Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab61e27a-0652-4856-ab05-5860ecb3c710",
   "metadata": {},
   "source": [
    "**TASK-2**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b008a8-84bc-40ce-a062-614c403fbb38",
   "metadata": {},
   "source": [
    "**OBJECTIVE - predict the profit for the\r\n",
    "corresponding startups through linear regression  single or multi variate)**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38ba4ea4-8fdd-4a54-96e4-fe343084e1a4",
   "metadata": {},
   "source": [
    "**IMPORTING LIBRARIES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "840d5f34-0a76-408b-9414-86280615a002",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623c35f3-c263-4d3c-8600-6b8946d9ec81",
   "metadata": {},
   "source": [
    "**DATA COLLECTION AND CLEANING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6aaa8cad-0541-418a-bf6c-312ff00b6593",
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</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>Florida</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>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</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>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</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>Florida</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>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NaN</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NaN</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</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>California</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>New York</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>Florida</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>California</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>New York</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>California</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    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47             NaN        299737.29    New York  111313.02\n",
       "22   73994.56             NaN        303319.26     Florida  110352.25\n",
       "23   67532.53             NaN        304768.73     Florida  108733.99\n",
       "24   77044.01             NaN        140574.81    New York  108552.04\n",
       "25   64664.71             NaN        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52             NaN        118148.20     Florida  103282.38\n",
       "29   65605.48             NaN        107138.38    New York  101004.64\n",
       "30   61994.48             NaN         91131.24     Florida   99937.59\n",
       "31        NaN             NaN         88218.23    New York   97483.56\n",
       "32        NaN             NaN         46085.25  California   97427.84\n",
       "33        NaN             NaN        214634.81     Florida   96778.92\n",
       "34        NaN       157693.92        210797.67  California   96712.80\n",
       "35        NaN        85047.44        205517.64    New York   96479.51\n",
       "36        NaN       127056.21        201126.82     Florida   90708.19\n",
       "37        NaN             NaN        197029.42  California   89949.14\n",
       "38        NaN             NaN        185265.10    New York   81229.06\n",
       "39        NaN             NaN        174999.30  California   81005.76\n",
       "40        NaN             NaN        172795.67  California   78239.91\n",
       "41        NaN             NaN        164470.71     Florida   77798.83\n",
       "42   23640.93             NaN        148001.11  California   71498.49\n",
       "43        NaN             NaN         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41\n",
       "49       0.00       116983.80         45173.06  California   14681.40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r\"D:\\MLCOE\\task2\\regression.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f47b909f-2d30-4bf1-96e0-68ef17b3420a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          12\n",
       "Administration     18\n",
       "Marketing Spend     0\n",
       "State               0\n",
       "Profit              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a27512fe-5a14-4f48-85e7-0168c1fdaebc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prakh\\AppData\\Local\\Temp\\ipykernel_19268\\831124899.py:1: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  df=df.interpolate()\n"
     ]
    },
    {
     "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>136897.800000</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.700000</td>\n",
       "      <td>151377.590000</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.510000</td>\n",
       "      <td>101145.550000</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.410000</td>\n",
       "      <td>118671.850000</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.340000</td>\n",
       "      <td>91391.770000</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.900000</td>\n",
       "      <td>99814.710000</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.460000</td>\n",
       "      <td>147198.870000</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.130000</td>\n",
       "      <td>145530.060000</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.520000</td>\n",
       "      <td>148718.950000</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.880000</td>\n",
       "      <td>108679.170000</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.080000</td>\n",
       "      <td>110594.110000</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.960000</td>\n",
       "      <td>91790.610000</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.750000</td>\n",
       "      <td>127320.380000</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.390000</td>\n",
       "      <td>135495.070000</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.240000</td>\n",
       "      <td>156547.420000</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.610000</td>\n",
       "      <td>122616.840000</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.110000</td>\n",
       "      <td>121597.550000</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.160000</td>\n",
       "      <td>145077.580000</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.160000</td>\n",
       "      <td>114175.790000</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.700000</td>\n",
       "      <td>153514.110000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.860000</td>\n",
       "      <td>113867.300000</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.470000</td>\n",
       "      <td>118912.080000</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.560000</td>\n",
       "      <td>123956.860000</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.530000</td>\n",
       "      <td>129001.640000</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.010000</td>\n",
       "      <td>134046.420000</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.710000</td>\n",
       "      <td>139091.200000</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.870000</td>\n",
       "      <td>144135.980000</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.600000</td>\n",
       "      <td>127864.550000</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.520000</td>\n",
       "      <td>132125.888571</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.480000</td>\n",
       "      <td>136387.227143</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.480000</td>\n",
       "      <td>140648.565714</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>58798.350833</td>\n",
       "      <td>144909.904286</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>55602.221667</td>\n",
       "      <td>149171.242857</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>52406.092500</td>\n",
       "      <td>153432.581429</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>49209.963333</td>\n",
       "      <td>157693.920000</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46013.834167</td>\n",
       "      <td>85047.440000</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>42817.705000</td>\n",
       "      <td>127056.210000</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>39621.575833</td>\n",
       "      <td>130524.951250</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>36425.446667</td>\n",
       "      <td>133993.692500</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>33229.317500</td>\n",
       "      <td>137462.433750</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>30033.188333</td>\n",
       "      <td>140931.175000</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>26837.059167</td>\n",
       "      <td>144399.916250</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.930000</td>\n",
       "      <td>147868.657500</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22909.335000</td>\n",
       "      <td>151337.398750</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.740000</td>\n",
       "      <td>154806.140000</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.230000</td>\n",
       "      <td>124153.040000</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.460000</td>\n",
       "      <td>115816.210000</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>135426.920000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.050000</td>\n",
       "      <td>51743.150000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>116983.800000</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</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   165349.200000   136897.800000        471784.10    New York  192261.83\n",
       "1   162597.700000   151377.590000        443898.53  California  191792.06\n",
       "2   153441.510000   101145.550000        407934.54     Florida  191050.39\n",
       "3   144372.410000   118671.850000        383199.62    New York  182901.99\n",
       "4   142107.340000    91391.770000        366168.42     Florida  166187.94\n",
       "5   131876.900000    99814.710000        362861.36    New York  156991.12\n",
       "6   134615.460000   147198.870000        127716.82  California  156122.51\n",
       "7   130298.130000   145530.060000        323876.68     Florida  155752.60\n",
       "8   120542.520000   148718.950000        311613.29    New York  152211.77\n",
       "9   123334.880000   108679.170000        304981.62  California  149759.96\n",
       "10  101913.080000   110594.110000        229160.95     Florida  146121.95\n",
       "11  100671.960000    91790.610000        249744.55  California  144259.40\n",
       "12   93863.750000   127320.380000        249839.44     Florida  141585.52\n",
       "13   91992.390000   135495.070000        252664.93  California  134307.35\n",
       "14  119943.240000   156547.420000        256512.92     Florida  132602.65\n",
       "15  114523.610000   122616.840000        261776.23    New York  129917.04\n",
       "16   78013.110000   121597.550000        264346.06  California  126992.93\n",
       "17   94657.160000   145077.580000        282574.31    New York  125370.37\n",
       "18   91749.160000   114175.790000        294919.57     Florida  124266.90\n",
       "19   86419.700000   153514.110000             0.00    New York  122776.86\n",
       "20   76253.860000   113867.300000        298664.47  California  118474.03\n",
       "21   78389.470000   118912.080000        299737.29    New York  111313.02\n",
       "22   73994.560000   123956.860000        303319.26     Florida  110352.25\n",
       "23   67532.530000   129001.640000        304768.73     Florida  108733.99\n",
       "24   77044.010000   134046.420000        140574.81    New York  108552.04\n",
       "25   64664.710000   139091.200000        137962.62  California  107404.34\n",
