Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression is one of the most fundamental algorithms in the Machine Learning world. It is the door to the magical world ahead.\n",
    "But before proceeding with the algorithm, let’s first discuss the lifecycle of any machine learning model. This diagram explains the creation of a Machine Learning model from scratch and then taking the same model further with hyperparameter tuning to increase its accuracy, deciding the deployment strategies for that model and once deployed setting up the logging and monitoring frameworks to generate reports and dashboards based on the client requirements. \n",
    "A typical lifecycle diagram for a machine learning model looks like:\n",
    "\n",
    "<img src=\"MLApplicationFlow_bold.PNG\" width= \"300\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take our discussion of Linear Regression further"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Regression Analysis?\n",
    "\n",
    "Regression in statistics is the process of predicting a Label(or Dependent Variable) based on the features(Independent Variables) at hand. Regression is used for time series modelling and finding the causal effect relationship between the variables and forecasting. For example, the relationship between the stock prices of the company and various factors like customer reputation and company annual performance etc. can be studied using regression.\n",
    "\n",
    "\n",
    "Regression analysis is an important tool for analysing and modelling data. Here, we fit a curve/line to the data points, in such a manner that the differences between the distance of the actual data points from the plotted curve/line is minimum. The topic will be explained in detail in the coming sections.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The use of Regression\n",
    "\n",
    "Regression analyses the relationship between two or more features. Let’s take an example:\n",
    "\n",
    "Let’s suppose we want to make an application which predicts the chances of admission a student to a foreign university. In that case, the \n",
    "\n",
    "The benefits of using Regression analysis are as follows:\n",
    "\n",
    "   * It shows the significant relationships between the Lable (dependent variable) and the features(independent variable).\n",
    "   * It shows the extent of the impact of multiple independent variables on the dependent variable.\n",
    "   *  It can also measure these effects even if the variables are on a different scale.\n",
    "\n",
    "These features enable the data scientists to find the best set of independent variables for predictions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "\n",
    "Linear Regression is one of the most fundamental and widely known Machine Learning Algorithms which people start with. Building blocks of a Linear Regression Model are:\n",
    "* Discreet/continuous independent variables\n",
    "* A best-fit regression line\n",
    "* Continuous dependent variable.\n",
    "i.e., A Linear Regression model predicts the dependent variable using a regression line based on the independent variables.\n",
    "The equation of the Linear Regression is:\n",
    "\n",
    "                                                Y=a+b*X + e \n",
    "\n",
    " Where,\n",
    " a is the intercept, \n",
    "b is the slope of the line, \n",
    "and e is the error term. \n",
    "The equation above is used to predict the value of the target variable based on the given predictor variable(s).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Problem statement:\n",
    "\n",
    "This data is about the amount spent on advertising through different channels like TV, Radio and Newspaper. The goal is to predict how the expense on each channel affects the sales and is there a way to optimise that sale?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%` not found.\n"
     ]
    }
   ],
   "source": [
    "# necessary Imports\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "% matpllotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv('Advertising.csv') # Reading the data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales\n",
       "0           1  230.1   37.8       69.2   22.1\n",
       "1           2   44.5   39.3       45.1   10.4\n",
       "2           3   17.2   45.9       69.3    9.3\n",
       "3           4  151.5   41.3       58.5   18.5\n",
       "4           5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() # checking the first five rows from the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the **features**?\n",
    "- TV: Advertising dollars spent on TV for a single product in a given market (in thousands of dollars)\n",
    "- Radio: Advertising dollars spent on Radio\n",
    "- Newspaper: Advertising dollars spent on Newspaper\n",
    "\n",
    "What is the **response**?\n",
    "- Sales: sales of a single product in a given market (in thousands of widgets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      "Unnamed: 0    200 non-null int64\n",
      "TV            200 non-null float64\n",
      "radio         200 non-null float64\n",
      "newspaper     200 non-null float64\n",
      "sales         200 non-null float64\n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info() # printing the summary of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "TV            0\n",
       "radio         0\n",
       "newspaper     0\n",
       "sales         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum() # finding the count of missing values from different columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's showcase the relationship between the feature and target column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x269b21705c0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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EDUsL2nx9bn4h0u8QEVF4SfNqkr/nMOVi0eUanlU5GQ8kG4ccEwkwaFha0ObrkxOjyz+H+R0iIgovaV6N+/ccplw8ulzDsygn44HSwB5aooSGDUsL2nzdu8y/+zuVkoGRsolKyej7HSIiCi9M7vXq7UWK+vfue3CYcvF468rYaDlUXfEjuydTVDnDYjxQWthDS5TQoCE8ly/9HGrzdQeAYQAGsPR/pJs/cw6zx2rcEJ6IMlGrNwbnxYRC5V4E9yKF/XvXoOsBc2y+uXWlbZgoOZ3I5zutnsyk5YxCl3iQnYdIPjZoiRIKO4Rn0Obr7lPMZquz/NrM3sOYnlojLbnuf+U4Zr5io2SAw4CIKHVp3cAPyr1Ady+Sy5t/h/29ly5DT0mO8bEq1q5djZMnT0f6u2F1ULS45YxKh3jgkOh84JBjooTiDEvrFWehhiRDk5Yvns02hwERUeqGDUVMcxEZkQvliLgeUPHosqhUVKrFQ29e4ZDo/GAPLZEAUYel9Yr6FDPpE0VdhgERUT4NykGHZk+l2mMiuhcp6fWAikeHnsy4VIkHv/umdReM8V4oJ9hDSyTI+FgVmzaMx0qCUZ5iiniimOeLJxGpLygHjVRKqfeYyOhFSnI9oOJRrSdTtKzjIei+aaRS4r1QTrCHlkgRYZ9iiuhddS+eM093z6HNy8WTiNS2nIP2dveYnGu2M+kxUaUXiYqLdVCeoPumc822bx7isdcPG7RECgmz+EiU3tVBK/fdNL0et924EfYPf8yLJxGlzu8GvlZv9OW3VruTSo9JlMWfiKIKs5Iu66Acg+6bNm0Y54OEHGCDlkgzQT0bvUk4zDzbiVUj2LRhPM3iExEt672Bd/Pb5/7uENpL958dBzh05BRXHiVtcSXdbA27b+KDBP2xQUukgKh7oA0bmpT2FgBERKJMT62BWTLRXspf7Y4jJX9x70lKg+zrMetxOBzSnW9s0BJlLO6T20FPFLmKMRHpam5+AWXTQNPzmuj8xR4zSovM6zHrcTTsic0vrnJMlCFZe6BxFWMi0pXs/MW9JylNsuoz6zHReWzQEmUozmbqvRuD+8n7FgBElG/33nwpKpLyV5y8SxSXjOtxrd7Ay2/OwWQ9JgLAIcdEmYr65DbK8CLOFyEi3XhzHBwHP/3hS/HRzRcJzV8cwUJpE3k9dmPENICFZqfr31iPqajYQ0uUoShPbuMML8p6M/OwwvQ6E1F0OsVWb45rth3sff4t4Z/DESyUBRHXY2+MeBuzowLrsU45g8jFHlqijIV9cpvXhZ64qAWRHLrFVpo5jiNYSEd+MTJaMfHJO6/CdZdPcpEpKiz20BIpIMyT2zwOk+OiFkRy6Bhbaec4XUawELn8YqTjQEhjVsecQeRig5ZoCFWG3+RxmBwXZyGSQ8fY0inHqXJdoGKRGSMq5QzGF0XFIcdEA6g2/CZvw+Ty2OtMpAJdY0uHHKfadYGKRVaMqJIzeuNrx9bNmL5kItUykH7YQ0sUQNXhN3kaJqdTjwyRTnSOLZVznKrXBSoWGTGiQs7wi6+dT77I+KKh2ENLFCCvizCpRoceGSIdMbbE43WB8izrnOEXX+US44uGY4OWKIAqw2+KYHysyosVkQSMLbF4XaC8yzJn+MVXq834ouE45JgogArDb4iISB28LhDJ4xdfOx64nvFFQ7GHlmiArIff6KJWb/AYESmOcSoGrwtE8njja6RSwoqVI6jVG4wzGogNWqIh3OE37jLyOtzApHnjKmLFT95oE4njF09prsyrazxHKTeHcmfrRz9+D7PHati0YRzvv3Bl37/rWgdp0fhYFYdmT2Hm6cMol020Wh2uJk4DsUFLFIJO2zSkfePqrkjomtl7GNNTa0LfROh0bIlU5xdP01NrEsdpks/XIZ51LXcRfeFrNr5x4J3lnz92w0X4pbus5Z95LvXnvbdw85asnEX5wDm0REPotE1D2mVNuhG7TseWSHVB8XT0xOlEcZr081WPZ13LXUQ/+vF7XY1ZAHj2wDv40Y/fA8BzmRdJ7y2oeNigJRpCp8SadlmTrvip07ElUl1QPAFIZWVeXeNZ13IX0eyx2sDXeS7zgauJU1Rs0BINoVNiTbusSVf81OnYEqkuKJ42rludysq8usazruUuok0bxge+znOZD957i7HRMlcTp6E4h5ZoCDexzuztnpOjYmLNoqxJVvzU6dgSqW5QPKWxMq+u8axruYvo/ReuxMduuAjP9syhdReG4rnMDzdntQ0TJafDc0gDsUFLFIJO2zRkUdYkK37qdGyJVDcontJYmVfXeNa13EX0S3dZ+NgNFweucsxzmR/jY1WsXbsaJ0+ezroopDg2aIlC0mmbBp3KCuhXXiKVZR1PWX9+XLqWu4jef+FK3+16XDyXRMXCObREBeTuqTt/5lzo3+UqkUR6C4rl+TPnGONEIYS5HjKeiNLHHlqigunao88Btt1tBe7Rx/38iPIhKJb3v3IcM1+xUTLAGCcaIMz1kPFElA320BIpSkbPaN8efc124B593M+PKB+CYvlHP35v8fVmO3aMcwQHFUGY6+Hy7ySIJ1kYp5R3UntoLcv6PQAPLP24x7btRyzLugPAZwGsALDbtu3fkVkGIh3J6hkdtEdf73yjKL9LROoKiuXZY7VEMc4RHFQUYa6Hql4zGadUBNJ6aJcarncB2AzgegA3Wpb1IIDPA/g5ANcA+KBlWXfLKgORjmT2jEbZo4/7+RHlQ1Asb9owHjvGOYKDiiTM9VDFaybjlIpC5pDjYwA+bdt2w7btJoBXAVwF4HXbtmdt224B+EsA/1xiGYi0M+gpb1LezcpXVEuoVkqBe/T1/S43NifSUlAsv//ClYuvV0qRY1xmniJSTZjr4fLvxIgnWRinVBSG4zjDfyshy7KuBPAdAI8BsGzb/qWl1+8A8Iht23eFeJspALPSCkmkiPkz5/Ar/+vfo9FsL79WrZTw+d+5ExOrRoR9xolTdaxbM7b8nn6vDXpdQZsAHMm6EAJMgbmOIggboyJjPI08RYGY64aQdd0K874qXTMZp6S50LlO+irHlmX9EwB7APwrAC0s9tK6DACdKO83N3cGnc7wRrgOGzGrXkaWL7m4Zdx2t4WZvZ45L3dbaJxt4ORZccOELlhRxsSqEZw8eXroHJsLVpSFf35Yw46haRqYnFyVYonSETbXAXrEwiAsf3xR58f5xfLatavRONuIHONp5KkwdK8/QLjvUPRcF/Y8y54zOux6GDeeZAmKUyxd/1WnS3yznOLEyXWyF4X6CIAvAfgt27b/i2VZWwBs8PzKegA/klkGIh3dNL0e01NrMDe/gMmJUd8hS7V6Y+C/h+WdY+Oa2XsY01NrOLyYSGFpx25vzgmTp4jSxOtZP8ZpdKLuryg90hq0lmVdAuBvAGy1bfvZpZe/u/hP1hVYHGbyL7C4SBRRKvKSpEQ+gVZ1ZUYiGizN2A3KOd4VXgEomzPykvvJn3t+31to8nrmY3ysGvv7Fy12uCq0nmT20P42gFEAn7Usy33tTwFsw2Kv7SiAvQD+WmIZiJbplKQGlVX0E2gVV2YkouHSit1BOefQ7Cnl86pOuZ+i857fVsdBp909k43Xs/iKFjvs4deXtAatbdu/CeA3A/75J2R9LpEfnZLUsLKK7pVxV2bsmmPD1YyJlJdW7AblnKMnTiufV3XK/RSd3/ktmQYqJlAumbyeJVDE2OGINX1JXxSKSAU6JamjJ07D6C5qV1ll9Mpwjg2RntKI3aCcA0D5vKpT7qfo/M5vtWziofuvxcrRCq9nCWQZO95hzmulflI3jljTFxu0VAi6JKn9rxzHrr2votkOLqusXpkkc2yIKDuyYzco52xct7qr9wYAGq2OUnlVl9xP8QSd343rVvN6llBWsdM7zHnH1s2YvmRC6me6OGJNX2zQUiHokKTc4T29jdmKz+bs7FElojT55ZxavQH07mWfwt72UeiQ+yk+nl95sji2fsOcdz75Ih596ObUzinvr/TEBi0pR9aKejKTlIgy+w3vGSmb+NQnrsO1myb7fp89qkSUpt6cMze/gGqlhLON9vJr1UrJd0hiliulysr9RVv9VVVsgMiT9rH1uw8ql9KfIuDNdYxzPbBBS0qRvaKejEagqDL7De9xAGxct1pQSYmIxAk7JFGFlVJF534VvhOdxwe88qR5bCcnRtFotrteO9doZzZFgHGuDzPrAhC5vENNzjbaaLQ6mNl7eHFYm6JEltkd3lMtm1hRLaHqM9SYiEgVYXKWjnl9mDx+JyJl9KyK2btIZloY53phDy0pQ8fVKEWXmUOniEgnw3KWjnl9mDx+JyIVzM0voFo2Q01jSKMsjHN9sEFLyshiRb2kcyPCljnK53DoFBGprjenBeUsmXndLUN1Rbr5kisnk25EzANNYy6pX2y12tnEFuNcL2zQkjLSXlFPxNyIMGVOew5GmItOVjeCRCSHG9MjlRLONdtSbzqj5DRZeb2rDA6w7W4rtbltXFk3f9JorGW1uJCIe5C07mP8YmvHA9dnEluMc72wQUtKSWvIrd/S8DN7D2N6ak3kzxxU5vkz54R9ThhhLjpZ3ggSkXhuTDuOg2bbQaVswgCk3HTGyZ2i87rI/B0Xp4fkRxqNtawWFxoUK2sFvIeMet8bW5dfOomTJ08L/5w4ZWGcq4uLQpFyxseq2LRhXGriGDQ3Io6gMp84VRf6OYOEWcCg73eabS5yQKQxb0y7e1g3Wx1pC5jEzZ0i87ro/B1XGtcqkiuNhX+yXFxIRKxkEW8qxZZKZaFgbNBSIaU1N2LdmrHU5mCEueiociNIRGL4xbRLRmyrMK9MhTJQPqRxTczyuisiVhhvpAM2aKmQ0toiZ2LVSGpb8YS56PDCRJQvfjHtkhHbKmwv1leGSolz2yiWNK6JWV53RcSrCjFPNAzn0FJhpTU3Iq3PCbOAQd/vLM2h5YWJSE/emPabQ5vGHLcs8oe3DNZlF6JxltMmKLo0Fv7JenEhEfGqQswTDcIGLWkt6aqBaW2Rk9bnhLno8EaQKF+8MZ3GKseAGtuLuWWYWDWCkz55LKtVZUkvaTTWsm4QiojXtGKecUtxsEFL2nruwNvYuftg6qsGqi7MRWfYjSAR6UWFBqZKslpVlvSURvwwRofzi9v7tqzOulikAc6hJS3V6g3sfPLFTFYNFKVWb2D2WE2rMhNRPuQ5/2S5qizpIc/1X1dBcTt/5lzWRSMNsIeWtDQ3v4ByyUCjef41d9VAFZ6AukNmqiv8y8LeAyLKSm/+2bF1M6YvmcjNUL9Bq8rq/L1IjCJff70xHnYf2rQExe2JU3VcsILNFRqMNYS0NDkxilZbzdV6uy6WS4sueS+WaW9STkTk8ss/O598EVs/djl2P/NGLm7yuZo7BSny9TfoQZYqguJ23ZoxrvVBQ3HIMWlpfKyKHQ9cr9wy8n1DZprtvqFu3AuWiLLin3+AJ77+em6G6HKbEQpS1Ouv33DenU++qFSMB8XtxKqRrItGGmAPLWlryw0X4+LJFUoNkQsz1I29B0SUFb/802w5KJdMtNrt5dd0H6Kb9aqypKaiXn/97k3KJfVinHFLcbGHlrQ2PlbFpg3jyiS9MBdL9h4QUVb88s+v//y16OTwJl+16wNlr6jXX797k1ZbzRhn3FIc7KGlzKS1AEmaC530baC+NIe293OHPYXMy+IsRJS+YfmjN/9cfukkWs3W+by1NIc2rdyTRb5jji0W7/nWpRdQZB3tuzfpONjxwPWpfnfGHMnEBi1lIq1VBrNYzdB7sbQuuzBwMYOgPemKvAIjESUTNn/05p+sbvKzyHfMscUSdL5VblTJqKN+D7JOnjwtqMSDMeZINg45ptSltUdglnsRukNmoi5mwP0TiSiupPkj7aF+WeQ75thi0fF8yyxzFsN5dTwHpB82aCl1aa0yqONqhjqWmYjUoFv+yKK8uh0jSkbH861jmQfJ2/chNbFBS6lLa5VBHVcz1LHMRKQG3fJHFuXV7RhRMjqebx3LPEjevg+piQ1aSl1aqwzquJqhjmUmIjXolj+yKK9ux4iS0fF861jmQfL2fUhNXBSKMpHWAiS6rGbolUWZufogkXhZxJVuOS+L8up2jCgZHc/mgFE9AAAgAElEQVS3t8wjlRLONduo1RtalN2PjueA9MIGLWUmaJVfXT9HpDTLzNUHicTLMq50y3lZlFe3Y0TJ6Hi+x8eqODR7KjfXZx3PAemDQ46JCmz+zDmuPkgkGFf1JKKkmEeIwmODlkgRtXoDs8dqqV6sTpyqR1p9MIsyEqmsVm/gtaPvdsVEklU9GWNE/ubPnBsYG3mLHa4OTBQehxwTKSCr4Ynr1oyFXn2QQ5OJurkxUS6baLU6yzERd1VPxhiRv/2vHMfMV2yUDPjGRh5jh6sDE4XHHlrSkl+vSFblSPpEOMthRROrRkKtPsihT0TdvDFRX2h1xUScVT1lx5gKvVcqlIH0sxwbzbZvbATFzo9+/F4q9U1WvebqwEThsYeWlBBlNdCgXpG0iXoiPGhYkSorfmZdRiLVDIuJqKt6yowxFXqvsiwDV3HX27DY8Pt3x3Hw+7teQKVkSq1vfvX6vi2rhb2/SqsDM45IZWzQUuai3Oh4n8Q2Wh0AwMzew5ieWpNqgvWWwxW3HCoMKxq2+qAKZSRSSZiYiLKqp6wYE5mrdCyDCo15SmZYbPj9e7O9+HOr3QYgp74F1evbbtwo7DMANVYHZhyR6jjkmDIVdZid7EUSwg4dElkOd1hRpWRgpGyiUjKUG1bEoU9E3bwxMTZaFhIT99x8KSolQ2iMpbGwzLC8mdXiNpwqkQ/LsVYp+cZG7/WpXDJQKXff3sqob0H1+sSputDPESnO8Oi04ohTEigJ9tBSpqIOs5PZUxjlCaTocpw914IDwDEAwBjy29lQaegTkQrcmGgbJkpOJ3ZMeHMPDAM/8+GNuNF6H84128tzcuOSPboiTN7MaoQHp0rkx03T63HbjRth//DHvtcf7/VppFLCZ2a+1/XvUetbmOG1QfV63ZoxNM6q1yjrjdUdWzdj+pKJoX+XRhyxB5iSYg8tZSrqjY6MXhEg+hNIkT2W3zjwNr7w1dfQajtoNDtoKtyLMD5WxaYN47wZJFoyPlbFVRsviB0Tvbmn2erg//nOEfzBrhfwh08cxCOP78P+Q8cTlU/W6IqweTOrER6cKpEvE6tGBl5/3OvT+y9cmai+7X/lOB55fN/Q+Auq1xOrRmJ/R1n8YnXnky+Gus+QHUccSUEisIeWMuVeEGb2dj+ZG3ThidorEuZJa5wnkCJ6LGv1Bp545vW+1032IhAVgl/uaXcctAE0Bc3/S5KrBuXPKHkzixEeca4vlA9x61vU+d66jFzyi9VyKdx9hog4EpVHiIKwQUuZi3NBGB+rYu3a1Th58vTA3ws7jCXuE8ikizXMzS+gbBpotbs/u9XusBeBqAD8ck8vETd3cXLVsPwZZ4RN2jeoujQ4SLw49S1O40qFRZuG8YvVVjt8L2uSOBKdR4j8cMgxKUHGUNYow1iyHBLndy/74B1XKn+BJKLkenNPpWyi1DONPoubuzD5U5fF4jhVgsLKa+PKL1Z3PHB9pJiIE0d5yiOkNvbQUiw67EcW9Ulr1kPiTANodRw8eMeV+KnNF0v/bCJSQ2/uOXTkVObDZMPmT/aA+tPhGkn98jxMvTdWL790cugot7jc+v/eQpN5hFLBBi1FpstqdHGetIoYOuS9kVmL4Tc2TORExeCXC7yvbdowDkCNnBAlf+ow5DJNulwjyV/viskiVhsXKcnDkjRi1Vv/W+1O3yg05hGSgQ1aiiTqgglZyuJJa++NzJ0f3oi//+7RoTc2TORE+ebXyIGDwIZP1jkhzz1VMul0jaRg42NVHJo9pdyDCdUflvjV/5IBVMomyswjJBEbtBSJbqvRpdnT4ZfI93znSNfv8MaGqHj8csOuPa8ChoGmwg0fFXqKdaPbNZL8qfhgQsUy9fKr/9VKCQ/dfy1WjlaYR0gaNmgpEh0XTEirp8MvkffijQ1R8fjlBtMwgJ50oWJ+yLqnWDc6XiOpn4oPJlQsU6+g+r9x3Wplykj5xFWOKRKuRhcszPYbvLEhKh6/3NBxnNBzy0gfvEbmg4oPJlQsUy/Wf8oKe2gpMg5D8+c358xvDi2PF1GxBM1HBcA5qjnEa6T+VJxDrmKZ/LD+UxbYoKVYOAzNn9+y+HfeeDETO1HBBd3k8cYvn3iN1J+KDTMVy+SH9Z/SxgYtkWC9iZyJnYgA/1zA/ECkLhXjU8UyEWWNc2iJSJpavYHZYzXU6o2si0IFwPpGRHQecyIVBXtoiUgK1ffLo3xhfSMiOo85kYqEPbQUG5/8URDvfnlnG200Wh3M7D3MukJSsL4RwGsSkasIOZHxTl7soaVY4jz5q9Ubyi9kkCbZxyPL4x20X97RE6e5uToJl2R/RpFxwhwXX9Jjx96oYktSf9KK2zTzgw571iahUry757W6Qv/jqjM2aCky75M/18zew5ieWhOYKFVKPiqQfTyyPt5+++U1mm089qWXUWYdIMEmJ0bRaLa7Xms020P3ZxQZJ1nHnM6SHrv5M+ciX5MoP5LUn7TiNu38oMOetXHFuQeVpeu8OsC2uy3m/YxwyDFFNujJn58iDH2JQvbxUOF4926uXikZgGGgyTpAshjG4J97iIwTb4OK9TsaEefhxKl6pGsS5UeS+pPWtTKLa3LvNbhaNpXcszaOqPegsvSd12abeT9D7KGlyKI++cv70JeoZB8PVY63d7+89xaa+JMv/wBnG+d70YpcB0isufkFVMtmV/2qls2++uUd8icyTgY1qFi/BxNxHtatGcttbxQNlqT+pHWtjPI5Iocl67JnbVSq9D6rcq9Fi9igpcjcJ38ze7uHzwQFsCrJRxWyj0fc4ZcyuPvl1eoN1gGSJkxM9Q7523r7FcLqJBtU8YnIhxOrRiJdkyg/ktSftO5Nwn6OjGHJedyzNuo9qCy8t1ULG7QUS5Qnf6okH1GSPkFN5XgYBuA43T9nKG91gNQyrH75zbna/cwb2Hr7Fdj9zBuJ66Rfg2rr7VcsD4FjPQ8mKjfktTeKBotbf9zruKgckLSMKs0L1YEK8d53Xpfm0PJ8ZYMNWootypO/OMlHtRVDa/UGnjv4Dp56/q3ECxslTcaDjk3Y4ZdpU+ECRPk1qH4FDQ2bWj+ORx++BUdPnAYAbFy3WsjnH37rXTzx9ddRLpnopLAAjGq5MipRuSGPvVE0XNT64zdaY2r9uNQdB9ZdMIZ/s+2DOLc0Wqq3Mfvym3MwFR++qlqeUSHevXXPuuxCNM5y/mxW2KCl1ERJPqqtGLr/lePYtfdVNNuLvZ7NpdeTPEGNm4yHHRuVh8GocAGi/AqqX4Ni4tDsKWG5Znysiv9++B/xV998EwDQai8+VJLZ06JaroyLuYGSCFt/gkZrPPrwLanF56YN433/bhrAQrPT9beqXLeB/OQZGdy6N7FqBCfZoM0MVzkm5aiwSq9fedzGrFfaK+uFOTZ5Xt2QKI6gmAAgNNfU6g088czrfa+bkvKEarmSSHVprpA7LD69/+5tzI4qdt1mniEdsIeWlBtGotrKcX7lcaX9BDXsseHwXqJufjExe6wmNNfMzS+gbBpo9Tz8arU7UvKEarlSV6pdA0meNEcwDYtPv38frZj45J1X4brLJ5Wpi1mt0kwUBRu0BafiMBLVhsz6lQcAKhk8QY1ybDiEj6hbb0yIzjWTE6PwSRV48I4rpcSiarlSRypeA0meNBcoHBaffv/ecaBUYxbIdpVmorA45LjA0txUfPZYLfT7qjZktrc8lZKBn//JTfjfHr4FN02vj/z9RJYl62NDpLOw8RQ2xr3vN1oxUS4Z+OWfvgo/tfniTMufhTTzYlwcSllMN02vx6MP34LffnAzHl26jnuJqrvD4lPl+PUKU05VYkmHvENysIe2wNIYrhb3iZ1qQ2aDypPFE0nVjg2RzobFU9QYTzs+VcwHuvTUcMh2cQWNYBJdd4fFp4rx62dYOVWIJV3yDsnBBm2ByR6ulnRfNdWGzPaWJ8t941Q7NkQ6C4qnuDGednyqlA902k+TQ7bJS1bdHRafKsXvIIPKmXUs6ZR3SA7pQ44tyxq3LOsHlmVNLf28y7Ks1y3LenHpf/fLLgP5kz3cRdRqgqoOIUlztcQ8UPU8kjpUqyN5jXGZx1mnY6bLkE+KL0pd16nuqibrWOK5I6k9tJZlfRjAnwG4yvPyPwVwm23bx2R+NoUjc7iLiCd2Kg8hyfqJpE5UPo+kBhXrSB5j3O8437dltbD31+2Y6TLkk6KLmlMmJ0a7evgAoNGSs0J5HmUZS7rlHRJPdg/trwH4FIAfAYBlWWMANgL4vGVZL1mW9QeWZXFhqoyNj1WxacO48OST9Ild2osMhH2S6/4eAD7dD0GVxSJIXfNnzilbR+65+VJUSoawGM+yFzooFufPnBP2GVn31MQh6xpI2Yl93XGcwT8LFicfqDaSxSurWNIx75BYoXtoLcu6zLbtH1qWdS+AGwDstG17ftDf2Lb9Py79rfvSegDPAngYwDyApwD8KhZ7cUOZnFwV9lexdq24p86yqF7GpOW7b8tq3HbjRpw4Vce6NWOYWDUS+m/fPfouymWz64lpuWyibZjL5RJ1/J478DZ2PvkiyqXFPSR3PHA9ttzQvzKp3+99/nfvGvj9RJ/j+TPnYh3PIDLr4PyZc5idfRelkgkMOI/DqB4nMkTJdYDex+iA/Y8we4aLRa0jYYWNH2+sG4aBX/jYlbj75qnAvwlTzrB5RpagnHriVB1XbbxA2Ockyftx6Vz/XXn4DnHIuK8Lc//g9zcjI2XUF1rLr42MlGPlobVrVw/NNXHygegcIrLOib438QpTzizyTi9dYliXckYRqkFrWdZ/Wvr/f4zFxudXAXwewCeifJht2z8EsDxn1rKsxwD8S0Ro0M7NnUHHb6O/HmvXrsbJk6ejFC91qpdRZPkuWFFG42wDJ8+Gf6JYcjpo9Qz/abU6KDkdnDx5Wlj5avUGdu4+iEarg0Zz8bWduw/i4skVfYtA+f3eow/fggvGqr7fT/Q5Fj0sU2YddMtqGsBCM/g8Ji2jaRqRG386CJvrAPVzySD7XzmOXU8fRnNArIv8rDDx4xfrf/X11/DBqy5EwyeHhTn+YfOMTEE5dd2aMSn1J07ej0Pn+u8K8x2KnuuinOdh9w+i/iaonH/3zdcH5po4+UB0DhEZNzKnjEQtZ1p5p5cueUiHcsbJdWGH+94I4CEsNkb/3Lbt7QAujVY8wLKs6yzL8jaCDQDNqO9D/VQeghJXWkNIwi4mkPWiAzoN3fWW1duYHeVQIPJw60lvY7YioY6EiR83jx49cVp4rGedP4DgnJpFT0bR5fGarZI49w9x/sbvPIaZQhEnH2SZQwbVV53uTSi/wg45Nm3b7liWdSeAf7v02liMzzMA/LFlWc8COAPg1wH8eYz3IQ/vk7FWu4N7b5nCRzdflIsGQ+8iAwAwe6yGyYlRrBX0GWEXE8h6wQgV9nkLy6+soxUTn7zzKlx3+aRy5aVs+NWTkbKJT33iOly7aVL6Z3njpzeP9nYYJV1gJEyeqdUb0hdU4SJI4kU9b7IX5qJFcep6lL/pPY9bb78CU+vHUR6pDL1Wx1nEaNjfyMofw3pfdbo3ofwK26B9w7KsvQAuA/BNy7K+COD7UT/Mtu2XLMv6dwC+A6AC4Eu2bT8R9X3oPL+9t/7mW7PY8/xb2K7AKqEiuHuf9SbVHVs3Y/qSCSHvv+2eqzGztzth+ybilBeM8NJpFT+/snYcsDFLXfzqiQNg4zrxN/eD4scvj5aMxZ7i8rCcENKwPJPmKs+67Hupg6jnLWi/zNtu3JhGcQsnTl0P8zd+5/ELX30NoxUTbQd9Q6h7r9WR7jtC/I2s/BFmf1ed7k0ov8I2aLdjcbjxt23bblqW9S0AfxH2Q2zbnvL89+MAHo9SSArm92QMAJpLQz7ysqm0X1Ld+eSLePShm0N9v2FPLsM8lZ2bX0C1UsLZRnv5tWqllNpTyDgXwKzoVFbKznI9edpGyYDUejKoTs4eq/Xl0WqlhIfuvxYrRyuRezyC8k1Qnglz00jqiXPegnqzTpyq44IVUndSJIGC7r3cKTZhHoiJ6kGWmT+C6uvRE6e7ciOv95S1UNnTtu33LMt6DcBPW5a1C8D3bduuyy0aheH3ZMyVpyEffkm1XAr3/YY9ufTefG7aMB74Pio8hdRpuKBOZaXs3DS9HrfduBH2D38svZ4E1cmg2F6zehTnmm2/two0LN/49f5wyJ6e4py3oLq2bs2Y74JjpKZB915A+AdiUXqQg+5VZOYPv+/ZaLbx2Jde7mqs83pPWQu1KJRlWdsA7ALwCID/AcDfWpb1axLLRSG5T8Yqpf4nhXka8uGXVFvt4d9v2GIF+185jkce34c/fOIgHnl8H/YfOh74Xqrsc6bTnok6lZWyM7FqJLV64lcn/WL7Ix/YgM/MfC9UbnDFXRxFhYdlFF2c88aFufLBex5Hq6W+f293HGxct1pYXht0ryIzf/TW10rJAAwDTZ8cx+s9ZSns+JYdAG4G8Jxt2/9oWdaNAL6CCNvtkDzuk7HnDr6Dp55/S9icL5X4DWnZ8cD1Q7/fsFUBow7T4VNIonzyxvZIpYTPzHwv8hC+uD0lHLKnp7jnjdeRfPCexyPHa9j9zBuL9cABtt1tCTuvw4YUy84f3u/53kITf/LlH3RNveJoElJB2AZt27btmmVZAADbtv/BsqzWkL+hFI2PVXHfRzZhy+aLlLlIil5xr/cm4PJLJ4fupTXoyWWSm8+sjy0RiefGtt+c2jC5IUlPicxGThqrJxdV3PPG64gehsWOex43bRjHjdb7MDe/AOsy//2q4wpzryL7IYn7PWv1BkeTkJLCNmhPWZZ1PRYXoIRlWZ8EcEpaqSg2VS6Sslbci/r9hj25ZGImol5xG6ZJe0pk5O80V08uKlWuuyRW1Nhx68HEqhGcFNigDZuP0qiHHE1CqgrboP0tAH8F4HLLso4BOAvg56SVirSm2oqdQU8uexNzq+Pg3psvTb18RCRflF7KJDdtKg0nVS0XE4kma/SBSrGjWiNSpRxH5Aq7yvGrlmX9BICrAJQWX7KbUktGSohzsVBxxc6gJ5duYv7mwXewZ98RfOW7R7Hn+bfYi0GUI3F6KZPctKnSYzdsDQH3u63NonBECcWJ67D3NKrdx6jWiFQlxxG5BjZoLcv6nwP+6S7LsmDb9mcllIkU8dyBt7Fz98HIQ9V0XLFz7/Nvodl20GwvLnTAXgyifEjS06L7TVtQLj5yrIb/8MUD5xfY27oZ05dMZFRKoujixHWUBrCK9zG65yMimYZt23PdkP9RTtXqDex88sXI208A6mxvE9awXgwi0leR49svF2+9/QrsfvaNrty+88kXQ+V2IlVEjeuoW2rpdh9DVHQDe2ht296eVkFILXPzCyiXDDQ8A8ujDLdRbXjMICo+iSUiMYoe37252K8hUC5x2w3SS9S4jjOEWKf7GKKiCzWH1rKsmwH8awCrABhYnEe7ybbtjRLLRhmanBhFq53sJlCX4TGqLbhAROIwvvtzcW9DoNUuTgOf8iFqXCdZubxIuYJIV2FXOf7PAP4CwD8D8KcAfh7Al2QVirI3PlbFjgeu75tDm9fEziexRPnF+D7PryGw44HrC31MSE9R4poPtojyLWyD1rFt+z9YlnUhgMMAHgDw3+QVi1Sw5YaLcfHkisLcBPJJLFF+Mb7P620IXH7pJE6ePJ11sYgiixLXfLBFlF9hG7Tule5NANfatv0dy7LakspEQ8jad80PbwKJiPKHuX2wNK+zlJ4i1HvWXSqisA3aFyzL2g3gdwHssSzrKgAtecWiIHH2XSMiIqJweJ0lXbHuUlEN27bH9VsAHrNt+zUAn176+SFppSJfUZedJyIiovB4nSVdse5SkYXtof1TALAsaw7An2BxgahHAHxCUrnIR5xl53XFITP+/I4LjxWpyq2bI5USzjXbqdZRxgXFUaTrbFF5cwOA3OQJXesuczWJELZBeyOAD2Fx656/sG37f7Esi4tCpSzpfoq6JA0OmfHnd1zggMeKlOTWV8dx0Gw7qJRNGEAqdZQ5RF2qX4eKvm9x3nlzQ6PVARwH1UopF3kiy7obN66Zq0mUsA1a07btjmVZdwL4t0uvjUkqEwVIsuy8LknDO2TGNbP3MKan1ih585MWv+Oya8+rgGGgyWNFivGrr249lV1HmUPUpcN1iNu75JdfbgCAs43FNU51zxNZ1d24cc1cTSKFbdC+YVnWXgCXAfimZVlfBPB9ecWiIHGWnc8iacR5WlerN/Dym3MwNRwyI5vfUCLTMIDul7qOleo9IZQ/bp17b6HZV19dsuNZ9rA7v2HUaxO/a/7pdPPK7V3yyS83eJmmgZffnMN1l08OPOcqX1vTrrtJ4lrXIdKkprAN2u0A7gfwbdu2m5ZlfQuL82gpA1GXnU87acR5Wuf+jWkAC83up6cc7uU/lKjjOOht0brHSoeeEMoXb51rdRx02h3f35MdzzKH3S0Po8Zij3OlZMAwDOzYuhnTl0wkfv880+3mtQjbuxTN5MRoX++s10KjjS9+zUbHCZ4aocO1Nc26mySuObyfRAq1yrFt2+/Ztv2Xtm0fWfr5T23brkstGQmTZtKIs8qe92+8jdnRagnVssnhXjg/lKhaNrFi6bhsv/cabO95bds9VwMAVzqkVPXGfbPVAQwDldLi/wCgUjZTiWe/WBHxmd7v6A6fbrYdNFod7HzyRcbXELx5JSU4Tt9LI5Xzt8ILzU7gNZOrCPdLEteycjUVU9geWtJYmvMq4jyt8/ub0YqJT9551dChP0USNJSo97XZYzWtekJIf34xXC2beOj+a7FytJL6Kscyht0NGq5YLjG+huHcVMra3PwCqpXS8pxZYPHB+V0fvARfe+Fo1wN1v2umbqMM0pA0rjm8n0Rhg7YgvEnDvbms1RvCk0fUp3W1egPvLTTR6hme2HGQeWNWxjyZpO/pN5So9zX2hFDagurcxnWrAUTbFkNU3IkcdheUp1ytttoriaqCN6+UJd+pOx0HH7pmHb7y3aNdr/tdM2VeW0XEdlb5IWlc9+bqrPNc1p9P8bBBWyDjY1Ucmj0ldf5HlKd13rkoHQcoGehaPj/LRCJjnkxac2/YE0JpC6pzUfONivPT/PKUWTK759A+cL2yK4mqhnNTKStBeer9F64Mdc2UdW0VEdtZ5wdRce33Pe7bslpACeN/vo55tojYoC2QtFaZDPO0zq8slaUhihvXrc7shqdWb+DoidPYtfdVNNvnn8QmPU5pr/CpS08In4SqJ+456a1zAPDI4/t86zzQ32ur4iq4QXnqf/rEdVizenR5GPXll07i5MnTqZYj62NDpIPefDY9tQa/8YnrAKDrXiPsNVP0tVVEbOclPwR9j9tu3Jjp5+t2HIuKDdoCSXP+x7CndX5lKZsGVo5WMksc7pM5w0BXYxZIfpyymHsjuidEdOOTT0Kz5Xc+k54Tb50Lmsv93MF3sOf5t/qewKs4P21Qnnr/hSszLUfWx4ZIdb357NYPbMC3XzoWmN/CXjNFXltFxLbo/JDVg+ag73HiVB0XrJDfXGGe1RsbtAWi0txKlcoCBG+47kpaNtW+b1SiG598Epotv/M5PbVG6Dnxq/OtdgdPPf/W8irB7mfcduNGJWNElTKpUg4iXfhdY5498E7X76hwzRER2yLzQ5YPmoO+x7o1Y2iclb+SNPOs3kJt20Pqq9UbmD1WG7h8vEpLpGdRlkHHKGgF0xFBW42odOyjkrFVwaAnoSRX0Pk8euK00HPiV+fvvWUK5Z7PME0D3zt0AgCUixFV4laVcsQR5tpEJNqgVcldSa85Iuq2iNgWlR9kbUsU9jgFfY+JVSOJPj8snfMssYc2F6I8UVNpbmWaZRl2jPyezFXKJj71ieuEzelV6dhHIWMYDp+EZifofAIQfk785tXuff6trt9ZaLTxf/3NS2i3F+Py0YdvUSpGVIlbVcoRBacVUFb8rjG9kuQ3kXVbRGyLeA8Z1/qoxynrPJf151N87KHVTO+TrjhP1MbHqti0YVyJQE2jLGGOkd+Tue33XI1rN4ndOiit7yuyR0RG45NPQrMzaIudoHOSpE5567z3vI9WS8u/c/bc+bgEoEx+cqmSM1UpRxhhr03swSUZ/K4xH7vhIiHXnCQ9mUH1XURsJ30P0df6uMcp6zyX9efnURp5nj20GvF70rXugrFMJrHrtDpt2KeOeXgyJ6NHRNZWBXk43joadD79zonoOuV+xstvzuGLX7Ox0Dw/x01U7tIpP+VVmLzLHlySyS+fffzWTYlzQ9yeTFH1XVZ+E32t5yJLBKSX59mg1UTQIjr/ZtsHUx+6qdtNSJSnjqJXBk6TzIWWZDU+dT7eOht0Pr3nRFadGh+r4rrLJ9E7IlBE7tItP+XVsLzLheEoDb3XGBHXnDg9maLqu+z8JvJaz6lFlGae55BjTQQ96TrXbKc6dFPWogEyFWV4q18dMQAcPSFmf0wOw8mXoPPpHRokc/Eub1yOjZaFxKWO+SmvhuVdLgxHuopzTyGivsfJb3GGeoq61hfl3ouCpZnn2UOriUFPujZtGE9t6GZaQ0hED6lRZXhr2O8V5/v71ZFzrQ4e+9LL2M5eKgqh9+n/1tuv8N16R9QTdjcu24aJktNJHJcc4qaWQXmXvTekM7+67V63qyv6c01QfR+plDB7rNb3Hn7X/qj5TYXRKqrce1E20szzbNBqYtjchrSGbqZROWUl4ayHt4b9XnG/v1tHdu15Fc32+XPUXHqKy6F8NIjf0KDdz7yBD06/D9956fjya+0OcOjIKWE3RuNjVaxduxonTyYfSTA5MYpGs931WqPZZiMpQ0F5V9bcfKK0eOt213XbAbbdbXXlSL/6/pEPbMBnZr7X9fN3XjoWaTeGoPsvlYb0Z33vRdlJM8+zQasRFZ50ya6cKiVhkcJ+r6Tf/6bp9Vi1ooL/+F9fxp7hUrYAACAASURBVDkJi+1Qfvk9/TcNYP8Pjne91nEc7FI5Jg0DcJzun0lJKlzTiJIKe9321veRSgmfmfle199848A7Xe/b+x5R7r84WoVUkVaeZ4NWM4OedKW1sqfMypnXJBz2e4n4/hvXre66nweS96Jz1dh8q9UbeG+hiVbv8OKOg5Jpot3pdL1uGlAyJufmF1AtmzjbON9LWy2bSpZVFt1ilb03pBO/+Ipy3Xbr++yxWt/f9EqyGwOH9JNK0sjzbNDmRNpzJWRVzrwm4bDfS8T3F92LrsI8HJLHe3477Q5KpoFq2VyeQ/tfvv563990HCgZk3nNH2ExVonkCYqvyYnRrp5WAGi0Bq814JereiXZjYFD+qlo2KDNgSyH6fY+rUzaO5DXJBz2eyX5/t5jL6oXPa9DwGlRrd7Arr3dc64rJvDQ/ddi47rVGB+rYsVIGZ/7u0Nwf6VkGtiuaEz2xk+r3cE9N18a6T106+F0MVaJzhMdx4PiCwD6hkX1/tzD71p/6wc24Ns9c2iTlH3QwlWTE6NYG/udKS5dry86YINWMXEqe1bDdHufVg5b0CCsvM6rCvu94nz/oCfHWaway4Strt5z89zBd7oaswBQLplYOVpZPndufXS3f3Ibuqpyy/vcwXfw1PNv4avfPYq9z78VKh/p2MPpntP3FpqpXgcY56QqGXE8bPuRaqXUPdWhUlqOPW+snKk3MXushk0bxn2v9R+/dZPQuApcuKrjYMfWzZi+ZCLxZ1A4Ol5fdMIGrULiVvY4w12S8ntaOWxBgyjyOq8q7PeK8v1l9sxEHcLJhK0uvy15nnr+rb7f89uWZ3ysims3TaZVVCH2PP8Wmq0Omks/D4sJHXs4vee01e6gdwSjrOHWjHNSlaw4HnYtDPo3b6wsNNrw/tbHbrgIv3SX1VUuWfc+fsdl55Mv4tGHblY2v+WJjtcX3ZhZF4AWxdkwu0vE4S5J+T2t7CVr8+Sk3I3Gf/Tj9yJvOB7l/UW977D3k7lxtTssKszG6InrMEnjd26e+PrrKPmE8L23TAFA5DrcW09Fx0EUcWIizQ3gReg9p822AzgOKiFiVeTnMs5JJb6rtUeMY7/cNeha2PdvlRK23XM1AHTFSu9d2bMH3sGPfvxe7O8atuyA/3Epl8Tmtyxzvup0u77oiD20KQuav5Bk2PDc/MLA4S4yhFnQoNVx8N5CE7V6Q5knUO7TUsdx0Gwv3vwZgLAeBtFDetz3M43F4/ngHVfipzZf3PU7shfCCTsEOq8rVOeB/7kx0epZvRgATv5/Z/HI4/si9b4Nmn7Q6jj42ZsvxZbNF6VWD+LEhG4LSvmd02qlhIfuvxYrRyvShgIzzv1xCLYa/OJ4odHGkeOLw3yHnadBow+mp9bgNz5xHYD+qRfe66R12YVonG2EWsl49lgN779wZdKvPbTsfsel1RaT32r1Br558B3s2XcE5ZLJURs+dLu+6IgN2hQNauwkqexZBMqwBQ0arQ467Q7+5Ms/UCa5+Q35aC79t4ihH6KH9Pi93xe++hoAdDVq01hIK8wwKCZsdfmdm3PNNj48/T5899A/dr3+nZe7952NM1S3d/rBl781i6f2HcH2e69JJQ/EiQndFqQLijfZc5wZ5/04BFsd42NVbL39iuVrpWv3M28s//+g8zRoWOih2VNDz7F7nZxYNYKTZxuhHvxv2jCe9CsPLbu3F9mb33Y8cH3iXLH/lePY9fTh5XupZrvd99mk3/VFR2zQpmRYYydJZU8zUIatpPvxWzfh6InTeOyvX0Kzg+VeYxWS26Bh0iJ6GAYN6YnzvnPzC/Ar7hNffx03Wu+LtTedTEzY6gq6yfvv9klUS4BncEefYbERZvoBADTbjvQ8kHSlbxXiKKys4o1x3o1z49QztX4coxUTC83z58Q0Fq+dLc8ieN5VioMWVjMN4OiJ07HOcW+s+M2hFdU7G2bkRG9+u/zSSZw8eTr2Z7p1v9nqH+nDURv9dLq+6IgN2pSEaewkqewiAiXuUJzeBQ1WjlZQLpnLT+oANZLboKelInoY/N6/2epgpFKK/X7nfC4U5ZI5cMP2LDFhq8vvJs9xgEZ/FesSZ6huEJl5wC8/LW+pEYEKcRRWVvHGOD+PQ7DVMzkx2rdAWqvjoFwy0eq5L/nmwXew9/m3AhdWW2h2cPitd2Of495Y8a5yLKoxCyx+51bKIycGPcws+qiNIDpdX3TDBm1Kws5fSFLZk/yt383gfVtWL/97lKfQqg5J8z4t9ZtDmzTJdL0/3OHMDj4z873YQ9BMw0C7Z4Gvts8qtCphwlaT302eX0P01uvW44VX/zHRUN1bP7AB33rpWN+Te1l5wC8/fe7vDsEsmSjnfBhoVvHGOF+k6vWuyPxy0tbbr1geduxqdRzs2Xeka+syv5VS//6/vd230GaUc+yNlfGxqtCGrOvQ7Cl02ufzX8k0+nK36DU+gh5mVkr9n00kGxu0KRE5f8GvJzXJghRBjdXbbty4/HOUp9AqD0nzPi0dqZRwrtkW2sNw0/R6bHzfavz+rhcAAM2WAyDeUMu5+QVUy2bXYl/A4iq0KhxL0os3Ls2l4W+9Riol/NQNF+Of/dQViYfqfvzWTb4LhaS1UFHbAdoRtu1JqlZvaLNXL4mj8vWuyPxy0oqRctd5uvfmS/GV7x7tGk1WKZtwDKDhGclSNg389IcvXe7J7T3HWS0I5n7uSKWEmacPw7uluGmga4SKjG17eut+FgsAErnYoE2RiPkLfj2pcJBoQYqgxuqJU3VcsGKxikR9Cq3ykDTZPQvnmm1UfIY2RR2C5nfMK2UTH918kbCyUrG4cfnym3P4y68exrlWb0x3luM1aoz0/s34WBUf/8gmfHTzRdLzQJhhzzKHgT534G380RMHlstQMoBfvW86lz3C1E/l612R9eak3vMELO5X7dVxHAA9D8c6Dj66+SLfXDZsdJss3s9ttjvoHfjbOzVJ9BofLtZ9UgX3oU3Z+FgVmzaMx+6Z7d3/b9eeV7Er4Z6AQY3VdWvGusoddj/SON81T/uXiRqC5nfMt/PJPyU0PlbFdZdPwum7BQIevOPKvvqVNDaT5Lwon+GNlUrJ6O+xlTjceefug10x33aAXXtezUU+o3DSqOeUnHuegMVG3tbbr+i+xt57DbYP2G/We46D9mSeP3NO6nfo/dxW2+kaNg305zuZ2/aw7pMK2EOrEd8Nww2j92Fi5J6IoCFT7tLzLllP4vK25YH3eJbLJlqtTuwhaHz6STJ0DT8esMexTrHZGyuHjpxKZRjo3PwCTJ+FUUyDCwMRqag3r229/QpMrR/vusYm2XvdO7pNBr/PrZRNOI6DSsD0DpHT3ohUxAatRvyesAUNj4n61C1sw0n0cN28bnngHs+2YaLkdBJ9Fy6+QjIMi3kdY9MbK2k9DJqcGEXHZ7hzx+HCQESq8ctru595A48+fEtfA3BYzhg0uq1xVt7oDL/PNQD83vYPDVwXRPS2PUQq4ZBjjfgOQR0wPCbO+ycZNhJlaKL7u0dPnA5cbEp342NVXLXxAmVv/okGxbzviBDTwMtvzg2NcVWmEKQ13HnH1s1dx6pkANvvvYaxT6SYQQtcRhU0FWti1Yjv74vIi+5CUL1DpbfdczXef+HKofmOw4Mpr9hDK5js1e78eh1q9QZ+4xPXAchudc0oQxO9v+u37xu3PCDKnl8vwEKjjS9+zUbHQWCM6zRM2StJ7t5yw8W4eHIFVznOUFYrzZJeBq1x4a1DAELVp7AjQUTkxTBDpYmKig1agdK6kfMOhVHh5jHK0ES/3y0Zi/M/ypLnuhFReEHb/CwsbWfhF+M6DlMGxOTR8bEqrt00KamENMhzB97Gzt0HtXuIQukLWjPk0Oyp5RzQaHUAx0G1UgpVn4YNTxaRF8MOlSYqKjZoBanVG9j19GE0Pclm155Xpe97qMLNY5Q9av1+t1op4aH7r8XK0QqfNBIpxLvNzxe/Zi83ZgH/GI+SC1ShSh6lwYJ6YGv1BnY++WLf+dv4vtXC9xknvQTVGb/tex55fF9XHQKwvAd80nwgIi/qmFuzwJEaxcUGrSDfPPhOV2MWAJptB88dfAf3fWSTlM+UmeBq9QbePfpuqAWNomxTE/S7HKJHpCZ3m58wUwOi5ALvjcda4aUOjzeK6hvUgz43v4ByyUCjef73HcfB7+96oWvFV/bY5ltvQ2bYqAtvr+rssVpfDvBKmg9EbOUnajvAPFNhxCJlh4tCCVCrN7Bn3xHff3vq+bekLYwiK8Htf+U4Hnl8H373P+3DI4/vw/5Dxwf+fpQ9auPsZ0tE2Qobt2F/z80xf/jEQTzy+D48d+DtNL9OF94oqi1or0/3ujo5MYpWzx6czbaDVtuJvTc76aU3n3zj4NsD60wvvxzglTQfiLjv4b3TYMPyBOUfe2gFWHxCbKLZbvf9W1nik/6guSDD5nLMzS9gpFLyHY7lTQru8Jsww22ibJFRlL1Vo/RyE6UpzrCssHEbZzug/2P3Qfzetg/i/ReuFFbesOLkUUrPsB708bEqdjxw/fIc2ma7A8MwukZMscc9v/zyyRNffx3lUnd/zaA60JsD/ObQxqk73rw1KC+GzW9hc3ARh91ypA2xQSvAoKd7sp70uwlremoNHn34llDJyx2O4QBotjqolAwYhtE3fCtuUoiyX6qsvVVVSeTusS6XTbRaHQ59IWXsf+U4du19FaZhoOM42H7vNaHrZti4HfR7fjmm2erg93e9gF/xKYuMYWS9eaIoD9l0FKYH3V1l2n1Y+5mZ7w38fcoPv3xSXtpBwctvJeNh82qT5IOgvOU3WiVKfhuWg6O+nyr3TElxpA2xQSuA9+me4zhoth1UyiYMQMqT/jg3eH5PMZttB4DT1QOrc1JQZf5E3F5uItlq9QY+99QhLI7QXIzzz/3doVTrZtADwFbb6YsTGQs2DbrRZHyqJ2wPuvf8sce9OPzySccBHrzjSux+5o3AlYyHzat1f45j/sy5UHlLdH6L+n55Wh2cI22IDVpBvE/3eofzinwCFjcB+j3FdPUO33KTgrd3EVhcOEHVp3gqrVTKoS+kqqMnTqPdu7iTs/h6mC1nROQyN8d8fs+rfXMfe+NEdCyplCcoPL/es0HXI/a4F0dQQ+am6fW40XrfwJWMZcX+iVP1UHlLdH47euI0eu/ygt4vaHXwoO0WdYglxn2xsUErkN8TftG9hnETYJRh0W5SaBsmSk4Hh2ZP4ZHH96X6FK9Wb+DoidMAEGoFZJUakTr3clM+ufF05Njp2O8hMpfdNL0eG9+3Gr+/64WuRm1vnIiOJZXyBEXjXl/96uF9W1YH/j7lX1BDZthKxqak2F+3ZixU3vLLb612J1Z+2//K8b6tI4M+F/BfHdwvF6oy8i0sxn1xcZVjiWSsuhb3Bs+7Ql6lvHjaKyVj4GqlV228AABSXzlu/yvH8en/8zv47O7v47O7v49PP/btoSstq9SI9B7rsdEyVyOkTO1/5Tg+/di38dnd38d//X9/2PfvJdPAxnX9DQIvGbns/ReuxK/ce83AOBG9sqdKeYKiC6qH82fOZV00ytj4WBWbNowH5ga/2F9otHHkeE14WSZWjURaFb7kaWd3HODQkVORPs+Ni97GbKVkBOZLv9XBe3MhVw4mnbCHViIZvQFJ5gkMGhad5ncYpFZvYNfTh7suPG0H2LXn1YFDg1SbP9Hby83GLGWhVm9g195X+4YZA0C1bMCBge0h4kRWHggTJyKHkamWJyiaoHp44lQdF6zg7QwFGx+rYuvtV+ALX32t6/Xdz7yBG633Cc8BYfPW9NQamCUT7aXGaLvTv5bAMH5xMVIx8alfuC5wKknv6uB+uZAjWkgnvAJItPgELNzwjyiS3OBFHY6Rdo/G3PwC/Kb6mkZ3EvXbEke1+RPjY1WsXbsaJ0/GH+ZJFFet3sDLb871zakCgGrFxC/fZeG6yydDxYnMPBAmTkQOI1MtT0TVO59Nl/ltIgTVw3VrxtA4y14jGmxq/ThGKyYWmvK3dBoUl95/m5tfQNk04Bn5G7lMQQtjDRt5410d3K+cHNFCOmGDVqJDs6f6hnTc+oENQhJnWvME0u7RmJwYhd9U345zPokO2hKH8yeIzseIaRpo+HTPOg5CN2aB/PVs6poneuez3fqBDfj2S8e0md+WVFA9nFg1gpNs0NIQfvcXMhpog1YP7o3hrbdfkbjR6MbF5556dfm9Ou0ODh05NTQfDMqFecv7lG9SG7SWZY0D2AfgZ23bPmJZ1h0APgtgBYDdtm3/jszPz5I71K/3VvJbLx3Dx2/dpFVCSLNHY3ysiu09iblkANvvvWa5N4Jb4hAF81vJ16tkhhtm3Ev3nk3d+Z3XZw+80/U7RciFrIcUVxoNtEGrBwPoi+Hdz7yBrbdf0bfFUNQyTU+tgWkA7aWf246YfMB4I11Ia9BalvVhAH8G4Kqln1cA+DyALQD+AcAey7Lutm37aVllyNLi0FkD6GnSmga0nH+QZo+Gm0D9VjnmnA6iwfxiZLRawi/cdhnWT46FWjU8iK49m3kwaOs1V1FyIeshxSW7gTZo9WD3v71KpoGp9eN49OFbEpVp8XNNNNvt5ddE5QPGG+lAZg/trwH4FIAvLP38IQCv27Y9CwCWZf0lgH8OIJcN2sWhLf1D/ToOpMw/yNs8qvGxqu9iBpzTQTSY73yqjoMPTa/TIjfkLZeJMmjrNRdzIdF5QblEZgNt2OrBQfcvScvEeyMqOsPxaXSJZFnWEQAfBXAzgHtt2/6lpdfvAPCIbdt3hXyrKQCz4ksoz3MH3sYfPXFgOcmUSwZ+6xdvwJYbLhb+OTuffBHlkoFW28GOB64X/hkqKdr3pdA2ATiSdSEEmELCXKdrjOha7rT0Hp87P3QJ/v6Ff+DxKh7muiGyzCWDPltmuZg/KYdC57o0G7QfAfAztm3/8tLrdwL4tG3bPxPyraYAzM7NnUFnyFNqAMqsLlurN3yHzgJiylirN/DI4/u65mRUyyYeffiWxE8gVTmGfmr1hhZb4qh8DAH1ywcML6NpGpicXAXk7CYvbK4D/I+RTj2da9euxptvzUnLZbKlGUcyVjnWIQ8Monv5gXDfoei5Lux5lnlfFIabz8Kscpzm6sp+5dQhblhOsXQoZ5xcl+Yqx28D2OD5eT2AH6X4+ZkIGjo7iDchAfD9b5FzSoM+b22kUqeLW+IQDSZqWF2cm684fxOUy46eOI2Vo5WuvbPd39ehsS5a73nl/Daibn65xDQNvHDoROJ1BPz45bthqwfLitne904rfxNlLc0G7XcBWJZlXYHFISb/AouLROVa1MTgXdK90eoAjoNqpYRGsw0YBqpls2sZ+KTzJno/z3EcVEomOo6D3/zFGzB9yUSs701E2Ut6Y9K7xUSYbWHi/A3gPwes0ergsb9+CQDQbDuolM3FnpylvFiErWr88IaTKJhfLllotPF/f/11AIsPyn71Z68Rkjf88t19W1YLjdG475Vm/ibKWmoNWtu2FyzL2gbgSwBGAewF8NdpfX4WoiaGoO02zjaWVq1znOX/9i7HHncZ+qDPO7f08x89cQD/+298hDdMRBpKemPilx+GbQMR529