnSSHxsj2k8rQ7eoy0/96bIxGPRiNesiyxppb0XzHHbBhg70YbdYIdXXW76Jdo6CL04ovfa9KJGjl4suskdBJgpri0OXqcuR4eI3VxmjUg9GohyxrNP4UymyaT5vm72Ng1qwz4wRJ+AEo0eDwpyASzL3mli3w/vfDq6/676vj8qdtGz4IRqMejEY9pK3R+FPwwG2usFe3j9fAsdfAdGT/CeUaHGspgnZJrV0Ld999xgy1F14mPcKS5TnXRYxGPRiNesiyxpquFFrntk6K81ucVr6QLMqYQtH20rRpTLK9NEnDC60TGsI4nCkufjt92ttEdkdH8ONVwq0cs4bRqAejUQ9Z1ljTlUJjfeOkuLDOXsJ6DHPaXnKb4lqugVcbYzuc8fLtrMvns1s5Zg2jUQ9Gox6yrLGmK4VdR3a5xjv/ZfuZmAhbibita6i4BmG+u8YwJNHF5cSrHLOE0agHo1EPmdboNVd1KoQo6xScPPXiU5HSFY3Vua1w9qPS+gHn8SdWNDc9FWlVtJOgRviiErUcq4nRqAejUQ9payQrK5qzxo6BHaHTBOn+qYSfvwYoa03kLY1hVjSXE7aLKyxRyrHaGI16MBr1kGWNNV0pjJ8aD50mdPdPGX6e3aCsW2f6uHt8CMJ2cYUlSjlWG6NRD0ajHrKssabXKQweG5yw8hkUr3UMQdcQBFnXULLPnEE4lpu0T5aIUo7VxmjUg9Goh7Q1mnUKHmzevTl0mrhmLYJ05ZTss2Sz6z5ZIko5VhujUQ9Gox6yrLGmK4VAtsXLiNs/H6Qrp2RK6nCb9u4e3UQpx2pjNOrBaNRDljVW28nOlKf4Yr7tNquP/5JLrAohzAt77Vr//Yv7bD8AKxdF12swGAxhqOmWQt9wX6R0YdYxuOG3otm5zw0f6vPcJytELcdqYjTqwWjUQ6Y1es1VnQoh7jqFQ786FCt9kTDrFtw8tZWvQSjZZ86h2OsUkkZXOSaJ0agHo1EPaWvErFNwp6e/J/Yxwq5bCDKltWSf1h7XfbKEjnJMGqNRD0ajHrKssaYrhYa6htjHCLtuIYjJiZJ9Tja4x2cIHeWYNEajHoxGPWRZY2KVgojcJSJHRWSvI+58Efm+iDxtf57n2LZRRJ4RkQMi8o6kdDlZkV8R+xhh7QoFmdJass/ACvf4DKGjHJPGaNSD0aiHLGtMsqWwGXhnWdwngIeVUpcBD9u/EZHFwE3AFXaaO0REk7V/b7bu3Rr7GGHXLYRep9C61XWfLKGjHJPGaNSD0aiHLGtM0kfzvwEvl0XfCNxjf78H+GNH/H1KqXGl1CHgGeDqpLQVWTp/aexjhF23EHqdwpGlmV+noKMck8Zo1IPRqIcsa6z2OoV5SqlhAKXUsIjMteMvAn7q2O+IHZcoYyfGYh8jyrqFMOsUtu0bY9Xi2DITRUc5Jo3RqAejUQ9Z1piVxWtuTiNdjTKJSCfQCTCvZR5dhS5WXb6KwmCBkeMjdLZ10t3XTevcVhrrG9l1ZBerr1zNjoEdjJ8aZ03rGjbv3kxbcxvf2vct+o/2s37Jenr6e2ioa2BFfgVb925l6fyljJ0Yo/9o/8Qxm2Y20ZHrYNv+bXTkOhgaHWJgZIDOGzpZf1E3zY3NtLe0s31gO48+t5yBkQEGjw1OpM/NyZFvyrPz4E5W5lfSO9TL8NjwxPZ8U56W2S0UBgsTedq2fxtLL14aOE9gzYGOnSd7uzNPyxe65+lHh39E/9H+wHkKe5105Km7r3uiHIPkKex10pGnQ8cO0X+0P7HrpCNPX3r8S1w598rM3HtueRodH6X/aH9m7j23PH3p8S/RPLs5tXuv4stYJWgQT0RywA6l1JX27wNAh91KaAYKSqlFIrIRQCn1aXu/B4EupVRFTxRxDeKl7Tw7CEajHoxGPRiNekhbY5YM4j0ArLO/rwO+44i/SUQaRORS4DLg8aTFZNl5dhGjUQ9Gox6MRj1kWWNi3UcishXoAC4QkSPA7cBngPtF5APAs8CfAiilnhSR+4F9wEngw0opD88D+mia2ZT0KWJjNOrBaNSD0aiHLGtMrFJQSq322LTMY/9NQFUnXXbkOqp5ukgYjXowGvVgNOohyxprekXztv3b0pbgi9GoB6NRD0ajHrKscUp7XhORFwEXP2aBuQB4SZOcpDAa9WA06sFo1EPaGhcopS502zClK4W4iEiv1wh8VjAa9WA06sFo1EOWNdZ095HBYDAYSjGVgsFgMBgmqPVKIbuThc9gNOrBaNSD0aiHzGqs6TEFg8FgMJRS6y0Fg8FgMDiouUrBzflP1hCRi0XkERHZLyJPisitaWsqR0TOEZHHReTntsZPpq3JCxGpE5GficiOtLV4ISKDItIvIrtFJLpBrwQRkTki8i0Recq+NzNl/1lEFtnlVwy/EZGPpq2rHBH5r/Yzs1dEtorIOWlrclJz3Uci8gfAGPD1oqG+rGEbC2xWSj0hIrOBPuCPlVL7UpY2gYgIcK5SakxEZgA/Bm5VSv3UJ2nVEZGPAe3Aa5RSmXR5JSKDQLtSKrPz60XkHuBHSqmvikg9MEspdSxlWa7YTrqeB96slIqzlkkrInIR1rOyWCl13Dbv812l1OZ0lZ2h5loKHs5/MoVSalgp9YT9fRTYTxX8S4RBWRSNws+wQ+b+YYjIfOCPgK+mrWUqIyKvAf4A+BqAUupEVisEm2XAwSxVCA6mAzNFZDowCxhKWU8JNVcpTDVs8+NvBB5LWcok7