       "26   75328.870000   144135.980000        134050.07     Florida  105733.54\n",
       "27   72107.600000   127864.550000        353183.81    New York  105008.31\n",
       "28   66051.520000   132125.888571        118148.20     Florida  103282.38\n",
       "29   65605.480000   136387.227143        107138.38    New York  101004.64\n",
       "30   61994.480000   140648.565714         91131.24     Florida   99937.59\n",
       "31   58798.350833   144909.904286         88218.23    New York   97483.56\n",
       "32   55602.221667   149171.242857         46085.25  California   97427.84\n",
       "33   52406.092500   153432.581429        214634.81     Florida   96778.92\n",
       "34   49209.963333   157693.920000        210797.67  California   96712.80\n",
       "35   46013.834167    85047.440000        205517.64    New York   96479.51\n",
       "36   42817.705000   127056.210000        201126.82     Florida   90708.19\n",
       "37   39621.575833   130524.951250        197029.42  California   89949.14\n",
       "38   36425.446667   133993.692500        185265.10    New York   81229.06\n",
       "39   33229.317500   137462.433750        174999.30  California   81005.76\n",
       "40   30033.188333   140931.175000        172795.67  California   78239.91\n",
       "41   26837.059167   144399.916250        164470.71     Florida   77798.83\n",
       "42   23640.930000   147868.657500        148001.11  California   71498.49\n",
       "43   22909.335000   151337.398750         35534.17    New York   69758.98\n",
       "44   22177.740000   154806.140000         28334.72  California   65200.33\n",
       "45    1000.230000   124153.040000          1903.93    New York   64926.08\n",
       "46    1315.460000   115816.210000        297114.46     Florida   49490.75\n",
       "47       0.000000   135426.920000             0.00  California   42559.73\n",
       "48     542.050000    51743.150000             0.00    New York   35673.41\n",
       "49       0.000000   116983.800000         45173.06  California   14681.40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.interpolate()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5a18848-4b49-43e5-b1b0-2784b27d00e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          0\n",
       "Administration     0\n",
       "Marketing Spend    0\n",
       "State              0\n",
       "Profit             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "128fc5ac-20c8-4a19-9eae-a40e1311211a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d9d26fe-483b-4e75-9962-1e411416eef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "57ff8a42-2057-4ac3-9866-f2e7106d017d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of         R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.200000   136897.800000        471784.10    New York  192261.83\n",
       "1   162597.700000   151377.590000        443898.53  California  191792.06\n",
       "2   153441.510000   101145.550000        407934.54     Florida  191050.39\n",
       "3   144372.410000   118671.850000        383199.62    New York  182901.99\n",
       "4   142107.340000    91391.770000        366168.42     Florida  166187.94\n",
       "5   131876.900000    99814.710000        362861.36    New York  156991.12\n",
       "6   134615.460000   147198.870000        127716.82  California  156122.51\n",
       "7   130298.130000   145530.060000        323876.68     Florida  155752.60\n",
       "8   120542.520000   148718.950000        311613.29    New York  152211.77\n",
       "9   123334.880000   108679.170000        304981.62  California  149759.96\n",
       "10  101913.080000   110594.110000        229160.95     Florida  146121.95\n",
       "11  100671.960000    91790.610000        249744.55  California  144259.40\n",
       "12   93863.750000   127320.380000        249839.44     Florida  141585.52\n",
       "13   91992.390000   135495.070000        252664.93  California  134307.35\n",
       "14  119943.240000   156547.420000        256512.92     Florida  132602.65\n",
       "15  114523.610000   122616.840000        261776.23    New York  129917.04\n",
       "16   78013.110000   121597.550000        264346.06  California  126992.93\n",
       "17   94657.160000   145077.580000        282574.31    New York  125370.37\n",
       "18   91749.160000   114175.790000        294919.57     Florida  124266.90\n",
       "19   86419.700000   153514.110000             0.00    New York  122776.86\n",
       "20   76253.860000   113867.300000        298664.47  California  118474.03\n",
       "21   78389.470000   118912.080000        299737.29    New York  111313.02\n",
       "22   73994.560000   123956.860000        303319.26     Florida  110352.25\n",
       "23   67532.530000   129001.640000        304768.73     Florida  108733.99\n",
       "24   77044.010000   134046.420000        140574.81    New York  108552.04\n",
       "25   64664.710000   139091.200000        137962.62  California  107404.34\n",
       "26   75328.870000   144135.980000        134050.07     Florida  105733.54\n",
       "27   72107.600000   127864.550000        353183.81    New York  105008.31\n",
       "28   66051.520000   132125.888571        118148.20     Florida  103282.38\n",
       "29   65605.480000   136387.227143        107138.38    New York  101004.64\n",
       "30   61994.480000   140648.565714         91131.24     Florida   99937.59\n",
       "31   58798.350833   144909.904286         88218.23    New York   97483.56\n",
       "32   55602.221667   149171.242857         46085.25  California   97427.84\n",
       "33   52406.092500   153432.581429        214634.81     Florida   96778.92\n",
       "34   49209.963333   157693.920000        210797.67  California   96712.80\n",
       "35   46013.834167    85047.440000        205517.64    New York   96479.51\n",
       "36   42817.705000   127056.210000        201126.82     Florida   90708.19\n",
