50 START UPS LINEAR REGRESSION.ipynb

{
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    "<h1>CONTENTS<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#MULTIPLE-LINEAR-REGRESSION-ON-50-START-UPS\" data-toc-modified-id=\"MULTIPLE-LINEAR-REGRESSION-ON-50-START-UPS-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>MULTIPLE LINEAR REGRESSION ON 50 START UPS</a></span><ul class=\"toc-item\"><li><span><a href=\"#IMPORTING-REQUIRED-LIBRARIES\" data-toc-modified-id=\"IMPORTING-REQUIRED-LIBRARIES-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>IMPORTING REQUIRED LIBRARIES</a></span></li><li><span><a href=\"#IMPORTING-DATA-(-CSV-)\" data-toc-modified-id=\"IMPORTING-DATA-(-CSV-)-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>IMPORTING DATA ( CSV )</a></span></li><li><span><a href=\"#DATA-CLEANING\" data-toc-modified-id=\"DATA-CLEANING-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>DATA CLEANING</a></span><ul class=\"toc-item\"><li><span><a href=\"#CHECKING-FOR-NULL-VALUES\" data-toc-modified-id=\"CHECKING-FOR-NULL-VALUES-1.3.1\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>CHECKING FOR NULL VALUES</a></span></li><li><span><a href=\"#NON-NUMERIC-TO-NUMERIC\" data-toc-modified-id=\"NON-NUMERIC-TO-NUMERIC-1.3.2\"><span class=\"toc-item-num\">1.3.2&nbsp;&nbsp;</span>NON-NUMERIC TO NUMERIC</a></span></li></ul></li><li><span><a href=\"#RANDOM-SAMPLING\" data-toc-modified-id=\"RANDOM-SAMPLING-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>RANDOM SAMPLING</a></span></li><li><span><a href=\"#HYPER-PARAMETER-TUNING\" data-toc-modified-id=\"HYPER-PARAMETER-TUNING-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>HYPER PARAMETER TUNING</a></span></li><li><span><a href=\"#DESCRIBING-DATA\" data-toc-modified-id=\"DESCRIBING-DATA-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>DESCRIBING DATA</a></span><ul class=\"toc-item\"><li><span><a href=\"#REMOVING-OUTLIERS\" data-toc-modified-id=\"REMOVING-OUTLIERS-1.6.1\"><span class=\"toc-item-num\">1.6.1&nbsp;&nbsp;</span>REMOVING OUTLIERS</a></span></li></ul></li><li><span><a href=\"#ASSUMPTIONS-CHECK\" data-toc-modified-id=\"ASSUMPTIONS-CHECK-1.7\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>ASSUMPTIONS CHECK</a></span><ul class=\"toc-item\"><li><span><a href=\"#MULTICOLLINEARITY\" data-toc-modified-id=\"MULTICOLLINEARITY-1.7.1\"><span class=\"toc-item-num\">1.7.1&nbsp;&nbsp;</span>MULTICOLLINEARITY</a></span><ul class=\"toc-item\"><li><span><a href=\"#METHOD-1-:-CORRELATION-MATRIX\" data-toc-modified-id=\"METHOD-1-:-CORRELATION-MATRIX-1.7.1.1\"><span class=\"toc-item-num\">1.7.1.1&nbsp;&nbsp;</span>METHOD 1 : CORRELATION MATRIX</a></span></li><li><span><a href=\"#METHOD-2-:-VIF\" data-toc-modified-id=\"METHOD-2-:-VIF-1.7.1.2\"><span class=\"toc-item-num\">1.7.1.2&nbsp;&nbsp;</span>METHOD 2 : VIF</a></span></li></ul></li></ul></li><li><span><a href=\"#LINEAR-REGRESSION-MODELING\" data-toc-modified-id=\"LINEAR-REGRESSION-MODELING-1.8\"><span class=\"toc-item-num\">1.8&nbsp;&nbsp;</span>LINEAR REGRESSION MODELING</a></span><ul class=\"toc-item\"><li><span><a href=\"#COEFFICIENTS-OF-VARIABLES\" data-toc-modified-id=\"COEFFICIENTS-OF-VARIABLES-1.8.1\"><span class=\"toc-item-num\">1.8.1&nbsp;&nbsp;</span>COEFFICIENTS OF VARIABLES</a></span></li><li><span><a href=\"#COEFFICIENT-OF-DETERMINANT\" data-toc-modified-id=\"COEFFICIENT-OF-DETERMINANT-1.8.2\"><span class=\"toc-item-num\">1.8.2&nbsp;&nbsp;</span>COEFFICIENT OF DETERMINANT</a></span></li></ul></li><li><span><a href=\"#CHECKING-FOR-ASSUMPTIONS\" data-toc-modified-id=\"CHECKING-FOR-ASSUMPTIONS-1.9\"><span class=\"toc-item-num\">1.9&nbsp;&nbsp;</span>CHECKING FOR ASSUMPTIONS</a></span><ul class=\"toc-item\"><li><span><a href=\"#MEAN-OF--ERROR\" data-toc-modified-id=\"MEAN-OF--ERROR-1.9.1\"><span class=\"toc-item-num\">1.9.1&nbsp;&nbsp;</span>MEAN OF  ERROR</a></span></li><li><span><a href=\"#NORMAL-DISTRIBUTION\" data-toc-modified-id=\"NORMAL-DISTRIBUTION-1.9.2\"><span class=\"toc-item-num\">1.9.2&nbsp;&nbsp;</span>NORMAL DISTRIBUTION</a></span></li><li><span><a href=\"#HETEROSCEDASTICITY\" data-toc-modified-id=\"HETEROSCEDASTICITY-1.9.3\"><span class=\"toc-item-num\">1.9.3&nbsp;&nbsp;</span>HETEROSCEDASTICITY</a></span></li></ul></li><li><span><a href=\"#METRICS\" data-toc-modified-id=\"METRICS-1.10\"><span class=\"toc-item-num\">1.10&nbsp;&nbsp;</span>METRICS</a></span><ul class=\"toc-item\"><li><span><a href=\"#MSE\" data-toc-modified-id=\"MSE-1.10.1\"><span class=\"toc-item-num\">1.10.1&nbsp;&nbsp;</span>MSE</a></span></li><li><span><a href=\"#RMSE\" data-toc-modified-id=\"RMSE-1.10.2\"><span class=\"toc-item-num\">1.10.2&nbsp;&nbsp;</span>RMSE</a></span></li><li><span><a href=\"#MAPE\" data-toc-modified-id=\"MAPE-1.10.3\"><span class=\"toc-item-num\">1.10.3&nbsp;&nbsp;</span>MAPE</a></span></li><li><span><a href=\"#ACCURACY\" data-toc-modified-id=\"ACCURACY-1.10.4\"><span class=\"toc-item-num\">1.10.4&nbsp;&nbsp;</span>ACCURACY</a></span></li></ul></li><li><span><a href=\"#REMOVING-OUTLIERS\" data-toc-modified-id=\"REMOVING-OUTLIERS-1.11\"><span class=\"toc-item-num\">1.11&nbsp;&nbsp;</span>REMOVING OUTLIERS</a></span><ul class=\"toc-item\"><li><span><a href=\"#COFFEICIENTS-OF-DETERMINANT\" data-toc-modified-id=\"COFFEICIENTS-OF-DETERMINANT-1.11.1\"><span class=\"toc-item-num\">1.11.1&nbsp;&nbsp;</span>COFFEICIENTS OF DETERMINANT</a></span></li><li><span><a href=\"#MEAN\" data-toc-modified-id=\"MEAN-1.11.2\"><span class=\"toc-item-num\">1.11.2&nbsp;&nbsp;</span>MEAN</a></span></li><li><span><a href=\"#NORMAL-DISTRIBUTION\" data-toc-modified-id=\"NORMAL-DISTRIBUTION-1.11.3\"><span class=\"toc-item-num\">1.11.3&nbsp;&nbsp;</span>NORMAL DISTRIBUTION</a></span></li><li><span><a href=\"#HETEROSCEDASTICITY\" data-toc-modified-id=\"HETEROSCEDASTICITY-1.11.4\"><span class=\"toc-item-num\">1.11.4&nbsp;&nbsp;</span>HETEROSCEDASTICITY</a></span></li><li><span><a href=\"#METRICS\" data-toc-modified-id=\"METRICS-1.11.5\"><span class=\"toc-item-num\">1.11.5&nbsp;&nbsp;</span>METRICS</a></span></li></ul></li><li><span><a href=\"#APPLYING-LASSO\" data-toc-modified-id=\"APPLYING-LASSO-1.12\"><span class=\"toc-item-num\">1.12&nbsp;&nbsp;</span>APPLYING LASSO</a></span><ul class=\"toc-item\"><li><span><a href=\"#MEAN\" data-toc-modified-id=\"MEAN-1.12.1\"><span class=\"toc-item-num\">1.12.1&nbsp;&nbsp;</span>MEAN</a></span></li><li><span><a href=\"#NORMAL-DISTRIBUTION\" data-toc-modified-id=\"NORMAL-DISTRIBUTION-1.12.2\"><span class=\"toc-item-num\">1.12.2&nbsp;&nbsp;</span>NORMAL DISTRIBUTION</a></span></li><li><span><a href=\"#HETEROSCEDASTICITY\" data-toc-modified-id=\"HETEROSCEDASTICITY-1.12.3\"><span class=\"toc-item-num\">1.12.3&nbsp;&nbsp;</span>HETEROSCEDASTICITY</a></span></li><li><span><a href=\"#METRICS\" data-toc-modified-id=\"METRICS-1.12.4\"><span class=\"toc-item-num\">1.12.4&nbsp;&nbsp;</span>METRICS</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b8c80e",
   "metadata": {},
   "source": [
    "\n",
    "## MULTIPLE LINEAR REGRESSION ON 50 START UPS\n",
    "\n",
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e40968",
   "metadata": {},
   "source": [
    "### IMPORTING REQUIRED LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87d02058",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.set_option ( \"display.max_columns\" , None)\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.metrics import mean_squared_error \n",
    "\n",
    "from sklearn.linear_model import LinearRegression \n",
    "\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df0f0c03",
   "metadata": {},
   "source": [
    "### IMPORTING DATA ( CSV )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16019b7f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "DF = pd.read_csv ( r\"A:\\PANDAS DATA\\DATA SETS\\50 STARTUPS\\50_Startups.csv\" )\n",
    "\n",
    "Start_Ups = DF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aaa4d1d",
   "metadata": {},
   "source": [
    "### DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e1267b",
   "metadata": {},
   "source": [
    "#### CHECKING FOR NULL VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2be70433",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This data set has no null values\n"
     ]
    }
   ],
   "source": [
    "def Null_Values ( DF ) :\n",
    "    \n",
    "    Total = dict( DF.isnull ( ).sum ( ) [ DF.isnull ( ).sum ( ) > 0 ] )\n",
    "    \n",
    "    if len ( Total ) > 0 : \n",
    "    \n",
    "        List = list ( dict(DF.isnull ( ).sum ( ) [ DF.isnull ( ).sum ( ) > 0 ] ) )\n",
    "\n",
    "        Categorical = { i : DF [ i ].isnull ( ).sum ( ) for i in list ( DF.select_dtypes ( 'object' ) ) } \n",
    "\n",
    "        Numeric = { i : DF [ i ].isnull ( ).sum ( ) for i in list ( DF.select_dtypes ( np.number ) ) } \n",
    "        \n",
    "        print (\"The Categorical Null Values are : \\n\\n{}\\n\\nThe Numeric Null Values are : \\n\\n{}\\n\\n\".format ( Categorical , Numeric ) )\n",
    "        \n",
    "    else :\n",
    "        \n",
    "        print ( \"This data set has no null values\" )\n",
    "        \n",
    "Null_Values ( DF )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b37882b2",
   "metadata": {},
   "source": [
    "#### NON-NUMERIC TO NUMERIC "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e29cb720",
   "metadata": {},
   "source": [
    "\n",
    "        The following function is used for the data frame, where all columns are nominal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b942a1a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <td>151377.59</td>\n",
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       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
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       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
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       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>1</td>\n",
       "      <td>166187.94</td>\n",
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       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>2</td>\n",
       "      <td>156991.12</td>\n",
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       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>0</td>\n",
       "      <td>156122.51</td>\n",
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       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>1</td>\n",
       "      <td>155752.60</td>\n",
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       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>2</td>\n",
       "      <td>152211.77</td>\n",
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       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>0</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>1</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>0</td>\n",
       "      <td>144259.40</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>1</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>0</td>\n",
       "      <td>134307.35</td>\n",
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       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>1</td>\n",
       "      <td>132602.65</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>2</td>\n",
       "      <td>129917.04</td>\n",
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       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>0</td>\n",
       "      <td>126992.93</td>\n",
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       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>2</td>\n",
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       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>1</td>\n",
       "      <td>124266.90</td>\n",
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       "      <th>19</th>\n",
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       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
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       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>0</td>\n",
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       "      <td>153773.43</td>\n",
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       "      <td>2</td>\n",
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       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>1</td>\n",
       "      <td>110352.25</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>1</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>2</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>0</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>1</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>2</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>1</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>2</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>1</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>2</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>0</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>1</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>0</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>2</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>1</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>0</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>2</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>0</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>0</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>1</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>0</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>2</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>0</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>2</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>1</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>0</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend  State     Profit\n",
       "0   165349.20       136897.80        471784.10      2  192261.83\n",
       "1   162597.70       151377.59        443898.53      0  191792.06\n",
       "2   153441.51       101145.55        407934.54      1  191050.39\n",
       "3   144372.41       118671.85        383199.62      2  182901.99\n",
       "4   142107.34        91391.77        366168.42      1  166187.94\n",
       "5   131876.90        99814.71        362861.36      2  156991.12\n",
       "6   134615.46       147198.87        127716.82      0  156122.51\n",
       "7   130298.13       145530.06        323876.68      1  155752.60\n",
       "8   120542.52       148718.95        311613.29      2  152211.77\n",
       "9   123334.88       108679.17        304981.62      0  149759.96\n",
       "10  101913.08       110594.11        229160.95      1  146121.95\n",
       "11  100671.96        91790.61        249744.55      0  144259.40\n",
       "12   93863.75       127320.38        249839.44      1  141585.52\n",
       "13   91992.39       135495.07        252664.93      0  134307.35\n",
       "14  119943.24       156547.42        256512.92      1  132602.65\n",
       "15  114523.61       122616.84        261776.23      2  129917.04\n",
       "16   78013.11       121597.55        264346.06      0  126992.93\n",
       "17   94657.16       145077.58        282574.31      2  125370.37\n",
       "18   91749.16       114175.79        294919.57      1  124266.90\n",
       "19   86419.70       153514.11             0.00      2  122776.86\n",
       "20   76253.86       113867.30        298664.47      0  118474.03\n",