cvbms1XHQaXfQ9KSnpud9/XJhEfCGk2gwby4xTQMLjXbXv7c7DnYJyBtB+a5cKeM//+0PhMRo3HhPO38TZU16g9a27SnPfz8D4Cdkf6YMfk/IBj01i5MYwmzL4PIOK467DP2wz2t3HBw9cTrykGkiypaIG5M40xmSToHw5rL3Fpr4ky//4PwDvQB53aom6JpT9BtO9k6Ta1BdcHPJy2/O4QtfOYxGz8rDhgG8/OYcrrt8MnY98h3abAB/9rc/6Hr4FjdGk8R7FvmbKEtpDjnWlt8TMjgY+NQsTmIIsy2Dq3dYsXdOrffnQaJ8HhHpoVZv4OU359DpdI+8cBwn0o1JnOkMIraOcOeA1eqNUPkpj1tTBPXKFP2Gk73T5ApTF8bHqrju8sm+bXQAoNHs4Itfs9FxEKoe+TWe/fJdq+OgUi51NWjjxmiSeM8qfxNlxcy6AKrzPiE722ij0epg197D2LX31a7XZvYeRq3eWP67OInBHSZTLZtYUS2hZBooGVj8b2Mxka2ollAtm33Dive/chyPPL4Pf/jEQTzy+D7sP3R86HdzP69S9q8GJXNxZWYi0oObB/7ya6+hZyYBmm0HI5VS6PfqzUd+eUfE34R5r0pp8aauUja78mKS91eV3zXHvb4U+YZz0HGhYolaF4yAkWgLzU6oehR0f+WX7x6840phMZok3rPO30RpYw+th98TuKAhJYAB4Hyi6X1qFndua+/wYbcM3v92V/us1RvLPRlxh6XcNL0eq1ZU8B+/9PLy3FnXL97FREaki6A58a5K2cS55uDhu73iTGcI8zdhh41636soqxwP6pXZtGE89poJuht0XC5PuSwc9pwddwSKGbLncm5+AdWyOXDqwqBez2H3V375bt2Fq7Fz98HEMZpkjRRAXv7WGWM3v9igXRI0fGVyYhStnidkHQeAM/ypWdzE4Lctg+vQ7Km+cq67YCzRMLSN61ajd0BOpWzi7pun0DjLp99EOhg2J94AYvUSxNliYtDfBK0KGvW98nozMqxXRscbThE3kar0TnPYc3aeO/A2du4+CNNY7F31CqoLYaZWDapHYYb99uaoLTdcjIsnVwiJ0aTxLjp/6yAo3zB2841DjjF4+Mqh2VPotLvnQmy/52psv/eaUMMyxseq2LRhXNh+kH7lHFnavsIryoXeb5jJ9nuuxsSqkcRlJqJ0BN24jVZMZYaOBeWw+TPnMi2XSsIM+xN5XZEtznQYPyoMh+Sw5+zU6g3sfPJFNFqdrsbsaIh7sN5687EbLgpdj+I+SBEZozrFe9aC8g1jN//YQ4vgJ3BHT5zGzNOH4V1PwDSwPNQk7afkQeU812wnHoam41N/IjrPb3ja1tuvwNT6cWViOiiHnThVxwUreDly5SUfi16VOevjUvRFubI0N7+AcslAo3n+tdGKiU/eedXQlYr96s3Hb90Uqh4lHfZL6RmUbxi7+cc7CAQ/gQPQFwDlkrkcAGkPyxj0pHDThvHEF3rdh5kQFV3WN/zDBOWwdWvGOL2hRx7ysYybyCyPiyrDnotocmK0b7XijoPQ2+74TeWSOReV0jco3zB2849DjhE8lGnjutVKBcCwIVcclkJEKueBoBzG6Q35lLebSBWGPRfV+FgVOx64PrNjr3JepUWD8g1jN//YQ7sk6AmcakNN+KSQiHTGHFYceRyuyfqbHZGLLVH+DMs3jN18Y4PWw28Iit+2Ee52OVkJM1SGS5MTFUOt3sC7R99FyeloE+t5GE5L4a4zebyJZP3NjqhjL+MeyX3P6grWjawMyzeM3fxigzaE8bGq73Y5qi73zaXJiYrBjfVy2USr1WGsU2qiXGd4E0kqkXGP1PWeDrDtbou5OCPMN8XEObQetXoDs8dqfct4p7Xc9//f3r1H2VXVCR7/1iOVoghVmvAICBjoyI7RVh46oKJAB3WM736YwUcb39222mtWO3ZPj46tPXb32OooPmiXDcTpXuPEVplWCIqi0CqgrYIomN0iCYgmjASnCqgU9Zw/zqlQqdStV+695+xzvp+1WKRu3cevzt37d87v7H32afT5S32PxcTajM+SVJyZfX14ZJzR8Ukub/FtCMwbgmreAsO2XQ+taLuHvOfYRFOPu2yb9eN3vnSO0ObmO2PXjuW+m3XGcDGxOoIrpW+uvj42Psl1N/+CFz3jlKZ/nnlD06p2Cwzbdn20ou228rjLtlk/fufL4wgtC5+xa/VKjc0cVV0o1iqeWZfqaM1AL+Oz+jrAVTfsbsnskdl5o9WjwSqvKq1e7D6xXlrRdpdz3HXZVT/hl/c9NO/72jbrx+98+Sxomf/sGmTz8bdsWk93Vwe9LVjue6HPh+yMzTs+cQMf+MzNvOMTN3DT7XvnfK+FliZfzGdJKr/+vh5e8LTHHvL49L2ym2m+0WC1VxmmolXpFhjuE+ulFW33kPdc0bXgcdf4xBR/cfl3Gx7LNXpdO9pmGXJMXZmPls8pxyx8du2m2/ay/do76O7sYHxikosufFxTh/+XcnZv2rYdO9m4bvWcSXi+Vd6qdGZdqrvzzngMV96wm7GJR/p0K/rzfKPB55/xmCQLmRSVaSpaVVYvdp9YP61ouzPfM5x6NKP7HykG52pjkBW18x3LFdE2y5Rj6sh8tHyO0DL/GbuZxeTI2CTjE1Nsv/aOwz5zNTQ8yr/d/esDtwBq9qhqo5uAV+nMulR3/X09vOb5j6dnRVdL+3M7R4Nnc7QgU8apaI32Mylxn1hPzWy70zkK4JTj+xlYtfKQz9q6eQPdXR2HvHa+Y7l2t80y5pi6MR8tnyO0uUZn7FqxgECjW220a1S1KmfWJWX9+VlnnUy8876W9ud2jQbP5GjBI6q2EFOZuE/Ucs2Vo1543lGHPO+cjWs5+dij+IvLv8v4EnJoO9umOaYczEfL4wjtDHOdsWt2MTnXrTamz4C1c1S1CmfWJWUGVq1seX8+MBrsaEEhnIrWWu4TtVSNctTggw/P+fwTjj6S1y4jh7arbZpjysN8tHSO0C5gupjctuPgM3CtXN59Lp6xkVQ0RwuK0+x9kaTD0yhH3Xv/MI8+Yu7D6zIfy5ljlDIL2kVoZgKa6wzY+OQUD42MHRilbaS/r8fEImlZhoZHm5LD2pWHHC04VJkPhqW6aZSjjlvdx+j+0YY5t8zHcuYYpcqCdpGalYBmngHr7u7k4dEJJicmueSKH9f+GjFJrZHitaiOFsytzAfDUp00ylEDq1bype/dnVzOnWaOUYosaGdp1ijGfKbPgA2OTPDfLr2JsUnYPzoBzH87HklaqqXe9qtMHC2op3bsh6VmmCtHDT748CE59/KrfpJEzpVSvs54bAAAHG9JREFUZUE7QztHMfr7epjoGKe7q5OxiYkDj9f5GjFJzZf6taiOFtRLirMJVG+zc9S99w8fknPHJqa4/uZf8MJnnNLu8KRacJXjXBErah63us9rxCS1lNeiKhWubK0qOG51H+MTk4c8fuWNd9mWpRaxoM3NN4rRKgOrVnoDZUkt5Y3alYoi9sNSsw2sWsnzn77ukMe7bctSyzjlOFfUKIbXiElqNfOMUuBsAlXF+Wc8hqtuvIuxGdfR2pal1nGENlfkKIY3UJbUauYZlZ2zCVQV/X09vMa2LLWNI7QzTI9i3H3vAwCcfNxRS34PV2eUlCJz1/INPvgwu/YMue2awNkEKov5cuJi8qVtWWofC9pZbt91/7JXWHR1RkkpMnct30237WXblyNdHbjtmsSVrVW0+XLiUvKlbVlqD6ccz3A4Kyy6OqOkFJm7lu/AthubcNtJFTFfTjRfSuVkQTvD4ayw6OqMklJk7lo+t51UPfP1a/u8VE5OOZ7hcFZYdHVGSSkydy2f206qnoX6tX1eKp9aj9AODY+ya8/Qgakii1lhcfZrprk6o6SyaZSvZjJ3Ld+Bbbeiy213mBbTVqV2mC8nli1ftqPf2DeVgtqN0E6vTLd77xDbr73jkIv6G61KNzQ8ynU3/4KrbthNd1fnnAsBuKKdpLJYysIlZctdKa24fM7GtTzrrJOJd96XRLxlVJdFyVJq13U3X05sd75s1G7a0W/q0jcbsc+mo1YF7XTH7OyAkbHJg363bcdONq5bfeAM3OykcfnVOw/cIHtsYuKQ10xzRTtJRZu5cMm0ufLVTGXJXSkeQA2sWskpx/cXHUaSltNWU5Riu667+XJiu/Jlo3bTjn5Tl77ZiH02LbWZcjyzY84uZqHxRf3TrxsbX/xrJKlIqS5c4gqi9ZNqW10K27WWY752045+U4e+2Yh9Nj21KWjn6pgzzbyof+b1AvO9zoUAJJVRqosVVfEAyuvP5pdqW12KKrZrtd587aYd/WY5n1GVfGefTU9tphzP1TEBenu6mMynEvT39RwyxWDLpvVzvm5FV4eLf0gqpemFS7btOHi6VNnzVdWKG6esLSzVtroUVWvXao/52k07+s1SP6NK+c4+m57aFLRzdcwtm9azbm3/geQw1/UC26+9gy2b1h9YQGp8Yopn/OZaLnzKSZxw9JEF/kWS1Nj0wiV33/sAACcfd1TBES2sSsVN3a8/W4qyLUrWbFVq11qcZiwmtFC7aUe/WexnVC3f2WfTU5uCFhbumI2mGKxb28/73/z0A6scf+f2e7nhx3uTPvskqfpu33V/cmfMq1LczDdlLdW/qZXKsihZq1SlXWth1//gHi7efnNT8u5C7aYd/WYxn1HFfGefTUutClqYv2MuNMVgx413MTYxNe8qx5JUBimfMa9CceOUNc1WhXat+Q0Nj3LxZ29pat5Nod1UNd+lsO2Vqc2iUIvR39fDlk3r6e7qoHdF50E3zPYCcUkpqVrOSm2xkekpaz3dnRzR03XQ/kRSNe0bHKG7q3HeTS2PLZb5TkWr3QjtfNc13HTb3vxa2U7GJib57fNOPTBNpKpnnyRVU5VyVrMXG2nG9W2L4ZS15mrX9yYt15qBXsYnDs674xOTPDQyxjduvufAeiypXAKyFCmu26DqqFVBO99B0VzT8/7pGz+jt6eLC8440QvEJSWlKjmr2VOn270Sp1PWmqNKK6iquvr7enjby04/cA3t6NgEk1PwiS/8iJGxyYOem8olIEuR4roNqobaFLQLHRTtGxyhc477zX7maz/lrHAs/X09nm2XlJQq5KxmLjaS8nXFdeb3ppScd+aJnLjmCO6+9wE++vkfMTY+OeftH1NfNGk2+6mKVJtraBe6niybJjJ5yOu6Z11z1t/XwynH99s5JSUh9ZzVzKnTVbuuuC783pSa/r4ejuxdQfccAyXTUr0EpBH7qYpUm4J2oYOi/r4eLrrwcYe8bnKKSiUcSUpJMxcbqdJ1xXXi96YUzdVuAXorumiS/VRFqs2U48VcT3bBGScC2TTj7s4OJqeoXMKRpNQ0a+p0Va4rrhu/N6Vorna7ZdN61q3tT/YSkPnYT1Wk2hS0sLiDogvOOJGzwrFJX3MmSVXTrMWVqnBdcR35vSlFdWu3dft7VR61KmhhcQdFrkopSdVljk+T35tSVLd2W7e/V+VQm2toJUmSJEnVYkErSZIkSUqSBa0kSZIkKUkWtDMMDY+ya88QQ8OjRYciSYUwD0pSeZmjpUPVblGoRm66bS/brj54qfFzNq4tOixJahvzoCSVlzlampsjtGRnu7ZdvZPR8Un2j04wOj7Jth07PfslqTbMg5JUXuZoqbHaFbRzTdXYNzhCV2fHQc/r6uxg3+BIu8OTpEKYB9PnVETVSd3auzlaaqxWU44bTdVYM9DLxOTUQc+dmJxizUBvQZFKUnuZB9PmVETVSR3buzlaaqw2I7SNpmr88r6H2Dc4wpZN6+np7uSIni56ujvZunmDN4aWVBv9fT1s3byhFnmwaiM7TkVUndS1vZc9R1ctryottRmh3Tc4QuesqRpTwF9c/l1WdHUyMTnFlk3rWbe2nzUDvaVJEJLULudsXMvGdavZNzhS2TxYxZGdRlMR7773AY7sXVHZ71L1NN/U26q381bk6KHh0cN+vyrmVaWlNgXt7j1DjIxOHPTY2PgkAOMT2ePbr72D97/56ZVPiJLUSH9fT2Vz4MyRnWnbduxk47rVSf/Nc01FHB2f5KOfu5Xu/IStB5iqirpPvW1mjm5GIVrVvKq01GLK8dDwKNu/fschj3fP+uu9uF6Sqquqi6rMnoq4orsTpqYYm5iq1ZRM1UPZp96mYvDBh5sydbuqeVVpqcUI7b7BEWb1NVau6MzP8D1ylq9OZ/gkVUszpo1VXZVHdmZORXxoZIxLrvgx+2fMSmo0JXNoeJRf3/1ruqYmbTdKxuFOvS1LviwyjnvvH27K1O0q51WloxYF7e69Q4yMTR702NQUnP2E4/j2rXsPPHbuk453hy4pOdf/4B4u3n6z1y8tYHpkZ9uOg6fYVSXvT09FHBoeXdQB5vR0w+7uTsbHJ203Sspyp96W5XrPouM4bnVfUwrRqudVpaHyBe3Q8Cjbrz10uvGLn7mOf/7m7oMe+9ate3jRuafYCSUlY2h4lIs/e4vXLy1SHRa+WswB5szr3qbbju1GVVeW6z3LEMfAqpVNK0TrkFdVbpUvaOea29/b00V/38rarpInqTr2DY7Q3dXB6Ngjj5nL5lflha+mLXSAWeeVYlVfZWn3ZYmjmYVoHfKqyqvyBe1cc/snJ6c45fh+5/xLSt6agV7GJ8xlOtR8B5he96Y6Kku7L0scYCGqaihkleMQwjdCCLeFEG7J/zu7VZ/VaDW8E44+0lXyJCWvv6+Ht73sdHOZlmTmvrGvt9t2o1ooywrJZYlDqoq2j9CGEDqA04DHxhjH2/GZjaZUOOdfUhWcd+aJnLjmCHOZlmR6HzjR0ekqx6qNshz7lSUOqQqKmHIc8v9fE0JYA3wqxvixVn9ooykVTrWQVAXmMi1Hf18PxxxzFL/61QNFhyK1TVnyZVnikFLXMTU1tfCzmiiE8DTgD4G3AiuA64D/GGP86gIvXQfsOtzPH3zwYe69f5jjVvcxsGrl4b6dpPI4BdhddBBNsI4m5LrZzH1SZdQ615nLpNpYdK5r+whtjPFG4Mbpn0MIlwKbgYUKWgD27XuQycmFi/C5zjgXfc+v2cp+Vtz4Dl/ZYyx7fLBwjJ2dHaxZs6qNEbXHYnMdLLyNypb7ZkuhHc7H+IuVevywuL+h7rnumGOO4kvX/bTUuQzSaY/G2VzG2TzLyXVtXxQqhHBuCGHTjIc6gLFGz2+Wmff82j86wej4JNt27GRoeLTVHy1JhTH3SaqCwQcfNpdJmlMRqxw/CvjbEEJvCOEo4NXAFa3+0Pnu+SVJVWXuk1QF994/bC6TNKe2F7QxxiuBq4Cbge8Dl+XTkFuqTPf8kqR2MfdJqoLjVveZyyTNqZD70MYY3xVjfHyM8bQY40fa8Zne80tSHZn7JFXBwKqV5jJJcyritj2F8Z5fkurI3CepCsxlkuZSq4IWvOeXpHoy90mqAnOZpNkKmXIsSZIkSdLhsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJqkVBOzQ8yq49QwwNjxYdiiRpCczfkpbKvCHVS3fRAbTaTbftZdvVO+nq7GBicoqtmzdwzsa1RYclSVqA+VvSUpk3pPqp9Ajt0PAo267eyej4JPtHJxgdn2Tbjp2esZOkkjN/S1oq84ZUT5UuaPcNjtDV2XHQY12dHewbHCkoIknSYpi/JS2VeUOqp0oXtGsGepmYnDrosYnJKdYM9BYUkSRpMczfkpbKvCHVU6UL2v6+HrZu3kBPdydH9HTR093J1s0b6O/rKTo0SdI8zN+Slsq8IdVT5ReFOmfjWjauW82+wRHWDPSa1CQpEeZvSUtl3pDqp/IFLWRn7ExokpQe87ekpTJvSPVS6SnHkiRJkqTqsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkipb0A4++DC79gwxNDxadCiSJKnJhoZH2bVniMEHHy46FCVkut14fChVR3fRAbTCTbftZduXI10dMDE5xdbNGzhn49qiw5IkSU1w02172Xb1Tro6O5iYgq3PC+7ntaCD2o3Hh1JlVG6Edmh4lG1X72R0bIL9oxOMjk+ybcdOz8RJklQBB/bz45PZfn5swv28FnRIu/H4UKqMyhW0+wZH6OrsOOixrs4O9g2OFBSRJElqFvfzWg7bjVRdlSto1wz0MjE5ddBjE5NTrBnoLSgiSZLULO7ntRy2G6m6KlfQ9vf1sHXzBnpWdHFETxc93Z1s3byB/r6eokOTJEmH6cB+vrsz28+v6HI/rwUd0m48PpQqo5KLQp2zcS3POutk4p33sWag12QlSVKFnLNxLRvXrWbf4Ajh1KMZ3e91kFrYzHbj8aFUHZUsaAEGVq3klOP7iw5DkiS1QH9fD/19PQysWsmvLGi1SNPtRlJ1VG7KsSRJkiSpHixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlCQLWkmSJElSkixoJUmSJElJsqCVJEmSJCXJglaSJEmSlKTuogNYgi6Azs6ORb9gKc8tStljNL7DV/YYyx4fzB/jjN91tSWY1ltyrlvO88vG+Itl/MVb6G8w16XzPRtncxlnc5U9zuXkuo6pqanWRNN85wLfLDoISaX1TOBbRQfRBOY6SfMx10mqg0XnupQK2pXAU4E9wETBsUgqjy7geOBfgYcLjqUZzHWS5mKuk1QHS851KRW0kiRJkiQd4KJQkiRJkqQkWdBKkiRJkpJkQStJkiRJSpIFrSRJkiQpSRa0kiRJkqQkWdBKkiRJkpJkQStJkiRJSpIFrSRJkiQpSd1FB9AKIYSXA+8EVgAfjjF+vOCQCCF8AzgWGMsfehPwGxQcZwihH7gBeEGMcXcI4ULgQ8ARwPYY4zvz550O/D3QD/wL8AcxxvEC4rscOBd4KH/Ke2KMVzSKuw3xvRt4Wf7jVTHGd5RpGzaIr2zb8L3A7wJTwKUxxg+VaRuWWRlz3WIsNu+U0VL6fFktpc+VVQjhA8DRMcatqeWFsh4PlFmZc11KOSGFfhNCeCHwbuBI4JoY4x+XcXuGEF4J/Of8x6tjjG8vyzYt+7F9K1RuhDaE8BjgfWQH7KcDbwwhbCw4pg7gNODJMcbTY4ynA/dQcJwhhLOBb+WxEUI4ArgMeDHweOCpIYTn5U//R+AtMcbTgA7gDe2OL/cU4FnT2zEvxOaLu5XxXQg8BziD7Ds8K4Rw0TyxtHUbNojvpZRrG54H/BbwpDyut4YQnjxPLG1vh2VVxly3GEvMO6WyjD5fOsvoc6UTQtgEvHrGQ8nkhbIeD5RZmXNdSjkhhX4TQjgV+DvgJWQ56sx825Vqe4YQ+oCLgfOAJwPPzNtC4du07Mf2rVK5gha4EPh6jPH+GONDwOfIzkQXKeT/vyaE8MMQwlsoR5xvAP4I+GX+878Dfhpj3JWfoflH4PdCCI8Fjogx3pQ/bxvwe+2OL08gJwOXhRBuDSG8J4TQ2SjuNsS3B/iTGONojHEM+AlZAinLNpwrvpMp0TaMMV4PXJB/5rFks0YeNVcsBbbDsipDDlmOReWdooJbwKL7fJFBzmcpfa7AMBsKIawmK27+Kv85tbxQ1uOBMivz9kkiJyTUb15KNoJ4T749twDDlGx7Al1kNdSRZLMGVpDNuCjDNi37sX1LVHHK8QlkCWbaHrIvs0iPBq4F3krW6K8DtlNwnDHG1wOEML1/nXPbnTjP4+2Oby3wdeDNwCBwJfA64MGC4rtt+t8hhMeRTTn6aINY2r4NG8T3TOB8SrIN8zjHQgjvAd4O/BMla4clVsZct6Al5J3SWWKfL60l9Lky+iTwX4CT8p9Tih1KejxQcqXNdQnlhFT6zXpgNITwRbKT71cCt1GyWGOMD4QQ3gXsJCu4rwdGKUGcZT+2b5UqjtB2kl0XNK0DmCwoFgBijDfGGH8/xjgYY7wPuBR4LyWLk8bbrhTbNMZ4Z4zxpTHGPTHGYbKdxuai4wshPAH4KvCfgDsbxFJYjDPji5nSbcMY47uBY8h2tqc1iKUU7bBEqrI9kvs7FtnnS22Rfa5UQgivB34eY7x2xsNJtZ+EjgfKpPTfcZlzQmL9pptsRP51wNOAs4FTKVmsIYQnAa8FHktWGE6QTT0vVZy5Uh/bN0sVC9p7gONn/LyWR4bdCxFCODe/dmFaB7CbksVJ421Xim0aQvjNEMLvzHiog2yKR2HxhRCeQXa2/c9ijJ+eJ5ZCYpwdX9m2YQhhQ74oAXmB/QWyEeTSbMMSq8r2SOrvWEKfL6Ul9rmy2QI8J4RwC1kR+CLg9aQRO5DU8UCZlLqPJZATUuo3e4GvxRh/FWPcD1xBVuCWLdbnAtfGGP9vjPFhsum651O+OKFkx6WtUsWC9mvAphDCMfk1l78DfLngmB4F/G0IoTeEcBTZRfmvpHxxfgcIIYT1IYQu4OVkK7fdBYzkSRvgVcDVBcTXAXw4hPDoEMIK4I1kyW7OuFsdTAjhJOD/AC+PMf7v/OHSbMMG8ZVqG5Kdef1UCGFlCKGHbNGCT84VS4naYVmUMdctR1Ftb8mW0ueLinERFt3nigxyLjHGZ8cYn5gvpPRfgS/GGF9DWnkhleOBMiltrkshJyTWb64EnhtCeFS+7Z5Hds10abZn7ofAhSGEI0O20NsLyaYdl3Gblua4tJUqV9DGGH9Bdp3AN4BbgP8VY/xuwTFdCVwF3Ax8H7gsxvhtyhfnCLAV+DxwO9m1AZ/Lf/0K4H+EEHYCq8hWd2t3fLcCfw18O4/vlhjjZxaIu5XeDvQCHwoh3JKf/dw6Tyzt3oZzxfd0SrQNY4w7OLhv3JAfFDSKpfB2WBZlzHXLUWD/XY6l9vnSWUafS0EyeSGV44EyKXmuSzknlK7fxBi/A7yfbJXe24G7gEso2faMMV4DfIasD99Kdj3831DObVrqY/tm6Ziamlr4WZIkSZIklUzlRmglSZIkSfVgQStJkiRJSpIFrSRJkiQpSRa0kiRJkqQkWdBKkiRJkpLUXXQAUiMhhIuBZ+U/bgR2AfuBJwPXxBifO+v5Z5Ldm+6k/EbXklQJIYQrgc/FGLfltwY5P8b4/4qOS5KkolnQqrRijG+b/ncIYTfwihjj90IIxwO7QggnxRh/PuMlbwL+3mJWUpXFGE8vOgZJksrCglbJiTHuCSF8kexG0X8JEEJYBbyMbPRWkkophHA+8BHgIbIb2X8bOBM4CugAXh9j/HYI4QTg08AJwF3AsTPeYwo4JsZ4XwjhXcBFwDjwb8BbYox72/cXSaqiPFe9D7gTeCKwgmzg4F+B/w6cB3QBNwNvA14LnBVjfFUIYQWwD/jjGOPlIYRzgQ8Cm4DLgccBk8D38/d8Vv6edwEbyGbjbY0x/iSEcBrwcbIceTxwC7AlxjgSQhgH/gZ4HnAk8Ocxxi/k8b8OeDPZ5ZX7yHLjzhDCNmA18BvAlTHGP23+1lO7eQ2tUvUJ4DUhhI7854uA62KMdxcYkyQtxhPJctabgKOBp8UYN5IVsH+WP+fjwE0xxieQHSxumP0mIYTXkB3IPTXG+CTgx8C2lkcvqS7OBj4YYzyDrBD9K7IcNU5WvD4Z+CVZUfkF4LkhhE7gXLKTds/O3+dFwOeBlwJH5bNMnpr/7tT8/08BPprnssuBf8gffwPw6RjjOcB64BTg+fnvuoDhGONZZIMal4UQjgkhnAe8GnhmHvv7gStm/F19McYnWMxWhwWtkhRjvA4YBi7IH3oj8LHCApKkxft5jPGuGOONwDuBN4UQPgD8LtmoLcCF5MVpjPEO4OtzvM/zgMtjjA/lP38E2BRC6Gll8JJq464Y4y35v39ANrL5AuDFwM359fwvATbmAwo/B84C/j3w18AF+cDDdEH7LeAJIYTryArjD+f5DeCHMcZv5v++DDgjhLAG+FPgVyGEdwCXkM1amc6TkB/7xRhvBX5ENtr7fLLi94Y8xvcDjw4hrM5f861mbByVh1OOlbJLgNeFEO4HVsUYry06IElahAcBQgjPJytCPwj8M7ATeGX+nCmyKcjTxud4n678edM6yfbrHXM8V5KWav+Mf0/npC6yqcRXw4FLvnrz51wBbAaeQ1ZUvhzYAuyPMf4sf/564Hzgt4CvhRDeCDzAwTluOodNAJ8hy2ufBa4CTqZxbuzMX9MF/MP0CGw+anwC8Ov8eQ8ubTOo7ByhVcr+J1lCfDPZ9DxJSsmzgS/FGC8Bvkc20tGV/+7LZDNPCCGczCOzUWb6MvDaEMKR+c9vA/7FhfEktdBXgLeEEHryQvFTZKOxkE07fjnQGWP8JXAN2ejo5wFCCH9INp34mrzY/ArZGgIAp4cQnpT/+43ADflK7s8F3htj3J7/7mweyZMAv5+/95lkl2Zcn7/vRfkiogB/ADjoUWEWtEpWjPEBsuT5H8iKW0lKyd8B54cQfkQ2ne9nwCn5QeIfARtDCD8BLiVbCGW2S4GvAd/Nn3cm8Iq2RC6prv4S2E22GNTtZKOlfwIQY7ydbCR3unj8CnASeUFLdqzWBdweQvg+MABcnP9uL/C+PB++BHhV/vifA1fkj3+SrGBdPyOeZ4QQfkA2TXlLjPHXMcZryBaZ+moI4VayIvu3Y4wzZ7SoQjqmpvxuJUmSJLVfvqLyx2KMT1zi6w6s+N6SwJQMR2glSZIkSUlyhFaSJEmSlCRHaCVJkiRJSbKglSRJkiQlyYJWkiRJkpQkC1pJkiRJUpIsaCVJkiRJSfr/rkTvJhnbUGEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the relationship between the features and the response using scatterplots\n",
    "fig, axs = plt.subplots(1, 3, sharey=True)\n",
    "data.plot(kind='scatter', x='TV', y='sales', ax=axs[0], figsize=(16, 8))\n",
    "data.plot(kind='scatter', x='radio', y='sales', ax=axs[1])\n",
    "data.plot(kind='scatter', x='newspaper', y='sales', ax=axs[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions about the data\n",
    "\n",
    "A generic question shall be: How the company should optimise the spends on advertising to maximise the sales?\n",
    "\n",
    "These general questions might lead you to more specific questions:\n",
    "1. What’s the relationship between ads and sales?\n",
    "2. How prominent is that relationship?\n",
    "3. Which ad types contribute to sales?\n",
    "4. How each ad contributes to sales?\n",
    "5. Can sales be predicted based on the expense of the advertisement?\n",
    "\n",
    "We will explore these questions below!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the relationship diagrams above, it can be observed that there seems to be a linear relationship between the features TV ad,  Radio ad and the sales is almost a linear one. A linear relationship typically looks like:\n",
    "<img src=\"LinearGraph.png\" width=\"300\">\n",
    "\n",
    "Hence, we can build a model using the Linear Regression Algorithm.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Linear Regression\n",
    "\n",
    "Simple Linear regression is a method for predicting a **quantitative response** using a **single feature** (\"input variable\"). The mathematical equation is:\n",
    "\n",
    "$y = \\beta_0 + \\beta_1x$\n",
    "\n",
    "What do terms represent?\n",
    "- $y$ is the response or the target variable\n",
    "- $x$ is the feature\n",
    "- $\\beta_1$ is the coefficient of x\n",
    "- $\\beta_0$ is the intercept\n",
    "\n",
    "$\\beta_0$ and $\\beta_1$ are the **model coefficients**. To create a model, we must \"learn\" the values of these coefficients. And once we have the value of these coefficients, we can use the model to predict the Sales!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimating (\"Learning\") Model Coefficients\n",
    "\n",
    "The coefficients are estimated using the **least-squares criterion**,  i.e., the best fit line has to be calculated that minimizes the **sum of squared residuals** (or \"sum of squared errors\").\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The mathematics involved\n",