       "37   39621.575833   130524.951250        197029.42  California   89949.14\n",
       "38   36425.446667   133993.692500        185265.10    New York   81229.06\n",
       "39   33229.317500   137462.433750        174999.30  California   81005.76\n",
       "40   30033.188333   140931.175000        172795.67  California   78239.91\n",
       "41   26837.059167   144399.916250        164470.71     Florida   77798.83\n",
       "42   23640.930000   147868.657500        148001.11  California   71498.49\n",
       "43   22909.335000   151337.398750         35534.17    New York   69758.98\n",
       "44   22177.740000   154806.140000         28334.72  California   65200.33\n",
       "45    1000.230000   124153.040000          1903.93    New York   64926.08\n",
       "46    1315.460000   115816.210000        297114.46     Florida   49490.75\n",
       "47       0.000000   135426.920000             0.00  California   42559.73\n",
       "48     542.050000    51743.150000             0.00    New York   35673.41\n",
       "49       0.000000   116983.800000         45173.06  California   14681.40>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9073345b-31d8-40a7-a75d-d0733037c2be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</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    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c695dc40-2186-4f76-8224-f89c5800b1bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RD_Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing_Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>136897.800000</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.700000</td>\n",
       "      <td>151377.590000</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.510000</td>\n",
       "      <td>101145.550000</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.410000</td>\n",
       "      <td>118671.850000</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.340000</td>\n",
       "      <td>91391.770000</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.900000</td>\n",
       "      <td>99814.710000</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.460000</td>\n",
       "      <td>147198.870000</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.130000</td>\n",
       "      <td>145530.060000</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.520000</td>\n",
       "      <td>148718.950000</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.880000</td>\n",
       "      <td>108679.170000</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.080000</td>\n",
       "      <td>110594.110000</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.960000</td>\n",
       "      <td>91790.610000</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.750000</td>\n",
       "      <td>127320.380000</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.390000</td>\n",
       "      <td>135495.070000</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.240000</td>\n",
       "      <td>156547.420000</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.610000</td>\n",
       "      <td>122616.840000</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.110000</td>\n",
       "      <td>121597.550000</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.160000</td>\n",
       "      <td>145077.580000</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.160000</td>\n",
       "      <td>114175.790000</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.700000</td>\n",
       "      <td>153514.110000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.860000</td>\n",
       "      <td>113867.300000</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.470000</td>\n",
       "      <td>118912.080000</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.560000</td>\n",
       "      <td>123956.860000</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.530000</td>\n",
       "      <td>129001.640000</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.010000</td>\n",
       "      <td>134046.420000</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.710000</td>\n",
       "      <td>139091.200000</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.870000</td>\n",
       "      <td>144135.980000</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.600000</td>\n",
       "      <td>127864.550000</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.520000</td>\n",
       "      <td>132125.888571</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.480000</td>\n",
       "      <td>136387.227143</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.480000</td>\n",
       "      <td>140648.565714</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>58798.350833</td>\n",
       "      <td>144909.904286</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>55602.221667</td>\n",
       "      <td>149171.242857</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>52406.092500</td>\n",
       "      <td>153432.581429</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>49209.963333</td>\n",
       "      <td>157693.920000</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46013.834167</td>\n",
       "      <td>85047.440000</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>42817.705000</td>\n",
       "      <td>127056.210000</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>39621.575833</td>\n",
       "      <td>130524.951250</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>36425.446667</td>\n",
       "      <td>133993.692500</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>33229.317500</td>\n",
       "      <td>137462.433750</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>30033.188333</td>\n",
       "      <td>140931.175000</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>26837.059167</td>\n",
       "      <td>144399.916250</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.930000</td>\n",