G6Z3cBR4PtKqcxpBD4P/DVwOmUdfihgp4j02T5DssbrgBeBu+2uuK+KyLlpi6rATUDmfF4qpZ4HPotlEHQY+LVSame6qkoxlUKGEZFGYBvwUaXUb9LWU45S6pRSagkwH7haRDLVHSciK4CjSqm+tLUE4Bql1FXAu4AP292cWWI6cBVwp1LqjcBvsX2sZw27a+sG4JtpaylHRM7Dcj98KdACnCsi701XVSmmUsgodj/9NmCLUurbaeuphN2NUADema6SSVwD3GD3198HXCci96YryR2l1JD9eRT4J6rgozwkR4Ajjtbgt7AqiSzyLuAJpdQLaQtx4W3AIaXUi0qpV4FvA/8pZU0lmEohg9iDuF8D9iul/iFtPW6IyIUiMsf+PhPrZn8qVVFlKKU2KqXmK6VyWN0JP1BKZepfGYCInGtPKMDuklkOZGp2nFLql8BzIrLIjlqG5f8ki6wmg11HNs8CbxGRWfZzvgxrzDAz1FylYDv/2QUsEpEjtsOfrHEN8D6sf7bF6XXXpy2qjGbgERHZA/w71phCZqd8Zpx5wI9F5OdYHgf/RSn1vZQ1ufEXwBb7mi8B/iZdOZMRkVnA27H+gWcOu6X1LeAJoB/rHZyp1c01NyXVYDAYDN7UXEvBYDAYDN6YSsFgMBgME5hKwWAwGAwTmErBYDAYDBOYSsFgMBgME5hKwXBWISKnyixlRl51KyKP6tRWdux2EfmihuPMFJEf2iZHckXrvyLSKiKbYws11BzT0xZgMGjmuG16IzZKqcRWmiqlegEdJrJvBr6tlDplrYWaOH6/iMwXkUuUUs9qOI+hRjAtBUNNYPsr+KSIPGH7Lfg9O/5CEfm+Hf8VETksIhfY28bszw4RKTh8CWyxV6MiIm32P/U+EXnQNntefu4/tW3n/1xE/s1xzB329+86Wja/FpF19j//vxeRfxeRPSLyQY+srQW+47FtO9ZKboMhMKZSMJxtzCzrPvo/HNteso3O3Ql83I67Hcv8xVVYNocu8TjuG4GPAouxLIZeY9un+hLwJ0qpNuAuYJNL2v8JvEMp9QYsQ20lKKWut1s3HwAOA/9sf/+1UupNwJuAPxeRS53pbMNvr1NKDXpo7gWu9dhmMLhiuo8MZxuVuo+Kpg/6gP9sf/994N0ASqnvicivPNI+rpQ6AmCbC88Bx4Arge/bDYc6LHPI5fwE2Gw7VHE1v2C3Tr4BvEcp9WsRWQ68XkT+xN7ltcBlwCFHsgtsDV4cxbLEaTAExlQKhlpi3P48xZl7Xzz29UrrTC/Ak0qpim4plVIfEpE3Yzn72S0iS5zbbS9h9wH/t1KqaAhPgL9QSj1Y4dDHgUquHM+x9zEYAmO6jwy1zo+B9wDY/87PC5H2AHCh2L6KRWSGiFxRvpOILFRKPaaU+p/AS8DFZbt8BtijlLrPEfcgsMHuokJE8uVObZRSvwLqxNvHb56MWVs1ZB9TKRjONsrHFD7js/8ngeUi8gSWHf5hYDTIiZRSJ4A/Af7WtnC6G3fb+H9vD27vBf4N+HnZ9o/bGoqab8ByH7oPeMJO9xXcW/Y7sbrA3Hgr8C9B8mIwFDFWUg01jYg0AKeUUiftf/x36prSWg1E5I3Ax5RS7yuLbwB+CPy+UupkKuIMUxIzpmCodS4B7heRacAJ4M9T1hMKpdTPROQREalTSp1ybLoE+ISpEAxhMS0Fg8FgMExgxhQMBoPBMIGpFAwGg8EwgakUDAaDwTCBqRQMBoPBMIGpFAwGg8EwgakUDAaDwTDB/wbr2r7vR76NJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size (l)\")\n",
    "plt.ylabel(\"CO2 Emission (g)\")\n",
    "plt.grid(color='g', linestyle='--', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Creating train and test dataset\n",
    "\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"polynomial_regression\">Polynomial regression</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Sometimes, the trend of data is not really linear, and looks curvy. In this case we can use Polynomial regression methods. In fact, many different regressions exist that can be used to fit whatever the dataset looks like, such as quadratic, cubic, and so on, and it can go on and on to infinite degrees.\n",
    "\n",
    "In essence, we can call all of these, polynomial regression, where the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Lets say you want to have a polynomial regression (let's make 2 degree polynomial):\n",
    "\n",