       "21   78389.47       153773.43        299737.29      2  111313.02\n",
       "22   73994.56       122782.75        303319.26      1  110352.25\n",
       "23   67532.53       105751.03        304768.73      1  108733.99\n",
       "24   77044.01        99281.34        140574.81      2  108552.04\n",
       "25   64664.71       139553.16        137962.62      0  107404.34\n",
       "26   75328.87       144135.98        134050.07      1  105733.54\n",
       "27   72107.60       127864.55        353183.81      2  105008.31\n",
       "28   66051.52       182645.56        118148.20      1  103282.38\n",
       "29   65605.48       153032.06        107138.38      2  101004.64\n",
       "30   61994.48       115641.28         91131.24      1   99937.59\n",
       "31   61136.38       152701.92         88218.23      2   97483.56\n",
       "32   63408.86       129219.61         46085.25      0   97427.84\n",
       "33   55493.95       103057.49        214634.81      1   96778.92\n",
       "34   46426.07       157693.92        210797.67      0   96712.80\n",
       "35   46014.02        85047.44        205517.64      2   96479.51\n",
       "36   28663.76       127056.21        201126.82      1   90708.19\n",
       "37   44069.95        51283.14        197029.42      0   89949.14\n",
       "38   20229.59        65947.93        185265.10      2   81229.06\n",
       "39   38558.51        82982.09        174999.30      0   81005.76\n",
       "40   28754.33       118546.05        172795.67      0   78239.91\n",
       "41   27892.92        84710.77        164470.71      1   77798.83\n",
       "42   23640.93        96189.63        148001.11      0   71498.49\n",
       "43   15505.73       127382.30         35534.17      2   69758.98\n",
       "44   22177.74       154806.14         28334.72      0   65200.33\n",
       "45    1000.23       124153.04          1903.93      2   64926.08\n",
       "46    1315.46       115816.21        297114.46      1   49490.75\n",
       "47       0.00       135426.92             0.00      0   42559.73\n",
       "48     542.05        51743.15             0.00      2   35673.41\n",
       "49       0.00       116983.80         45173.06      0   14681.40"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LE = LabelEncoder ( )\n",
    "\n",
    "def Encoding ( DF ) :\n",
    "    \n",
    "    Columns = list ( DF.select_dtypes ( 'object' ) )\n",
    "    \n",
    "    DF.loc [ : , Columns ] = DF.loc [ : , Columns ].apply ( LE.fit_transform )\n",
    "    \n",
    "    display (DF)\n",
    "    \n",
    "Encoding ( DF )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b04b555",
   "metadata": {},
   "source": [
    "### RANDOM SAMPLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15de256d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shapes of sampled data sets are (after standard sampling ratio = 0.2 test size ) : \n",
      "\n",
      "Train Data = (40, 5)\n",
      "\n",
      "Test Data = (10, 5)\n",
      "\n",
      "Train_X Data = (40, 4)\n",
      "\n",
      "Train_Y Data = (40,)\n",
      "\n",
      "Test_X Data = (10, 4)\n",
      "\n",
      "Test_Y Data = (10,)\n"
     ]
    }
   ],
   "source": [
    "def Sampling ( DF , Y ) : # Y is the target variable , accepted in string format\n",
    "    \n",
    "    global Train , Test , Train_X , Test_X , Train_Y , Test_Y\n",
    "    \n",
    "    Train , Test = train_test_split( DF , test_size = 0.2 , random_state = None ) # Random State = None ( small dataset )\n",
    "    \n",
    "    Train_X = Train.drop ( columns = [ Y ] )\n",
    "    \n",
    "    Train_Y = Train [ Y ].reset_index ( drop = True  )\n",
    "    \n",
    "    Test_X = Test.drop ( columns = [ Y ] )\n",
    "    \n",
    "    Test_Y = Test [ Y ].reset_index ( drop = True  )\n",
    "    \n",
    "    print ( \"The shapes of sampled data sets are (after standard sampling ratio = 0.2 test size ) : \\n\" )\n",
    "    \n",
    "    print ( \"Train Data = {}\\n\\nTest Data = {}\\n\\nTrain_X Data = {}\\n\\nTrain_Y Data = {}\\n\\nTest_X Data = {}\\n\\nTest_Y Data = {}\".format ( Train.shape , Test.shape , Train_X.shape , Train_Y.shape , Test_X.shape , Test_Y.shape ) )\n",
    "        \n",
    "Sampling ( DF , 'Profit' )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965ddd40",
   "metadata": {},
   "source": [
    "### HYPER PARAMETER TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f4aa4c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters :  {'copy_X': True, 'fit_intercept': True}\n"
     ]
    }
   ],
   "source": [
    "Parameters = { 'fit_intercept': [ True , False ],\n",
    "                  \n",
    "               'copy_X': [True , False ] }\n",
    "\n",
    "LR = LinearRegression (  )\n",
    " \n",
    "grid_search = GridSearchCV ( LR , Parameters )\n",
    "\n",
    "grid_search.fit ( Train_X , Train_Y )\n",
    "\n",
    "print( 'Best hyperparameters : ' , grid_search.best_params_ )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4dad6f",
   "metadata": {},
   "source": [
    "### DESCRIBING DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f03eb4af",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='R&D Spend', ylabel='Count'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Administration', ylabel='Count'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Marketing Spend', ylabel='Count'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State', ylabel='Count'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Profit', ylabel='Count'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    }
   ],
   "source": [
    "def Plots ( DF ) : \n",
    "    \n",
    "    sns.set_palette( \"pastel\" )\n",
    "\n",
    "    plt.figure ( figsize =  ( 10 , 10 ) )\n",
    "\n",
    "    for i in list ( DF.select_dtypes ( np.number ).columns ) :\n",
    "        \n",
    "        display ( sns.histplot ( x = i , data = DF , bins = 40  )  )\n",
    "\n",
    "        plt.show ( )\n",
    "\n",
    "        print ( \" \" )\n",
    "        \n",
    "Plots(Start_Ups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac38f299",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Column ---->  R&D Spend\n",
      "\n",
      "\n",
      "Mean = 73721.61559999999\n",
      "\n",
      "Median = 73051.08\n",
      "\n",
      "Standard Deviation = 45440.915562565344\n",
      "\n",
      "Kurtosis = 2.2385354431575326\n",
      "\n",
      "Right Skewed\n",
      "\n",
      "The distribution is Platykurtic\n",
      "\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Column ---->  Administration\n",
      "\n",
      "\n",
      "Mean = 121344.63959999995\n",
      "\n",
      "Median = 122699.795\n",
      "\n",
      "Standard Deviation = 27736.20965129446\n",
      "\n",
      "Kurtosis = 3.225071135368654\n",
      "\n",
      "Left Skewed\n",
      "\n",
      "The distribution is Leptokurtic\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Column ---->  Marketing Spend\n",
      "\n",
      "\n",
      "Mean = 211025.09780000005\n",
      "\n",
      "Median = 212716.24\n",
      "\n",
      "Standard Deviation = 121061.23118271727\n",
      "\n",
      "Kurtosis = 2.3282988718702486\n",
      "\n",
      "Left Skewed\n",
      "\n",
      "The distribution is Platykurtic\n",
      "\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Column ---->  State\n",
      "\n",
      "\n",
      "Mean = 1.0\n",
      "\n",
      "Median = 1.0\n",
      "\n",
      "Standard Deviation = 0.8246211251235321\n",
      "\n",
      "Kurtosis = 1.4361702127659575\n",
      "\n",
      "The distribution is Platykurtic\n",
      "\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Column ---->  Profit\n",
      "\n",
      "\n",
      "Mean = 112012.63920000002\n",
      "\n",
      "Median = 107978.19\n",
      "\n",
      "Standard Deviation = 39901.08281667283\n",
      "\n",
      "Kurtosis = 2.936141114531469\n",
      "\n",
      "Right Skewed\n",
      "\n",
      "The distribution is Platykurtic\n",
      "\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "def Statistics ( DF ) : \n",
    "\n",
    "    for i in list ( DF.select_dtypes ( np.number ).columns ) :\n",
    "        \n",
    "        global Mean , Med , SD , Kur\n",
    "                    \n",
    "        Mean = np.mean ( DF [ i ] )\n",
    "\n",
    "        Med = np.median ( DF [ i ] )\n",
    "\n",
    "        SD = np.std ( DF [ i ] )\n",
    "        \n",
    "        Kur = DF [ i ].kurt ( ) + 3 \n",
    "        \n",
    "        print ( '\\n\\nColumn ----> ', i )\n",
    "        \n",
    "        print ( \"\\n\\nMean = {}\\n\\nMedian = {}\\n\\nStandard Deviation = {}\\n\\nKurtosis = {}\".format ( Mean , Med , SD , Kur ) )\n",
    "        \n",
    "        if Mean != Med :\n",
    "            \n",
    "            if Mean > Med :\n",
    "                \n",
    "                print ( \"\\nRight Skewed\" )\n",
    "                \n",
    "            else :\n",
    "                \n",
    "                print ( \"\\nLeft Skewed\" )\n",
    "                \n",
    "        if Kur > 3 :\n",
    "            \n",
    "            print ( \"\\nThe distribution is Leptokurtic\" )\n",
    "            \n",
    "        else : \n",
    "            \n",
    "            print ( \"\\nThe distribution is Platykurtic\\n\" )\n",
    "            \n",
    "        print ( \"----------------------------------------------------------------\" )\n",
    "            \n",
    "            \n",
    "Statistics ( Start_Ups )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5017400a",
   "metadata": {},
   "source": [
    "    We can see that the distributions are platykurtic / leptokurtic, therefore the data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf06c37",
   "metadata": {},
   "source": [
    "#### REMOVING OUTLIERS"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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v/+qrr55++unc3NzOnTubZp52r9Bmnv41+y1tnL+uZvplRO0J7/P5tJJ//OMfe/To0aRJk9TU1CNHjnz//fdTp07dunWriKxYseLaa699++23QxuZ+tjM6LFixYrJkyeLSM2aNW+88cZOnTolJyfPnTs3LS2ta9euw4cPdzgcL774oq48fvx4y7J8Pp+mFdi/f/9ll11mTssxjysq03Ju0aJFZmamLrzgggtuueWWNm3auN3uHTt2TJkyZenSpSLy/vvv33jjja+//nrCowya12Djxo1SElBo2rSpRBswovGUZs2a/fGPfzz//PObNWtmzvy0adO2bNkiIitXrhw8ePDcuXNjnPmVK1dOmzZNRNq3b3/33Xe3b9/e4XB8++23DzzwwKFDhyzLmjNnTv/+/a+55hpzisyDq666atWqVfoJbN68+dixYzt27BgIBFavXv3ss8/u2bOnS5cupmNI2OdH/19+/fXXxo0bm4UDBw4cMmSI9uJZu3bt008/vW3bNhF5/PHHa9euPXLkSCbgAACUWWI7SMSDoRMAUKXFM3Ti66+/Dv2t6dGjR2Q5f/3rX80KOiA/bByBefXIkSNRa2K6kaelpema7du3D+slrvr3768rpKam6hL96dG/rVq1Cq3tpEmTSjv2xYsXm9WGDh0auUK3bt30VT0z2pFeuyPq3+nTp4eek8ihE7ravffeG7qXsKETTzzxxBdffFFaJSdNmmQquXHjxtDNtfBhw4ZJSCv6pptuKq0o09fD6XSWto6WaYZOtG3b1o576IS+g9dee62p8LJlyyJ38frrr5sVFi9eHHpEsUUOnZgxY4Yu8ZYoKirSlc8++2yzmpYfen2ij5988kmtQFQatVHr16+3I878LbfcEnrmn3322chCWrZsqa+2atUqdNda1EcffSQlYYs777wzcvOBAweGfgKfe+45s3dzOGYK0oyMjN27d0cWMmbMGHMg2dnZdrQPKgCg6joBQydI8AMAqCh6FzQ9PX3ZsmUi4vV6fT6f3+8vLi4WkWeeecYMMXjvvfekZBIKvWkcOj+fdtovKiry+Xw+n8/r9eo6uv6DDz6oYwFq1qy5fv16j8ejq/n9fr/fr13H33vvPe2mXlBQMHXqVAnp6m9oJ4Jx48aNGjVKRHwlQtc0qRPatWunrd/i4uLQHa1YsSIlJUVK6Ylw/LT1+MADD1x44YWhlVQaZBk1apRpuuvBhp7MsOPt06fPa6+9FlqUHoh2mti/f7+ubAa2FBcXm31FTn5ZJjrnwtatW02QYsmSJT169AgEAubt8/l8wWBw6NCho0eP1nXGjx8vIk6nM+pBHZPmH01LS3OX0F4Go0aN0g+PiNx4440ulysQCIT2aNDH48eP7927t5Ry5keOHHn99dfr+tphIWoldS+33367tufNweowmTfeeENX27p1q37y9YpNP1F6+LZtd+3aVd/csE/gu+++qx/1yGQlWpk5c+aYcUybNm1q2LChnmdzwkXk2Wef1Q+YiDz++OMSbSwJAAAxEGgAAFQUve/6zDPPiIjX69VU/y6XKykpSdszgwYN0jW/+uorCZnGUv6317o2mUzL0OPxaEp8bXqZ28hTpkwRkYKCgtBZMB0OR0FBgYj87W9/0yWvvvqqiGgJobXVKv39738XEZ32TzmdTm1R//rrryYzwj/+8Q8RKS4uTkpKcrlcLpdLZysQkaefflrim8Oy3AKBgDYLRcSyLJfLZdrM2pg0d6QXLVokIm63O7LFq5vryAiv1xt6eu2SuQYiG6v6Juq+EtKd/rnnntMHF1988QUXXKB3VELnsND3RZu7IrJq1aojR47owIFy7G7ZsmWrVq2aN2/ehx9++OGHH86ePXvMmDEZGRn64dHpG2bOnCmlTJ9R1jPv8Xgiz7wm4Jg4caKUfNL0I5ScnCwiXbt2NT101q1bJyK2bWshe/fuXb9+vb6k/Va8Xm/oJ1BDeM8//3xkzc3/i34+RWTs2LF16tQJ+3+xLEvjHc8++6wumTVrloi4XK7yRXYAAL9NBBoAABVF22MDBgyQkt4NYTp16qQPfvnlFxEprfUYdaE2/jds2GA6xt9www0ikpqa6vpfqampImJ6769du1ZETEqIUFdccYWUjNg3CzUThIiYBJBJSUl6vzesHa7HOHToUAnJ5lgRdH5NbeW6XK7QJrHWwXS///nnn6X0NnP9+vV12EhY/4sYzfhE3dk27d73339fl4wcOVJENIoUSnscpKWlmU+L5kEsX00mT57crVu3AQMGXHHFFVdcccW111773HPPaccBt9s9dOhQPWOl5Zss65kvTefOndPS0mzbDv2/0J4LoYXs2LFDQgINZuROSkqKThQS9sbpB/Kqq64SEQ06hBYuIvn5+Rq8EJG7775bRFJTU3WyTEO75Pz+97/XM3/w4EFNcUKnBgBA/EgGCQCoQG63WzMjRm3rmqSJ2iyPuk5ptOm1cuVKfdqoUaMDBw5oNofQ1TT5f0pKSuht8IMHD6anp0fuUTMsRKa+0zbnt99+q091OszItqiWVrNmzYyMDG2bVQS94W9Z1r59+2bOnLl48eJt27YdOnQoEAjoATqdTlP/QCCgfUmithL1QMJSY54YehSHDx8+cOCALklLS8vNzT1w4IDmgzBrWpbl9/sbN25sBtps2bKlX79+CbzB7nK5/H7/ddddN2PGDIn2AQits2VZWVlZM2fO/Pzzz2Oc+WAwWFRUlJycHPXMt23bViJCWg6HQ3dt/i/CpiYxs3KYT2DUw3E4HM2bN9+5c2foQv24mhJcLpfX692zZ09RUZHO+hF6mC6XKy0trVatWllZWSKyY8eOjIwMAg0AgPgRaAAAVCDtDV6aMkUWom6rrSmdrq9OnTrxbGXbdn5+fnp6emTDqV69ejFqZebU1Pz8kYEG01Bs2LBhBQUazLCCYcOGhaZIjCFGJoXTTz9dSm+vVig9e6G3/S+66KJjbqXjUzQlR/k+PIMHD+7bt292drZmedi7d+/8+fO1GjNnzvzmm29++OEHfSnszTVL4j/zMU6s9ho45pk3H1E9WG32i4hOKhEMBsMiROYT2KhRo7BAgxZl/l/8fn/z5s2PeQh6wjXewdAJAED8CDQAACpQRTdOwm75HpM2t0KnIQgVe2pGzfUgIubWemmqVatWplrFybR1O3fubG5N69PWrVvXrl1bm69JSUl5eXkvvPDCMQvU2+/HE+45TvpGxJ9wQbNgHE9P/sGDB1999dWhSyZPnvyvf/3rtttuE5FNmzZdffXV77//fmlRhi5dunz33XdmeeSZz8/Pj5oiIYxWvqxn3ozH0fhdjH+u0j7JR44ckbKcOj3hBw8eLFM9AQAg0AAAqMJ0UHogEGjbtu2sWbP27t17zAyFeXl5LVq0EBHtMR5/Y89kZNBseTFUUIIGv9/v8XgmTpxoogwvvvjiHXfcEXXleAINJzHEoPSGvLZ7P/jgg1q1auXn58eolcPhyM3N1YED5RvuoRN25ufnm6a4y+W69dZbc3Jy7r//fsuy5s+f/9577w0YMCB0RIme+UmTJpkowz/+8Y+77ror6i7iCTSUj2Yb0fpLzISjJigWRo8oGAzWqVNnwYIFBQUFOu6jtHL0hPfp00cqbCIVAMApid8MAECVpK1THewgIj6fr0OHDh06dIi/hKj5IGOoX7++Pigtz18wGNQwh5kVMpK5mRw726LeSQ5brm1jnR9BRCZMmKBRBtu2fT6fNhf9fn9KSsq2bdviPaqTRBvJ5u0TkZ49e9asWbOsJZRvvzp3hi7RsQbjxo2bOXPm1q1bRWT48OEDBgzQeRb0Q6JnXid6EJFnn31WowyRZ/7HH38sR62OST8VOmJCRDIzM6Vkjs/Q82A+gWaYj6GVbNiwoT7Ny8vr3r17mepQoROpAABOMfxmAAAqu6h3XLXZc+655+pTTcsnIkVFRb5jKeuADl3fzHqgeftLm/AvGAzqJBpRmRvppd1z1uPS6QYi6+D1ek2YQ3v7a0jCzDqpt53NJIiVlr6nZ5xxhlny3nvviUh+fv4x374YWSfKQRMWiMhLL70kIg6HIycn55VXXpGSc66NfJ/PZ1rvt9xyi5Ry5jds2JDAuhl6ujRZqZTkJQ2dllKZkUF79uwJK0E/V+3bt9enRUVF+jEuLCxM+P8LAAAEGgAAlV3k7X0paXrpNJNq5syZIuJwONyl0Ju9LperrPdmdUPtQC4iOTk5W7ZskYhB8toA/uijj0REpwYMpY3ABg0a6NNdu3ZpyZEJ/0VkyZIlUWuiPf9VWlqaiLjd7tDWph7aa6+9ZpaUb3yEbhW5baJGW+hsIBKSA/Lll18WkbS0NJ08MpJOJ2lZ1jFHx5SVnrTevXt36NBBazVu3DgRcblc5t0JPfOnnXaaiDidzthnPuE1vPjii82SefPmScQclhoxeeONNyQiD6tWtWnTpjrfioi8+OKLIlLaP4sJneiMnhVxUACAUxi/HACASiS01a0J9kRk7969IuL1evX+qtfr9fv9TqdTO70PGjRIVxs9enRhYaHH4/H5fH6/PxAIBIPBQCCgUzz6/X6NQZSjqaybtGrVyoyeuP/++6XkZngwGAwGgz6fT3vjjx8/Pmoh2jw2t5Rzc3N1gENxcbFt28Fg0O/3a0PxtddeKygoiJrPT4MLSns9hG7u8/mcTueaNWv+85//lPUYowpNe5mTk6O703ehuLj4OHsWaJN+9OjR+vTbb7/VTg16MvXt03fQ7/d7vV7LslwuV0VkCnA4HHrmJ06cKCWJCaZPny4hc3ZocEHp+Aifzxd25teuXbtw4cKEV09KhvmkpqaaWMOIESNEJDk5Wfeun/mkpCS/33/vvfdKRBRMZwkVkZEjR+qSV1555ccff9SpPcNOuP6/WJZlwnMAAJQJgQYAQOWiU/SJSKtWrXTJm2++KSIpKSl6o9Xj8YS2Nk3OAtu227RpU1BQoDdjnU6nw+FwOp1Op9Ns8vzzz7/77rtScu83/irp+o899pgumT9//muvvaZNX73lq1GG0aNH//DDD1HL12hFgwYNTKeGO++8U0SSk5Mty3I4HHonf926ddozP6yhqHeVU1NTdU5KKWmih22enZ3do0eP0A3LNzuD1rZmzZom3PPOO+/o7vRdSEpKOs4mqB7RZZddZsa/DBw48NNPP3U6ndq+1RPrdDpdLpeGXVasWKEhnoR35tePx0UXXXT22Wdr4boj80lLSkoyCQ6invmcnJyePXsmtlahtFYmx+f+/ft///vfaxcY0wHh8OHDrVu31lykkf2A9IQ/8sgjZlaUjh077ty5U0sIPeHm/2X27Nn/+Mc/JOYkqQAARCLQAACodLRVM3jwYBGxLOuVV17p1q3b2LFjn3jiiSeffPLee+/VRq/e5q1Xr97s2bN1w59++iktLW3UqFHr1q3T5pbP59uzZ8+CBQuGDRtWq1ate++9V3MclLWlqo202267zWRquOWWW4YOHWrG7a9fv/6SSy554YUXUlNTmzRpEtkwczgc2vYzvSH++9//9u7de9OmTSJi2/bOnTvvueeejh07ikj79u3DQhWWZenmd999t4i4XK6PP/64R48ea9eu1WPZs2fPY489Vrdu3cLCwi5dupgZCsrHxFbMZJB33nnnJZdc8uCDD/7tb3976qmn7rnnnq+++kqOowlqIkqffvqpac9fcsklffv2/eSTTzTjRjAYzM3NXb58+YMPPtimTZvzzz9/7ty5Ut7oSWx6vBMmTJD/7dSgPSxERBNAulyuRYsWnX/++WvWrNEzv3fv3scffzwjIyMvL+/4z3xpNCdIu3btHn74YV3y7bffOhyOq6+++sknn3z88cf/+Mc/1qxZc8eOHRdeeOGVV14ZWYLJfrp48WJdUlBQ0KJFi+uuu27lypU6k4Xf7//1118//fTTu+++u0GDBtdee+3XX38tBBoAAGVln3Da1VB/AnVJIBA48dUAAJSPfo1rF83VoegAACAASURBVHFtfu/bt8+2bb/fb17VxomIpKWl6VZhX/Ver9e27Tlz5uhqZ599tq6jq5mVTZr9ML169TL7Utr+jMdzzz1n27aOONAdma4Tb731lqlbJD3Ao0eP1qpVK0b5n3/++dChQ/XxtGnTQgs0x9WlS5cYJbzzzjtTp07Vx0OHDjVHajZv27ZtjM1btWp15MgR87SgoMBUXmsybNgwfWn48OHHPN7s7OzSdjRmzBjbtvPy8mzbfvvtt3Vh27ZtQ99Krfk111yjr44bNy5sj7paVlZW6AwUMbRs2dLU7Zh0tby8PCnpo/Hmm2+Wdsjm9J599tm6r/T09NByQl+K6swzzww98/n5+WFnXvuqiMhtt90WtRq6sskGMmHChKirmfEmkbTOGqQTkcmTJ0c94StWrIjnbEvJJ1D/XwAApwb9ddYExosWLbJLvxgoN3o0AADKRm+K6v1PfRw5gYI27cxqkeyS9Pj6VG9fGw6HQ1fYtWvXXXfdFTnrYWSn/UGDBu3du/fSSy+NUfO6devefvvtGgUIHXxh8vxpfexS7pbrbILVqlXbt29f7969o64za9asPn366OyDkcwt5dWrV19++eVR15k5c+Y111xjJi8IzUFoTsvGjRsvu+yyqJtffvnlW7ZsCT0/hYWF5rFufvTo0dDCYx9vnTp1fv3110GDBpkxFKErSEmwKazMMKb5rauF7lEPql69ellZWQ888ECMvINOp7Nfv37/+te/pIw5KfXzqTuNccimE8dTTz2lSw4ePPjEE09IyX0REVm3bl1pb9yf/vSnH3/8MTSzRowzH3keQpnTpQ8iV5s4ceLSpUs7d+4curBmzZrjxo07cOCAiOzcuVMXaurHyBPerVu3wsLCG264IWoFVPXq1QcNGvTQQw9JtP84AABisEr7kas4fr/f5XJdddVVCxYs0L2HzQINAKjM9Eu7uLh427Ztp512Wn5+vt5g1+X6NxAIbN68OSUlJRAItGzZsrRCCgsLd+7cqWkXmjZtGraObdumPVlYWJiTk6MJHQsKCmrUqNGgQQPz86Gtd31cWFj4n//8Z9WqVZmZmcXFxdWrV2/SpEmHDh169OhhkiOEyczM1ByTzZs3T01Njf2rZF7dtm3bW2+9tXbtWr/f36hRo379+g0cOFDX6dmzpw4rmDZt2ogRI0yeyLDjyszMnDt37rp16/Ly8ho1anTxxRcPGDBA1zlw4MD+/fsty6pdu3bdunVDqxS6+TvvvLN27dr8/PyMjIwuXbr079/f9AvYsWNHcnJyUVFRixYtwt6d/fv3Hzx40LbtjIyMjIyM+H+Fjxw5cujQoUAg4HA48vLyGjZsWLNmTU3JWVBQkJmZqckCmjVrFna6srKyDh06FAwGTz/99PT09Mg96rWBPl6+fPnSpUu3bNly6NAhj8fTqFGjs84667zzzovdmyC2JUuWpKSkHD16tFOnTrVr1z7mIa9cudKyrIKCgtNOO+33v/+9rh925vWNy8jIOOeccwYMGGDOfGZmpsfjiXrms7Ozc3JyYpx5XbJ79+78/Hz9XNWoUSNsNb120poEg8Fdu3b5fL7atWvXqVPHrJOenp6bm6sns3v37qGnV+m7pqUtWrRo+fLl27dvz8/PT01NPeOMM9q3b9+9e3etPwDg1KO/Cxs3bmzfvv2iRYv69esXdq1y/Ag0AAAqL82EH+cvn+bJj33r1efzabq746mVxjui/nJ5vV6PxxM70CAiwWBQZ2qMWvgxJ1bQpn7UzXUsfWLvP2tn+4qY7iFsL8d8r7XHQUXXJIYYb1xFnPmozBAPTd8Y+lJhYWFKSsrWrVtbt26tS/Ly8tLS0kxYoawV9vl8mvE0oUcAADjJTkCggV8OAEB5BINBE6rW3PulvRqjJXPM1XTOiNDVQl8KW6LNIc0OICUdHKSkv4PT6dTp+iL3YhLdRR5IVLojndgytEuF1vaYm5u6RZagcwdIyJmJWiXdS4wKxD6xsQuPFFmmCt089h7jOcPmvVZhb59GdsrX4tUC9XFpEaLS1g8LXZX2xoW+9eZgy33mY58uU6zOammWW5alw1vuu+8+XdKhQwedDzVqZETL0bBFaSc8sRedAIDfDgINAIDyiN1ai7OfWmJXU/G39kM3KdP6Kmqjt0zJ+WM0m+OPd5Rj8/L1IjyeMuM/w/EEAsqqrGUec/3Y8Y4YBxtnNUorQTsmrFmzJicnp1+/flGjAGPGjPnoo48sy7Jte/z48XKsPjIkXwAAVAQCDQAAAFWABhrWr19/0003tWnTpn///r169WratKnb7c7Kyvriiy+mTp26b98+jTJ07dp10KBBUt64EgAAx4NAAwAACWZuIHO7GAmXlJQkIps3b37yySeffPLJyBVs227duvXy5cuFNFgAgJOE3x4AABIsJydHH+hcgyc+7zJOSRq36tixY9SZXFRaWtpTTz21efNmTXhBlAEAcFLQowEAgITRrHtjxozZsmVLcXFxv379hH4NSBD9ILVp02br1q0ismbNmi1btmRlZRUWFiYlJTVq1Khz584mBhF1pgkAAE4MAg0AACSM3kAeOnRo6ELae0igQCCgs1F07ty5c+fOkSv4fD6Xy8WnDgBwEhFoAAAgwXT6QynXFBhAbDomwkxLaaautG3b4XA4nU7mpAQAnHQEGgAASLDY0x9WKDO/5ikT4AgGg5rkQm/jn+zqVAqcBwBAJUegAQCAU4Rt2zHiC9rJQsXTUtUWfuhtc71nruLZ/JjrxFNO6Dq2bZsb+AAAoNIi0AAAwCnCsqzCwsLi4uLi4uJ69eqFvRr/bXDbtn0+n8fjKW0Fv99vWVbsThPH3F0gEPB6vTFGl+ikCYWFhUePHk1OTq5evbplWcykAABA5UegAQCAKs/v97tcrnXr1nXs2FFErr322lmzZunC8hWlUYa8vLw1a9bs2rUrPz/f5XLVr1+/Xbt2TZs21WJNVsLy1dmEGLTfRGQ5OmLil19+adWqlYhceeWV8+fPJ8oAAEDlx681AABVWzAY1Jb/TTfdpEv++c9/SslcmzqEwev1WiUmTpyoS2IU9eabb7Zq1eq000674IILbrzxxhEjRvz5z3++/PLLmzVrlpaWNnr06MLCQqfT6XA4IodIaJ6IvXv3WqVwu9116tQ599xzH3zwwd27d2u0QsMKoZxOZyAQaNmy5S233CIiCxYsWLFihYj4/f4Enj0AAJBwBBoAAKjatKn/73//+/vvvxeRv/71r6mpqYFAIHRIQug4CF0/MtmBdivYsmXL6aefPnTo0G3btkXdXUFBwQsvvJCamjpt2jQRiRprCNtjGL/ff+DAgdWrVz/55JONGzceOHCg1icy1qA0MiIit912m4i4XK54EkAAAICThaETAABUYaYPwujRo3XJww8/HHuTqNMfap7FhQsX/ulPfwpdc8CAAd27d69Tp05RUdEPP/wwb968zMxMffWuu+5as2bNq6++qrGGsEENoU8bNGhQq1atgoICjW4UFxfv2bPHvPree++1a9du48aNkSkYtFNDjRo1hg0bNnPmzE2bNs2fP/+qq64iUwMAAJUZP9IAAFRhem//3Xff/eWXX0TkmmuuSUtLC+vOEHWTUJrc8euvvw6NMowdO9br9c6ePfvuu+8eMmTITTfd9Nxzz+3cufO///1vSkqKiDgcjtdee+2ee+6RUvpHmMdvvvnmxo0bd+7cuWPHjh07duzevdu27blz52qvB7fb/cMPPzz00ENR66bljB8/Xp/qAzo1AABQmRFoAACgqjLdGR577DFdct9998n/NvLjL+Siiy4yCz/44IOnn35aRAKBgC+Ernb06NF69erptJeTJ0/+5JNPLMuKkTrh8OHDIlJcXKyTZerCQYMGaecILfb555+XaBEEjZj87ne/a9eunYhs3rx5+fLlEt/0mQAA4KQg0AAAQFWlje0tW7b88MMPIqIZFqUsM1lKSVTinnvuyc/P11EVEyZMuPzyy30+n/aMcIewbVvnpPzuu+/MtsOGDZOy9DIIBoOBQKC4uLhBgwYjRowQEafTWVBQoFkhwgqxLEvzVg4fPlyXPPvssxKtDwUAAKgkCDQAAFBVaTt/+vTp+nTAgAEi4vf74w80aCghEAhMnjxZRHw+X9OmTbVbhMvlihx/YVmWx+Px+XwNGzbUZBBut3v//v3vv/++lN7LQIMCDodDJ5hwOBxmbstLL71USrotmOwPYfTVQYMG6dMPPvjAtm2tdpyHCQAATiQCDQAAVEnBYFA7IMyZM0eXXHPNNWUtRNvqb731lpTME6GjMHw+X4wuAxrIuP/++6Vk4IPGKcqRoLFmzZrmcX5+ftR1tCYZGRnNmjXTJQsWLJAyjhABAAAnDIEGAACqJO0+8NNPP2VlZemSnj17ShnHFGho4J133hERHaEwePBgOVbIQHsTJCcnX3jhhbpk6dKlulX8jX9dM3T6iTp16pRWSa1bnz59dMncuXPj3AsAADjxCDQAAFAlaaDhiy++0KcNGjTQLgnxBxps29Y0kF9//bUuadWqVVJSUjyFaFcIHfigHSs0T0TU4QwaUwgGg5oMMhgMmvEd7777rpTEONq3bx971yauoUfN3BMAAFROBBoAAKjCli1bpg90UoYyJWgwrfQDBw7ogw4dOpSpkM6dO0tJaGDTpk1SynAGTbLgdDpNjgZNALFkyZJ58+bpvs4777xatWpp8oXIEnSdTp066dP9+/cXFxeXtjsAAHByEWgAAKBK0ga59iMQkTZt2kgZJ33UAIFGGfRxo0aN4ixEW/7169eXiEEQkV0StEDtPaF27tw5atSo3r17m1dff/31GLvWMs844wyzZMeOHUKgAQCASsl17FUAAEAlEwwGNdCwd+9eXdKgQQMp16SP2jXA4XAEAoFq1aqVqZDU1FQpiQ6UlspRRK6++mrLsmIEBb788suWLVuW1p3BVOm0004zS37++eezzjorznoCAIATiR4NAABUYYcOHdIHodM3lEloWKGsE0bq+tq7IbTDQqTIKENKSoqI3H777bZtx5/GUvNQiEhubq6UsQcHAAA4MejRAABAFaZpFKUkI2OZaONfuwloyODgwYNSlvEIGubQAIGJdERu3rBhw/T09IKCAofDEQwG9+7dW1hYWFhYKCLTp08/99xzb7755jj3aLo86IGXowcHAACoaPRoAACgCjN3+H0+X1m31Z4IOlxCbdu2TUIa8zFoV4LMzEyzpEWLFlJKy//NN99cv3799u3bt23btn379oKCgq1bt2oiSRG55ZZbZs2aJfH1pzDraGCFHA0AAFRCBBoAAKjCatSooQ/MGIoy0Ya6SXbw3XffiYjT6YxzSMKXX34pIn6/X0TOPvtsKSXQoHUrLi62bVv32LJly++++65Lly66wvXXX5+Tk+N0Oo8ZazA9OGrXri0lsRIAAFCp8PMMAEDVo7kbpWTeBynJClmmO/yWZWk/iL59+4qI0+k8dOjQ9u3b4ylHe1IsWLBARILBYHp6et26dSWOlr/GGjQD5aeffmqW33rrrVL6UAitT15enlmiE2QAAIBKiEADAABVkgYa2rVrp083b94sZb/Dr+sPHTpUShr5kyZNkmMFGrQLw3fffbd7927NATl48GBdHjVSoAsdDodlWfo3KSnJ7/fXqlXrkUce0XUWLFiwceNGh8OhhYfR+vz8889mSYyRGgAA4OQi0AAAQBXWo0cPffDDDz+IiMvlKtNEDBpo6Ny5c8uWLbWFP23atOzsbJfLVVrSh0AgoMGFu+66S0qCDmPGjJEyNvs1E8Sjjz5qUkLce++9UkqsRA9q3bp1+jQjIyM5ObmsewQAACcGgQYAAKokbZBfeOGF+nTPnj0aGijT6AnTg2Dy5MlS0m7/4x//KCJut9vr9QYCgWAIn8+n+33++edXrVqlEYdrrrmmWbNmtm3Hk0XSMAM3HnroIV3y6aef/vDDD6V1ahCRJUuW6INevXqJiN/vJ0cDAACVED/PAABUSdrGbt68eUZGhi5ZtmyZlH0iBg0WXHrppQMGDLBt2+VyrV69+oILLhARj8fjdDp1Rw6Hw+FwuN1uy7KmTZumvQ/8fr/b7X7zzTfLsV8p6dRgRk+IyOjRo02VjGAwqCkhPv/8c12iIzXK1HcDAACcMAQaAACokhwOh/YI+L//+z9dMm/evPIVpS32f//7323bttVuAl9++WVaWtrLL79cVFQkIcMZVqxY0b17dx00oVavXp2UlBQIBMI6F8QTdzApLR9++GFdop0apGRERmhRubm5O3bs0CVXXXWVxDcNJwAAOPEINAAAUFXpSIfhw4fr03fffVdipmnQlnlk+9w0+Ddu3NizZ0/dvKCg4I477khJSWnQoEGbNm2aN29uWdb555+/cuVK3So5OfmHH37o0KFDMBiMWqaIuN1uiZmiUg/hscceM0v++te/6lGYUIXW7d///rc+/cMf/uB2uwOBAIEGAAAqJwINAABUVdqAP+uss1q1aiUiv/7665o1ayTamAKv1yslLfZDhw5JRI8Dp9OpS7788ssXX3wx9KV9+/Zt2bIlMzPT7NHhcIwePbqgoOCss84KBoNR4wiFhYUion0u8vPzI/doDkFrNX78eF3y8ccff/HFF6a2ZtzEyy+/rCvcd999pZUGAAAqA9exVwEAAJWSw+Hwer0ej+fhhx++7rrrRGTixImzZs2KnIvB4/H85S9/cTqdOTk5ffr0kWj9GizL0m4Cd9xxxx133DFv3ry333571apV+/btM5ELfdC4ceOJEyeKiM/n0z4LYeWIyGmnnda/f/9q1art37//zDPPjLrH0PXHjx+/Y8eOpKSkffv2bdq0qXfv3rpcAwq7du3SGErTpk21/qSBBACg0rJO/A0Bv9/vcrmuuuqqBQsW6N5LuxkCAABiM7+h9evXz8rKEpGCgoKUlJRyjyywbdtMYBlm+vTpZphG69atN2/eLCIVPYRBLxuGDx8+ffp0EXn77beHDBmiCytupwAAnML0Z3Tjxo3t27dftGhRv379ot45OB407wEAqMLMZJATJkzQJU899VTkasFg0Ov1+ny+4uJiHZJQGsuyND+C1+vVARfG7bff/re//U3X2bJli+kZETUlRDAY9JeIZ3qI0PVNDYPBoMvlKiws1ChD8+bNhwwZInRnAACgcuN3GgCAqk1b3ddff32bNm1E5Mknn/T7/U6nMzSg4HA4PB6P2+1OSkqKpwOCZVkej8fj8QRLaGkPPvjg6NGjtUPiF198cfXVV2vhkaEEh8PhKhFPXCB0fVND3dEDDzygT1966SUR0XkxjlkgAAA4WfidBgCgajOdGmbMmCEitm3ffPPNkqB0iY4SJlvkxIkTb7jhBn11/vz5Q4cOTdS+wuigjJ07d77wwgsi0qdPn379+okIgyYAAKjk+KkGAJyaAoGA5hr4Ldz91rZ3165ds7KyCgsLtX9BwhvklmVpSog33njjxRdf1Dkms7OzpfREj8e5OxGpU6fO1q1bk5OTGzduLKdKXicdJ+J0OpmhEwBwSiLQAAA4Nf02W3H16tWr0PIdDod2XqhWrZouadKkScXtS0SqV69evXp1XWLb9ikQZZCSkSwnuxYAAFSUU+HXGgCAUHo/Pzs7+5lnngld8lsQKFFxu7AsS2emOAH7EhFNDxEIBILBYOS0nVWO+Sg+88wzOTk58lv6cAIAfjsINAAATjWasODRRx8dN27cvHnz5LfUlnOWqNC9aMqGE7yvU6Mvg34U58yZM27cuMcff1xKPq4AAJxKToXfbAAAQul9b+3h7/P55LcUaEAlpx9FDS5oZ5BToJsGAABhCDQAAE5N2n7ThIi05VBJ6EdRu4GcGn00AACIRDJInCDmdmLkdVXonUauugAkFn0ZAACIfb3N1TgSjkBD1RYMBm3bDp293LIsy7Iq4RdEjCpVwtoCAAAAx6RjoMzVeFW8FD/mq0A5EGioqjT/ttvtjvqqz+ezLCvhM6hHitFPAQAAADgl2bbt8/lcLlfUhLh+v18v1Ct64B49EVBp8XGsenSiL6fTqVGGoqKi9evXf/bZZ4sWLfr2229zc3NFxO12u1wuXbNCK+MoUdoKWoHc3Fy3261pw7Ozs81yzYb14YcfavS3Q4cO5hgrtNoAAABA+fj9fsuyPB6PXgNv3759yZIln3zyybJly3bv3i0iLpfL4/FYllXR08o4QkRdIRgM6nV19+7d9Xp79uzZ8r8pafPz81NSUhwOh2VZ+/btM8uB40GPhirGtm39HikoKPj73//+xhtv/Pzzz2Hr1KpVq3///g899FCTJk1EJBgMnvQAp8vlMt+zob0w9IvPfJcdPHjwxNcNAAAAiEcwGDS9hl9//fWpU6d+++23YetYlnXxxRePHTv2oosucrlceoPwZFT2f+jNSCkJMYTe1XO5XEVFRebxia8bTkn0aKhKbNvW/leTJk1KS0t74oknIqMMIpKbm/vqq682bdr09ttvFxGHw5HwDgKmwNatW2tw9PPPP5fSJwMP/XqN/Ko132iV4VsYAAAAiEpv+69atapWrVrDhg2LjDKIiG3bn3322cUXX9y5c+fs7Gyn0xmaTy1R9Kp71KhReil+9913S+mX4iKSlJSkD/SeX+iYjtgX6kD5ELKqMkzHhD/96U8LFy40y2vWrHnxxRe3bt3a7Xbv3Llz6dKlu3bt0pdeeeWVzz77bOvWrW63u4L6NWRlZemD+GeqjxxLlp+fr0+PHDmS8BoCAAAAiTJ16lRt1Ru9e/fu3LlzrVq1srKyVq1aZaIP33//fd26dVetWtW1a9eEX4rrFbW5eNYHMS7FTQ/iyHVMdwYRKSgoSE9PT2A98ZtFoKFqMN9NV1111cKFC7WTQvXq1efOnXvppZeGrbxly5ZBgwZt2LDB5XJlZma2bNkyMzNTN0l4rCE5Ofnw4cMi4vF4pOxJaHT9gQMH/vLLL0lJSSbcG7ucRKW9iZ3JMuxb+KQPPwEAAMDJohfSr732WmiU4amnnrr//vvD1iwqKhoxYsSMGTP06Xnnnbdx48a2bdv6/f4EDkzQLgmmn4Jeipc196T2X6hWrdqePXs8Hk9RUVGjRo2OWU6iLpLLdCl+PDvCSUGgoWrQFvgLL7ywYMECzXfQunXrzZs366t+v19X0DFjrVu3Xr9+/ZAhQ+bMmeN0Onft2vV///d/c+bMCfvnDAQCGtp0OBwxvvVKW02/K01wVHtq+Xw+rYnmkjjml6lWyel06pdabMESoSl8tXqaZrK0rTRhj23bLpfL4XCEbRLWmU1zCEdWXrc6ZvJLAAAAnGL00vHHH3+85ZZbzML169e3b99eSiaDExHbtt1ud3Jy8muvvda7d++hQ4fqmr169Tpw4IBmag+9jPR6vXqNqmGCqCIvZXW53+93u93ap1hKOix4vV6zoa4f5wE2aNAg9Glp17qRF962bXu93tiX/eYwtf56RDGu3qPO2REIBHR5jA1RqdBeqgL0/zk/P3/06NEi4vf7k5KSNmzYICJer1f/ad1ut840oe1kEZk9e/Y555yjbeO5c+euWrVK/nfgltPp9Hg8Ho8n9ndQ1NXMt0m1atV0Se3atUUkJSVFa3LMYstxEnSnmsI3rHpOp9N8xYdxOBwej0erpFEGs4muEFqaySEcWXndKvT7HQAAAKc8bdyKyHXXXWcWfv755+3bt/f5fGYyOL3a1JkmAoHADTfc8Mgjj4iI2+0+ePDgmDFjJOL+lrlGjbH3sEtZszwlJUVEatWqpU9r1KghImlpae4Sun6i5tc0IYbQq2gRMVfOMWa7C62/3owMK8SwbTsYDEZe8IuI0+nU5UQZqgp6NFQB+pX06KOPiohGLt9++22Xy+X1eiO/mCzLcrvd2t3gvffea9q0qTa/x44du3TpUv3P1K+JXbt2vfzyy2lpaenp6XfeeWfkfnW1devWzZkzx+PxnHnmmTfccIMuPHz48MSJE+vXr5+Tk6MrT58+vUuXLgcPHnQ6nZZlHT16tEmTJjfffHPsQ9PStm/f/uqrryYnJ9etW/eOO+6Iega05mvXrp0yZcry5cuzsrK8Xm9qamrTpk379+8/ZswYPRWhcWJ9fPjw4QkTJqSmporI+PHjnU5nUVHRM888s2DBgp9//vnIkSP16tXbtWuXhio0vvDxxx//85//XLt27YEDB/RM1qhR44wzzujatesVV1xx4YUXSuWYywMAAAAVTa/6Vq9evXr1ar1iHDJkSJ8+fQKBQOhkaobe+RORRx99dMaMGZq7/bnnnnv66ac1MaSZRe6pp54SkaKionHjxqWmpoZdXurTgoKCp59+Ojk5ORgMPvDAA6b5PWXKFLfbvXz5cn26fPnymTNnZmVl6TVzMBj0+Xxjx47VTG2xDy0YDGpN/H7/2LFjU1JSImuixR48eHDSpEkLFy7ctWtXQUGBx+M5/fTTL7zwwjFjxpx55pkSkrpeQq6WJ0+efOTIkYKCgttvv71p06Yi8tZbb7322mtbtmzJy8s7evToihUrunXrplfdlmVt2bJl0qRJy5Yt27dvX1FRkcPhqFat2umnn96hQ4c//OEPV1xxReS5QmVkn3Dau/7KK680ew8EAie+GlWFOTk1a9bUt6xRo0a6JBgMHnPDyy+/3LzXBw4csG3b7/frW/D+++/r8rS0tLB9qeLiYtu2J0+erKu1adPGLNT+FLHVr19fd2fbtkn3KCKHDh0yy7W0t99+W1+qXbt2ZE308YEDB7p27Rpjdy+99FLYGdDD3LhxY+in/YMPPojxX7Bv374zzjgj9nGNHj3a1BxA5aS9vUaMGCEi77zzjlkCnHT6UdQfvrvuusvmwwlUenpJef3115urwV27dtklV7Ol0X/tt956S0oSKMyYMUOX64YFBQWmwO3bt0cWqE9D55g7evSoeTV2Pwi1f/9+U/927drpwlmzZpnq6S5C09/BawAAIABJREFU07Hv3r07rCbm8V133RVjX9ddd52uZloo5nrehB5WrFjh8/kik01++umnZncDBw6MfVBNmzYNKx/loJ8KbdMtWrTIroAfI+JAlZ3GIDdv3nzo0CENJd50000i4vP54knTojNcarR1wYIFEpJYpXr16vogIyMjaglavhkcUadOHfOSfrXF7rkUT8ZajUSamminr7CjcDgcO3bsqF279tdff60L3W53p06dLrjggrZt25o177jjDu2fFsbU/4wzzvjoo4+uuOIKfZqamtqoUSM9xsLCQhHx+/3NmjUz3+bJyckdO3bs2bNnt27dWrRoYQoMHfwGAACAU5j25JeSC2kRadOmTZMmTeRYV8L66pAhQ6Tk6nHOnDkSMmhXxz4oc70aKXQ17aKrzKAJI7JpEOcog9BiQ3cnIX0ZOnfuPHXqVLO8devWPXv27NKli+nTMWvWLL0paFlWWB8K04jIzs5u3rz5wYMHdbX69evr4GtzS/Lyyy//97//bTZs1apV9+7dzz///Hbt2plK6nU7Kj+GTlR2+o+6dOlSEdHOWn/6058kjqSyuoL289esDYsXL77ppps0F4uETHITu+Vsvim0EP2uadmyZVFRUVJS0hlnnPHLL7+IyKJFi/r162ey6ZoUkvEcnV0yXC1s7l+NMvj9fhOCTU1NnT17tgkWaK2GDh2qX9yPP/54z549L7744tCkvqb+e/fuHTBggIhceeWVU6ZMMT0XVq1apefq/vvv19l9nE7nRx99FDmdx9q1a++8804tmc5aQFVhvvEqaCZzIH6WZWlf63hmgwZQGegPx549e44ePaqjDC655BIRiTqEOZTJR9C+fXu9b6wZ00KTqZePGXpcrVq14cOHT58+XURuu+22V155JfQauKioKDk5WeK4ai3tx9EMT7jsssu+//57XfjII4888sgjoS2RKVOmjBo1SkS++eabESNGvPjii2HlmCv8YcOG5ebmNmnS5M033+zZs6cu3Lx5c1pamoisXLnyo48+0oWPPvpo5B3E3bt3P/PMMytXrox9OKgkCDRUDfq/rRGBNm3aSNyBhrS0tFq1auXm5orI+vXrRcTpdIa158vBtm2dTccEKcKmt4wzH2zso9BXhw4dqu3/2rVrZ2Vl6benbduaUsHtds+ePbtGjRrmSzYzM9MMjQvdhR71uHHj/v73v4fu5bzzztMH8+bN0wfz58/XKIPuxbIsPZaOHTuagXCJTXUJoOLot5Pb7SY+iMpA7/7F0+cZQGWgl5RbtmwREU2Rds4558S5rV6sduzYUQMNhw8f1sZ/QqLeYUEEveINvbTWFY4nrKmBhnffffc///mPLlm6dGmvXr30sd/v17nYRo4c2bJlyz/84Q8i8tJLL40fP75Ro0aaxz20NJfLlZub26FDh7Vr14Yub9OmjVbSXIqbPJpScgGvOeAaNWr0j3/8w2zIz3olx9tT2em/0J49e/SpZVmnnXaaxDdNrn6LNW7cWJ/u27cvzg3jLFlCukXoAx0rZeahLHf5es/Hsqzs7OzZs2frwo8//tjlchUXF2vJ2tTXb5+XX35Z+1Pt2rVr9erVoRVTehqbNWumUQYdlaSVNGGXvXv36oMePXqISHFxse7F5NTRMXXlPigAJ5h+3Wmkkr4MqCT0o6g/MQAqP73sNFeJItK8eXOJr5Wr2+oIXL2ePHDggBxf49/Qa92w6S01n8LxX4pLyOyY999/vy4ZO3Zsr169fD6fZmEwc1UGg8FLL71UE/CJiOZ3i+y1oVfRH374oYgUFRWZepp+0Dt37tQ1+/btKyL5+fm6F62GrqnX8MdzXDhhCDRUdnqhfPToUX0a/z0Q/c+XkEFfFXpZY+IXlmU5ShxPgVr5N954Q5+2atXq3HPP1Ul39YvG7/frF5aO1Bo8eLCuqSPowr6D9Mv9vvvuE5Hi4mKdmDesntprS0S++eYbEUlKStJEKV6vV6MeZvKe4zkuACeMfukNGTJEp+OxgEpAP4o33HCDkPQHqAosyxKRvLw8syQyp1jsbXV9veDUcqxE3PbTAsOK0vksE3gpvn379h07dugSvZA20QG9FA8Gg/prO3z4cF1NL8WjzsfRu3fvxo0bB4PB5ORkU0/TCdqcWB0zrlfmeikeDAYty9JpRAk0VBUEGqqGcnwfmfZwFf1v1EM2PbX0mswVjSatueyyy3TN7777TiLOmIZ7tU9X2JgOTQMhImao2KWXXjpjxgx9SWf91eiG5mKlmxZQtSQlJZkLmpPTsgRK6IcwOTk5bLwhgErOCrmwLOuldej6ViJCDCeGNiU+//xzfdquXTvN6ZiUlBT1Uly7IYjI9u3bJVpKSCm5FI/sIKynyJTw6quvjho1qqCgwLIscykuIj6fL3JEBiotxplXdvqPZyJ8ZeqVoP+Hhw8f1qfmjn3lZzprbdq0SZfMmjVrw4YNubm5mlnHrKnfYtWrV9cvNRHZv3+/lCTODCu2YcOGEu0rXpc8/fTT8+fP173ffPPNN998c8+ePfv27du7d+8ePXqYrBOagKcCDhpAgmkqmXfeeSc0gyxQGbz//vv9+/cn3Q9Q+YVdiouITpoQ/7ahwyV0qrUqcRdQL49NPoWff/558ODBhw4dCruQ1ktxj8djwge2bZs8lGF+97vfRd2XfhkOGTLkr3/96+7du0VkypQpU6ZMadOmTd++ffv06dOrV69atWppL4nQhJeozHiTKjv9VtI8C5Zl2bZ94MCB2rVrx/MNpV8EP/30kz7VZvZx5rk9MczR6VeziGzevHnz5s3xbBvj21/vIEUGGjQXfatWrRYtWqSZhNWyZcuWLVumj/v06XP//fdffPHFunIVCkgDv1n6TaIdmnw+X9RunMAJph9FBk0AVYXet9OraLV9+/ZevXrFM5ZWt/3xxx+l5ApcZ3OsQvfkNcWbw+E4cuTIO++8E+dWBQUFUXNeaqa5yMN3OBx6J2/dunVdu3Y1tw/1+n/KlCki0qpVq5EjR44YMYIoQ1XB+1Q1nHvuudOmTdNLk/Xr1/fu3fuYgQYTQzUz03bq1ElEdIxT/Ls+uTFXExxt3Lhx7dq1tQ9V1DXN8bZt27YcO9Ji+/XrZ9v25MmTp0+fHhbXWLx48eLFizt37vzNN9/oSIoq9CMB/JbptaBmrqoSN5FwCjN9iatE0B+AlFwi6uWlhgi//vrrm2+++ZjXgcFgUJvEZmLIunXrasi7TJfiJzc7mAbrg8FgWlpamzZtjhw5EqPymrjB5/NFzR8RdYmhV9fp6ek//vjjxx9//Nxzzy1ZsiT0V3vr1q133nnnPffc880333Ts2JFL8cqPQENlp/9COpGM/qvPmzcvnkCDxgU/+eQTEUlKSiouLg69Vx+5i9KclFv3ZqdpaWmaCPOFF14YMGBAOUooE+2LNWrUqFGjRgWDwW+++ebzzz9///33Ne+DiKxZs6ZLly7ff/+95trkCw6oKkyChpNdEfzWlXb9DaBy0v/W2rVr16tXT8fnLly4UEQ0QXiMS0G9Vj9y5Ij2aJCSdGB+v79M3wAn9+vCjBnp16+fmX4yTmWtuenXcNlll2nyta1bty5ZsmThwoU6V4WI+Hy+Tp06HThwID09neHMlRzNpMpOv7+aNm3aqFEj/cLSPIXHzLmqwwSmTp0qJZkdLr/8cglJhWgexL6vkp2dHX9tE3i3UIsyQ7n++9//ikhxcXHonD2lKfdOXS6Xbdua29bhcJx33nkPPPDAt99+m5eXp3P2OJ3OtWvX6ngKpp8AAAA4tTkcDr3VN2jQIBFxOp179uzRW1CxLwX11X/+859Scll+4403lrZyjKvx+FNCJJZeipuewitXrtQHYZPZJ/ZSXEco66W4iLRq1er222//4IMPbNt+9tlnpSR48dhjjwldwyo9Ag2VncPh0G5aI0eOFBGPx5Ofnz9p0iQJmTg3km6yYsWKVatWaaiif//+Ho8nNFOrjpISkdzc3KiF6H/yihUrYlTPhCr1yyhRd/gty9Kju/DCC3WJpmnUKScjb0uahN7ljnToPD1alMfjsSxLe3/p3JZpaWnz58+vV6+errN48WIh0AAAAPAboJed99xzj5Q0bvWx3qCKuonf79dREg899JCIeL3e1NRUc8/PXMeaBzk5ORJxx04vNXXa9dh1i9z2+GnJvXv31qdZWVmZmZmmVjEuxctdEw1h6KW49m4wl+IiMmbMmNtuu00L/+KLL6SkU0n5jxAVjEBDFaDju8aMGZOSkqIRhL/85S8//fSTx+PRWV7M/7O2lr1er8ZN+/fvLyVfBxMmTDAF6ldD06ZN9WlRUVFWVpb8b8vZ5/O5XC6v1/vRRx/FqJsmdRcRHeDg9/sDgcDxhzOlJGYxbNgwfZqVlaWz8uock7qC0sMJBALagap8wQ6dxVe/0bR8p9Ppcrl0Qp3CwkIR6dKli66soRnGTQAAAJzyNKDQvHlzncPI6XR+9dVXL7/8sogEg0G9dAzNB6RX0SIydOjQwsJCjTg89dRTIuLz+UKb6M2aNdMH2ls29BZ9IBDQQl566aUYdUtNTdUHmpRNr4cTcimufZ+7d+9erVo1XaJBEw0BSMiluF4S61W0maat3Hs0rZvQS/GioiIpGUsuIkeOHBEuxSs93p4qwOFwaE7EWbNmmYVnnXXW7t273W63hkX9fr+O+HI6nRpl6Nix4/79+/U/9sEHH2zevHkwGNSn+gWXkZGRnp6upb3wwgtS0jdM/731O/Gmm26Sku5eYVXSr5gWLVroEo22Jicna1PffOmUj365iEj79u27deumCwcOHJiTk5OcnKx795XQ2KdO5Lt//349V/EHU/Vb2Ofz7du3T7/RNBxryg8EAjo/8Pr163WTJk2aCD0aAADA/7N35+FRVNnfwE8t3Z10Z4dAQgIBAggSdpFVdtkUBhx0XBkX3EbBFUZ0cBQRGZXREVdmfuqrjooOOIgKCIgIAWURwg6RTUIgMWRPeq2q949j7pTpJCShuwPJ9/Pw8HSqq6tvdzq3T506915oGjjq++CDD6giHXDvvfd+9NFH5tCRQ1BJkjiKfuSRR95//32Ornv06PHAAw9Qxbm0iO3FmTMPdrbZbBx88hm7JElLly4VYxYq4Vj3kksu4R9//PFHClwoTqb64ieffJK3/Pvf/16yZAm/ZBEqc8UBh+I8bz2P9ahHnHz8+HF+9/hd4nkl+d3gS5uiuKNVq1aEoRMXPCQaLg789zx58uRHHnmEiCRJKi8vb9269fz587nGgU+zOYPw0UcfRUREZGRk8An5iBEjnnnmGTKl/UTvNnXqVN7y/PPPf/HFF7IsWywW8ed96623fvjhh1QxEKMS/tseNWqUOMLatWsrJWLP+bpqSAfw4AUi+vjjj3mLz+dLSEhYunQpv15LBX5d+/btmzZtWkJCQl17N7FcRatWrW666SaeGVi8FeLduOeee06ePMkPmTJlCpkmuQAAAACARozrXiMjI7lon914441Tpkzh+JBPvzko5bnD//73v/Pc4Var9dtvvyUi8xBmduedd/KNgwcPTp8+nYg4+ORahnfeeYdjzirxoYYOHco/ZmZmzp8/n4twGUf755Nu4Fh35syZXbp04S3XX3/97NmzyRQqc8UBEeXk5PzjH/+w2WyfffYZv9jaTwbJcfvgwYM7duy4cuVKXiNPRPt8jvPJJ58sWrSI97/pppuoqvcTLihYdeKiwdftX3zxRVVV//a3v/HGJ5544oknnujYsWP79u0VRcnOzt61a5d4iK7r48eP56lxK82Ly3/58+fP5+keiGjChAndunUbPny4qqqHDh3iP/K2bdvefffd3KFUOq/mH//0pz9xGZWmaVdeeaU4uGEY3bt3z8jI8H8h5mZUmny70rq4vM5NmzZtvvnmmxEjRvCzTJkyxeFwDBs2rH379larNT8/PzMzc8eOHaJj5eqDSq/0nHjGig8//PDDDz+0WCyXXXZZ586d4+LiPB5PZmbmmjVrRN7kgQceaN26tai5AAAAAIBGj+cpHDZs2Nq1a8WVtqVLly5durRFixZdu3Z1OBwFBQUZGRmlpaV8r67rsbGxBw4ciImJqbREAsfAAwcOHDJkyHfffSfL8quvvvrxxx9PmDChefPmp0+fXrNmDS9y8frrrz/00EM8s7sZH6FLly7dunXbs2cPVZwXmPfh1Rn4opoIs/1PzquLlsUyEDt27Gjbtm1ubi4RLViwYMGCBYMGDerWrVtERERJScmJEycyMjJOnz7Nj3I4HP5vXc1PxCIjIw8ePMjrTaSlpXXt2jUxMVGW5VOnTm3YsIEHehNRx44d77vvPvI7cYALDX49Fw3RKSxYsGD06NFTpkwRkzhmZmaKhXPM3nrrrbvuuouIDMOo1KfwOXx4ePjmzZsHDhzIG/fs2cP9FLNarceOHfv000/5R56FodIR4uLiVq9eXWnhTO7OCgsLxRZz5+h2u8U8lJy/5EFlRCT6ZfOrNgxj+PDhmZmZI0eO/Pnnn3l/zp74i42N5USDGNxRXl4u7uVsS5VlFJXmp9iyZUuVhWp//vOfFyxYQBV5nyrbAAAAAACND19LGzlyZG5u7uTJk9PT03l7bm4un4RXcscdd/zrX/+iilXnzXeJhdLXrVvXoUOHEydOEFFeXh6vLid8/vnnEyZM+NOf/sQ/ejyesLAwcQQ+7Pr16y+77LLjx4/7N4DjWw59RSTPgbc5HuYZEFiljIY4ZcjJybnmmmu4WoGI0tPTxcuvJDk5mX6bUxBPbX4iMzE/mtiyd+/evXv3+u85YMCA7777jqo6u4ELDRINFxkuJRoxYkR+fv7y5cv/+c9/bt682bxsRFhYWJ8+fW655Za7776bt1R3Ssx/nAMGDMjNzX3wwQc/++wzURSQmJh42223zZ07l4giIiL69u0bFhbGcyWYD8VHGD16tGEYS5cu3bx5c1ZWltPplGW5oKCgR48eYk+bzTZ69GhuP8/4wMfhDiUlJaVfv37h4eEdOnQwH5lxn85d8Jdffrlo0aItW7bwHDDMbrd37Nhx+PDh119/fb9+/cRL5qeIjo4ePHiw3W4X81ZWeje4DZGRkdnZ2StXrly1atUPP/xw8uRJc/+blJQ0fvz4xx9/nGfQrHnZZAAAAABolDgujY+P37Rp0549e1555ZXVq1eL0bVEJMtyx44dJ0+e/NBDD7Vo0YKIxCxplXCuQVXV48ePz5s379VXX+USBiJyOBzjxo174YUX2rZtm5+fP3ToUJvNpqqqebY1qghimzVrduzYsS1btqxatero0aMcJJtnGeP9r7rqqoyMDJfLxTOsmQ+lqurYsWN9Ph/nFKpspyzLy5YtO3DgwIsvvrhq1ars7Gyxg6IoKSkpAwcOnDx58uTJk8Ux+bSFiKZMmXLmzJnS0tKEhASqqq6Bt2zfvn3NmjVffvllenr6kSNHzMNAYmJi+vfv/8gjj3AtCS74XRTqvxxgvfl8PlVVJ02atHz5cn52nLbVCc8iay4WcrvdJSUlhmHY7XZztRIPjqr5vTX/oRYXF5eVlUVFRfmXPNWAf6F1fBF1VulZnE4nJ0cjIiLEdLuBVVRU5HK5LBZLbGyseIs4MYyPK8AFzuv1WiyW++677/XXX//kk0+uvfZa3tLQ7QL49cP50Ucf3Xjjjffff/+iRYvw4QS46PhXKOTn53u9XpvNFhMTIzbyIhQ1D7Y1nwe53e7CwkKLxSLma6/NGbV/Y4Kh0mmFYRj5+fk+ny8sLCw6Otq8Z6CyAOXl5WVlZYZhREVFiToOCtXrbfT43Grv3r3dunVbvXr16NGjA/5lhIqGiw/PIstzvcqyrKqqzWYTl+vJtNBjbf4IecUKIlJVNSoqKioqirfzwblcSqyXW+UBxcSz5HcGLlID5iV2/GfBNd9bXc5CPAu/5PDwcHPCVdxV88FrnoCX3zd+1ZIkRUdHm/tNXo4Ig8EAAAAAmjhzhMxxo0gNkCkurU3cyFG9z+ezWCw2m61ly5a8XcTn54yTefKIKkNxc+jLB6Rq4uGa76WKCghey9NisUiS1KxZs0ovmXfzP18458EZh+L8MmVZttvt5quJXHAhpmmHCx/Omi5WkiTxGATOlZq313UBW+6zzMcRC/PQuXqESo2pTs0HqeUCPDW8ZFmWq2tA7Vf3Ee+b/8rD5jcEAAAAAJo4EWFWGTfWHBhXwnFmpVDcPHfj+YfidK6pE2t5Lc3/rIEqBj7U0IBaHrxSKF7pKXC176KDX9hFL1Bl/BfRcIBgN/UieisAAAAAoAEhFL/ojg+hgd8iAAA0ThypIF6BCxCmMQMAgMYN4RcAADROPF6U/w/9zMcAVeKPIn8sxbhlAACARgaJBgAAaGz4XI4nqeZldFDXABcI/ijyx5LnOUMWDAAAGh/M0QAAAI0NTxk1f/78AQMGTJgwgWo9ExVAsPFH8ZprrlmyZMnEiRMJH04AAGiM8N0GAACNDV80ttvtf/jDHxq6LQBVu+666/gGym0AAKDxwXcbAAA0Trqu87LbDd0QgMp0Xfd4PPhwAgBAY4WKBggRTdMMw6jNUsAAAAGBDgcuWLIsn3PRewCAwNI0jYgURWnohkCTgEQDhIKu66JTMwwDy3oBAAAAAISMYRgcjSMUh9DApR4IBVmWy8vLDxw4QESSJKFYFAAAAAAgZCRJysrKOnXqFLIMEBpINEBwcY3Wrl27HA7HpZdeOnLkSCKSZRm5BgAAAACAYPP5fES0cOHC1q1bJycnv/DCC1QRogMEDxINEFy8PPi8efP4x2+++Wb//v1EhEQDAAAAAEBQ6brOa+jOmTOHtzz55JNEpCgKonEIKiQaILi4Oqu0tJSIeOIrr9fbwG0CAAAAAGhKRATOBQ4AwYbJICEUuDqLqxvCw8MbujkA0CSgLjTgJEmTZR7ca2ga5i0PPEmSsFQKAAQDx+FUcRUQINiQaIBQcLvdVDFcAmvqAEBooLcJgv+9pXh3AQAuIiLRABAaSDRAKJivK/I4MQCAYDt58qTb7UafExCGQZqmpqbulCSNSCKiI0d6SZIhyzr/COfP7XYnJCRER0fruo66BgAILDEjAyoaIDQQfkFwcV/Go8I4k2qxWBq4TQDQNKxatSr79Gl7dLSOoOr8SKTruur1Ko/c/yd7OBGRV6P/fLFQ08NVxW1gvqfzIxGRYchEBTk5EyZO7N+/Py48AgDAxQ6JBggikTo1D53gq4tIpgJAsEVERDRr3jy8sNDwen8dRoGep54MXZfdzjCLYicqJyJVscQVuCXJo6heMmTUNNSfYeiSZLjdclwctWhhDwtr6AYBQCOHIBxCA4kGCAXzShMoYwaA0NANQzcMfc8erbS02DA0TTNQ5V9fui57XTafu9xiJSLSfN6CLVs0TVNtOmF9tPriugWb1WrzeNS0ND0lBavNAQBA44BTPgguHmVqTjRg6AQAhIgkkSQZYWGGrg9s106WZV3XUdRQL4ahy5ozzGL5gshLRKpCg7t3l4hk1WugoqFeJCJd18Os1tPFxT+dOGGx2QgjJgAg+DAFDIQGEg0QCqhoAIAGYxi6YfTv3JmsVvJ6CQFWfeikq+QKI1XlRANJ8sAuXUiSSfUQ5mioN10nuz0iM3N/ZmZkQ7cFAAAggHDKB6GARAMANCBd0zwul9Xr9Xm9EhIN9aGTrpJHl3T917fP0LXycpIkUpBoqD9N162G4fF6ddQyAECoYI4GCA2c8kEoeDwecRtDJwAg1CRJISL5Vw3dmouUTNJvhkgoskwkkSwj0XBeZBkhPwCEEhINEBoIDiAUzBUNDH0cAMBFBZfcAQAaAwThEBpINEAoYBptAAAAAIAGh0QDhAYSDRAKmqY1dBMAAAAAAAAgFJBogFDw+Xx8Q1GUhm0JAAAAAECThYoGCA0kGiAUREUDEg0AAAAAAA0FkyJDaOBzBqEgEg1Y2xIAAAAAAKBxQ6IBQgoVDQAAAAAAoWEYldcMwtAJCA0kGiCkUNEAAAAAANBQkGiA0ECiAYKIc6jmTCoqGgAAAAAAGgoSDRAaSDRA0JnXtkRFAwAAAABAQ8FkkBAa+JxB0Hm9XnHbarU2YEsAAAAAAJoac32x/6wNAMGARAMEnbmiAYkGAAAAAIBQMicXUNEAoYHPGQSduaLBYrEQka7rDdccAAAAAIAmCnM0QGgg0QBBZ65osNlsDdgSAAAAAICmxlzRgEQDhAYSDRB0XNHAZVo8dAJjwwAAAAAAQgOxN4QeEg0QRNyp+Xw+qsie8tAJAAAAAAAIPVQ0QGgg0QBB51/RQJiHBgAAAAAgJDB0AkIPJ3sQdJxo4A6OKxpQvgUAAAAAEBpYdQJCD58zCDpzRYOqqg3dHAAAAACAJgQrvkHoIdEAQee/vCUAAAAAAIQehk5AaCDRAEFnTjSIORoAAAAAACAEMEcDhB4SDRB0nGjgTg1DJwAAAAAAQgmJBgg9JBog6PwrGjBODAAAAAAAoLFCogGCzn+OBmRSAQAAAABCA6tOQOjhcwZBZx46gckgAQAAAABCCUMnIPSQaIAg4iESmqaJLUg0AAAAAAA0FCQaIDSQaIAg4o4Mq04AAAAAADQUVDRA6CHRAEHncrnEbZvN1oAtAQAAAABoavwnYsfU7BBsSDRA0Hk8HnEbFQ0AAAAAAA0Fk0FCaOBzBkGHRAMAAAAAQEPB0AkIPSQaIOjMQyeQaAAAAAAACCVzogEgNJBogKAzVzRgjgYAAAAAgIaCigYIDSQaIOgwdAIAAAAAoKFg6ASEHhINEHQYOgEAAAAA0FDMiQZMBgmhgc8ZBJ3P5xO3LRZLA7YEAAAAAKCpwWKWEHpINEDQeb1ecRuJBgAAAACAUDIPl8DQCQgNtaEbAI2feY4GTjQ0+My35iILVcVfAQAAAACEjq7r5iqDYIej5udCogFCA6dYEHRc0cDJBU40NPjYMCQXAAAAAKChyLLc4PEwQFBF9aiMAAAgAElEQVThdAuCzjx0omEng9R1nfv0G2+80eVy6boeExPz7rvvmu8CAAAAAAgSjjnPnj178803x8TEFBQUXHbZZfPmzQtqLIpVJyD0kGiAoLsA52j46KOPxG1ONAAAAAAABBuf85eVla1atYq3HD9+fN68eUEdWcwHl2XZnM7ANTYIKny8IIi4U/Ofo6HBiY7V4XA0bEsAAAAAoKkJCwujilLf2NjYUD41KhogNJBogCDi8/kLsKJBzIijaVrDtgQAAAAAmhqORXl6cnOoHCQYOgGhh0QDBB1XNHAHZ7PZGro5v4GuFgAAAAAaNyQaIPSQaICgM68lqShKA7YEAAAAAKDp4BSDeXlLTM0AoYHPGQSduaLhAhk6AQAAAADQRPhXMQR17kkAQqIBgscwDO7UeOAZZ1JVFQudAAAAAACEjrmiAUMnIDSQaIBgEYlSt9stNqKiAQAAAAAglDBHA4QeEg0QROaKBu7guKIBHRwAAAAAQGhwHM4ROGZMg9BAogGCjudowNAJAAAAAIDQw4wMEHpINEDQmRcHRqIBAAAAAKChoLIYQgOJBggwn8/n8Xg4b8rL55gTDeY5GjRN0zQt9C0EAAAAAGisdF33+XzmMLuGySAr7QkQKLi8DAHmX7NgTjSIUWG6rvNtXdexnC8AAAAAwPkzDEOWZY6uxYiJKieD5BXiOHRHQA4Bh88TBIZIlH799df//e9/ydSLmbOknFzg7s/tdpeXl6NTAwAAAAA4f7qucwS+cePG06dPS5LkH2mLLbzn3r17CwsLEZBDwOEjBYHBiYaZM2eOGTNm8uTJv//978Vd/nM0SJK0dOnSsLAwh8OxcOFC+m1BFwAAAAAA1ImoSujXr9+QIUNatWq1Zs0a3uI/dIK3X3nlld26dYuNjd24cSMR+Xy+hmk6NEZINEAA6LrOGYT/+7//IyJJkpYtW7Zv3z6+19xniXTpjTfeyDceffRR3o5cAwAAAABA/XAs/dNPP23dupW33Hnnnea7GCcaJElasWLF2rVrOTh/7rnnQt1caOyQaIAAkGWZswmjRo0iIqvVSkSzZ8/me80VDdy1PfXUUx6Px2azEVFCQkLoGwwAAAAA0JhwmJ2amso3VFU9ceLEkiVLqJpSBb7axxcLe/ToEdK2QhOARAMEBvdonA11u91EtGLFilOnThGRx+OptPPTTz9NFQmIv/3tb0Tk8/kwNgwAAAAAoH4URdE0TZKkOXPmUEUdMWcT+PKeeYbIZcuWHT58WJIkDtQ5OMfKlxBAOLWDwFAUxTCM1NTUsWPHUkV39pe//IX8cqiPPfYYEVmtVl3X27RpM3XqVDINqQAAAAAAgHp78sknicjj8UiSlJWVlZ6enpiYKO4NCwsjoocffpgqyhlmzZpltVo1TRPLwwGcP5zdQcDw6hLmooZ3333X5XLFxcXxDvHx8WQqYSCil19+mVDOAAAAAABw3rioQVGUJ554giryCDNnzuSyBZ6pISkpKSMj48SJE5IkcX3xM88806CthsYJZ3cQMNyX9ezZc/DgwVQxU8Njjz0WGRnJO7Rs2XLmzJlEZLFYdF3v0qXL5MmTCeUMAAAAAACBwMMf5s6dSxXjlLds2fLGG29QxUVBp9P5yCOPUEXo/thjj1mtVq/Xi3IGCCyc4EEgcZ0CFzVw6vTNN988fvw433vo0KFXX32VKnq9RYsWEcoZAAAAAAACRJZlTdNkWebRypw+ePbZZ8UOy5cvX7duHVUE5JySQJYBAg4neBBInDIYPHhw9+7diUhRFLfbLRbU8Xq9LpeLO7LLLrts5MiRhHIGAAAAAIBAe+qpp6iiiqG8vFxsLysro4oI/IknnrBYLJyYaKBmQqOFjxQEkljnkidi4JlvzTtIksT9HZc2oJwBAAAAACCAFEXx+Xw2m43HLCuKYg7IJUmSJIkvBHIyAotNQDDgHA8CjBMHY8eOTU1NJb+ei+8dPnx4v379qGJsGAAAAAAABApH4OaiBsEwDL53zpw5qqp6vV5c9oNgwKcKAkwUNcyfP58qprcVuKd75ZVXyG/ZSwAAAAAAOH+8/ITdbudlLM1TMHA5g6IonIZAlgGCBB8sCDzusK677rqEhAQy9V/cx02aNCktLY1QzgAAAAAAEEw816PP5xNVxnzjySeflGUZi01A8CDRAIHH3RYRzZs3z7ydyxlefvll8iviAgAAAACAQOGZGhwOx4wZM6jigp8oZ3jyyScJl/0gmJBogKDgbuuOO+6IjY3VdV2WZe7dbr755pSUFO7gGrqNAAAAAACNFhcv8JU/HrPMhcbPPPMMEXm9XkwDCcGDRAMEhSRJXNTAo79sNhuXMCxcuJCIDMNo2OYBAAAAADRuPFNDZGTk9OnTicjhcGiapqrq7Nmz6bcTNwAEHBINECzceXGxltPpJKI77rijRYsWmqahXwMAAAAACI2nn36aiMrKyoiIB01gjXkINgzLgWCRZZkHTRw+fHj27NldunThMi3UaAEAAAAAhICiKLqux8bGbt26dd68eYMHD545cyZhsQkIPiQaIIi4C+vYseN//vOfShsBAAAAACDYOPbu27fv8uXLK20ECB4kGiDofD4fT8qgqirKGQAAAAAAQszn83GtMWvo5kDjh0QDBIuu60RkGIYkSZxf0HVdkqSgdm3iSc0bg/2kAAAAAAAh4x/xcrBdQ8RbaSXLehwBoE6QaICg0DRNluUq6xc0TQvSmX8NT2oYBpa6AAAAAICLGicIqgykDcOozZzruq4bhlHlbnwEXKKDgECiAQLM0A1JlrjzcpY5C/MLy8rKDMOwO+yxsbH2SDvfFdi1J7gSjA/o8XgK8gpKS0p1XbeF2WJiY6JioriqAvPrAgAAAMBFyhw/lxaXFuYXOp1OSZEi7BExcTFh9jC+lwNj/4dXKi4uOltUWFTodrstqiUiIiIuPk5RlZqPAFB7SDRAIIle6fD+wzt27Dh8/HBuYW65t5wMCreGN49u3ql1p169e3Xt0TXgWQYiOnXi1NatWw8dPXQ673SJu0Q3dJvFFueIa5fUrlf3Xr379lYt+MADAAAAwEXmfxfVDNr1465du3YdOXnkl6JfXJpLIslhdbSMbdmpfae+ffq279RelmUevOx/BCJylbt2bN2RsS/jRPaJgrICj8+jKmqELaJV81aXdrq0b7++LRNbVnkEgDrBeRcEkizLZ385u/KrlZv3bM5x5tia22I7xDaPaE4SuUpdR3OO7v1x78bdG/td2m/8+PEJSQnn/4yGYciy7Ha5v1759YZtG44VHlNj1Ng2sbFRsYqiuJ3u07+cPnTg0Ob9m3tv6z165OjOaZ3P/0kBAAAAAEKGz/lPHj+5atWqrQe3ntXPOuIdsZ1joxxRhmGUlZQdOH1g16Zd6TvTB/UYNO6qcVExUeaHiyzD7l27v1779a7ju1wWV2TLyKj2UTFhMZpPKy0o3Z6zffua7Zt3bB42YNjIMSN5XUzUNUC9IdEAgWGQIZF04uiJjz/9+Meff7S3sffv0z8+MV4UEUgkeTXv2TNnD/x44KuMr07lnvrDpD906trpvJ7UMCRJKiwoXPLxko0HNuqxes9xPZNSkqxh1l+f1JA0XSsuKD6059D6/etP5py8ftL1fS7vI8syj3ADAAAAALjASZK0e+fupcuX7j27t1mHZkN6DYltEatKqiEZRCSR5PF4crJy9u/Y/9n3n506c+r6669vldSKQ2WRL/hmzTcr1q044TqR0julQ9cO0dHRkiwZhkESkUFOlzPrSNbBHQdPrz596sypP1z/B7vdjroGqDckGiAAuA/KPZP74ZIPt57a2nFAx+59u/t8Po/T4yp3id1kSW7WotnwicP379q/Y9MObal2u/32Nu3a1K8L407T7XIv+XjJN/u+ie0c229YP0VRXE6Xp8hj3tMeYe8/ov/JlJPfr/n+P1/8x26322w2p9MZgFcOAAAAABA0HCdnHsxc8t8l+4v29xjZo1PXTh6Xx13qdhr/i2ZlWW7VplWrtq1+3Pzj5h8308d0+623xzSL4eJfItr07aZlq5edUc8MmjQoISnBXe4uLSk1P5Gqqh27dmyd2nrL2i1rdq5RJOXmP97MF+dQ1wD1gA8NnC+eV8YwjBWfr9hxakenAZ169u9ZVlLmKnMZkiGbGGS4yl2lRaVde3XtMbTH7tzdK75Y4XV7OdVa1+flLm/1ytUb922M7Rx7xegrNJ9WVlJm6L95UlmWPW5PcUFxcrvkK666ItOZue67dR6X55zHBwAAAABoQBxml5eWr/hyxb78fX2u7HNJ10tKCktcLr8wWzfKS8vd5e5+Q/q17tP6+6Pff/XFV1Qx5uLnoz9/ufbL0/LpYb8bFt8yvrig2OPxVAqYfT5fSVGJTPLQq4baUmzf7vz227XfiiMA1BUSDXC+eNnIndt2/rD/h5j2Md0v715SWGIYhqzIEv2mYxLz3BYVFHXq3inx0sStmVu3btlKde/CNE0joqyfszZu3+iL9fUb3s9Z7vR5fYqi+B9KlmVFVkqKSlomt+zSr8vuU7tlCZ98AAAAALigcVibvjH9x2M/tu3Vtt0l7YoLikkiWfYLs2VJVmRd00uLSvsM7GNvbU/fk75/936+99sN3x4uPNzzip4xcTFlJWWKovgXKfBkkx6Px+fxDRg5oMResmHLhrzcvPpdEQTA0Ak4X7x+xPYft+fpeUMvH+p1eUWNVpUkSZIMyV3m7tq36zc/fbNt57aBVwyUFKlOdVm8584dO48XHk8bk2ZRLO4yt6xU/3CJZFkuLy7v2LXj6aOnvbq30v2GQehCARoTWSYyiPg/o4EbA1BLOn9ojV//AUAjI0m//mnX5g+cY2NXuWt7xna33d21V1dnqZPkmq7PSbKk6ZrP50vrl7Z52eZt27dd2v3S0ydP/3jwx4jkiHad2pWVlNUcb8uy7HF5ImIiOvTqcHjD4T279gwfPRxFDVAPSDTAeeEeMO9M3sHjB6OSo+Kax52z/yLuwjyeqOioZm2bZZ7IPHXyVHLbZKPWIRU/qebV9mful6Kk5JRkl9MlyefoASVJ0n26oigpnVPIQuQlIhKLbAZutU0AuFA47FTmIouFdJUk/I3DhUpRyGIhq5XC7BThICJSVSIiBPYAjRL/gYeF1Xb/kydOHjtzLKFzgt1hLyksqem6GhERSZLkcXriW8Y7WjgOHj1IOp34+UR2UXaXAV10Xa/5cqA4gqvclZKakrk1c//h/ZxowEwNUFdINEAA5OTk5JXmJaYlnvNs/zc0apHU4sihI9nZ2cltk+v6pPl5+afPno5JiLGGWcuKz53dICKSyef1xcXHkZ2oiIjI6aT166mkhDSNZBmXjwAaj7Aw2rCBisqpZSZpTvL5yGpt6DYB+FOpqJAyj1KOjU6eJK2Yfs6ikhJ8JQE0TpJEuk5RUXTqVG0fkn0qu9BT2C2pm6ZpVItAm5MCqqI2S2z2y75fsrOyT5857VE88QnxPo9PqsUhJFnSvbo9wu5o5sjKzSopKomMjqxtcwEqINEAAVBUWOQ23PYou67VYfiBpmuOKIfX8BYVFVFdpmng2oeS0pIyV1lEVESdJlzQNd1mt4lEg2HQkSNUXk4+HyFLC9CYWK2Um0elLrKXkO4iiTArEVx4dCKZvD4qLSajmM766NRJstuprAxfSQCNlq6Tw0G5ubXYVSYiKiwq1BTNHlm3MNsgwx5lz9fyz+adLSotUsKVsLAwTa9VqoKIdNJlRXZEOUryS5zlzsjoyNqXHgMwJBogADRNMwxDqePwA36IQYama/V4Ul3TddIlpW6lpQYZiqSI8w1FIYeDJOnXigYAaDSsVrKHkc8g1UI+X0O3BqA6BskyqSpZrBQWRnb7r99K+EoCaKw40VBScu49ZZKJyKf5JEmSFblup/o6KYpCEnl9Xs2nybIsSZKh1y1ZICmSYRh1SnAACEg0QADYI+wWyeJyuupUXCArssvpUkixh9vr8aTh9nCbanM73UZd5nmTZMnj8pDr1x99Piov/3XoBEbDAjQmNhuVl5PLQ14fGRqq0OFCZZCuk89HHje5VCovp5JSKitFogGg0dJ10nVyuWqzpy7LssPuIB+5Xe66TZGgEIfZUVFR9nC75tG8Xq+qqlTrpIFhGO4yd7Q12hpmJSxyCXWHRAMEQHzz+ChrVEFuQZ0qkxVFyc/Nd6iOFi1aUMWAiNrgni4mNqZZRLPjvxznNK1hGOfuAXWyKJb84nwq+3VDeDjdcQeVl5PXi0QDQKMSHk7hdsorJLuFXKWkKFT76AogRGQinWJiqFNHataDkhJoeD8aNIgKCzFFMUCjpWkUG0uHD9OcObXav2XLlmFSWGFeYXLrusxoZlDRL0VR1qiEVgnxJ+LJTUWFRS0TW/q8vnMGzDxhpNfjLSso6xDTITo6ug7PC1ABiQYIgJatWrZp2WbnyZ0up0tVVU3Tau7Cfu2/vN4zx890iOuQnJxMdU+UOiIdqW1S92XsK8wvjI6Odrlc5z6CRLIqZx3NEhUNnNyw16eiAgAudFGR5PaRNZyU+gzPAggJnRSF7HaKcJArkmKiSJIoJga5b4BGi4PP2Nja7t86pXVidGL20ewuvbpIkkQG1TzPgqEbqkUtKy3LP5U/MGFgRFRE66TWMWrMqaOnklOSXeXnLqUwDMMWbsvJznEXulO7pSoWhSqWlgeoPSQa4LxwKYEtzNajS4+MrzOOHDjStXfXksISRVZq6AQN3XBEO346+JMvz9dzSM+ImAiqS/8lyzLXknXv1n3T7k0HMw4OHjPY7XTXXNTA00CezT2bdzTP/14MnQBoZGSZnG7yeEjSyOujugyxAggpXSevjzwecrvI4yUi0jRSFIz3AWiEeF4wVa3V5EEcGzdv0bx7p+7/3fbf7GPZSW2TykrKapgWzSBDN/TIiMgdm3ZYXdZe3XsRUZv2bbq06bLl0JbOPTuHhYV53J4aom4Opy0Wy6GMQ4m2xB7dexCRpml1nYsNAIkGOF/cH/Uf2H/77u27tu1KapMUFRtVVlLGs87476zrerg9vKykbO+WvZ2adxo4cCBVDEKr61P37NOz99beaw6sOdb2WLtO7YoLimVJ9l9i0zAMwzAsVossydu/2942qq1VsnoMj3kfzLwF0Pj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"
    }
   },
   "cell_type": "markdown",
   "id": "cdf91d82",
   "metadata": {},
   "source": [
    "        DEFINING UPPER AND LOWER LIMIT OF QUARTILES\n",
    "        \n",
    "        \n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad01d3ce",
   "metadata": {},
   "source": [
    "### ASSUMPTIONS CHECK\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f2d087",
   "metadata": {},
   "source": [
    "#### MULTICOLLINEARITY \n",
    "\n",
    "        Multicollinearity occurs when two or more independent variables in a linear regression model are highly correlated \n",
    "        with each other. \n",
    "        \n",
    "        This can cause problems in the estimation of the model coefficients and the interpretation of the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f4d0461",
   "metadata": {},
   "source": [
    "##### METHOD 1 : CORRELATION MATRIX "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b5871ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Correlation = DF.corr ( )\n",
    "\n",
    "Set_Corr = set ( Correlation.values [ ( Correlation > 0.5) | ( Correlation < -0.5)  ] )\n",
    "\n",
    "Set_Corr.remove ( 1.0 )\n",
    "\n",
    "sns.color_palette ( 'pastel' )\n",
    "\n",
    "sns.heatmap ( DF.corr ( ) , cmap = \"BuPu\" , annot = True )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74df2f1f",
   "metadata": {},
   "source": [
    "##### METHOD 2 : VIF\n",
    "\n",
    "\n",
    "        The VIF measures how much the variance of the estimated regression coefficient is inflated due to the presence of\n",