    "Take a quick look at the plot created. Now consider each point, and know that each of them has a coordinate in the form (X, Y). Now draw an imaginary line between each point and the current \"best-fit\" line. We'll call the distance between each point and the current best-fit line as D. To get a quick image of what we're trying to visualize, take a look at the picture below:\n",
    "\n",
    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Linear_least_squares_example2.svg/220px-Linear_least_squares_example2.svg.png\">\n",
    "\n",
    "What elements are present in the diagram?\n",
    "- The red points are the **observed values** of x and y.\n",
    "- The blue line is the **least squares line**.\n",
    "- The green lines are the **residuals**, which is the distance between the observed values and the least squares line.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before, we're labelling each green line as having a distance D, and each red point as having a coordinate of (X, Y). Then we can define our best fit line as the line having the property were:\n",
    "$$ D_{1}^2 + D_{2}^2 + D_{3}^2 + D_{4}^2 + ....+ D_{N}^2$$\n",
    "\n",
    "So how do we find this line? The least-square line approximating the set of points:\n",
    "\n",
    "$$ (X,Y)_{1},(X,Y)_{2},(X,Y)_{3},(X,Y)_{4},(X,Y)_{5}, $$\n",
    "\n",
    "has the equation:\n",
    "$$ Y = a_{0} +a_{1}X $$\n",
    "this is basically just a rewritten form of the standard equation for a line:\n",
    "$$Y=mx+b$$\n",
    "\n",
    "We can solve for these constants a0 and a1 by simultaneously solving these equations:\n",
    "$$ \\Sigma Y = a_{0}N + a_{1}\\Sigma X $$\n",
    "$$ \\Sigma XY = a_{0}\\Sigma X + a_{1}\\Sigma X^2 $$\n",
    "These are called the normal equations for the least-squares line. There are further steps that can be taken in rearranging these equations to solve for y, but we'll let scikit-learn do the rest of the heavy lifting here.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s see the underlying assumptions: -\n",
    "* The regression model is linear in terms of coefficients and error term.\n",
    "* The mean of the residuals is zero.\n",
    "* The error terms are not correlated with each other, i.e. given an error value; we cannot predict the next error value.\n",
    "* The independent variables(x) are uncorrelated with the residual term, also termed as **exogeneity**. This, in layman term, generalises that in no way should the error term be predicted given the value of independent variables.\n",
    "* The error terms have a constant variance, i.e. **homoscedasticity**.\n",
    "* No Multicollinearity, i.e. no independent variables should be correlated with each other or affect one another. If there is multicollinearity, the precision of prediction by the OLS model decreases.\n",
    "* The error terms are normally distributed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The general equation of a straight line is:$$𝑦={mx+b}$$\n",
    "It means that if we have the value of m and b, we can predict all the values of y for corresponding x.\n",
    "During construction of a Linear Regression Model, the computer tries to calculate the values of m and b to get a straight line.\n",
    "But the question is:\n",
    "###### How Do you Know this is the best fit line?\n",
    "The best fit line is obtained by minimizing the _residual_.\n",
    "Residual is the distance between the actual Y and the predicted Y, as shown below:\n",
    "<img src=\"residual.png\" width=\"300\">\n",
    "Mathematically, Residual is: $$r={y-(mx+b)}$$\n",
    "Hence, the sum of the square of residuals is:\n",
    "<img src=\"sumOfResiduals.png\" width=\"300\">\n",
    "\n",
    "As we can see that the residual is both a function of m and b, so differentiating partially with respect to m and b will give us:\n",
    "<img src=\"partialDerivatives.png\" width=\"300\">\n",
    "\n",
    "For getting the best fit line, residual should be minimum. The minima of a function occurs where the derivative=0. So, equating our corresponding derivatives to 0, we get:\n",
    "<img src=\"minima.png\" width=\"300\">\n",
    "\n",
    "This same equation can be written in matrix form as:\n",
    "<img src=\"matrix1.png\" width=\"300\">\n",
    "\n",
    "Ideally, if we'd have an equation of one dependent and one independent variable the minima will look as follows:\n",
    "<img src=\"minima2.png\" width=\"300\">\n",
    "\n",
    "But as the residual's minima is dependent on two variables m and b, it becomes a _Paraboloid_ and the appropriate m and b are calculated using _*Gradient Descent*_ as shown below:\n",
    "<img src=\"GradientDescent.gif\" width=\"300\"> Photo:Google"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let’s understand how to check, how well the model fits our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new values for 'slope' and 'intercept' are caluclated as follows:\n",
    "\n",
    "<img src=\"new_m.PNG\" width=\"300\">\n",
    "\n",
    "where, $\\theta_0$ is 'intercept' , $\\theta_1$ is the slope, $\\alpha$ is the learning rate, m is the total number of observations and the term after the $\\sum$ sign is the loss. Google Tensor board recommends a Learning rate between 0.00001 and 10. Generally a smaller learning rate is recommended to avoid overshooting while creating a model.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $R^2$ statistics\n",
    "\n",
    "The R-squared statistic provides a measure of fit. It takes the form of a proportion—the proportion of variance\n",
    "explained—and so it always takes on a value between 0 and 1. \n",
    "In simple words, it represents how much of our data is being explained by our model. \n",
    "For example,  $R^2$ statistic = 0.75, it says that our model fits 75 % of the total data set.\n",
    "Similarly, if it is 0, it means none of the data points is being explained and a value of 1 represents 100% data explanation.\n",
    "Mathematically $R^2$ statistic is calculated as :\n",
    "<img src=\"RSquared.PNG\" width=\"300\">\n",
    "                                              \n",
    "Where RSS:  is  the Residual Sum of squares and is given as :\n",
    "<img src=\"RSS.PNG\">\n",
    "                                                                \n",
    "RSS is the residual(error) term we have been talking about so far.\n",
    "And, TSS:  is  the Total sum of squares and given as :\n",
    "<img src=\"TSS.PNG\">\n",
    "                                                             \n",
    "TSS is calculated when we consider the line passing through the mean value of y, to be the best fit line.\n",
    "Just like RSS, we calculate the error term when the best fit line is the line passing through the mean value of y and we get the value of TSS.\n",
    "    <img src=\"TSS2.PNG\">\n",
    "    \n",
    "The closer the value of R2 is to 1 the better the model fits our data. If R2 comes below 0(which is a possibility) that means the model is so bad that it is performing even worse than the average best fit line.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusted $R^2$ statistics\n",
    "As we increase the number of independent variables in our equation, the R2 increases as well. But that doesn’t mean that the new independent variables have any correlation with the output variable. In other words, even with the addition of new features in our model, it is not necessary that our model will yield better results but R2 value will increase. To rectify this problem, we use Adjusted R2 value which penalises excessive use of such features which do not correlate with the output data.\n",
    "Let’s understand this with an example:\n",
    " \n",
    "<img src=\"adjr.PNG\" width=\"300\">\n",
    "We can see that R2 always increases with an increase in the number of independent variables.\n",
    "Thus, it doesn’t give a better picture and so we need Adjusted R2 value to keep this in check.\n",
    "Mathematically, it is calculated as:\n",
    "                                        <img src=\"adjr2.PNG\">\n",
    "In the equation above, when p = 0, we can see that adjusted R2 becomes equal to R2.\n",
    "Thus, adjusted R2  will always be less than or equal to R2, and it penalises the excess of independent variables which do not affect the dependent variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.032593549127693\n",
      "[0.04753664]\n"
     ]
    }
   ],
   "source": [
    "# create X and y\n",
    "feature_cols = ['TV']\n",
    "X = data[feature_cols]\n",
    "y = data.sales\n",
    "\n",
    "# follow the usual sklearn pattern: import, instantiate, fit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "lm.fit(X, y)\n",
    "\n",
    "# print intercept and coefficients\n",
    "print(lm.intercept_)\n",
    "print(lm.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the model\n",
    "\n",
    "How do we interpret the coefficient for spends on TV ad ($\\beta_1$)?\n",
    "- A \"unit\" increase in spends on a TV ad is **associated with** a 0.047537 \"unit\" increase in Sales.\n",
    "- Or, an additional $1,000  on TV ads is **translated to** an increase in sales by 47.53 Dollars.\n",
    "\n",
    "As an increase in TV ad expenditure is associated with a **decrease** in sales, $\\beta_1$ would be **negative**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction  using the model\n",
    "\n",
    "If the expense on TV ad is $50000, what will be the sales prediction for that market?\n",
    "\n",
    "$$y = \\beta_0 + \\beta_1x$$\n",
    "$$y = 7.032594 + 0.047537 \\times 50$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.409444"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the prediction\n",
    "7.032594 + 0.047537*50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we would predict Sales of **9,409 widgets** in that market.\n",
    "\n",
    "Let's do the same thing using code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TV\n",
       "0  50"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Let's create a DataFrame since the model expects it\n",
    "X_new = pd.DataFrame({'TV': [50]})\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.40942557])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the model to make predictions on a new value\n",
    "lm.predict(X_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Least Squares Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>296.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV\n",
       "0    0.7\n",
       "1  296.4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame with the minimum and maximum values of TV\n",
    "X_new = pd.DataFrame({'TV': [data.TV.min(), data.TV.max()]})\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.0658692 , 21.12245377])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make predictions for those x values and store them\n",
    "preds = lm.predict(X_new)\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x269b2214b38>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# first, plot the observed data\n",
    "data.plot(kind='scatter', x='TV', y='sales')\n",
    "\n",
    "# then, plot the least squares line\n",
    "plt.plot(X_new, preds, c='red', linewidth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Confidence\n",
    "\n",
    "**Question:** Is linear regression a low bias/high variance model or a high bias/low variance model?\n",
    "\n",
    "**Answer:** It's a High bias/low variance model. Even after repeated sampling, the best fit line will stay roughly in the same position (low variance), but the average of the models created after repeated sampling won't do a great job in capturing the perfect relationship (high bias). Low variance is helpful when we don't have less training data! \n",
    "\n",
    "If the model has calculated a 95% confidence for our model coefficients, it can be interpreted as follows: \n",
    "If the population from which this sample is drawn, is **sampled 100 times**, then approximately **95 (out of 100) of those confidence intervals** shall contain the \"true\" coefficients.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>6.129719</td>\n",
       "      <td>7.935468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>0.042231</td>\n",
       "      <td>0.052843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "Intercept  6.129719  7.935468\n",
       "TV         0.042231  0.052843"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "lm = smf.ols(formula='sales ~ TV', data=data).fit()\n",
    "lm.conf_int()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep in mind that we only have a **single sample of data**, and not the **entire population of data**. The \"true\" coefficient is either within this interval or it isn't, but there's no way actually to know. We estimate the coefficient with the data we do have, and we show uncertainty about that estimate by giving a range that the coefficient is **probably** within.\n",
    "\n",
    "Note that using 95% confidence intervals is just a convention. You can create 90% confidence intervals (which will be more narrow), 99% confidence intervals (which will be wider), or whatever intervals you like.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis Testing and p-values\n",
    "\n",
    "**Hypothesis testing** is Closely related to confidence intervals. We start with a **null hypothesis** and an **alternate hypothesis** (that is opposite to the null). Then, we check whether the data **rejects the null hypothesis** or **fails to reject the null hypothesis**.\n",
    "\n",
    "(\"Failing to reject\" the null hypothesis does not mean \"accepting\" the null hypothesis. The alternative hypothesis might indeed be true, but that we just don't have enough data to prove that.)\n",
    "\n",
    "The conventional hypothesis test is as follows:\n",
    "- **Null hypothesis:** No relationship exists between TV advertisements and Sales (and hence $\\beta_1$ equals zero).\n",
    "- **Alternative hypothesis:** There exists a relationship between TV advertisements and Sales (and hence, $\\beta_1$ is not equal to zero).\n",
    "\n",
    "How do we test this? We reject the null hypothesis (and thus believe the alternative hypothesis) if the 95% confidence interval **does not include zero**. The **p-value** represents the probability of the coefficient actually being zero.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    1.406300e-35\n",
       "TV           1.467390e-42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the p-values for the model coefficients\n",
    "lm.pvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the 95% confidence interval **includes zero**, the p-value for that coefficient will be **greater than 0.05**. If the 95% confidence interval **does not include zero**, the p-value will be **less than 0.05**. \n",
    "\n",
    "Thus, a p-value of less than 0.05 is a way to decide whether there is any relationship between the feature in consideration and the response or not. Using 0.05 as the cutoff is just a convention.\n",
    "\n",
    "In this case, the p-value for TV ads is way less than 0.05, and so we **believe** that there is a relationship between TV advertisements and Sales.\n",