    "$$y = b + \\theta\\_1  x + \\theta\\_2 x^2$$\n",
    "\n",
    "Now, the question is: how we can fit our data on this equation while we have only x values, such as **Engine Size**?\n",
    "Well, we can create a few additional features: 1, $x$, and $x^2$.\n",
    "\n",
    "**PolynomialFeatures()** function in Scikit-learn library, drives a new feature sets from the original feature set. That is, a matrix will be generated consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, lets say the original feature set has only one feature, *ENGINESIZE*. Now, if we select the degree of the polynomial to be 2, then it generates 3 features, degree=0, degree=1 and degree=2:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  2.  ,  4.  ],\n",
       "       [ 1.  ,  2.4 ,  5.76],\n",
       "       [ 1.  ,  1.5 ,  2.25],\n",
       "       ...,\n",
       "       [ 1.  ,  3.  ,  9.  ],\n",
       "       [ 1.  ,  3.2 , 10.24],\n",
       "       [ 1.  ,  3.2 , 10.24]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "\n",
    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "\n",
    "\n",
    "poly = PolynomialFeatures(degree=2)\n",
    "train_x_poly = poly.fit_transform(train_x)\n",
    "train_x_poly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**fit_transform** takes our x values, and output a list of our data raised from power of 0 to power of 2 (since we set the degree of our polynomial to 2).\n",
    "\n",
    "The equation and the sample example is displayed below.\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "v\\_1\\\\\\\\\n",
    "v\\_2\\\\\\\\\n",
    "\\vdots\\\\\\\\\n",
    "v_n\n",
    "\\end{bmatrix}\\longrightarrow \\begin{bmatrix}\n",
    "\\[ 1 & v\\_1 & v\\_1^2]\\\\\\\\\n",
    "\\[ 1 & v\\_2 & v\\_2^2]\\\\\\\\\n",
    "\\vdots & \\vdots & \\vdots\\\\\\\\\n",
    "\\[ 1 & v_n & v_n^2]\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "2.\\\\\\\\\n",
    "2.4\\\\\\\\\n",
    "1.5\\\\\\\\\n",
    "\\vdots\n",
    "\\end{bmatrix} \\longrightarrow \\begin{bmatrix}\n",
    "\\[ 1 & 2. & 4.]\\\\\\\\\n",
    "\\[ 1 & 2.4 & 5.76]\\\\\\\\\n",
    "\\[ 1 & 1.5 & 2.25]\\\\\\\\\n",
    "\\vdots & \\vdots & \\vdots\\\\\\\\\n",
    "\\end{bmatrix}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like feature sets for multiple linear regression analysis, right? Yes. It Does.\n",
    "Indeed, Polynomial regression is a special case of linear regression, with the main idea of how do you select your features. Just consider replacing the  $x$ with $x\\_1$, $x\\_1^2$ with $x\\_2$, and so on. Then the 2nd degree equation would be turn into:\n",
    "\n",
    "$$y = b + \\theta\\_1  x\\_1 + \\theta\\_2 x\\_2$$\n",
    "\n",