    "        multicollinearity. \n",
    "        \n",
    "        A high VIF value indicates that the variance of the coefficient estimate is significantly larger than it would be \n",
    "        if the predictor variable were not correlated with the other predictor variables in the model.\n",
    "        \n",
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe78aae",
   "metadata": {},
   "source": [
    "     1. We can see that in the presence of multicollinearity, the coefficient of Research & Development is varying the most. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d43a1b6",
   "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>Variables</th>\n",
       "      <th>VIF_Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R&amp;D Spend</td>\n",
       "      <td>8.386322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marketing Spend</td>\n",
       "      <td>7.674608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Administration</td>\n",
       "      <td>4.815916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>State</td>\n",
       "      <td>2.382637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Variables  VIF_Values\n",
       "0        R&D Spend    8.386322\n",
       "2  Marketing Spend    7.674608\n",
       "1   Administration    4.815916\n",
       "3            State    2.382637"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = DF.iloc [ : , : - 1 ]\n",
    "\n",
    "VIF = pd.DataFrame ( )\n",
    "\n",
    "VIF [ 'Variables' ] = A.columns\n",
    "\n",
    "VIF [ 'VIF_Values' ] = [ variance_inflation_factor ( A.values, i ) for i in range( A.shape[1] ) ]\n",
    "\n",
    "VIF.sort_values ( 'VIF_Values' , ascending = False )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a014f6d",
   "metadata": {},
   "source": [
    "### LINEAR REGRESSION MODELING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4574d190",
   "metadata": {},
   "source": [
    "            Best hyperparameters :  {'copy_X': True, 'fit_intercept': True}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1dd1505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LinearRegression ( copy_X = True , fit_intercept = True )\n",
    "\n",
    "LR.fit ( Train_X , Train_Y )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0955eaf0",
   "metadata": {
    "scrolled": false
   },
   "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>Predicted_Values</th>\n",
       "      <th>Actual_Values</th>\n",
       "      <th>Error</th>\n",
       "      <th>Absolute_Error</th>\n",
       "      <th>Error_Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>46828.384581</td>\n",
       "      <td>14681.40</td>\n",
       "      <td>-32146.984581</td>\n",
       "      <td>32146.984581</td>\n",
       "      <td>218.964026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>47542.034814</td>\n",
       "      <td>35673.41</td>\n",
       "      <td>-11868.624814</td>\n",
       "      <td>11868.624814</td>\n",
       "      <td>33.270228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>48697.538683</td>\n",
       "      <td>64926.08</td>\n",
       "      <td>16228.541317</td>\n",
       "      <td>16228.541317</td>\n",
       "      <td>24.995412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75230.381370</td>\n",
       "      <td>90708.19</td>\n",
       "      <td>15477.808630</td>\n",
       "      <td>15477.808630</td>\n",
       "      <td>17.063298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>68513.680337</td>\n",
       "      <td>81229.06</td>\n",
       "      <td>12715.379663</td>\n",
       "      <td>12715.379663</td>\n",
       "      <td>15.653732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>146657.223872</td>\n",
       "      <td>129917.04</td>\n",
       "      <td>-16740.183872</td>\n",
       "      <td>16740.183872</td>\n",
       "      <td>12.885287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55852.610677</td>\n",
       "      <td>49490.75</td>\n",
       "      <td>-6361.860677</td>\n",
       "      <td>6361.860677</td>\n",
       "      <td>12.854646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>61260.858318</td>\n",
       "      <td>69758.98</td>\n",
       "      <td>8498.121682</td>\n",
       "      <td>8498.121682</td>\n",
       "      <td>12.182119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>115261.582435</td>\n",
       "      <td>105008.31</td>\n",
       "      <td>-10253.272435</td>\n",
       "      <td>10253.272435</td>\n",
       "      <td>9.764249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>128752.417997</td>\n",
       "      <td>141585.52</td>\n",
       "      <td>12833.102003</td>\n",
       "      <td>12833.102003</td>\n",
       "      <td>9.063852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>88933.998355</td>\n",
       "      <td>96712.80</td>\n",
       "      <td>7778.801645</td>\n",
       "      <td>7778.801645</td>\n",
       "      <td>8.043198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45780.027285</td>\n",
       "      <td>42559.73</td>\n",
       "      <td>-3220.297285</td>\n",
       "      <td>3220.297285</td>\n",
       "      <td>7.566536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>119090.858263</td>\n",
       "      <td>111313.02</td>\n",
       "      <td>-7777.838263</td>\n",
       "      <td>7777.838263</td>\n",
       "      <td>6.987357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>89900.159997</td>\n",
       "      <td>96479.51</td>\n",
       "      <td>6579.350003</td>\n",
       "      <td>6579.350003</td>\n",
       "      <td>6.819427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>126325.283030</td>\n",
       "      <td>134307.35</td>\n",
       "      <td>7982.066970</td>\n",
       "      <td>7982.066970</td>\n",
       "      <td>5.943135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>73179.626099</td>\n",
       "      <td>77798.83</td>\n",
       "      <td>4619.203901</td>\n",
       "      <td>4619.203901</td>\n",
       "      <td>5.937369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101354.908357</td>\n",
       "      <td>107404.34</td>\n",
       "      <td>6049.431643</td>\n",
       "      <td>6049.431643</td>\n",
       "      <td>5.632390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>173830.567919</td>\n",
       "      <td>182901.99</td>\n",
       "      <td>9071.422081</td>\n",
       "      <td>9071.422081</td>\n",
       "      <td>4.959718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>131553.555967</td>\n",
       "      <td>125370.37</td>\n",
       "      <td>-6183.185967</td>\n",
       "      <td>6183.185967</td>\n",
       "      <td>4.931936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110919.907679</td>\n",
       "      <td>105733.54</td>\n",
       "      <td>-5186.367679</td>\n",
       "      <td>5186.367679</td>\n",
       "      <td>4.905130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>85588.831281</td>\n",
       "      <td>89949.14</td>\n",
       "      <td>4360.308719</td>\n",
       "      <td>4360.308719</td>\n",
       "      <td>4.847527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>117314.473361</td>\n",
       "      <td>122776.86</td>\n",
       "      <td>5462.386639</td>\n",
       "      <td>5462.386639</td>\n",
       "      <td>4.449036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>113104.665035</td>\n",
       "      <td>108552.04</td>\n",
       "      <td>-4552.625035</td>\n",
       "      <td>4552.625035</td>\n",
       "      <td>4.193956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>114266.096833</td>\n",
       "      <td>110352.25</td>\n",
       "      <td>-3913.846833</td>\n",
       "      <td>3913.846833</td>\n",
       "      <td>3.546685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>128159.606470</td>\n",
       "      <td>124266.90</td>\n",
       "      <td>-3892.706470</td>\n",
       "      <td>3892.706470</td>\n",
       "      <td>3.132537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>114766.629102</td>\n",
       "      <td>118474.03</td>\n",
       "      <td>3707.400898</td>\n",
       "      <td>3707.400898</td>\n",
       "      <td>3.129294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>103581.171279</td>\n",
       "      <td>101004.64</td>\n",
       "      <td>-2576.531279</td>\n",
       "      <td>2576.531279</td>\n",
       "      <td>2.550904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>170184.292636</td>\n",
       "      <td>166187.94</td>\n",
       "      <td>-3996.352636</td>\n",
       "      <td>3996.352636</td>\n",
       "      <td>2.404719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>99483.035341</td>\n",
       "      <td>97483.56</td>\n",
       "      <td>-1999.475341</td>\n",
       "      <td>1999.475341</td>\n",
       "      <td>2.051090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152570.655904</td>\n",
       "      <td>149759.96</td>\n",
       "      <td>-2810.695904</td>\n",
       "      <td>2810.695904</td>\n",
       "      <td>1.876801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>188232.227868</td>\n",
       "      <td>191792.06</td>\n",
       "      <td>3559.832132</td>\n",
       "      <td>3559.832132</td>\n",
       "      <td>1.856089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>98782.760535</td>\n",
       "      <td>99937.59</td>\n",
       "      <td>1154.829465</td>\n",
       "      <td>1154.829465</td>\n",
       "      <td>1.155551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>64503.672586</td>\n",
       "      <td>65200.33</td>\n",
       "      <td>696.657414</td>\n",
       "      <td>696.657414</td>\n",
       "      <td>1.068488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>157141.405065</td>\n",
       "      <td>156122.51</td>\n",
       "      <td>-1018.895065</td>\n",
       "      <td>1018.895065</td>\n",
       "      <td>0.652625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>153103.961809</td>\n",
       "      <td>152211.77</td>\n",
       "      <td>-892.191809</td>\n",
       "      <td>892.191809</td>\n",
       "      <td>0.586152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>193229.742403</td>\n",
       "      <td>192261.83</td>\n",
       "      <td>-967.912403</td>\n",
       "      <td>967.912403</td>\n",
       "      <td>0.503435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>97730.704066</td>\n",
       "      <td>97427.84</td>\n",
       "      <td>-302.864066</td>\n",
       "      <td>302.864066</td>\n",
       "      <td>0.310860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103450.945505</td>\n",
       "      <td>103282.38</td>\n",
       "      <td>-168.565505</td>\n",
       "      <td>168.565505</td>\n",
       "      <td>0.163208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>80897.162356</td>\n",
       "      <td>81005.76</td>\n",
       "      <td>108.597644</td>\n",
       "      <td>108.597644</td>\n",
       "      <td>0.134062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>96830.884528</td>\n",
       "      <td>96778.92</td>\n",
       "      <td>-51.964528</td>\n",
       "      <td>51.964528</td>\n",
       "      <td>0.053694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Predicted_Values  Actual_Values         Error  Absolute_Error  \\\n",
       "36      46828.384581       14681.40 -32146.984581    32146.984581   \n",
       "34      47542.034814       35673.41 -11868.624814    11868.624814   \n",
       "25      48697.538683       64926.08  16228.541317    16228.541317   \n",
       "0       75230.381370       90708.19  15477.808630    15477.808630   \n",
       "28      68513.680337       81229.06  12715.379663    12715.379663   \n",
       "23     146657.223872      129917.04 -16740.183872    16740.183872   \n",
       "1       55852.610677       49490.75  -6361.860677     6361.860677   \n",
       "11      61260.858318       69758.98   8498.121682     8498.121682   \n",
       "14     115261.582435      105008.31 -10253.272435    10253.272435   \n",
       "6      128752.417997      141585.52  12833.102003    12833.102003   \n",
       "20      88933.998355       96712.80   7778.801645     7778.801645   \n",
       "3       45780.027285       42559.73  -3220.297285     3220.297285   \n",
       "27     119090.858263      111313.02  -7777.838263     7777.838263   \n",
       "35      89900.159997       96479.51   6579.350003     6579.350003   \n",
       "17     126325.283030      134307.35   7982.066970     7982.066970   \n",
       "15      73179.626099       77798.83   4619.203901     4619.203901   \n",
       "7      101354.908357      107404.34   6049.431643     6049.431643   \n",
       "31     173830.567919      182901.99   9071.422081     9071.422081   \n",
       "24     131553.555967      125370.37  -6183.185967     6183.185967   \n",
       "4      110919.907679      105733.54  -5186.367679     5186.367679   \n",
       "19      85588.831281       89949.14   4360.308719     4360.308719   \n",
       "38     117314.473361      122776.86   5462.386639     5462.386639   \n",
       "29     113104.665035      108552.04  -4552.625035     4552.625035   \n",
       "8      114266.096833      110352.25  -3913.846833     3913.846833   \n",
       "32     128159.606470      124266.90  -3892.706470     3892.706470   \n",
       "12     114766.629102      118474.03   3707.400898     3707.400898   \n",
       "39     103581.171279      101004.64  -2576.531279     2576.531279   \n",
       "30     170184.292636      166187.94  -3996.352636     3996.352636   \n",
       "26      99483.035341       97483.56  -1999.475341     1999.475341   \n",
       "2      152570.655904      149759.96  -2810.695904     2810.695904   \n",
       "22     188232.227868      191792.06   3559.832132     3559.832132   \n",
       "10      98782.760535       99937.59   1154.829465     1154.829465   \n",
       "16      64503.672586       65200.33    696.657414      696.657414   \n",
       "33     157141.405065      156122.51  -1018.895065     1018.895065   \n",
       "18     153103.961809      152211.77   -892.191809      892.191809   \n",
       "37     193229.742403      192261.83   -967.912403      967.912403   \n",
       "9       97730.704066       97427.84   -302.864066      302.864066   \n",
       "5      103450.945505      103282.38   -168.565505      168.565505   \n",
       "13      80897.162356       81005.76    108.597644      108.597644   \n",
       "21      96830.884528       96778.92    -51.964528       51.964528   \n",
       "\n",
       "    Error_Percentage  \n",
       "36        218.964026  \n",
       "34         33.270228  \n",
       "25         24.995412  \n",
       "0          17.063298  \n",
       "28         15.653732  \n",
       "23         12.885287  \n",
       "1          12.854646  \n",
       "11         12.182119  \n",
       "14          9.764249  \n",
       "6           9.063852  \n",
       "20          8.043198  \n",
       "3           7.566536  \n",
       "27          6.987357  \n",
       "35          6.819427  \n",
       "17          5.943135  \n",
       "15          5.937369  \n",
       "7           5.632390  \n",
       "31          4.959718  \n",
       "24          4.931936  \n",
       "4           4.905130  \n",
       "19          4.847527  \n",
       "38          4.449036  \n",
       "29          4.193956  \n",