    "\n",
    "Note that we generally ignore the p-value for the intercept.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How Well Does the Model Fit the data?\n",
    "\n",
    "One of the most generic way to evaluate the fit of a linear model is by computing the **R-squared** value. R-squared explains the**proportion of variance**, i.e., the proportion of variance in the observed data which the model explains, or the reduction in error over the **null model**. (A null model only predicts the mean of all the observed responses, and thus it only has an intercept and no slope.)\n",
    "\n",
    "The value of R-squared lies between 0 and 1. A value closer to 1 is better as it means that more variance is explained by the model. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.611875050850071"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the R-squared value for the model\n",
    "lm.rsquared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is it a \"good\" R-squared value? Now, that’s hard to say. In reality, the domain to which the data belongs to plays a significant role in deciding the threshold for the R-squared value. Therefore, it's a tool for **comparing different models**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Linear Regression\n",
    "\n",
    "Till now, we have created the model based on only one feature. Now, we’ll include multiple features and create a model to see the relationship between those features and the label column.\n",
    "This is called **Multiple Linear Regression**.\n",
    "\n",
    "$y = \\beta_0 + \\beta_1x_1 + ... + \\beta_nx_n$\n",
    "\n",
    "Each $x$ represents a different feature, and each feature has its own coefficient. In this case:\n",
    "\n",
    "$y = \\beta_0 + \\beta_1 \\times TV + \\beta_2 \\times Radio + \\beta_3 \\times Newspaper$\n",
    "\n",
    "Let's use Statsmodels to estimate these coefficients\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9388893694594085\n",
      "[ 0.04576465  0.18853002 -0.00103749]\n"
     ]
    }
   ],
   "source": [
    "# create X and y\n",
    "feature_cols = ['TV', 'radio', 'newspaper']\n",
    "X = data[feature_cols]\n",
    "y = data.sales\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X, y)\n",
    "\n",
    "# print intercept and coefficients\n",
    "print(lm.intercept_)\n",
    "print(lm.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we interpret these coefficients? \n",
    "If we look at the coefficients, the coefficient for the newspaper spends is negative. It means that the money spent for newspaper advertisements is not contributing in a positive way to the sales.\n",
    "\n",
    "A lot of the information we have been reviewing piece-by-piece is available in the model summary output:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   570.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 27 Dec 2019</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:12:27</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   780.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   196</td>      <th>  BIC:               </th> <td>   793.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.9389</td> <td>    0.312</td> <td>    9.422</td> <td> 0.000</td> <td>    2.324</td> <td>    3.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.809</td> <td> 0.000</td> <td>    0.043</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>radio</th>     <td>    0.1885</td> <td>    0.009</td> <td>   21.893</td> <td> 0.000</td> <td>    0.172</td> <td>    0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>newspaper</th> <td>   -0.0010</td> <td>    0.006</td> <td>   -0.177</td> <td> 0.860</td> <td>   -0.013</td> <td>    0.011</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>60.414</td> <th>  Durbin-Watson:     </th> <td>   2.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 151.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.327</td> <th>  Prob(JB):          </th> <td>1.44e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.332</td> <th>  Cond. No.          </th> <td>    454.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  sales   R-squared:                       0.897\n",
       "Model:                            OLS   Adj. R-squared:                  0.896\n",
       "Method:                 Least Squares   F-statistic:                     570.3\n",
       "Date:                Fri, 27 Dec 2019   Prob (F-statistic):           1.58e-96\n",
       "Time:                        20:12:27   Log-Likelihood:                -386.18\n",
       "No. Observations:                 200   AIC:                             780.4\n",
       "Df Residuals:                     196   BIC:                             793.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.9389      0.312      9.422      0.000       2.324       3.554\n",
       "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
       "radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
       "newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
       "==============================================================================\n",
       "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
       "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
       "Kurtosis:                       6.332   Cond. No.                         454.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = smf.ols(formula='sales ~ TV + radio + newspaper', data=data).fit()\n",
    "lm.conf_int()\n",
    "lm.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the things to be learnt from this summary?\n",
    "\n",
    "- TV and Radio have positive **p-values**, whereas Newspaper has a negative one. Hence, we can reject the null hypothesis for TV and Radio that there is no relation between those features and Sales, but we fail to reject the null hypothesis for Newspaper that there is no relationship between newspaper spends and sales.\n",
    "- The expenses on bot TV and Radio ads are**positively associated** with Sales, whereas the expense on newspaper ad is **slightly negatively associated** with the Sales.\n",
    "- This model has a higher value of **R-squared** (0.897) than the previous model, which means that this model explains more variance and provides a better fit to the data than a model that only includes the TV.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "\n",
    "How do I decide **which features have to be included** in a linear model? Here's one idea:\n",
    "- Try different models, and only keep predictors in the model if they have small p-values.\n",
    "- Check if the R-squared value goes up when you add new predictors to the model.\n",
    "\n",
    "What are the **drawbacks** in this approach?\n",
    "-If the underlying assumptions for creating a Linear model(the features being independent) are violated(which usually is the case),p-values and R-squared values are less reliable.\n",
    "- Using a p-value cutoff of 0.05 means that adding 100 predictors to a model that are **pure noise**, still 5 of them (on average) will be counted as significant.\n",
    "- R-squared is susceptible to **model overfitting**, and thus there is no guarantee that a model with a high R-squared value will generalise. Following is an example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8971942610828956"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only include TV and Radio in the model\n",
    "lm = smf.ols(formula='sales ~ TV + radio', data=data).fit()\n",
    "lm.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8972106381789522"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add Newspaper to the model (which we believe has no association with Sales)\n",
    "lm = smf.ols(formula='sales ~ TV + radio + newspaper', data=data).fit()\n",
    "lm.rsquared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the model with the highest value of R-squared is not a correct approach as the value of R-squared shall always increase whenever a new feature is taken for consideration even if the feature is unrelated to the response.\n",
    "\n",
    "The alternative is to use **adjusted R-squared** which penalises the model complexity (to control overfitting), but this again generally [under-penalizes complexity](http://scott.fortmann-roe.com/docs/MeasuringError.html).\n",
    "\n",
    "a better approach to feature selection is**Cross-validation.** It provides a more reliable way to choose which of the created models will best **generalise** as it better estimates of out-of-sample error. An advantage is that the cross-validation method can be applied to any machine learning model and the scikit-learn package provides extensive functionality for that.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Categorical Predictors with Two Categories\n",
    "\n",
    "Till now, all the predictors have been numeric. What if one of the predictors is categorical?\n",
    "\n",
    "We’ll create a new feature called **Scale**, and shall randomly assign observations as **small or large**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales  Scale\n",
       "0           1  230.1   37.8       69.2   22.1  large\n",
       "1           2   44.5   39.3       45.1   10.4  small\n",
       "2           3   17.2   45.9       69.3    9.3  small\n",
       "3           4  151.5   41.3       58.5   18.5  small\n",
       "4           5  180.8   10.8       58.4   12.9  large"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# set a seed for reproducibility\n",
    "np.random.seed(12345)\n",
    "\n",
    "# create a Series of booleans in which roughly half are True\n",
    "nums = np.random.rand(len(data))\n",
    "mask_large = nums > 0.5\n",
    "\n",
    "# initially set Size to small, then change roughly half to be large\n",
    "data['Scale'] = 'small'\n",
    "data.loc[mask_large, 'Scale'] = 'large'\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the scikit-learn library, all data must be represented **numerically**. If the feature only has two categories, we can simply create a **dummy variable** that represents the categories as a combination of binary value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "      <th>Scale</th>\n",
       "      <th>IsLarge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales  Scale  IsLarge\n",
       "0           1  230.1   37.8       69.2   22.1  large        1\n",
       "1           2   44.5   39.3       45.1   10.4  small        0\n",
       "2           3   17.2   45.9       69.3    9.3  small        0\n",
       "3           4  151.5   41.3       58.5   18.5  small        0\n",
       "4           5  180.8   10.8       58.4   12.9  large        1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new Series called IsLarge\n",
    "data['IsLarge'] = data.Scale.map({'small':0, 'large':1})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's redo the multiple linear regression problem and include the **IsLarge** predictor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Coefficient of  TV  is:  0.045719820924362775\n",
      "The Coefficient of  radio  is:  0.1887281431342787\n",
      "The Coefficient of  newspaper  is:  -0.0010976794483516655\n",
      "The Coefficient of  IsLarge  is:  0.05742385085482776\n"
     ]
    }
   ],
   "source": [
    "# create X and y\n",
    "feature_cols = ['TV', 'radio', 'newspaper', 'IsLarge']\n",
    "X = data[feature_cols]\n",
    "y = data.sales\n",
    "\n",
    "# instantiate, fit\n",
    "lm = LinearRegression()\n",
    "lm.fit(X, y)\n",
    "\n",
    "# print coefficients\n",
    "i=0\n",
    "for col in feature_cols:\n",
    "    print('The Coefficient of ',col, ' is: ',lm.coef_[i])\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we interpret the coefficient for **IsLarge**? For a given TV/Radio/Newspaper ad expenditure if the average sales increases by 57.42 widgets, it’s considered as a large market.\n",
    "\n",
    "What if the 0/1encoding is reversed? Still, the value of the coefficient shall be same, the only difference being the sign. It’ll be a negative number instead of positive.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Categorical variables with More than Two Categories\n",
    "\n",
    "Let's create a new column called **Targeted Geography**, and randomly assign observations to be **rural, suburban, or urban**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "      <th>Scale</th>\n",
       "      <th>IsLarge</th>\n",
       "      <th>Targeted Geography</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>rural</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "      <td>suburban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales  Scale  IsLarge  \\\n",
       "0           1  230.1   37.8       69.2   22.1  large        1   \n",
       "1           2   44.5   39.3       45.1   10.4  small        0   \n",
       "2           3   17.2   45.9       69.3    9.3  small        0   \n",
       "3           4  151.5   41.3       58.5   18.5  small        0   \n",
       "4           5  180.8   10.8       58.4   12.9  large        1   \n",
       "\n",
       "  Targeted Geography  \n",
       "0              rural  \n",
       "1              urban  \n",
       "2              rural  \n",
       "3              urban  \n",
       "4           suburban  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set a seed for reproducibility\n",
    "np.random.seed(123456)\n",
    "\n",
    "# assign roughly one third of observations to each group\n",
    "nums = np.random.rand(len(data))\n",
    "mask_suburban = (nums > 0.33) & (nums < 0.66)\n",
    "mask_urban = nums > 0.66\n",
    "data['Targeted Geography'] = 'rural'\n",
    "data.loc[mask_suburban, 'Targeted Geography'] = 'suburban'\n",
    "data.loc[mask_urban, 'Targeted Geography'] = 'urban'\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to represent the ‘Targeted Geography’ column numerically. But mapping urban=0, suburban=1 and rural=2 will mean that rural is two times suburban which is not the case.\n",
    "Hence, we’ll create another **dummy variable**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "      <th>Scale</th>\n",
       "      <th>IsLarge</th>\n",
       "      <th>Targeted Geography</th>\n",
       "      <th>Targeted Geography_suburban</th>\n",
       "      <th>Targeted Geography_urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "      <td>rural</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>urban</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>rural</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>urban</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "      <td>suburban</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales  Scale  IsLarge  \\\n",
       "0           1  230.1   37.8       69.2   22.1  large        1   \n",
       "1           2   44.5   39.3       45.1   10.4  small        0   \n",
       "2           3   17.2   45.9       69.3    9.3  small        0   \n",
       "3           4  151.5   41.3       58.5   18.5  small        0   \n",
       "4           5  180.8   10.8       58.4   12.9  large        1   \n",
       "\n",