       "      <td>147868.657500</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22909.335000</td>\n",
       "      <td>151337.398750</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.740000</td>\n",
       "      <td>154806.140000</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.230000</td>\n",
       "      <td>124153.040000</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.460000</td>\n",
       "      <td>115816.210000</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>135426.920000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.050000</td>\n",
       "      <td>51743.150000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>116983.800000</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RD_Spend  Administration  Marketing_Spend       State     Profit\n",
       "0   165349.200000   136897.800000        471784.10    New York  192261.83\n",
       "1   162597.700000   151377.590000        443898.53  California  191792.06\n",
       "2   153441.510000   101145.550000        407934.54     Florida  191050.39\n",
       "3   144372.410000   118671.850000        383199.62    New York  182901.99\n",
       "4   142107.340000    91391.770000        366168.42     Florida  166187.94\n",
       "5   131876.900000    99814.710000        362861.36    New York  156991.12\n",
       "6   134615.460000   147198.870000        127716.82  California  156122.51\n",
       "7   130298.130000   145530.060000        323876.68     Florida  155752.60\n",
       "8   120542.520000   148718.950000        311613.29    New York  152211.77\n",
       "9   123334.880000   108679.170000        304981.62  California  149759.96\n",
       "10  101913.080000   110594.110000        229160.95     Florida  146121.95\n",
       "11  100671.960000    91790.610000        249744.55  California  144259.40\n",
       "12   93863.750000   127320.380000        249839.44     Florida  141585.52\n",
       "13   91992.390000   135495.070000        252664.93  California  134307.35\n",
       "14  119943.240000   156547.420000        256512.92     Florida  132602.65\n",
       "15  114523.610000   122616.840000        261776.23    New York  129917.04\n",
       "16   78013.110000   121597.550000        264346.06  California  126992.93\n",
       "17   94657.160000   145077.580000        282574.31    New York  125370.37\n",
       "18   91749.160000   114175.790000        294919.57     Florida  124266.90\n",
       "19   86419.700000   153514.110000             0.00    New York  122776.86\n",
       "20   76253.860000   113867.300000        298664.47  California  118474.03\n",
       "21   78389.470000   118912.080000        299737.29    New York  111313.02\n",
       "22   73994.560000   123956.860000        303319.26     Florida  110352.25\n",
       "23   67532.530000   129001.640000        304768.73     Florida  108733.99\n",
       "24   77044.010000   134046.420000        140574.81    New York  108552.04\n",
       "25   64664.710000   139091.200000        137962.62  California  107404.34\n",
       "26   75328.870000   144135.980000        134050.07     Florida  105733.54\n",
       "27   72107.600000   127864.550000        353183.81    New York  105008.31\n",
       "28   66051.520000   132125.888571        118148.20     Florida  103282.38\n",
       "29   65605.480000   136387.227143        107138.38    New York  101004.64\n",
       "30   61994.480000   140648.565714         91131.24     Florida   99937.59\n",
       "31   58798.350833   144909.904286         88218.23    New York   97483.56\n",
       "32   55602.221667   149171.242857         46085.25  California   97427.84\n",
       "33   52406.092500   153432.581429        214634.81     Florida   96778.92\n",
       "34   49209.963333   157693.920000        210797.67  California   96712.80\n",
       "35   46013.834167    85047.440000        205517.64    New York   96479.51\n",
       "36   42817.705000   127056.210000        201126.82     Florida   90708.19\n",
       "37   39621.575833   130524.951250        197029.42  California   89949.14\n",
       "38   36425.446667   133993.692500        185265.10    New York   81229.06\n",
       "39   33229.317500   137462.433750        174999.30  California   81005.76\n",
       "40   30033.188333   140931.175000        172795.67  California   78239.91\n",
       "41   26837.059167   144399.916250        164470.71     Florida   77798.83\n",
       "42   23640.930000   147868.657500        148001.11  California   71498.49\n",
       "43   22909.335000   151337.398750         35534.17    New York   69758.98\n",
       "44   22177.740000   154806.140000         28334.72  California   65200.33\n",
       "45    1000.230000   124153.040000          1903.93    New York   64926.08\n",
       "46    1315.460000   115816.210000        297114.46     Florida   49490.75\n",
       "47       0.000000   135426.920000             0.00  California   42559.73\n",
       "48     542.050000    51743.150000             0.00    New York   35673.41\n",
       "49       0.000000   116983.800000         45173.06  California   14681.40"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"Marketing Spend\": \"Marketing_Spend\"},inplace = True)\n",
    "df.rename(columns={\"R&D Spend\": \"RD_Spend\"},inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e69acb02-4506-4aa3-af30-6545b053804c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RD_Spend           float64\n",
       "Administration     float64\n",
       "Marketing_Spend    float64\n",
       "State               object\n",
       "Profit             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06cee545-fb2d-4443-beb0-0392e3e05668",
   "metadata": {},
   "source": [
    "**EXPLORATORY DATA ANALYSIS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "877a58b4-15c3-4928-98fd-340fd45fe642",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average profit : 112012.63920000002\n",