       "8           3.546685  \n",
       "32          3.132537  \n",
       "12          3.129294  \n",
       "39          2.550904  \n",
       "30          2.404719  \n",
       "26          2.051090  \n",
       "2           1.876801  \n",
       "22          1.856089  \n",
       "10          1.155551  \n",
       "16          1.068488  \n",
       "33          0.652625  \n",
       "18          0.586152  \n",
       "37          0.503435  \n",
       "9           0.310860  \n",
       "5           0.163208  \n",
       "13          0.134062  \n",
       "21          0.053694  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model = pd.DataFrame ( )\n",
    "\n",
    "Model [ 'Predicted_Values' ] = LR.predict ( Train_X )\n",
    "\n",
    "Model [ 'Actual_Values' ] = Train_Y\n",
    "\n",
    "Model [ 'Error' ] = Train_Y - Model [ 'Predicted_Values' ]\n",
    "\n",
    "Model [ 'Absolute_Error' ] = np.abs ( Model.Error )\n",
    "\n",
    "Model [ 'Error_Percentage' ] = np.abs ( ( Model [ 'Error' ] * 100  ) / Train_Y )\n",
    "\n",
    "Model = Model.sort_values ( 'Error_Percentage' , ascending = False )\n",
    "\n",
    "Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5203511",
   "metadata": {},
   "source": [
    "#### COEFFICIENTS OF VARIABLES \n",
    "\n",
    "\n",
    "    High coefficients of variables mean that the corresponding independent variable has a strong relationship with \n",
    "    the dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2561a472",
   "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>Values</th>\n",
       "      <th>Columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.800407</td>\n",
       "      <td>R&amp;D Spend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010174</td>\n",
       "      <td>Administration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.027361</td>\n",
       "      <td>Marketing Spend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1089.762090</td>\n",
       "      <td>State</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Values          Columns\n",
       "0     0.800407        R&D Spend\n",
       "1     0.010174   Administration\n",
       "2     0.027361  Marketing Spend\n",
       "3  1089.762090            State"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Coefficients = pd.DataFrame () \n",
    "\n",
    "Columns = list(DF.columns)\n",
    "\n",
    "Columns.remove ( 'Profit' )\n",
    "\n",
    "Coefficients [ 'Values' ]  = LR.coef_\n",
    "\n",
    "Coefficients [ 'Columns' ] = Columns\n",
    "\n",
    "Coefficients"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "f50a7031",
   "metadata": {},
   "source": [
    "#### COEFFICIENT OF DETERMINANT\n",
    "\n",
    "            R2 value tells us how much variance the model is able to capture ( SSR )\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "506ad872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient of determinant is : 95.05035861084991 and the adjusted value is 94.48468530923276\n"
     ]
    }
   ],
   "source": [
    "R2 = LR.score ( Train_X , Train_Y )\n",
    "\n",
    "P = Train_X.shape [ 1 ]\n",
    "\n",
    "N = Train_X.shape [ 0 ]\n",
    "\n",
    "ADJ_R2 = 1 - ( ( ( 1 - R2) * ( N - 1 ) ) /  ( N - P - 1 ) )\n",
    "\n",
    "print ( \"The coefficient of determinant is : {} and the adjusted value is {}\".format ( R2 * 100 , ADJ_R2 * 100 ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327e6f95",
   "metadata": {},
   "source": [
    "### CHECKING FOR ASSUMPTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5470c6",
   "metadata": {},
   "source": [
    "#### MEAN OF  ERROR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4061a155",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the error is :  -1.7280399333685637e-11\n"
     ]
    }
   ],
   "source": [
    "print ( \"The mean of the error is : \" , np.mean ( Model.Error ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d99a309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of absolute error is :  6344.162122366675\n"
     ]
    }
   ],
   "source": [
    "print ( \"The mean of absolute error is : \" , np.mean ( Model.Absolute_Error ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c43e2fc",
   "metadata": {},
   "source": [
    "#### NORMAL DISTRIBUTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01baa100",
   "metadata": {},
   "source": [
    "\n",
    "        The Errors are violating the assumption of Normal Distribution. \n",
    "        \n",
    "        The graph suggests that the kurtosis is higher than 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b93afc97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure ( figsize = ( 7 , 7 ) )\n",
    "\n",
    "plt.xlabel ( 'Error = Actual - Predicted' )\n",
    "\n",
    "plt.ylabel ( 'Frequency' )\n",
    "\n",
    "plt.grid ( )\n",
    "\n",
    "plt.hist ( x = Model.Error );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832de667",
   "metadata": {},
   "source": [
    "#### HETEROSCEDASTICITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "edbdc5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot ( x = Model.Actual_Values , y = Model.Predicted_Values , data = Model );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9a6f94",
   "metadata": {},
   "source": [
    "### METRICS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a8271a",
   "metadata": {},
   "source": [
    "#### MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "181bce3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean square value of error test is  91270989.63188198\n",
      "\n",
      "The mean square value of error train is  77776787.94248608\n"
     ]
    }
   ],
   "source": [
    "Predicted_Test = LR.predict ( Test_X )\n",
    "\n",
    "Error_Test = Test_Y - Predicted_Test\n",
    "\n",
    "Error_test_Percentage = abs(Error_Test) * 100 / Test_Y \n",
    "\n",
    "MSE = np.mean ( np.square ( Error_Test ) )\n",
    "\n",
    "print ( \"The mean square value of error test is \" , MSE )\n",
    "\n",
    "print ( \"\\nThe mean square value of error train is \" , np.mean ( np.square (Model.Error) ) ) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5359c51c",
   "metadata": {},
   "source": [
    "#### RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c5333a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The root mean square value of error test is  9553.585171645354\n",
      "\n",
      "root mean square value of error train is  8819.114918317262\n"
     ]
    }
   ],
   "source": [
    "RMSE = np.sqrt ( MSE )\n",
    "\n",
    "print ( \"The root mean square value of error test is \" , RMSE )\n",
    "\n",
    "print ( \"\\nroot mean square value of error train is \" , np.sqrt ( np.mean ( np.square ( Model.Error) ) ) ) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17b32e7",
   "metadata": {},
   "source": [
    "#### MAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "baa0c745",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean absolute percentage error (of test) is  6.142368258032201\n",
      "\n",
      "The mean absolute percentage error (of train) is  11.677243912327064\n"
     ]
    }
   ],
   "source": [
    "MAPE = np.mean ( Error_test_Percentage )\n",
    "\n",
    "print ( \"The mean absolute percentage error (of test) is \" , MAPE )\n",
    "\n",
    "print ( \"\\nThe mean absolute percentage error (of train) is \" , np.mean ( Model.Error_Percentage ) ) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0934afca",
   "metadata": {},
   "source": [
    "#### ACCURACY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6180fc06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train data is :  93.8576317419678\n",
      "\n",
      "Accuracy on test data is :  88.32275608767293\n"
     ]
    }
   ],
   "source": [
    "print ( \"Accuracy on train data is : \" , 100 - MAPE  )\n",
    "\n",
    "print ( '\\nAccuracy on test data is : ' , 100 - np.mean ( Model.Error_Percentage ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b478f2",
   "metadata": {},
   "source": [
    "### REMOVING OUTLIERS\n",
    "\n",
    "        We can see that the model is performing well on Test data and poor on train data, this is due to less records \n",
    "        in Test Data\n",
    "        \n",
    "        Another reason could be that the Train data is influenced by outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4bd45918",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Quartiles ( DF , Y , K ) :\n",
    "    \n",
    "    global Lower , Upper , Lower_Outliers , Upper_Outliers\n",
    "            \n",
    "    Lower = DF [ Y ].quantile ( q = 0.25 )\n",
    "\n",
    "    Upper = DF [ Y ].quantile ( q = 0.75 )\n",
    "\n",
    "    IQR = Upper - Lower\n",
    "\n",
    "    Lower_Outliers =  Lower - K * IQR \n",
    "\n",
    "    Upper_Outliers =  Upper + K * IQR \n",
    "\n",
    "    DF.drop ( labels = list ( DF [ DF [ Y ] >= Upper_Outliers ].index ) , inplace = True )\n",
    "\n",
    "    DF.drop ( labels = list ( DF [ DF [ Y ] <= Lower_Outliers ].index ) , inplace = True )\n",
    "    \n",
    "    DF.reset_index ( drop = True , inplace = True )\n",
    "\n",
    "Quartiles ( DF , 'Profit' , 0.75 ) ## K = 1 becasue the data is not vast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f50bd40d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted_Values</th>\n",
       "      <th>Actual_Values</th>\n",
       "      <th>Error</th>\n",
       "      <th>Absolute_Error</th>\n",
       "      <th>Error_Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>46828.384581</td>\n",
       "      <td>14681.40</td>\n",
       "      <td>-32146.984581</td>\n",
       "      <td>32146.984581</td>\n",
       "      <td>218.964026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>47542.034814</td>\n",
       "      <td>35673.41</td>\n",
       "      <td>-11868.624814</td>\n",
       "      <td>11868.624814</td>\n",
       "      <td>33.270228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>48697.538683</td>\n",
       "      <td>64926.08</td>\n",
       "      <td>16228.541317</td>\n",
       "      <td>16228.541317</td>\n",
       "      <td>24.995412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75230.381370</td>\n",
       "      <td>90708.19</td>\n",
       "      <td>15477.808630</td>\n",
       "      <td>15477.808630</td>\n",
       "      <td>17.063298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>68513.680337</td>\n",
       "      <td>81229.06</td>\n",
       "      <td>12715.379663</td>\n",
       "      <td>12715.379663</td>\n",
       "      <td>15.653732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>146657.223872</td>\n",
       "      <td>129917.04</td>\n",
       "      <td>-16740.183872</td>\n",
       "      <td>16740.183872</td>\n",
       "      <td>12.885287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55852.610677</td>\n",
       "      <td>49490.75</td>\n",
       "      <td>-6361.860677</td>\n",
       "      <td>6361.860677</td>\n",
       "      <td>12.854646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>61260.858318</td>\n",
       "      <td>69758.98</td>\n",
       "      <td>8498.121682</td>\n",
       "      <td>8498.121682</td>\n",
       "      <td>12.182119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>115261.582435</td>\n",
       "      <td>105008.31</td>\n",
       "      <td>-10253.272435</td>\n",
       "      <td>10253.272435</td>\n",
       "      <td>9.764249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>128752.417997</td>\n",
       "      <td>141585.52</td>\n",
       "      <td>12833.102003</td>\n",
       "      <td>12833.102003</td>\n",
       "      <td>9.063852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>88933.998355</td>\n",
       "      <td>96712.80</td>\n",
       "      <td>7778.801645</td>\n",
       "      <td>7778.801645</td>\n",
       "      <td>8.043198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45780.027285</td>\n",
       "      <td>42559.73</td>\n",
       "      <td>-3220.297285</td>\n",
       "      <td>3220.297285</td>\n",
       "      <td>7.566536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>119090.858263</td>\n",
       "      <td>111313.02</td>\n",
       "      <td>-7777.838263</td>\n",
       "      <td>7777.838263</td>\n",
       "      <td>6.987357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>89900.159997</td>\n",
       "      <td>96479.51</td>\n",
       "      <td>6579.350003</td>\n",
       "      <td>6579.350003</td>\n",
       "      <td>6.819427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>126325.283030</td>\n",
       "      <td>134307.35</td>\n",
       "      <td>7982.066970</td>\n",
       "      <td>7982.066970</td>\n",
       "      <td>5.943135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>73179.626099</td>\n",
       "      <td>77798.83</td>\n",
       "      <td>4619.203901</td>\n",
       "      <td>4619.203901</td>\n",
       "      <td>5.937369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101354.908357</td>\n",
       "      <td>107404.34</td>\n",
       "      <td>6049.431643</td>\n",
       "      <td>6049.431643</td>\n",
       "      <td>5.632390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>173830.567919</td>\n",
       "      <td>182901.99</td>\n",
       "      <td>9071.422081</td>\n",
       "      <td>9071.422081</td>\n",
       "      <td>4.959718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>131553.555967</td>\n",
       "      <td>125370.37</td>\n",
       "      <td>-6183.185967</td>\n",
       "      <td>6183.185967</td>\n",
       "      <td>4.931936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110919.907679</td>\n",
       "      <td>105733.54</td>\n",
       "      <td>-5186.367679</td>\n",
       "      <td>5186.367679</td>\n",
       "      <td>4.905130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>85588.831281</td>\n",
       "      <td>89949.14</td>\n",
       "      <td>4360.308719</td>\n",
       "      <td>4360.308719</td>\n",
       "      <td>4.847527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>117314.473361</td>\n",
       "      <td>122776.86</td>\n",
       "      <td>5462.386639</td>\n",
       "      <td>5462.386639</td>\n",
       "      <td>4.449036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>113104.665035</td>\n",
       "      <td>108552.04</td>\n",
       "      <td>-4552.625035</td>\n",
       "      <td>4552.625035</td>\n",
       "      <td>4.193956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>114266.096833</td>\n",
       "      <td>110352.25</td>\n",
       "      <td>-3913.846833</td>\n",
       "      <td>3913.846833</td>\n",
       "      <td>3.546685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>128159.606470</td>\n",
       "      <td>124266.90</td>\n",
       "      <td>-3892.706470</td>\n",
       "      <td>3892.706470</td>\n",
       "      <td>3.132537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>114766.629102</td>\n",
       "      <td>118474.03</td>\n",
       "      <td>3707.400898</td>\n",
       "      <td>3707.400898</td>\n",
       "      <td>3.129294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>103581.171279</td>\n",
       "      <td>101004.64</td>\n",
       "      <td>-2576.531279</td>\n",
       "      <td>2576.531279</td>\n",
       "      <td>2.550904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>170184.292636</td>\n",