       "  Targeted Geography  Targeted Geography_suburban  Targeted Geography_urban  \n",
       "0              rural                            0                         0  \n",
       "1              urban                            0                         1  \n",
       "2              rural                            0                         0  \n",
       "3              urban                            0                         1  \n",
       "4           suburban                            1                         0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create three dummy variables using get_dummies, then exclude the first dummy column\n",
    "area_dummies = pd.get_dummies(data['Targeted Geography'], prefix='Targeted Geography').iloc[:, 1:]\n",
    "\n",
    "# concatenate the dummy variable columns onto the original DataFrame (axis=0 means rows, axis=1 means columns)\n",
    "data = pd.concat([data, area_dummies], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the encoding say?\n",
    "* rural is encoded as Targeted Geography_suburban=0 and Targeted Geography_urban=0\n",
    "* suburban is encoded as Targeted Geography_suburban=1 and Targeted Geography_urban=0\n",
    "* urban is encoded as Targeted Geography_suburban=0 and Targeted Geography_urban=1\n",
    "\n",
    "Now the question is: **Why have we used two dummy columns instead of three?**\n",
    "\n",
    "Because using only two dummy columns, we can capture the information of all the 3 columns. For example, if the value for Targeted Geography_urban as well as Targeted Geography_rural is 0, it automatically means that the data belongs to Targeted Geography_suburban.\n",
    "\n",
    "This is called handling the **dummy variable trap**. If there are N dummy variable columns, then the same information can be conveyed by N-1 columns.\n",
    "Let's include the two new dummy variables in the model:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TV', 'radio', 'newspaper', 'IsLarge', 'Targeted Geography_suburban', 'Targeted Geography_urban'] [ 0.04574401  0.1878667  -0.0010877   0.07739661 -0.10656299  0.26813802]\n"
     ]
    }
   ],
   "source": [
    "# create X and y\n",
    "feature_cols = ['TV', 'radio', 'newspaper', 'IsLarge', 'Targeted Geography_suburban', 'Targeted Geography_urban']\n",
    "X = data[feature_cols]\n",
    "y = data.sales\n",
    "\n",
    "# instantiate, fit\n",
    "lm = LinearRegression()\n",
    "lm.fit(X, y)\n",
    "\n",
    "# print coefficients\n",
    "print(feature_cols, lm.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we interpret the coefficients?\n",
    "- If all other columns are constant, the _suburban_  geography is associated with an average **decrease** of 106.56 widgets in sales for $1000 spent. \n",
    "\n",
    "- if $1000 is spent in an _urban_ geography, it amounts to an average **increase** in Sales of 268.13 widgets\n",
    "\n",
    "**A final note about dummy encoding:** If we have categories that can be ranked (i.e., worst, bad, good, better, best), we can potentially represent them numerically  as (1, 2, 3, 4, 5) using a single dummy column\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi- Collinearity\n",
    "\n",
    "**Origin of the word:** The word multi-collinearity consists of two words:Multi, meaning multiple, and Collinear, meaning being linearly dependent on each other. \n",
    "\n",
    "For e.g.,\n",
    "\t\tLet’s consider this equation\n",
    "\t\t$ a+b=1  => b=1-a $\n",
    "        \n",
    "It means that ‘b’ can be represented in terms of ‘a’ i.e., if the value of ‘a’ \t\tchanges, automatically the value of ‘b’ will also change. This equation \t\tdenotes a simple linear relationship among two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Definition:** The purpose of executing a Linear Regression is to predict the value of a \tdependent variable based on certain independent variables.\n",
    "\n",
    "So, when we perform a Linear Regression, we want our dataset to have variables which are independent i.e., we should not be able to define an \tindependent variable with the help of another independent variable  \tbecause now in our model we have two variables which can be defined based \ton a certain set of independent variables which defeats the entire purpose.\n",
    "\n",
    "* Multi-collinearity is the statistical term to represent this type of a relation amongst the independent variable- when the independent variables are not so independent😊.\n",
    "* We can define multi-collinearity as the situation where the independent variables (or the predictors) have strong correlation amongst themselves.\n",
    "\n",
    "<img src=\"multicollinearity_pic.PNG\" width=\"500\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mathematical flow for multicollinearity can be shown as:\n",
    "<img src=\"multicollinearity_flow.PNG\" width=\"500\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Why Should We Care About Multi-Collinearity?\n",
    "* The coefficients in a Linear Regression model represent the extent of change in Y when a certain x (amongst X1,X2,X3…) is changed keeping others constant. But, if x1 and x2 are dependent, then this assumption itself is wrong that we are changing one variable keeping others constant as the dependent variable will also be changed. It means that our model itself becomes a bit flawed.\n",
    "* We have a redundancy in our model as two variables (or more than two) are trying to convey the same information.\n",
    "* As the extent of the collinearity increases, there is a chance that we might produce an overfitted model. An overfitted model works well with the test data but its accuracy fluctuates when exposed to other data sets. \n",
    "* Can result in a Dummy Variable Trap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Detection\n",
    "* __Correlation Matrices and Plots:__ for correlation between all the X variables.\n",
    "        \n",
    "        This plot shows the extent of correlation between the independent variable. Generally, a correlation greater than 0.9 or less than -0.9 is to be avoided.\n",
    "    <img src=\"cor.PNG\" width=\"500\">\n",
    "* __Variance Inflation Factor:__ Regression of one X variable against other X variables.\n",
    "\n",
    "     VIF=$\\frac {1}{(1-R squared)}$\n",
    "\n",
    "            The VIF factor, if greater than 10 shows extreme correlation between the variables and then we need to take care of the correlation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remedies for Multicollinearity\n",
    "\n",
    "* **Do Nothing:** If the Correlation is not that extreme, we can ignore it. If the correlated variables are not used in solving our business question, they can be ignored.\n",
    "* **Remove One Variable**: Like in dummy variable trap\n",
    "* **Combine the correlated variables:** Like creating a seniority score based on Age and Years of experience\n",
    "* Principal Component Analysis\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularization \n",
    "When we use regression models to train some data, there is a good chance that the model will overfit the given training data set.  Regularization helps sort this overfitting problem by restricting the degrees of freedom of a given equation i.e. simply reducing the number of degrees of a polynomial function by reducing their corresponding weights.  \n",
    "In a linear equation, we do not want huge weights/coefficients as a small change in weight can make a large difference for the dependent variable (Y). So, regularization constraints the weights of such features to avoid overfitting. Simple linear regression is given as:\n",
    "\n",
    "$y = \\beta_0 + \\beta_1x1+ \\beta_2x2 +\\beta_3x3+...+\\beta_PxP$\n",
    "\n",
    "Using the OLS method, we try to minimize the cost function given as:\n",
    "\n",
    "<img src=\"RSS_reg.PNG\" width=\"300\">\n",
    "\n",
    "To regularize the model, a Shrinkage penalty is added to the cost function.\n",
    "Let’s see different types of regularizations in regression:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LASSO(Least Absolute Shrinkage and Selection Operator) Regression (L1 Form)\n",
    "LASSO regression penalizes the model based on the sum of magnitude of the coefficients. The regularization term is given by\n",
    "\n",
    " regularization=$ \\lambda *\\sum  |\\beta_j| $\n",
    "\n",
    "Where, λ is the shrinkage factor.\n",
    "\n",
    "and hence the formula for loss after regularization is:\n",
    "\n",
    "<img src=\"L1.PNG\" width=\"300\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ridge Regression (L2 Form)\n",
    "Ridge regression penalizes the model based on the sum of squares of magnitude of the coefficients. The regularization term is given by\n",
    "\n",
    " regularization=$ \\lambda *\\sum  |\\beta_j ^ 2| $\n",
    "\n",
    "Where, λ is the shrinkage factor.\n",
    "\n",
    "and hence the formula for loss after regularization is:\n",
    "\n",
    "<img src=\"ridge.PNG\" width=\"300\">\n",
    "\n",
    "This value of lambda can be anything and should be calculated by cross validation as to what suits the model.\n",
    "\n",
    "Let’s consider $\\beta_1$ and $\\beta_2$ be coefficients of a linear regression and λ = 1:\n",
    "\n",
    "For Lasso, $\\beta_1$ + $\\beta_2$ <= s  \n",
    "\n",
    "For Ridge, $\\beta_1^2$ + $\\beta_2^2$  <= s  \n",
    "\n",
    "Where s is the maximum value the equations can achieve\n",
    ".\n",
    "If we plot both the above equations, we get the following graph:\n",
    "\n",
    "<img src=\"ridge_vs_lasso.PNG\" width=\"300\">\n",
    "\n",
    "The red ellipse represents the cost function of the model, whereas the square (left side) represents the Lasso regression and the circle (right side) represents the Ridge regression.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Difference between Ridge and Lasso\n",
    "Ridge regression shrinks the coefficients for those predictors which contribute very less in the model but have huge weights, very close to zero. But it never makes them exactly zero. Thus, the final model will still contain all those predictors, though with less weights. This doesn’t help in interpreting the model very well. This is where Lasso regression differs with Ridge regression. In Lasso, the L1 penalty does reduce some coefficients exactly to zero when we use a sufficiently large tuning parameter λ. So, in addition to regularizing, lasso also performs feature selection.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Why use Regularization?\n",
    "Regularization helps to reduce the variance of the model, without a substantial increase in the bias. If there is variance in the model that means that the model won’t fit well for dataset different that training data. The tuning parameter λ controls this bias and variance tradeoff. When the value of λ is increased up to a certain limit, it reduces the variance without losing any important properties in the data. But after a certain limit, the model will start losing some important properties which will increase the bias in the data. Thus, the selection of good value of λ is the key.\n",
    "The value of λ is selected using cross-validation methods. A set of λ is selected and cross-validation error is calculated for each value of λ and that value of λ is selected for which the cross-validation error is minimum.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Elastic Net\n",
    "\n",
    "According to the Hands-on Machine Learning book, elastic Net is a middle ground between Ridge Regression and Lasso Regression. The regularization term is a simple mix of both Ridge and Lasso’s regularization terms, and you can control the mix ratio α. \n",
    "\n",
    "<img src=\"elasticNet.PNG\" width=\"300\">\n",
    "where α is the mixing parameter between ridge (α = 0) and lasso (α = 1).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**When should you use plain Linear Regression (i.e., without any regularization), Ridge, Lasso, or Elastic Net?**\n",
    "\n",
    "According to the Hands-on Machine Learning book, it is almost always preferable to have at least a little bit of regularization, so generally you should avoid plain Linear Regression. Ridge is a good default, but if you suspect that only a few features are actually useful, you should prefer Lasso or Elastic Net since they tend to reduce the useless features’ weights down to zero as we have discussed. In general, Elastic Net is preferred over Lasso since Lasso may behave erratically when the number of features is greater than the number of\n",
    "training instances or when several features are strongly correlated.\n",
    "\n",
    "No let's see the Python Implementation of these concepts:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's start with importing necessary libraries\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.linear_model  import Ridge,Lasso,RidgeCV, LassoCV, ElasticNet, ElasticNetCV, LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a function to create adjusted R-Squared\n",
    "def adj_r2(x,y):\n",
    "    r2 = regression.score(x,y)\n",
    "    n = x.shape[0]\n",
    "    p = x.shape[1]\n",
    "    adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
    "    return adjusted_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
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       "      <td>0.76</td>\n",
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       "      <td>0.72</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR  CGPA  \\\n",
       "0           1      337.0        118.0                4.0  4.5  4.5  9.65   \n",
       "1           2      324.0        107.0                4.0  4.0  4.5  8.87   \n",
       "2           3        NaN        104.0                3.0  3.0  3.5  8.00   \n",
       "3           4      322.0        110.0                3.0  3.5  2.5  8.67   \n",
       "4           5      314.0        103.0                2.0  2.0  3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit  \n",
       "0         1             0.92  \n",
       "1         1             0.76  \n",
       "2         1             0.72  \n",
       "3         1             0.80  \n",
       "4         0             0.65  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data =pd.read_csv('Admission_Prediction.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>250.500000</td>\n",
       "      <td>316.558763</td>\n",
       "      <td>107.187755</td>\n",
       "      <td>3.121649</td>\n",
       "      <td>3.374000</td>\n",
       "      <td>3.48400</td>\n",
       "      <td>8.576440</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.72174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.481833</td>\n",
       "      <td>11.274704</td>\n",
       "      <td>6.112899</td>\n",
       "      <td>1.146160</td>\n",
       "      <td>0.991004</td>\n",
       "      <td>0.92545</td>\n",
       "      <td>0.604813</td>\n",
       "      <td>0.496884</td>\n",