    "Now, we can deal with it as a 'linear regression' problem. Therefore, this polynomial regression is considered to be a special case of traditional multiple linear regression. So, you can use the same mechanism as linear regression to solve such problems.\n",
    "\n",
    "so we can use **LinearRegression()** function to solve it:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         52.24740714 -1.83074346]]\n",
      "Intercept:  [105.50929735]\n"
     ]
    }
   ],
   "source": [
    "clf = linear_model.LinearRegression()\n",
    "train_y_ = clf.fit(train_x_poly, train_y)\n",
    "# The coefficients\n",
    "print ('Coefficients: ', clf.coef_)\n",
    "print ('Intercept: ',clf.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, **Coefficient** and **Intercept** , are the parameters of the fit curvy line.\n",
    "Given that it is a typical multiple linear regression, with 3 parameters, and knowing that the parameters are the intercept and coefficients of hyperplane, sklearn has estimated them from our new set of feature sets. Lets plot it:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "XX = np.arange(0.0, 10.0, 0.1)\n",
    "yy = clf.intercept_[0]+ clf.coef_[0][1]*XX+ clf.coef_[0][2]*np.power(XX, 2)\n",
    "plt.plot(XX, yy, '-r' )\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Engine size (l)\")\n",
    "plt.ylabel(\"CO2 Emission (g)\")\n",
    "plt.grid(color='g', linestyle='--', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"evaluation\">Evaluation</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.00\n",
      "Residual sum of squares (MSE): 884.09\n",
      "R2-score: 0.80\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x_poly = poly.transform(test_x)\n",
    "test_y_ = clf.predict(test_x_poly)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y_ ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"practice\">Practice</h2>\n",
    "Try to use a polynomial regression with the dataset but this time with degree three (<b>cubic</b>). Does it result in better accuracy?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.   ,  2.   ,  4.   ,  8.   ],\n",
       "       [ 1.   ,  2.4  ,  5.76 , 13.824],\n",
       "       [ 1.   ,  1.5  ,  2.25 ,  3.375],\n",
       "       ...,\n",
       "       [ 1.   ,  3.   ,  9.   , 27.   ],\n",
       "       [ 1.   ,  3.2  , 10.24 , 32.768],\n",
       "       [ 1.   ,  3.2  , 10.24 , 32.768]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write your code here\n",
    "poly3 = PolynomialFeatures(degree=3)\n",
    "train_x_poly3 = poly3.fit_transform(train_x)\n",
    "train_x_poly3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         37.42798216  2.32946591 -0.35277257]]\n",
      "Intercept:  [121.02291366]\n"
     ]
    }
   ],