       "      <td>166187.94</td>\n",
       "      <td>-3996.352636</td>\n",
       "      <td>3996.352636</td>\n",
       "      <td>2.404719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>99483.035341</td>\n",
       "      <td>97483.56</td>\n",
       "      <td>-1999.475341</td>\n",
       "      <td>1999.475341</td>\n",
       "      <td>2.051090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152570.655904</td>\n",
       "      <td>149759.96</td>\n",
       "      <td>-2810.695904</td>\n",
       "      <td>2810.695904</td>\n",
       "      <td>1.876801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>188232.227868</td>\n",
       "      <td>191792.06</td>\n",
       "      <td>3559.832132</td>\n",
       "      <td>3559.832132</td>\n",
       "      <td>1.856089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>98782.760535</td>\n",
       "      <td>99937.59</td>\n",
       "      <td>1154.829465</td>\n",
       "      <td>1154.829465</td>\n",
       "      <td>1.155551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>64503.672586</td>\n",
       "      <td>65200.33</td>\n",
       "      <td>696.657414</td>\n",
       "      <td>696.657414</td>\n",
       "      <td>1.068488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>157141.405065</td>\n",
       "      <td>156122.51</td>\n",
       "      <td>-1018.895065</td>\n",
       "      <td>1018.895065</td>\n",
       "      <td>0.652625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>153103.961809</td>\n",
       "      <td>152211.77</td>\n",
       "      <td>-892.191809</td>\n",
       "      <td>892.191809</td>\n",
       "      <td>0.586152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>193229.742403</td>\n",
       "      <td>192261.83</td>\n",
       "      <td>-967.912403</td>\n",
       "      <td>967.912403</td>\n",
       "      <td>0.503435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>97730.704066</td>\n",
       "      <td>97427.84</td>\n",
       "      <td>-302.864066</td>\n",
       "      <td>302.864066</td>\n",
       "      <td>0.310860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103450.945505</td>\n",
       "      <td>103282.38</td>\n",
       "      <td>-168.565505</td>\n",
       "      <td>168.565505</td>\n",
       "      <td>0.163208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>80897.162356</td>\n",
       "      <td>81005.76</td>\n",
       "      <td>108.597644</td>\n",
       "      <td>108.597644</td>\n",
       "      <td>0.134062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>96830.884528</td>\n",
       "      <td>96778.92</td>\n",
       "      <td>-51.964528</td>\n",
       "      <td>51.964528</td>\n",
       "      <td>0.053694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Predicted_Values  Actual_Values         Error  Absolute_Error  \\\n",
       "36      46828.384581       14681.40 -32146.984581    32146.984581   \n",
       "34      47542.034814       35673.41 -11868.624814    11868.624814   \n",
       "25      48697.538683       64926.08  16228.541317    16228.541317   \n",
       "0       75230.381370       90708.19  15477.808630    15477.808630   \n",
       "28      68513.680337       81229.06  12715.379663    12715.379663   \n",
       "23     146657.223872      129917.04 -16740.183872    16740.183872   \n",
       "1       55852.610677       49490.75  -6361.860677     6361.860677   \n",
       "11      61260.858318       69758.98   8498.121682     8498.121682   \n",
       "14     115261.582435      105008.31 -10253.272435    10253.272435   \n",
       "6      128752.417997      141585.52  12833.102003    12833.102003   \n",
       "20      88933.998355       96712.80   7778.801645     7778.801645   \n",
       "3       45780.027285       42559.73  -3220.297285     3220.297285   \n",
       "27     119090.858263      111313.02  -7777.838263     7777.838263   \n",
       "35      89900.159997       96479.51   6579.350003     6579.350003   \n",
       "17     126325.283030      134307.35   7982.066970     7982.066970   \n",
       "15      73179.626099       77798.83   4619.203901     4619.203901   \n",
       "7      101354.908357      107404.34   6049.431643     6049.431643   \n",
       "31     173830.567919      182901.99   9071.422081     9071.422081   \n",
       "24     131553.555967      125370.37  -6183.185967     6183.185967   \n",
       "4      110919.907679      105733.54  -5186.367679     5186.367679   \n",
       "19      85588.831281       89949.14   4360.308719     4360.308719   \n",
       "38     117314.473361      122776.86   5462.386639     5462.386639   \n",
       "29     113104.665035      108552.04  -4552.625035     4552.625035   \n",
       "8      114266.096833      110352.25  -3913.846833     3913.846833   \n",
       "32     128159.606470      124266.90  -3892.706470     3892.706470   \n",
       "12     114766.629102      118474.03   3707.400898     3707.400898   \n",
       "39     103581.171279      101004.64  -2576.531279     2576.531279   \n",
       "30     170184.292636      166187.94  -3996.352636     3996.352636   \n",
       "26      99483.035341       97483.56  -1999.475341     1999.475341   \n",
       "2      152570.655904      149759.96  -2810.695904     2810.695904   \n",
       "22     188232.227868      191792.06   3559.832132     3559.832132   \n",
       "10      98782.760535       99937.59   1154.829465     1154.829465   \n",
       "16      64503.672586       65200.33    696.657414      696.657414   \n",
       "33     157141.405065      156122.51  -1018.895065     1018.895065   \n",
       "18     153103.961809      152211.77   -892.191809      892.191809   \n",
       "37     193229.742403      192261.83   -967.912403      967.912403   \n",
       "9       97730.704066       97427.84   -302.864066      302.864066   \n",
       "5      103450.945505      103282.38   -168.565505      168.565505   \n",
       "13      80897.162356       81005.76    108.597644      108.597644   \n",
       "21      96830.884528       96778.92    -51.964528       51.964528   \n",
       "\n",
       "    Error_Percentage  \n",
       "36        218.964026  \n",
       "34         33.270228  \n",
       "25         24.995412  \n",
       "0          17.063298  \n",
       "28         15.653732  \n",
       "23         12.885287  \n",
       "1          12.854646  \n",
       "11         12.182119  \n",
       "14          9.764249  \n",
       "6           9.063852  \n",
       "20          8.043198  \n",
       "3           7.566536  \n",
       "27          6.987357  \n",
       "35          6.819427  \n",
       "17          5.943135  \n",
       "15          5.937369  \n",
       "7           5.632390  \n",
       "31          4.959718  \n",
       "24          4.931936  \n",
       "4           4.905130  \n",
       "19          4.847527  \n",
       "38          4.449036  \n",
       "29          4.193956  \n",
       "8           3.546685  \n",
       "32          3.132537  \n",
       "12          3.129294  \n",
       "39          2.550904  \n",
       "30          2.404719  \n",
       "26          2.051090  \n",
       "2           1.876801  \n",
       "22          1.856089  \n",
       "10          1.155551  \n",
       "16          1.068488  \n",
       "33          0.652625  \n",
       "18          0.586152  \n",
       "37          0.503435  \n",
       "9           0.310860  \n",
       "5           0.163208  \n",
       "13          0.134062  \n",
       "21          0.053694  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LinearRegression ( copy_X = True , fit_intercept = True )\n",
    "\n",
    "LR.fit ( Train_X , Train_Y )\n",
    "\n",
    "Model = pd.DataFrame ( )\n",
    "\n",
    "Model [ 'Predicted_Values' ] = LR.predict ( Train_X )\n",
    "\n",
    "Model [ 'Actual_Values' ] = Train_Y\n",
    "\n",
    "Model [ 'Error' ] = Train_Y - Model [ 'Predicted_Values' ]\n",
    "\n",
    "Model [ 'Absolute_Error' ] = np.abs ( Model.Error )\n",
    "\n",
    "Model [ 'Error_Percentage' ] = np.abs ( ( Model [ 'Error' ] * 100  ) / Train_Y )\n",
    "\n",
    "Model = Model.sort_values ( 'Error_Percentage' , ascending = False )\n",
    "\n",
    "Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5dce720",
   "metadata": {},
   "source": [
    "#### COFFEICIENTS OF DETERMINANT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2aa06136",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient of determinant is : 95.05035861084991 and the adjusted value is 94.48468530923276\n"
     ]
    }
   ],
   "source": [
    "R2 = LR.score ( Train_X , Train_Y )\n",
    "\n",
    "P = Train_X.shape [ 1 ]\n",
    "\n",
    "N = Train_X.shape [ 0 ]\n",
    "\n",
    "ADJ_R2 = 1 - ( ( ( 1 - R2) * ( N - 1 ) ) /  ( N - P - 1 ) )\n",
    "\n",
    "print ( \"The coefficient of determinant is : {} and the adjusted value is {}\".format ( R2 * 100 , ADJ_R2 * 100 ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ce4ade",
   "metadata": {},
   "source": [
    "#### MEAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "37633d57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the error is :  -1.7280399333685637e-11\n",
      "\n",
      "The mean of absolute error is :  6344.162122366675\n"
     ]
    }
   ],
   "source": [
    "print ( \"The mean of the error is : \" , np.mean ( Model.Error ) )\n",
    "\n",
    "print ( \"\\nThe mean of absolute error is : \" , np.mean ( Model.Absolute_Error ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d23226d",
   "metadata": {},
   "source": [
    "#### NORMAL DISTRIBUTION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7e8de973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure ( figsize = ( 7 , 7 ) )\n",
    "\n",
    "plt.xlabel ( 'Error = Actual - Predicted' )\n",
    "\n",
    "plt.ylabel ( 'Frequency' )\n",
    "\n",
    "plt.grid ( )\n",
    "\n",
    "plt.hist ( x = Model.Error );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01893f3c",
   "metadata": {},
   "source": [
    "#### HETEROSCEDASTICITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ab2a493c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot ( x = Model.Actual_Values , y = Model.Predicted_Values , data = Model );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb6ae61",
   "metadata": {},
   "source": [
    "#### METRICS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7a78d0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The mean square value of error test is  91270989.63188198\n",
      "\n",
      "The mean square value of error train is  77776787.94248608\n",
      "\n",
      "The root mean square value of error test is  9553.585171645354\n",
      "\n",
      "The root mean square value of error train is  8819.114918317262\n",
      "\n",
      "The mean absolute percentage error (of test) is  6.142368258032201\n",
      "\n",
      "The mean absolute percentage error (of train) is  11.677243912327064\n",
      "\n",
      "Accuracy on train data is :  93.8576317419678\n",
      "\n",
      "Accuracy on test data is :  88.32275608767293\n"
     ]
    }
   ],
   "source": [
    "Predicted_Test = LR.predict ( Test_X )\n",
    "\n",
    "Error_Test = Test_Y - Predicted_Test\n",
    "\n",
    "Error_test_Percentage = abs(Error_Test) * 100 / Test_Y \n",
    "\n",
    "MSE = np.mean ( np.square ( Error_Test ) )\n",
    "\n",
    "print ( \"\\nThe mean square value of error test is \" , MSE )\n",
    "\n",
    "print ( \"\\nThe mean square value of error train is \" , np.mean ( np.square (Model.Error) ) ) \n",
    "\n",
    "#### RMSE\n",
    "\n",
    "RMSE = np.sqrt ( MSE )\n",
    "\n",
    "print ( \"\\nThe root mean square value of error test is \" , RMSE )\n",
    "\n",
    "print ( \"\\nThe root mean square value of error train is \" , np.sqrt ( np.mean ( np.square ( Model.Error) ) ) ) \n",
    "\n",
    "#### MAPE\n",
    "\n",
    "MAPE = np.mean ( Error_test_Percentage )\n",
    "\n",
    "print ( \"\\nThe mean absolute percentage error (of test) is \" , MAPE )\n",
    "\n",
    "print ( \"\\nThe mean absolute percentage error (of train) is \" , np.mean ( Model.Error_Percentage ) ) \n",
    "\n",
    "#### ACCURACY\n",
    "\n",
    "print ( \"\\nAccuracy on train data is : \" , 100 - MAPE  )\n",
    "\n",
    "print ( '\\nAccuracy on test data is : ' , 100 - np.mean ( Model.Error_Percentage ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76863529",
   "metadata": {},
   "source": [
    "### APPLYING LASSO \n",
    "\n",
    "        A high Lasso score indicates that the model has a large number of nonzero coefficients, \n",
    "        \n",
    "        which can be interpreted as a sign of overfitting or high complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "501ebc41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lasso score is :  95.0503585218569\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "L = Lasso ( )\n",
    "\n",
    "L.fit ( Train_X , Train_Y )\n",
    "\n",
    "print ( 'The lasso score is : ' , L.score ( Train_X , Train_Y ) * 100 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3167a96e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted_Values</th>\n",
       "      <th>Actual_Values</th>\n",
       "      <th>Error</th>\n",
       "      <th>Absolute_Error</th>\n",
       "      <th>Error_Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>46829.765869</td>\n",
       "      <td>14681.40</td>\n",
       "      <td>-32148.365869</td>\n",
       "      <td>32148.365869</td>\n",
       "      <td>218.973435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>47540.719160</td>\n",
       "      <td>35673.41</td>\n",
       "      <td>-11867.309160</td>\n",
       "      <td>11867.309160</td>\n",
       "      <td>33.266540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>48696.100348</td>\n",
       "      <td>64926.08</td>\n",
       "      <td>16229.979652</td>\n",
       "      <td>16229.979652</td>\n",
       "      <td>24.997628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75230.427772</td>\n",
       "      <td>90708.19</td>\n",
       "      <td>15477.762228</td>\n",
       "      <td>15477.762228</td>\n",
       "      <td>17.063247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>68512.417316</td>\n",
       "      <td>81229.06</td>\n",
       "      <td>12716.642684</td>\n",
       "      <td>12716.642684</td>\n",
       "      <td>15.655287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>146656.009642</td>\n",
       "      <td>129917.04</td>\n",
       "      <td>-16738.969642</td>\n",
       "      <td>16738.969642</td>\n",
       "      <td>12.884353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55852.666507</td>\n",
       "      <td>49490.75</td>\n",
       "      <td>-6361.916507</td>\n",
       "      <td>6361.916507</td>\n",
       "      <td>12.854759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>61259.442877</td>\n",
       "      <td>69758.98</td>\n",
       "      <td>8499.537123</td>\n",
       "      <td>8499.537123</td>\n",
       "      <td>12.184148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>115260.328262</td>\n",
       "      <td>105008.31</td>\n",
       "      <td>-10252.018262</td>\n",
       "      <td>10252.018262</td>\n",
       "      <td>9.763054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>128752.563532</td>\n",
       "      <td>141585.52</td>\n",
       "      <td>12832.956468</td>\n",
       "      <td>12832.956468</td>\n",