       "      <td>0.14114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>125.750000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>8.127500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.63000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250.500000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>375.250000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.82000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Serial No.   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
       "count  500.000000  485.000000   490.000000         485.000000  500.000000   \n",
       "mean   250.500000  316.558763   107.187755           3.121649    3.374000   \n",
       "std    144.481833   11.274704     6.112899           1.146160    0.991004   \n",
       "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
       "25%    125.750000  308.000000   103.000000           2.000000    2.500000   \n",
       "50%    250.500000  317.000000   107.000000           3.000000    3.500000   \n",
       "75%    375.250000  325.000000   112.000000           4.000000    4.000000   \n",
       "max    500.000000  340.000000   120.000000           5.000000    5.000000   \n",
       "\n",
       "             LOR        CGPA    Research  Chance of Admit  \n",
       "count  500.00000  500.000000  500.000000        500.00000  \n",
       "mean     3.48400    8.576440    0.560000          0.72174  \n",
       "std      0.92545    0.604813    0.496884          0.14114  \n",
       "min      1.00000    6.800000    0.000000          0.34000  \n",
       "25%      3.00000    8.127500    0.000000          0.63000  \n",
       "50%      3.50000    8.560000    1.000000          0.72000  \n",
       "75%      4.00000    9.040000    1.000000          0.82000  \n",
       "max      5.00000    9.920000    1.000000          0.97000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['University Rating'] = data['University Rating'].fillna(data['University Rating'].mode()[0])\n",
    "data['TOEFL Score'] = data['TOEFL Score'].fillna(data['TOEFL Score'].mean())\n",
    "data['GRE Score']  = data['GRE Score'].fillna(data['GRE Score'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>250.500000</td>\n",
       "      <td>316.558763</td>\n",
       "      <td>107.187755</td>\n",
       "      <td>3.118000</td>\n",
       "      <td>3.374000</td>\n",
       "      <td>3.48400</td>\n",
       "      <td>8.576440</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.72174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.481833</td>\n",
       "      <td>11.103952</td>\n",
       "      <td>6.051338</td>\n",
       "      <td>1.128993</td>\n",
       "      <td>0.991004</td>\n",
       "      <td>0.92545</td>\n",
       "      <td>0.604813</td>\n",
       "      <td>0.496884</td>\n",
       "      <td>0.14114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>125.750000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>8.127500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.63000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250.500000</td>\n",
       "      <td>316.558763</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>375.250000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.82000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Serial No.   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
       "count  500.000000  500.000000   500.000000         500.000000  500.000000   \n",
       "mean   250.500000  316.558763   107.187755           3.118000    3.374000   \n",
       "std    144.481833   11.103952     6.051338           1.128993    0.991004   \n",
       "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
       "25%    125.750000  309.000000   103.000000           2.000000    2.500000   \n",
       "50%    250.500000  316.558763   107.000000           3.000000    3.500000   \n",
       "75%    375.250000  324.000000   112.000000           4.000000    4.000000   \n",
       "max    500.000000  340.000000   120.000000           5.000000    5.000000   \n",
       "\n",
       "             LOR        CGPA    Research  Chance of Admit  \n",
       "count  500.00000  500.000000  500.000000        500.00000  \n",
       "mean     3.48400    8.576440    0.560000          0.72174  \n",
       "std      0.92545    0.604813    0.496884          0.14114  \n",
       "min      1.00000    6.800000    0.000000          0.34000  \n",
       "25%      3.00000    8.127500    0.000000          0.63000  \n",
       "50%      3.50000    8.560000    1.000000          0.72000  \n",
       "75%      4.00000    9.040000    1.000000          0.82000  \n",
       "max      5.00000    9.920000    1.000000          0.97000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data looks good and there are no missing values. Also, the first cloumn is just serial numbers, so we don' need that column. Let's drop it from data and make it more clean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>324.000000</td>\n",
       "      <td>107.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>316.558763</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322.000000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314.000000</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GRE Score  TOEFL Score  University Rating  SOP  LOR  CGPA  Research  \\\n",
       "0  337.000000        118.0                4.0  4.5  4.5  9.65         1   \n",
       "1  324.000000        107.0                4.0  4.0  4.5  8.87         1   \n",
       "2  316.558763        104.0                3.0  3.0  3.5  8.00         1   \n",
       "3  322.000000        110.0                3.0  3.5  2.5  8.67         1   \n",
       "4  314.000000        103.0                2.0  2.0  3.0  8.21         0   \n",
       "\n",
       "   Chance of Admit  \n",
       "0             0.92  \n",
       "1             0.76  \n",
       "2             0.72  \n",
       "3             0.80  \n",
       "4             0.65  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= data.drop(columns = ['Serial No.'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the data and analyze the relationship between independent and dependent variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see how data is distributed for every column\n",
    "plt.figure(figsize=(20,25), facecolor='white')\n",
    "plotnumber = 1\n",
    "\n",
    "for column in data:\n",
    "    if plotnumber<=16 :\n",
    "        ax = plt.subplot(4,4,plotnumber)\n",
    "        sns.distplot(data[column])\n",
    "        plt.xlabel(column,fontsize=20)\n",
    "        #plt.ylabel('Salary',fontsize=20)\n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data distribution looks decent enough and there doesn't seem to be any skewness. Great let's go ahead!\n",
    "\n",
    "Let's observe the relationship between independent variables and dependent variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Chance of Admit']\n",
    "X =data.drop(columns = ['Chance of Admit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,30), facecolor='white')\n",
    "plotnumber = 1\n",
    "\n",
    "for column in X:\n",
    "    if plotnumber<=15 :\n",
    "        ax = plt.subplot(5,3,plotnumber)\n",
    "        plt.scatter(X[column],y)\n",
    "        plt.xlabel(column,fontsize=20)\n",
    "        plt.ylabel('Chance of Admit',fontsize=20)\n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, the relationship between the dependent and independent variables look fairly linear.\n",
    "Thus, our linearity assumption is satisfied.\n",
    "\n",
    "Let's move ahead and check for multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\virat\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\virat\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "scaler =StandardScaler()\n",
    "\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "variables = X_scaled\n",
    "\n",
    "# we create a new data frame which will include all the VIFs\n",
    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
    "# we do not include categorical values for mulitcollinearity as they do not provide much information as numerical ones do\n",
    "vif = pd.DataFrame()\n",
    "\n",
    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables, i) for i in range(variables.shape[1])]\n",
    "# Finally, I like to include names so it is easier to explore the result\n",
    "vif[\"Features\"] = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.152735</td>\n",
       "      <td>GRE Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.793345</td>\n",
       "      <td>TOEFL Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.517272</td>\n",
       "      <td>University Rating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.776393</td>\n",
       "      <td>SOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.037449</td>\n",
       "      <td>LOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.654369</td>\n",
       "      <td>CGPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.459411</td>\n",
       "      <td>Research</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF           Features\n",
       "0  4.152735          GRE Score\n",
       "1  3.793345        TOEFL Score\n",
       "2  2.517272  University Rating\n",
       "3  2.776393                SOP\n",
       "4  2.037449                LOR\n",
       "5  4.654369               CGPA\n",
       "6  1.459411           Research"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have the correlation values for all the features. As a thumb rule, a VIF value greater than 5 means a very severe multicollinearity. We don't any VIF greater than 5 , so we are good to go. \n",
    "\n",
    "Great. Let's go ahead and use linear regression and see how good it fits our data.\n",
    "But first. let's split our data in train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(X_scaled,y,test_size = 0.25,random_state=355)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "378    0.56\n",
       "23     0.95\n",
       "122    0.57\n",
       "344    0.47\n",
       "246    0.72\n",
       "409    0.61\n",
       "197    0.73\n",
       "116    0.56\n",
       "83     0.92\n",
       "91     0.38\n",
       "106    0.87\n",
       "258    0.77\n",
       "444    0.92\n",
       "304    0.62\n",
       "12     0.78\n",
       "392    0.84\n",
       "462    0.62\n",
       "204    0.69\n",
       "250    0.74\n",
       "370    0.72\n",
       "313    0.67\n",
       "126    0.85\n",
       "424    0.91\n",
       "457    0.37\n",
       "100    0.71\n",
       "17     0.65\n",
       "140    0.84\n",
       "40     0.46\n",
       "279    0.67\n",
       "64     0.52\n",
       "       ... \n",
       "362    0.91\n",
       "221    0.75\n",
       "209    0.68\n",
       "201    0.72\n",
       "406    0.61\n",
       "480    0.80\n",
       "403    0.91\n",
       "225    0.61\n",
       "177    0.82\n",
       "487    0.79\n",
       "152    0.86\n",
       "417    0.52\n",
       "336    0.72\n",
       "329    0.43\n",
       "447    0.84\n",
       "438    0.67\n",
       "333    0.71\n",
       "402    0.78\n",
       "45     0.88\n",
       "285    0.93\n",
       "31     0.74\n",
       "430    0.74\n",
       "95     0.42\n",
       "401    0.66\n",
       "255    0.79\n",
       "51     0.56\n",
       "291    0.56\n",
       "346    0.47\n",
       "130    0.96\n",
       "254    0.85\n",
       "Name: Chance of Admit, Length: 375, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression = LinearRegression()\n",
    "\n",
    "regression.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the model to the local file system\n",
    "filename = 'finalized_model.pickle'\n",
    "pickle.dump(regression, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92190162])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction using the saved model\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "a=loaded_model.predict(scaler.transform([[300,110,5,5,5,10,1]]))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8415250484247909"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8385023654247188"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_r2(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our r2 score is 84.15% and adj r2 is 83.85% for our training et., so looks like we are not being penalized by use of any feature.\n",
    "\n",
    "Let's check how well model fits the test data.\n",
    "\n",
    "Now let's check if our model is overfitting our data using regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7534898831471066"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7387414146174464"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_r2(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like our model r2 score is less on the test data.\n",
    "\n",
    "Let's see if our model is overfitting our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=None, copy_X=True, cv=10, eps=0.001, fit_intercept=True,\n",
       "    max_iter=100000, n_alphas=100, n_jobs=None, normalize=True,\n",
       "    positive=False, precompute='auto', random_state=None,\n",
       "    selection='cyclic', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lasso Regularization\n",
    "# LassoCV will return best alpha and coefficients after performing 10 cross validations\n",
    "lasscv = LassoCV(alphas = None,cv =10, max_iter = 100000, normalize = True)\n",
    "lasscv.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0341655445178153e-05"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best alpha parameter\n",
    "alpha = lasscv.alpha_\n",
    "alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=3.0341655445178153e-05, copy_X=True, fit_intercept=True,\n",
       "   max_iter=1000, normalize=False, positive=False, precompute=False,\n",
       "   random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now that we have best parameter, let's use Lasso regression and see how well our data has fitted before\n",
    "\n",
    "lasso_reg = Lasso(alpha)\n",
    "lasso_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7534654960492284"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "our r2_score for test data (75.34%) comes same as before using regularization. So, it is fair to say our OLS model did not overfit the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\virat\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([9.9446 , 1.62429, 7.676  , 2.58657, 7.3006 , 9.31714, 3.34469,\n",
       "       3.0428 , 5.83061, 8.5141 , 8.55828, 2.34786, 3.42601, 5.34558,\n",
       "       3.09892, 7.2427 , 7.43945, 7.75282, 8.17264, 1.79508, 7.422  ,\n",
       "       5.44549, 9.15021, 5.42831, 1.34016, 8.0355 , 1.81993, 2.40259,\n",
       "       2.7257...11 , 3.51759,\n",
       "       3.40962, 9.77374, 2.42211, 1.17214, 7.2834 , 3.93244, 2.20177,\n",
       "       0.84324]),\n",
       "    cv=10, fit_intercept=True, gcv_mode=None, normalize=True, scoring=None,\n",
       "    store_cv_values=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Ridge regression model\n",
    "# RidgeCV will return best alpha and coefficients after performing 10 cross validations. \n",
    "# We will pass an array of random numbers for ridgeCV to select best alpha from them\n",
    "\n",
    "alphas = np.random.uniform(low=0, high=10, size=(50,))\n",