   "source": [
    "clf = linear_model.LinearRegression()\n",
    "train_y_ = clf.fit(train_x_poly3, train_y)\n",
    "# The coefficients\n",
    "print ('Coefficients: ', clf.coef_)\n",
    "print ('Intercept: ',clf.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "XX = np.arange(0.0, 10.0, 0.1)\n",
    "yy3 = clf.intercept_[0]+ clf.coef_[0][1]*XX+ clf.coef_[0][2]*np.power(XX, 2)+ clf.coef_[0][3]*np.power(XX, 3)\n",
    "plt.plot(XX, yy3, 'm-' )\n",
    "plt.grid(True)\n",
    "#-----\n",
    "plt.xlabel(\"Engine size (l)\")\n",
    "plt.ylabel(\"CO2 Emission (g)\")\n",
    "plt.grid(color='g', linestyle='--', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 21.84\n",
      "Residual sum of squares (MSE): 876.09\n",
      "R2-score: 0.80\n"
     ]
    }
   ],
   "source": [
    "test_x_poly3 = poly3.transform(test_x)\n",
    "test_y_ = clf.predict(test_x_poly3)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y_ ) )"
   ]
  },
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    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "poly3 = PolynomialFeatures(degree=3)\n",
    "train_x_poly3 = poly3.fit_transform(train_x)\n",
    "clf3 = linear_model.LinearRegression()\n",
    "train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
    "\n",
    "# The coefficients\n",
    "print ('Coefficients: ', clf3.coef_)\n",
    "print ('Intercept: ',clf3.intercept_)\n",
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "XX = np.arange(0.0, 10.0, 0.1)\n",
    "yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
    "plt.plot(XX, yy, '-r' )\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "test_x_poly3 = poly3.transform(test_x)\n",
    "test_y3_ = clf3.predict(test_x_poly3)\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "Saeed Aghabozorgi\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                                    |\n",
    "| ----------------- | ------- | ---------- | ----------------------------------------------------- |\n",
    "| 2021-01-11        | 2.3     | Lakshmi    | Changed R2-score calculation in polynomial regression |\n",
    "| 2020-11-04        | 2.2     | Lakshmi    | Made changes in markdown of equations                 |\n",
    "| 2020-11-03        | 2.1     | Lakshmi    | Made changes in URL                                   |\n",
    "| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab                    |\n",
    "|                   |         |            |                                                       |\n",
    "|                   |         |            |                                                       |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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