       "      <td>9.063749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>88935.416912</td>\n",
       "      <td>96712.80</td>\n",
       "      <td>7777.383088</td>\n",
       "      <td>7777.383088</td>\n",
       "      <td>8.041731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45781.364629</td>\n",
       "      <td>42559.73</td>\n",
       "      <td>-3221.634629</td>\n",
       "      <td>3221.634629</td>\n",
       "      <td>7.569678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>119089.553413</td>\n",
       "      <td>111313.02</td>\n",
       "      <td>-7776.533413</td>\n",
       "      <td>7776.533413</td>\n",
       "      <td>6.986185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>89898.903973</td>\n",
       "      <td>96479.51</td>\n",
       "      <td>6580.606027</td>\n",
       "      <td>6580.606027</td>\n",
       "      <td>6.820729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>126326.811290</td>\n",
       "      <td>134307.35</td>\n",
       "      <td>7980.538710</td>\n",
       "      <td>7980.538710</td>\n",
       "      <td>5.941997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>73179.733817</td>\n",
       "      <td>77798.83</td>\n",
       "      <td>4619.096183</td>\n",
       "      <td>4619.096183</td>\n",
       "      <td>5.937231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101356.362039</td>\n",
       "      <td>107404.34</td>\n",
       "      <td>6047.977961</td>\n",
       "      <td>6047.977961</td>\n",
       "      <td>5.631037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>173829.433254</td>\n",
       "      <td>182901.99</td>\n",
       "      <td>9072.556746</td>\n",
       "      <td>9072.556746</td>\n",
       "      <td>4.960338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>131552.282780</td>\n",
       "      <td>125370.37</td>\n",
       "      <td>-6181.912780</td>\n",
       "      <td>6181.912780</td>\n",
       "      <td>4.930920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110919.968149</td>\n",
       "      <td>105733.54</td>\n",
       "      <td>-5186.428149</td>\n",
       "      <td>5186.428149</td>\n",
       "      <td>4.905187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>85590.424888</td>\n",
       "      <td>89949.14</td>\n",
       "      <td>4358.715112</td>\n",
       "      <td>4358.715112</td>\n",
       "      <td>4.845755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>117313.097236</td>\n",
       "      <td>122776.86</td>\n",
       "      <td>5463.762764</td>\n",
       "      <td>5463.762764</td>\n",
       "      <td>4.450157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>113103.407859</td>\n",
       "      <td>108552.04</td>\n",
       "      <td>-4551.367859</td>\n",
       "      <td>4551.367859</td>\n",
       "      <td>4.192798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>114266.238548</td>\n",
       "      <td>110352.25</td>\n",
       "      <td>-3913.988548</td>\n",
       "      <td>3913.988548</td>\n",
       "      <td>3.546814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>128159.784067</td>\n",
       "      <td>124266.90</td>\n",
       "      <td>-3892.884067</td>\n",
       "      <td>3892.884067</td>\n",
       "      <td>3.132680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>114768.186170</td>\n",
       "      <td>118474.03</td>\n",
       "      <td>3705.843830</td>\n",
       "      <td>3705.843830</td>\n",
       "      <td>3.127980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>103579.797889</td>\n",
       "      <td>101004.64</td>\n",
       "      <td>-2575.157889</td>\n",
       "      <td>2575.157889</td>\n",
       "      <td>2.549544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>170184.595343</td>\n",
       "      <td>166187.94</td>\n",
       "      <td>-3996.655343</td>\n",
       "      <td>3996.655343</td>\n",
       "      <td>2.404901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>99481.651410</td>\n",
       "      <td>97483.56</td>\n",
       "      <td>-1998.091410</td>\n",
       "      <td>1998.091410</td>\n",
       "      <td>2.049670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152572.285825</td>\n",
       "      <td>149759.96</td>\n",
       "      <td>-2812.325825</td>\n",
       "      <td>2812.325825</td>\n",
       "      <td>1.877889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>188233.874852</td>\n",
       "      <td>191792.06</td>\n",
       "      <td>3558.185148</td>\n",
       "      <td>3558.185148</td>\n",
       "      <td>1.855231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>98782.840308</td>\n",
       "      <td>99937.59</td>\n",
       "      <td>1154.749692</td>\n",
       "      <td>1154.749692</td>\n",
       "      <td>1.155471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>64505.013899</td>\n",
       "      <td>65200.33</td>\n",
       "      <td>695.316101</td>\n",
       "      <td>695.316101</td>\n",
       "      <td>1.066430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>157142.935344</td>\n",
       "      <td>156122.51</td>\n",
       "      <td>-1020.425344</td>\n",
       "      <td>1020.425344</td>\n",
       "      <td>0.653606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>153102.724597</td>\n",
       "      <td>152211.77</td>\n",
       "      <td>-890.954597</td>\n",
       "      <td>890.954597</td>\n",
       "      <td>0.585339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>193228.628642</td>\n",
       "      <td>192261.83</td>\n",
       "      <td>-966.798642</td>\n",
       "      <td>966.798642</td>\n",
       "      <td>0.502855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>97732.148518</td>\n",
       "      <td>97427.84</td>\n",
       "      <td>-304.308518</td>\n",
       "      <td>304.308518</td>\n",
       "      <td>0.312342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103450.923495</td>\n",
       "      <td>103282.38</td>\n",
       "      <td>-168.543495</td>\n",
       "      <td>168.543495</td>\n",
       "      <td>0.163187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>80898.688424</td>\n",
       "      <td>81005.76</td>\n",
       "      <td>107.071576</td>\n",
       "      <td>107.071576</td>\n",
       "      <td>0.132178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>96831.011148</td>\n",
       "      <td>96778.92</td>\n",
       "      <td>-52.091148</td>\n",
       "      <td>52.091148</td>\n",
       "      <td>0.053825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Predicted_Values  Actual_Values         Error  Absolute_Error  \\\n",
       "36      46829.765869       14681.40 -32148.365869    32148.365869   \n",
       "34      47540.719160       35673.41 -11867.309160    11867.309160   \n",
       "25      48696.100348       64926.08  16229.979652    16229.979652   \n",
       "0       75230.427772       90708.19  15477.762228    15477.762228   \n",
       "28      68512.417316       81229.06  12716.642684    12716.642684   \n",
       "23     146656.009642      129917.04 -16738.969642    16738.969642   \n",
       "1       55852.666507       49490.75  -6361.916507     6361.916507   \n",
       "11      61259.442877       69758.98   8499.537123     8499.537123   \n",
       "14     115260.328262      105008.31 -10252.018262    10252.018262   \n",
       "6      128752.563532      141585.52  12832.956468    12832.956468   \n",
       "20      88935.416912       96712.80   7777.383088     7777.383088   \n",
       "3       45781.364629       42559.73  -3221.634629     3221.634629   \n",
       "27     119089.553413      111313.02  -7776.533413     7776.533413   \n",
       "35      89898.903973       96479.51   6580.606027     6580.606027   \n",
       "17     126326.811290      134307.35   7980.538710     7980.538710   \n",
       "15      73179.733817       77798.83   4619.096183     4619.096183   \n",
       "7      101356.362039      107404.34   6047.977961     6047.977961   \n",
       "31     173829.433254      182901.99   9072.556746     9072.556746   \n",
       "24     131552.282780      125370.37  -6181.912780     6181.912780   \n",
       "4      110919.968149      105733.54  -5186.428149     5186.428149   \n",
       "19      85590.424888       89949.14   4358.715112     4358.715112   \n",
       "38     117313.097236      122776.86   5463.762764     5463.762764   \n",
       "29     113103.407859      108552.04  -4551.367859     4551.367859   \n",
       "8      114266.238548      110352.25  -3913.988548     3913.988548   \n",
       "32     128159.784067      124266.90  -3892.884067     3892.884067   \n",
       "12     114768.186170      118474.03   3705.843830     3705.843830   \n",
       "39     103579.797889      101004.64  -2575.157889     2575.157889   \n",
       "30     170184.595343      166187.94  -3996.655343     3996.655343   \n",
       "26      99481.651410       97483.56  -1998.091410     1998.091410   \n",
       "2      152572.285825      149759.96  -2812.325825     2812.325825   \n",
       "22     188233.874852      191792.06   3558.185148     3558.185148   \n",
       "10      98782.840308       99937.59   1154.749692     1154.749692   \n",
       "16      64505.013899       65200.33    695.316101      695.316101   \n",
       "33     157142.935344      156122.51  -1020.425344     1020.425344   \n",
       "18     153102.724597      152211.77   -890.954597      890.954597   \n",
       "37     193228.628642      192261.83   -966.798642      966.798642   \n",
       "9       97732.148518       97427.84   -304.308518      304.308518   \n",
       "5      103450.923495      103282.38   -168.543495      168.543495   \n",
       "13      80898.688424       81005.76    107.071576      107.071576   \n",
       "21      96831.011148       96778.92    -52.091148       52.091148   \n",
       "\n",
       "    Error_Percentage  \n",
       "36        218.973435  \n",
       "34         33.266540  \n",
       "25         24.997628  \n",
       "0          17.063247  \n",
       "28         15.655287  \n",
       "23         12.884353  \n",
       "1          12.854759  \n",
       "11         12.184148  \n",
       "14          9.763054  \n",
       "6           9.063749  \n",
       "20          8.041731  \n",
       "3           7.569678  \n",
       "27          6.986185  \n",
       "35          6.820729  \n",
       "17          5.941997  \n",
       "15          5.937231  \n",
       "7           5.631037  \n",
       "31          4.960338  \n",
       "24          4.930920  \n",
       "4           4.905187  \n",
       "19          4.845755  \n",
       "38          4.450157  \n",
       "29          4.192798  \n",
       "8           3.546814  \n",
       "32          3.132680  \n",
       "12          3.127980  \n",
       "39          2.549544  \n",
       "30          2.404901  \n",
       "26          2.049670  \n",
       "2           1.877889  \n",
       "22          1.855231  \n",
       "10          1.155471  \n",
       "16          1.066430  \n",
       "33          0.653606  \n",
       "18          0.585339  \n",
       "37          0.502855  \n",
       "9           0.312342  \n",
       "5           0.163187  \n",
       "13          0.132178  \n",
       "21          0.053825  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model = pd.DataFrame ( )\n",
    "\n",
    "Model [ 'Predicted_Values' ] = L.predict ( Train_X )\n",
    "\n",
    "Model [ 'Actual_Values' ] = Train_Y\n",
    "\n",
    "Model [ 'Error' ] = Train_Y - Model [ 'Predicted_Values' ]\n",
    "\n",
    "Model [ 'Absolute_Error' ] = np.abs ( Model.Error )\n",
    "\n",
    "Model [ 'Error_Percentage' ] = np.abs ( ( Model [ 'Error' ] * 100  ) / Train_Y )\n",
    "\n",
    "Model = Model.sort_values ( 'Error_Percentage' , ascending = False )\n",
    "\n",
    "Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee2b162f",
   "metadata": {},
   "source": [
    "#### MEAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0268d426",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the error is :  -1.4006218407303095e-11\n",
      "\n",
      "The mean of absolute error is :  6343.93405472469\n"
     ]
    }
   ],
   "source": [
    "print ( \"The mean of the error is : \" , np.mean ( Model.Error ) )\n",
    "\n",
    "print ( \"\\nThe mean of absolute error is : \" , np.mean ( Model.Absolute_Error ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7090c39c",
   "metadata": {},
   "source": [
    "#### NORMAL DISTRIBUTION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c8f89d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure ( figsize = ( 7 , 7 ) )\n",
    "\n",
    "plt.xlabel ( 'Error = Actual - Predicted' )\n",
    "\n",
    "plt.ylabel ( 'Frequency' )\n",
    "\n",
    "plt.grid ( )\n",
    "\n",
    "plt.hist ( x = Model.Error );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b22829",
   "metadata": {},
   "source": [
    "#### HETEROSCEDASTICITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c234a204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot ( x = Model.Actual_Values , y = Model.Predicted_Values , data = Model );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9637dba",
   "metadata": {},
   "source": [
    "#### METRICS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dad96417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The mean square value of error test is  91259520.16870753\n",
      "\n",
      "The mean square value of error train is  77776789.34088856\n",
      "\n",
      "The root mean square value of error test is  9552.984882679733\n",
      "\n",
      "The root mean square value of error train is  8819.114997599734\n",
      "\n",
      "The mean absolute percentage error (of test) is  6.141679767019481\n",
      "\n",
      "The mean absolute percentage error (of train) is  11.677247076702795\n",
      "\n",
      "Accuracy on train data is :  93.85832023298052\n",
      "\n",
      "Accuracy on test data is :  88.3227529232972\n"
     ]
    }
   ],
   "source": [
    "Predicted_Test = L.predict ( Test_X )\n",
    "\n",
    "Error_Test = Test_Y - Predicted_Test\n",
    "\n",
    "Error_test_Percentage = abs(Error_Test) * 100 / Test_Y \n",
    "\n",
    "MSE = np.mean ( np.square ( Error_Test ) )\n",
    "\n",
    "print ( \"\\nThe mean square value of error test is \" , MSE )\n",
    "\n",
    "print ( \"\\nThe mean square value of error train is \" , np.mean ( np.square (Model.Error) ) ) \n",
    "\n",
    "#### RMSE\n",
    "\n",
    "RMSE = np.sqrt ( MSE )\n",
    "\n",
    "print ( \"\\nThe root mean square value of error test is \" , RMSE )\n",
    "\n",
    "print ( \"\\nThe root mean square value of error train is \" , np.sqrt ( np.mean ( np.square ( Model.Error) ) ) ) \n",
    "\n",
    "#### MAPE\n",
    "\n",
    "MAPE = np.mean ( Error_test_Percentage )\n",
    "\n",
    "print ( \"\\nThe mean absolute percentage error (of test) is \" , MAPE )\n",
    "\n",
    "print ( \"\\nThe mean absolute percentage error (of train) is \" , np.mean ( Model.Error_Percentage ) ) \n",
    "\n",
    "#### ACCURACY\n",
    "\n",
    "print ( \"\\nAccuracy on train data is : \" , 100 - MAPE  )\n",
    "\n",
    "print ( '\\nAccuracy on test data is : ' , 100 - np.mean ( Model.Error_Percentage ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7804d879",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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