    "ridgecv = RidgeCV(alphas = alphas,cv=10,normalize = True)\n",
    "ridgecv.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8432446610176114"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgecv.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.8432446610176114, copy_X=True, fit_intercept=True,\n",
       "   max_iter=None, normalize=False, random_state=None, solver='auto',\n",
       "   tol=0.001)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model = Ridge(alpha=ridgecv.alpha_)\n",
    "ridge_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7538937537809315"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we got the same r2 square using Ridge regression as well. So, it's safe to say there is no overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNetCV(alphas=None, copy_X=True, cv=10, eps=0.001, fit_intercept=True,\n",
       "       l1_ratio=0.5, max_iter=1000, n_alphas=100, n_jobs=None,\n",
       "       normalize=False, positive=False, precompute='auto',\n",
       "       random_state=None, selection='cyclic', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Elastic net\n",
    "\n",
    "elasticCV = ElasticNetCV(alphas = None, cv =10)\n",
    "\n",
    "elasticCV.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0011069728449315508"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elasticCV.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# l1_ration gives how close the model is to L1 regularization, below value indicates we are giving equal\n",
    "#preference to L1 and L2\n",
    "elasticCV.l1_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNet(alpha=0.0011069728449315508, copy_X=True, fit_intercept=True,\n",
       "      l1_ratio=0.5, max_iter=1000, normalize=False, positive=False,\n",
       "      precompute=False, random_state=None, selection='cyclic', tol=0.0001,\n",
       "      warm_start=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elasticnet_reg = ElasticNet(alpha = elasticCV.alpha_,l1_ratio=0.5)\n",
    "elasticnet_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7531695370639867"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elasticnet_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we can see by using different type of regularization, we still are getting the same r2 score. That means our OLS model has been well trained over the training data and there is no overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Regression\n",
    "\n",
    "For understanding Polynomial Regression, let's first understand a polynomial.\n",
    "Merriam-webster defines a polynomial as:\n",
    "\"_A mathematical expression of one or more algebraic terms each of which consists of a constant multiplied by one or more variables raised to a non-negative integral power (such as a + bx + cx^2)\"._\n",
    "Simply said, poly means many. So, a polynomial is an aggregation of many monomials(or Variables).\n",
    "A simple polynomial equation can be written as:\n",
    "$$y = {a+bx + cx^2+...+nx^n+...}$$\n",
    "\n",
    "So, Polynomial Regression can be defined as a mechanism to predict a _dependent variable_ based on the polynomial relationship with the _independent variable_.\n",
    "\n",
    " In the equation, _$$y= {a+bx + cx^2+...+nx^n+...}$$_ the maximum power of 'x' is called the degree of the polynomial equation.\n",
    " For example, if the degree is 1, the equation becomes $$y={a+bx}$$ which is a simple linear equation.\n",
    "              if the degree is 2, the equation becomes $$y = {a+bx + cx^2}$$ which is a quadratic equation and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When to use Polynomial Regression?\n",
    "Many times we may face a requirement where we have to do a regression, but when we plot a graph between a dependent and independent variables, the graph doesn't turn out to be a linear one.\n",
    "A linear graph typically looks like:\n",
    "<img src=\"LinearGraph.png\" width=\"300\">\n",
    "\n",
    "But what if the relationship looks like:\n",
    "<img src=\"PolynomialGraph.png\" width=\"300\">\n",
    "\n",
    "It means that the relationship between X and Y can't be described Linearly.\n",
    "Then comes the time to use the Polynomial Regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can generalize the matrix obtained above (for Linear Regression) for an equation of n coefficients(in y=mx+b, m and b are the coefficients) as follows: \n",
    "![image.png](generalEquation.png)\n",
    "Where m is the _degree_(maximum power of x) of the polynomial and n is the number of observation points.\n",
    "The above matrix results in the general formula for Polynomial Regression.\n",
    "Earlier, we were able to visualize the calculation of minima because the graph was in three dimensions. But as there are n number of coefficients, it's not possible to create an (n+1)  dimension graph here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have the maths clear, now Let's focus on the Python implementation of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Necessary Imports for importing the required modules to be used\n",
    "import pandas as pd\n",
    "import numpy  as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline   \n",
    "# this makes sure that the graphs are printed in the jupyter notebook itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level  Salary\n",
       "0   Business Analyst      1   45000\n",
       "1  Junior Consultant      2   50000\n",
       "2  Senior Consultant      3   60000\n",
       "3            Manager      4   80000\n",
       "4    Country Manager      5  110000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the dataset\n",
    "dataset= pd.read_csv('Position_Salaries.csv') # the full namespace of the file can be provided if the file is not in the same directory as the .ipynb or.py file\n",
    "dataset.head()   # to see how the imported data looks like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, it can be seen that there are 3 columns in the dataset. The problem statement here is to predict the salary based on the\n",
    "Position and Level of the employee.\n",
    "But we may observe that the Position and the level are related or level is one other way of conveying the position of the employee in the company. So, essentially Position and Level are conveying the same kind of information. As Level is a numeric column, let's use that in our Machine Learning Model.\n",
    "Hence, Level is our feature or X variable.\n",
    "And, Salary is Label or the Y variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10]], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=dataset.iloc[:,1:2].values\n",
    "#x=dataset.iloc[:,1].values\n",
    "# this is written in this way to make x as a matrix as the machine learning algorithm.\n",
    "# if we write 'x=dataset.iloc[:,1].values', it will return x as a single-dimensional array which is not desired \n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  45000,   50000,   60000,   80000,  110000,  150000,  200000,\n",
       "        300000,  500000, 1000000], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=dataset.iloc[:,2].values\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, we divide our dataset into two parts\n",
    "1) The training dataset to train our model. And,\n",
    "2) The test dataset to test our prepared model.\n",
    "Here, as the dataset has a limited number of entries, we won't do a split.\n",
    "Instead of that, we'd use direct numerical values to test the model.\n",
    "Hence, the code above is kept commented.\n",
    "But, train test split can also be done, if you desire so:)\n",
    "    \n",
    "To learn Polynomial Regression, we'd follow a comparative approach.\n",
    "First, we'll try to create a Linear Model using Linear Regression and then we'd prepare a Polynomial Regression Model and see how do they compare to each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Linear Regression to the dataset\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the Linear Regression results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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(x, y, color = 'red')\n",
    "plt.plot(x, lin_reg.predict(x), color = 'blue')\n",
    "plt.title('Linear Regression')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the red dots are the actual data points and, the blue straight line is what our model has created.\n",
    "It is evident from the diagram above that a Linear model does not fit our dataset well. So, let's try with a Polynomial Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.   1.   1.]\n",
      " [  1.   2.   4.]\n",
      " [  1.   3.   9.]\n",
      " [  1.   4.  16.]\n",
      " [  1.   5.  25.]\n",
      " [  1.   6.  36.]\n",
      " [  1.   7.  49.]\n",
      " [  1.   8.  64.]\n",
      " [  1.   9.  81.]\n",
      " [  1.  10. 100.]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Polynomial Regression to the dataset\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_reg = PolynomialFeatures(degree = 2)  #trying to create a 2 degree polynomial equation. It simply squares the x as shown in the output\n",
    "X_poly = poly_reg.fit_transform(x)\n",
    "print(X_poly)\n",
    "poly_reg.fit(X_poly, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# doing the actual polynomial Regression\n",
    "lin_reg_2 = LinearRegression()\n",
    "lin_reg_2.fit(X_poly, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be noted here that for Polynomial Regression also, we are using the Linear Regression Object.\n",
    "###### Why is it so?\n",
    "It is because the Linear in Linear Regression does not talk about the degree of the Polynomial equation in terms of the dependent variable(x). Instead, it talks about the degree of the coefficients. Mathematically,\n",
    "$$y = {a+bx + cx^2+...+nx^n+...}$$\n",
    "It's not talking about the power of x, but the powers of a,b,c etc.\n",
    "And as the  coefficients are only of degree 1, hence the name Linear Regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "# Visualising the Polynomial Regression results\n",
    "plt.scatter(x, y, color = 'red')\n",
    "plt.plot(x, lin_reg_2.predict(poly_reg.fit_transform(x)), color = 'blue')\n",
    "plt.title('Polynomial Regression')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still, a two degree equation is also not a good fit. \n",
    "Now, we'll try to increase the degree of the equation i.e. we'll try to see that whether we get a good fit at a higher degree or not.\n",
    "After some hit and trial, we see that the model get's the best fit for a 4th degree polynomial equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Polynomial Regression to the dataset\n",
    "poly_reg1 = PolynomialFeatures(degree = 4)\n",
    "X_poly1 = poly_reg1.fit_transform(x)\n",
    "poly_reg1.fit(X_poly, y)\n",
    "lin_reg_3 = LinearRegression()\n",
    "lin_reg_3.fit(X_poly1, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "# Visualising the Polynomial Regression results\n",
    "plt.scatter(x, y, color = 'red')\n",
    "plt.plot(x, lin_reg_3.predict(poly_reg1.fit_transform(x)), color = 'blue')\n",
    "plt.title('Polynomial Regression of Degree 4')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see that our model now accurately fits the dataset. This kind of a fit might not be the case with the actual business datasets.\n",
    "we  are getting a brilliant fit as the number of datapoints are a  few."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cloud Deployment (Google Cloud Platform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the training is completed, we need to expose the trained model as an API for the user to consume it. For prediction, the saved model is loaded first and then the predictions are made using it. If the web app works fine, the same app is deployed to the cloud platform. The application flow for cloud deployment looks like:\n",
    "\n",
    "<img src=\"testing_pipeline.PNG\" width= \"300\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-requisites:\n",
    "* Basic knowledge of flask framework.\n",
    "* Any Python IDE installed(we are using PyCharm).\n",
    "* A Google Cloud Platform account.\n",
    "* Basic understanding of HTML.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Flask App\n",
    "\n",
    "As we’ll expose the created model as a web API to be consumed by the client/client APIs, we’d do it using the flask framework. \n",
    "The flow of our flask app will be:\n",
    "<img src=\"flask_flow.PNG\" width= \"300\">\n",
    "\n",
    "Create the project structure, as shown below:\n",
    "<img src=\"folder_structure.PNG\" width= \"300\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deployment to G-cloud:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Go to https://cloud.google.com/ and create an account if already haven’t created one. Then go to the console of your account.\n",
    "* Go to IAM and admin(highlighted) and click manage resources\n",
    "<img src=\"iam.PNG\" width= \"300\">\n",
    "\n",
    "* Click CREATE PROJECT to create a new project for deployment.\n",
    "* Once the project gets created, select App Engine and select Dashboard.\n",
    "<img src=\"dashboard.PNG\" width= \"300\">\n",
    "\n",
    "* Go to https://dl.google.com/dl/cloudsdk/channels/rapid/GoogleCloudSDKInstaller.exe to download the google cloud SDK to your machine.\n",
    "* Click Start Tutorial on the screen and select Python app and click start.\n",
    "<img src=\"tutorial.PNG\" width= \"300\">\n",
    "\n",
    "* Check whether the correct project name is displayed and then click next.\n",
    "<img src=\"setup.PNG\" width= \"300\">\n",
    "\n",
    "* Create a file ‘app.yaml’ and put  ‘runtime: python37’ in that file.\n",
    "* Create a ‘requirements.txt’ file by opening the command prompt/anaconda prompt, navigate to the project folder and enter the command ‘pip freeze > requirements.txt’.\n",
    "It is recommended to use separate environments for different projects.\n",
    "* Your python application file should be called ‘main.py’. It is a GCP specific requirement.\n",
    "* Open command prompt window, navigate to the project folder and enter the command gcloud init to initialise the gcloud context.\n",
    "* It asks you to select from the list of available projects.\n",
    "<img src=\"select_project.PNG\" width= \"300\">\n",
    "\n",
    "* Once the project name is selected, enter the command gcloud app deploy app.yaml \n",
    "--project <project name>\n",
    "    \n",
    "* After executing the above command, GCP will ask you to enter the region for your application. Choose the appropriate one.\n",
    "<img src=\"region_select.PNG\" width= \"300\">\n",
    "\n",
    "* GCP will ask for the services to be deployed. Enter ‘y’ to deploy the services.\n",
    "* And then it will give you the link for your app,and the deployed app looks like:\n",
    "\n",
    "<img src=\"final_snap.PNG\" width= \"300\">\n"
   ]
  },
  {
   "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": "3.8.8"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
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}