      "Average R&D Spend : 74076.616\n",
      "Average Marketing Spend : 211025.09780000002\n"
     ]
    }
   ],
   "source": [
    "avg_profit=df.Profit.mean()\n",
    "avg_RDspend=df.RD_Spend.mean()\n",
    "avg_spend=df.Marketing_Spend.mean()\n",
    "print(\"Average profit : {}\".format(avg_profit))\n",
    "print(\"Average R&D Spend : {}\".format(avg_RDspend))\n",
    "print(\"Average Marketing Spend : {}\".format(avg_spend))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8ac6e7b-81a9-40b0-8ff4-abab8928baf1",
   "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>RD_Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing_Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>74076.616000</td>\n",
       "      <td>129419.045500</td>\n",
       "      <td>211025.097800</td>\n",
       "      <td>112012.639200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45360.289591</td>\n",
       "      <td>21233.370447</td>\n",
       "      <td>122290.310726</td>\n",
       "      <td>40306.180338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>51743.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14681.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>40420.608125</td>\n",
       "      <td>118731.907500</td>\n",
       "      <td>129300.132500</td>\n",
       "      <td>90138.902500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73051.080000</td>\n",
       "      <td>134020.056250</td>\n",
       "      <td>212716.240000</td>\n",
       "      <td>107978.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>101602.800000</td>\n",
       "      <td>145035.661071</td>\n",
       "      <td>299469.085000</td>\n",
       "      <td>139765.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>157693.920000</td>\n",
       "      <td>471784.100000</td>\n",
       "      <td>192261.830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RD_Spend  Administration  Marketing_Spend         Profit\n",
       "count      50.000000       50.000000        50.000000      50.000000\n",
       "mean    74076.616000   129419.045500    211025.097800  112012.639200\n",
       "std     45360.289591    21233.370447    122290.310726   40306.180338\n",
       "min         0.000000    51743.150000         0.000000   14681.400000\n",
       "25%     40420.608125   118731.907500    129300.132500   90138.902500\n",
       "50%     73051.080000   134020.056250    212716.240000  107978.190000\n",
       "75%    101602.800000   145035.661071    299469.085000  139765.977500\n",
       "max    165349.200000   157693.920000    471784.100000  192261.830000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cfad41ee-e19d-4cc1-b6dc-23906b62f220",
   "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>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York</td>\n",
       "      <td>1933859.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Florida</td>\n",
       "      <td>1900384.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>1766387.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State      Profit\n",
       "2    New York  1933859.59\n",
       "1     Florida  1900384.39\n",
       "0  California  1766387.98"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['State'], as_index = False)['Profit'].sum().sort_values(by='Profit', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "51ba46a2-f54b-4032-b306-d434f58254b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Profit'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting barplot\n",
    "sns.set(rc={'figure.figsize':(20,10)})\n",
    "profit_state=df.groupby(['State'], as_index = False)['Profit'].sum().sort_values(by='Profit', ascending = False)\n",
    "sns.barplot(x = 'State' , y ='Profit' ,data=profit_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53afa322-604f-48a4-affc-cb924ae01f71",
   "metadata": {},
   "source": [
    "**FROM THIS GRAPH WE CAN CONCLUDE THAT NEWYORK IS GENERATING MORE PROFIT IN COMPARISION OF OTHER TWO STATES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "95c2444d-40a9-497c-ae8b-2e1305a5ed5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marketing_Spend', ylabel='Profit'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EFBkZSZ999pnyen5+PjVp0oQWLFhAREQnTpwgi8ViN/nxzTffUFBQkPJv6+I0Wfv37yeLxWLX1ty/f59q1apFa9euJaIHHUiLxUK3bt0yEgaXtGvXjiZPnqz8+86dO2SxWGjbtm1KvWaNFxHRpEmTqG3btkQkrkutFixYQIGBgUry55gs1qlTh5YtW6b821qX/PHHH6rHPH36dIqMjLQrTwkJCWSxWOj8+fPC+sls1nKyZ88eZVt2djYFBwfT+vXriUg/BqI6acqUKU65THBwMC1atEh6H47+/e9/U8+ePe22bdiwgXx9fZ3qOlfbKD169deCBQuoYcOGdot9p02bRpGRkar7unv3LoWHhzstHPr555+padOmSsxt2waZ+k/m/nck0/YbLe+yRDF19x4U7cNKq7yIcitHOTk55OvrSz/88IPd9u7du9Pw4cMF0RDTi5eR+4OI6M8//6Q6deoo+a0axzaY6EF9Gx0dTb6+vtSuXTunTnt8fDx5enralcGzZ89S06ZNNReAmlVvy9CLoRk5nExdIMoljdYnRA8Gl95//327bZ07d6YxY8aovv/48ePk6+ur1PdGmBVDIvW6cMKECdS0aVO7cvvDDz9QQEAA3bt3j4qKiig8PJymTJlit6/evXsruZCozVUTHR1NHTt2tPvMl19+Se+++y4RyeVCMmRy4StXrlC9evUoKirK7h6TGT+QycdtabVH1tesD7OIHs6TqS+Tk5Opc+fOVK9ePWratKlbD4u7E0ci/VySSHwfyo7lrF27lkJCQqQHE/Pz82nSpEnk6+urfMaWXp5ghOg+FvVHrL777jtq1aoVhYWFqeZxsmWosLCQOnbsSN27d1fuQTPGv1xpx2QY6dM6HoMruYLjPmTGrYzWBUTietCRTC5hhDtxtaVVLkXti0z/qF27dvTBBx/Yfd+oUaM0j7Uk+3h68RPlWkTi/ESUa969e5cCAwPtJq4KCgrotddeUxbbuFIuRfWiO9wZk3akdj+I2hKZHNdombOlla8a7TdoER3/mDFj6F//+pfdZ4YNG0a9e/dW/q2XG4iu/Zo1aygoKEj5gwZERJcvXyaLxaL0maOiomjw4MF2n4uJiaHx48drnpcoR/grxW/cuHGadTQR0fXr18lisdDx48c132M0P5syZQrVq1dP92ECI+2Jq2M3MveP3niAzFix6Nxl2g3RPtSUVHvsbgxtaV1H0Xi7kXZSL7d3NGHCBGrfvr3dtjfeeIM+/fRT5d9GxyTViGLYpUsXGjVqlN1n5syZQ02aNCGiB32owMBAu/KQmJhoF2szxmCMjhfIlG2Z+kWG7HjPqVOnKDg4mN58803VHMe2z/Dnn3+Sp6cnHTp0SPU79ebNRHWS1nGokZl/adWqFS1dulT3PSIlEUPRPXX48GEaOnSoXc7z9ddfU61atZR/BwcH0+eff263j549e0o9VGvmdXEkip+orMuMFYnqdZk82spIXSiK+a5du5zqgYkTJ1Lr1q119+vI3Ria1Y8Q9SeN5nAy/e6Sygtl5q/dnXt3pDZOc/78efL09KRdu3Yp77t9+zaFh4c7/UEoq5KYFzUjflZafSgz5g2MjLdZifoERuoPPe6uQSqONQR68xey9b7smhIzxltFMZTt2xcWFtJbb72llCHb/FumLdU7F5l6TU3fvn3t8mgRV9comRVDK60y5M7aB6u9e/dSgwYNqE2bNtJzHGbWDWrcjd/t27fJYrHQzz//rLy+c+dOslgsyh94eVg5jZVs3F2d/3Q3hjL9O1tqba1MXu1IdM/JzEcYHQfVImrXRWOlMnmBTJssyouMnq9MDM26l92NoS3Z9ahqeaFoTFWmzhAxYy2ompKIoUwZMnMtk5XousiMl8uQuRf15r9F481m9G9cWbsvWpsumkOUJYqfqB8ps77f6Do2tfFjV+otmWsnml+R4e76ftl+pmgdh+hcXCmHJTFuLTp/0bWXafdE884ya7qIjPfFROtbzeojuxtDIrn5SdG1lpkrcnU9r9Y6HrPWvYpiKCpDZtzHxbH2o7jWK+pRy0fMWKcuaveKa/5OdnxIb3zEKL28WOvYZc5fVI5lyqDRNp1IPPdk1ri/Hpm+hhnr9dSeN5RpS4mMlyGj6ztd4WH+b5Wzv7pTp07h7t27qF+/vrKtUqVK8PHxQUJCgtP7ExMT8Y9//AMvvfSSsi0kJASlSpVCUlJSiRyz2Y4fP45HHnkEmzZtQq1atexeS09PR506dey2+fj44NKlS7h8+bJyzvXq1VNer1mzJqpXr67Er2/fvujevTsAID8/H1u2bMG5c+cQFhamejzp6ekgIgQHByvbPDw84OXlhV9//RUA0KFDB3zwwQcAgKKiIhw4cAAJCQma+zRLlSpVEBsbC39/f2VbqVKlUKpUKWRlZaFq1aq4desWfvzxRxARTp8+jaSkJCWuzz//PL788ks8+eSTAIDLly/j22+/ha+vL6pUqQJAPl4//PADfv/9d3z00UeGziE3NxcTJ05Ex44dERoaCuBBDKdOnYr27dvbvdfDwwNZWVmG9i/yxBNPYNq0abBYLACAzMxMfP3116hRowZefvllnD9/Hjk5OXjxxRc193H69Gm7e9CRbPlISEjA6tWrMWXKFKlj37NnDxo1aoSNGzfi8ccf133vvn37sH37dnz66acoW7as1P6N0Dp2mfjp3fOOMjMzMXPmTAwYMEDZZ+nSpTF58mSl3szOzkZsbCwqVqyIwMBA1f3I1BX79u1D8+bN8eijjyrv6dSpE9avX49SpUrpHqcrtGK4b98+1KtXD0899ZSyrVGjRti5cycee+wx3LhxA2lpaXbtRpkyZRAcHKycS5UqVeDh4YE1a9YgLy8PmZmZ2Lp1q128ReXY0SuvvILY2FhUr15d2ebh8SB1sd6np0+fxlNPPYUnnnjCQCRcU7VqVezatQsXL15EYWEhVq9ejbJly8LLy0up47799lsUFhbi4sWL2LNnj3L+orrUaufOnZg8eTKio6M1j2HLli24ceMG8vLy8P3336Ny5cp47rnnVN/frl07TJ061a48Wf//7du3hfWT2dLS0gDArr2rWLEinn/+eaW904uBqE6Kjo5G27ZtAQD379/H8uXLce/ePaXul9mHo969ezsdi4eHB/Lz85Gdna1sc7WNEtGrvxITExESEoIyZcoo2+rVq4e0tDRcv37daV8LFizAvXv3nM5n586d6NKlC7777junz8jUfzL3vyOZtt9oeZelF1Mz7kHRPgD98iLKrRyVKlUKCxYsQOPGje22m5XT6MVL9v6w+vzzz1GtWjX069dP9bvU2mAAuHjxIv7880+sXbsWr776qtPn9u3bB4vFAk9PT2Xbyy+/jF27dmnmB2bV2zL0YmhGDidTF4jaYKP1SVFREX777Te73AAAQkNDVfuUAPDJJ58gODjYKf+WYUYMrdTqwvT0dPj7+9vlsD4+Prh//z6OHTuG1NRUXLp0Ca+99prdvhYvXoz+/fsDELe5avbt24e2bdvafWbIkCGYP38+ALlcSIYoFyYijBw5Eq+//jpCQkLsXpMZP5DJx21ptUcAMHHiRFgsFrz++uvC/cjUl/Hx8XjppZfw448/4plnnpE6Pi3uxNF6vFq5JCC+D2XHcnbs2IGhQ4fiyy+/lDqvnJwcJCQk4KuvvkJUVJTT63p5ghF68RP1R6xSU1PxxRdf4PPPP9fsc8qWobVr1+LMmTMYP368cg+6O/7lajsmQ7ZPq3YMRnMFtX3IjFsZrQsAcT3oSJRLGOVOXK30yqWofZHpH6Wnp9u9DgDe3t74/fffUVBQ4HSsJdnH04ufKNcCxPmJKNdMSkrCvXv30KZNG+X10qVLY9OmTXjjjTcAuFYuRfWiO9wZk3akdj+I2hKZHNdombPSy1eN9hu0iI4/MTHRKT+tV68ekpKSQEQA9HMD0bWvX78+5s2bh3LlytmdB/AgN7t37x4SExOdcsZJkyZh7NixmuclyhH+SvET3benT59GqVKlULNmTc33GMnPkpOTsWzZMowcOVKpV9TItifujN3I3D964wEyY8W21M5dpt0Q7UNNSbXH7sbQSu86isbbZdtJUW7vqGrVqjh79izi4+NBRDh06BDOnTuHgIAA5T1GxyTViGL44Ycfok+fPnaf8fDwUPqk6enpePHFF+3Kg4+PD4AHbQjg/hiMK+MFMmVbpn6RIVMOc3Nz8eGHH2LIkCFO35eWloYqVarY1YU1atRAlSpVVO9DvXkzUZ2kdxxqRHV0Xl4e0tLSDOVFakoihqJ7qlatWpg+fbqS85w7dw4//PCDMhaUmZmJrKws1ZxG7TrZMvu6OBLFT1TWZcaKRPW6TB4NGKsLZWLetGlTfPLJJ8q+jx49ip07dxpe6+BuDM3qR4j6k0ZzOJl+d0nlhTLz1+7OvTtSG6fZv38/Hn/8cbvxjEqVKuE///mPUztjVRLzombED9DvQ5kxbyA73mZL1CeQrT9E3F2DVBxrCLTmL4zU+7JrSswYbxXFULZvv2DBAuTn5yvzHFaybaneucjUa2qMrClxZ42SWTG00ipD7qx9AB5ci5iYGHz66afKGjsRs+sGNe7Gr1y5cqhYsSI2btyI7OxsZGdn44cffkDNmjVRqVIl5VhLOqexMhJ3V+c/3Y2hTP/OllpbK8qrHcncczLzEUbHQbWI2nXRWKlMXiDTJovyIqPnKxNDs+5ld2NoJbseVS0vlBlTlakzjH6vK2tB1ZREDGXKkFlrmaxkrotovFyWzL2oN/8tGm82o39jdE2KzNp00RyiLFH8RP1ImfX9RtexqY0fu1JvyVw70fyKGTEUjRHI9jNF6zhE5+LKMyQlMW4tOn/RtZdp90TzzjJrugDjfTHR+laz+sjuxlB2flJ0rWXmilxdz6u1jsesda+iGIrKkBn3cXGs/Siu9YpatPIRM9api9q94pq/kx0f0hofMUovp9M7dpnzF5VjmTJotE0HxOM0Zo37a5Hpa5ixXk/teUNAri0FjJcho+s7XVFG/Bb2/5uMjAwAwD//+U+77dWqVVNes3XlyhWn95YtWxaVK1fGn3/+WXwHWowiIiIQERGh+lq1atWczuvixYsAgBs3buDKlSuoUqWKXQfE+jnH+CUmJqJbt24oKipCx44d0axZM9XvtE5iXL58Ga+88oqy/dKlS7h//77dey9fvozmzZujoKAADRs2xFtvvSVxxq6rVKkSmjRpYrdt27ZtSE9Px0cffYSGDRvi0KFDGD58OEaMGIHCwkK89tprePfdd5321bt3b+zfvx9PPPEEvvnmG6eHYfXidfHiRUycOBHz5s1DxYoVDZ3D2rVrcf36daWjBDxIJB0nui5fvoy4uDh06dLF0P6NGDNmDNasWYOyZcti/vz5qFChAs6cOQMAWL58Ofbu3QsPDw80btwYQ4cOVRLyM2fOoEqVKujatStSU1Px/PPPY8CAAU4Jsl75yMrKwogRIzB69Gine1rL0KFDpc9t+vTpaNasmVMiawa9Y5eJn94972jRokUoV66c06IqqwULFmDGjBkoVaoUJk6cqBlLmboiNTUVzZo1w+TJk7Ft2zY88sgjaN68Od5//32nz7lLL4apqakIDg7G3LlzsXHjRqX8DB8+HJUqVdJtN06dOgXgwWKe0aNH44svvsCqVatQVFQEi8WCuXPnKu8/c+YMCgoK0KdPH5w6dQrVq1dHjx49NB/k+Mc//uFU/yxfvhz3799XksjTp0+jQoUKGDJkCH777TdUqVIFHTt2RPfu3ZUBSLOMGjUK77//Ppo1a4bSpUvDw8MDs2fPxnPPPYfnnnsOAwYMwJdffokZM2agsLAQ9erVUzrborrUau3atQCAQ4cOqR7DxIkTMWLECDRo0AClS5dGhQoVsGTJEs2BGrUEfdGiRfjHP/5ht6APUK+fzFatWjUAwJ9//qkcW2FhITIyMlC1alUA+jGQrZM2bdqEESNGgIgwePBguwk2I/Ua8N8JNav8/Hx8/fXX8PPzUzpf7rRRInr1V0ZGhtI5tLKNse0AkbXz+O9//xuVK1e2+8ykSZOkjkWr/pO5/7Xotf1Gy7ssvZiacQ+K9iEqL61atZLOrYAHE2MNGza023b06FHEx8dj9OjRqp8xQi9eMveH1enTp/Hjjz9i7ty5mpPHWm2wl5cXvvnmGwAPBm4dWXOjVatWYeXKlcjKykKdOnUQExNjt1jIVu3atU2pt2XI5CHu5HAydYEolzRSn1iPKScnBzVq1HD6jFqfcteuXfj999+xceNG3ThocTeGVlp1YbVq1XD69Gm79166dAnAgz6gdQA6JycHffr0wYkTJ/DMM89gwIABynEZaXOBBxPO165dw+OPP46PPvoIe/fuRaVKlfDGG2+gT58+KF26tFQuJEMUv6+//hrXrl3DggULsHDhQrvXZMYPZPJxK732aPv27dizZw82b96MXbt2Cc9Lpr7s2rWrcD+y3IkjoJ9LAuL7UHYsx/rd1nEMkUqVKmH9+vUAoPyvLdk8QUQvfqL+CPCgjfn3v/+NPn36wNfXV3U/smUoLy8Ps2fPRpcuXfDCCy8o290d/3K1HZMh26dVOwajuYLaPmTGrYzUBYBcPWhLJpcwyp24AuJyKWpfZPpH1apVc1owdunSJeTn5yMrK0v34b/i7uPpxU+UawHisS5RrpmamoonnngCp0+fxsyZM5GWloaXX34ZQ4cORe3atZXvAOTLJSCuF93hzpj0008/rWzXuh9EbYm/v78wx3WlzInyVSP9Bj2iHH3Dhg2q+em9e/dw8+ZNPPnkk7q5gejaP/PMM06LzWJjY1GuXDnUrVsX6enpKCoqQunSpTFkyBAkJCSgWrVqiIqKQqdOnTS/V5Qj/JXid/bsWVSpUgUdOnTAlStXYLFYMHToUGUy9MyZM3j88cfxySefYP/+/ahQoQJatmyJgQMHKmXVSH42a9Ys4R+BkW1P3B27kekj6o0HGJlXAtTPXabdEO3DUUm2x+7GEBBfRyPj7XrtpCi3d9StWzccPXoUPXr0QOnSpVFYWIh3330X7dq1U95jdExSjSiGjguH79y5g2+//RaNGjUC8KAMXb16FYWFhcq1tc1NAPfHYIyOF1hfA/TLtkz9IkOmHFoXtEZFRSEmJsbpWO/cuYPs7GxlIWt2djZu376NzMxMp+/TmjeTqZP0jkONaP4lOTkZhYWF2LZtGyZOnIjc3FzUrVsXw4cPV66BjJKIocw9ZdWiRQukpaXh//7v/zBnzhwADxZWlS1bVjWnUbtOVsVxXRyJ4icq66KxIpl6XSaPBozVhUZiXlhYiNq1a+P+/fvw8fHBe++9V6IxNKsfIepPGs3hZPrdJZUXbt68Wdiemjn3rjVOk5qaimeffRbbt29HbGwsrly5Ah8fH4wcOVJzgWJJzIuaET/ZdRvuzBvIjLc5ysjI0O0TyNYf7sZQtAbJ7DUEevMXrtb7emtKzBhvFcXwl19+Efbtjx49iiVLluD777/HlStX7N4rW6/rnYuRdXBWt2/fxpUrV5CYmIhVq1bh5s2bCAgIwPDhw1Uf1ndnjZIZMbTSK0Purn0YNWoUwsPDERERgaVLlwrfXxx1gxoz4jdlyhSMHTsWwcHBKFWqFKpVq4YVK1bAw8PjoeU0VrJxd2f+090YyvTvrLTaWltqebUjmXtOZj7CyDioLLV23cj8tygv0GuTRXmR0fOViaFZ97ItV2NoZD2qWl4oM6ZatmxZ3TpDxKy1oCLFFUOZMmTGWiZbMtdFNF7uCq17UW/+WzTeLDNeKNu/kV27L7M2XTSH6Aq1+BnpR2qt7zeyjk1r/NiVekvm2onmV8yIoZG5Rr32RLSOQ/ZcjDxDUlLj1lZq5y+69jLtnmjeOTU1FYD+mi7A9b6Y1vpWs/rItlyJoez8pN61lp0rcmU9r946nuJY96oWQ5m1C3qft9K7j4tj7YdM2Ta6XlGLXj5ixjp1I+2emfN3MuNDeuMjRohyOtlj1zp/UTmWKYNG16YD8s/+uDvur0a2r2HGej215w0BubbUlTJkdH2nK/iXxf8H3bt3DwCcJs8fffRR5Obmqr5fbaJd6/1/d6+//jq2b9+OTZs2oaCgACdPnsSSJUsAPOgoGYlHzZo1sWHDBkyYMAE//fQTvvjiC9XvrF69OurVq4fPP/8cKSkpyM/Px7Jly3Dy5Enk5+fbvbdSpUpYu3YtZs6ciVOnTmHEiBEmnbmc3377DTExMYiMjETTpk1x48YNXLp0CUOGDMH333+PiRMnYs+ePZg9e7bTZ4cPH441a9YgMDAQPXv2dBrE0IpXYWEhhg8fjs6dOxse5C8qKsI333yDTp064R//+Ifm+65fv4533nkHVatWxYABAwx9hxE9evTAunXr0LZtWwwaNAjHjx/HmTNn4OHhgWrVqmHBggUYOXIk9u3bh4EDB6KoqAgFBQVISUnB7du3MXjwYMTGxiIwMBD9+vXDwYMH7favVz4+/vhjBAUFOf2FITMkJCTg+PHjGDhwoOn7BvSPXRQ/I7Kzs7FmzRr06dNH82HtVq1aYcOGDejbty9Gjx6t+RCCTF2RnZ2NRYsWITc3F3PmzMHw4cOxefNmUx7uc6QXw+zsbGzcuBGnT5/GtGnT8MknnyApKQkDBw4EEUm1G3l5eTh9+jQiIyOxevVqxMbGoqioCB988AEKCwsBAGfPnsWtW7fQrVs3LF68GC1atEBMTAy+//57qXPYsWMHpk2bhp49eyqdmLNnzyIrKwstWrTA4sWL8dZbb+HLL79UrYPclZycjMcffxxz587F6tWr0aFDB3z44Yc4efIksrOzkZKSgq5du2Lt2rX48ssvkZaWhjFjxqjuy7EulXX69Gk8++yzWLp0KVatWoXQ0FC899570n+8ZerUqdi9ezc+/vhjPPLII3avqdVPZvP398eLL76IcePG4cqVK7h//z6mTZuGmzdvOrV37qhbty42btyIESNGYP78+Vi1apUp+y0oKMCIESNw9uxZjBs3DoB7bZS77t+/r3pfAnDKSVatWoXHH38cnTt3dvn7tOo/mftfi16u5G55Lw7uHpNMeTGSW6lJSUnBoEGDEBAQgDfffFP63Nyldn/Y+vrrr+Hp6an5x5Nk2mAt2dnZiI+Px5YtWzB+/HjMmDEDGRkZ6N69u2Z/paTqbVnu5HCiukAmlzRSn1jfD8j3KZcuXYrw8HB4e3vrxsEdMv0krbrw9ddfx9GjR/HVV18hLy8PFy5cwMyZM1GqVCm7X+aJjo5G27ZtsWTJEoSFhWHgwIFO+biVXpsLQNnn1KlT8fTTT2PRokXo27cvFi5cqHm/q+VC7jp16hTmzJmj+UvNMnmgkXxc6xpcuXIFY8eOxaRJk6R/HcPd+tJMojgC+rkkIL4PjY7l/J2I+iPAg4edHn30Ubzzzjuq+zBShrZs2YLbt2+jb9++dtvdGf9ypx0zi+wx6OUKWvuQGbcy2jc3Wg+KconiohdXUbkUtS8y/aN27dph1apV+OWXX1BYWIj4+HisW7cOAIR9qJLo42kR5VpGxrqs73fMNbOzs3H//n2MHTsWvXr1wqJFi/DCCy+gR48eOHfuHABzx4yKm2hM2pbW/WA0p1PLcV0pc0bGHEX9BiMcj1/t/K3/zsvLc+u71CxfvhwrVqzAhx9+iCeffFKp16yLFpcsWYL27dtj/PjxykI8NaIcwdbDjN+ff/6JO3fuICcnB6NHj8a8efPw1FNPISoqCsnJyQAe3HO5ubkICAjAV199hQEDBmDt2rUujXVeuHABO3bsEI7Vy7QnxTF2Y7SPaCTP0Dp3I+NqZsbPlpntsdEYylxHI+PtWu2kTG7v6M8//8TNmzcxduxYrFu3DiNHjsTSpUulx71dpRfDu3fvYuDAgcjNzVX6x61atcKtW7cwefJk5OTk4Pr165gwYQLKlCkjPTYrGoMxOl4AyJVtM+sXW44x3Lt3LzZv3oxJkyY5/cFrAGjcuDEef/xxjBo1Crdu3UJ2djbGjRun5He2tObNZMqy6DjUiOZfrAtby5cvjy+//BITJ05ESkoKunfvrvnAloziiKGRe+qLL77A8uXLUbVqVXTv3h13795F6dKl0bZtW8yfPx9Hjx5FYWEhtm7dil27dmmW9eK6LiKO8TNa1h3HimTqdZk82mhdaCTmRUVFWLFiBWJjY5GTk4N33nlH6fuXRAwfRj9CJoczsl5Edp+yHGNo5vy1zFiF1jhNdnY20tPTMW/ePAwbNgzz589HmTJl8Pbbb0s/PFES86KuxE+2D+XOvIHMeJsjUZ9A5v5xhTtrkAD31xBozV+4U+/Lrikxi2MMRX37nJwcfPjhh/jwww9VHxx1pS11ZLReAx5cN+DBL1FNnjwZM2fORG5uLt5++22nXysze42S0RjaKq45sO+++w7nzp0z9IcKiqNukGE0fkSEkydPIigoCCtXrsQ333yDp59+GgMHDlR+NRgo+ZwGMBZ3M6+90Rga6d9ptbW21PJqW67ec2rzEUbGQWWptetGxkpFeYE763XdPV+1GBbHvexqDGXzGq28UGZMVVRnuPK9tsxqt4srhjJlyOz1Va6MdTuOl7uiOOa1zOzfuFoXqK1NF80hukIUP1E/Umt9v5H8XGv82JV6S+baGZlfkeHK+n7R52XJnouRcljS49Zq52/02mu1e3rzzq6s6TJCa31rcfSRXYmhq/OTtmTnioy2N0bXgpmx7lUrhrLrRF29j4tj7UdxrFfUopePmLFO3Ui7Z+b8nWh8SDQ+YoReDI0cu9b5i8qxTBl0Za2l7LM/Zo/7A3J9DTPW6+k9byhqS80sQ6Yj9j9n69atZLFY6N69e3bbhwwZQu+++67T+z/55BP617/+5bS9Xr16tHTp0uI6zBITHR1NUVFRdtvmzJlDAQEB5OXlReHh4bRq1SqyWCx05swZWrx4MdWvX99pPx07dqRPP/1U83sWLFhAfn5+lJubq/p6RkYG9ezZkzw9PcnHx4feffddGjNmDHXo0EFzn5s3byaLxUIXL16UPFv37Nixg2rVqkW9evWi+/fvExFRnz59KCYmxu59a9asIR8fH7px44bqfu7evUshISE0e/Zsze+yjdecOXPo9ddfV2J34cIFslgsFB8fLzzmhIQEslgslJycrPmec+fOUbNmzahp06Z0/vx54T7NUFhYSK1ataKRI0dSYWEhZWZm2r1++PBhslgsdPjwYSIiys7Odrpn+/TpQ3369NH8DtvysWHDBmrUqBHdunVLed1isdC6deukjzk8PJxmzZql+lpMTIxqPWEG0bHLxM+W2j1v+11+fn50+/ZtqWPr3bs3devWTfU1mbrC39+fOnXqZPf6li1byGKx0PXr16WOQYYohi1btqTGjRtTXl6e8vqRI0fIYrHQkSNH6NixY6r30dSpU+m1114jIqKFCxdSZGQkFRQUKK+npaWRxWKhHTt2EBHRvXv3KDs7224fY8eOpZYtWwrPYdWqVeTt7U3R0dFUWFiobM/NzaWsrCy79y5cuJACAwPt3ueuy5cvk6+vLyUkJNhtf+utt2jAgAE0duxY6t69u91rBw4cIIvFQidOnLDbrlaXOoqPjyeLxUIXLlxQtv3+++/k6elJly5dUrbl5eVRs2bNaMKECbrHn5eXRyNHjiQvLy9as2aN7ntt66fikJycTB06dCCLxUK+vr40cuRIGjRoEA0ePNjufWoxsKVXJ9kaM2YMRUREuLUPIqI7d+5Qnz59yN/fn3bv3q1sd6eNMsqx/mrbti199tlndu85e/YsWSwWOn78uN32Zs2aOb1XjWzbYFv/ydz/MmzbfnfKuxFabYIZ96DaPmTKiyu5lVVSUhKFhobS66+/7tQ2mkErXlr3h9W9e/coMDCQVq5cqblv2TZ41qxZFB4ebrfN+t227dy1a9fIy8uLtmzZorofs+ttWXp5iJXRHE6mLhDlkkbqEyKizMxMslgsTtd7xYoVVLt2bbttly5dUn2vq4zG0JZeXbhmzRoKDg4mLy8vql+/Pq1fv548PT1p165dFBcXRxaLxakMv/POO/TOO+/YbZNtc69du0YWi4Xef/99u+2LFi2iwMBAKioqstuulQsZZRu/+/fvU5s2bWjRokXK6473mMz4gZF8XO0aFBUVUc+ePenjjz9Wtq1bt44sFovuuRitL6Oioig6Olp3n7KMxlGUSxKJ70OjYzmu5CSiuBvtQ2pxvI9F/ZFDhw5RnTp17Prrtnmc0TLUvXt3p3uPyL3xL3faMaO06kGZYxDlCnr7EI1bGe2bG6kHZXIJdxmNq6hcWum1L0Ti/lFubi598skn5OvrS15eXtS2bVtatGgRWSwWunv3rtS5FXcfj8g5fjK5luxYl1auuXDhQqcco6ioiNq0aaPUB0bLpSOZ9shVRsekrfTuByM5nVaOa7TMGRlzFPUbjFA7frW47N69mywWi93xWenlBnrXvqioiGbMmEEWi4VmzJihbLeWLcdr8PHHH1Pr1q1V9yWTI1j9FeJ3+/Ztu/a6sLCQWrduTWPHjiUiovz8fKdYW3Ppa9euOR2H3jWYM2cOhYWFCXNfmfbE7LEbUR9RbTzAyLyS3rnLjquZGT8rM9tjV2Iocx1dGW+3bSdlcns1LVu2pDlz5thtmzFjBoWEhKheAyNjklr0Ynj16lVq37491a1bl44ePWr32s8//0xhYWHk5eVFtWvXpqVLl1JYWBitWLHC6TtcGYMxMl5gS1S2jdYvMhxjeOPGDQoLC7MbS1Jrr3/77TeKjIwkT09PqlWrFn3++efUqVMnmjJlit37tObNRGVZ9jgcycy/OPaVr1y5Qp6enhQXF6e7by3FFUOj9xTRg/6Kp6cnbdiwgYiIsrKy6IMPPiBvb2/y8vKit956i6ZPn06hoaGqny+u66JH7T42UtbVxopk6nVRHu1qXWg05kQPyoLFYqFff/1Vd99aXIlhcfQj9MqCkRxOdr1IceeFRttTd+fetcZpxo4d6zQ/fe/ePQoJCbErn2pKal7Ulfi5um7D6LyBaLxNjahPIHP/GOXuGiR31xBozV+YWe/rrSkxY7xVLYaivn1MTAz169dP2Yda/m20Xlc7F1fWwd24ccOuH5KTk0MhISG0cOFCu/eZuUbJlRhayc6BGV37cO7cOapduzb9/vvvyjbRHEdx1Q0irsQvLi6OgoOD6c6dO8p+bt26RUFBQbR06dKHltMYibuZ85+ulkHZ/p1WW6vGMa+2cuWe05uPkBkHdYVtu250/tvx81r01utqtRWunq9WDIvjXrYyEkMjeY1WXigzpiqqM/SYuRZUVnHEUK8MmbGWyZGRsW6t8XJ3aN2LonFdtTrbzP6NLdm1+3pr00VziK5Si5+RfqTj+n7Z/Fxv/NiVekt07YzMrxhldH2/3ucdqZVjV89FVA4fxrg1kf35G7n2Wu2eaN7ZyJouK1f7YrbrW4ujj2xlJIauzE86XmuZ/Ntoe2N0HY/Z615tY2hknaja5x1ptUdmr/0ojvWKakT5iFnr1I22e2bM34nGh2TGR2ToxdDVY3csgzLlWFQGXVmb7sqzP+6O+xPJr0MxY72e3vOGorbU3TJUnM+c8C+L/w/65z//CQC4evWq3farV6+ievXqTu+vUaOG03vz8vJw69YtVKtWrfgO9CEaNGgQfvvtN+zevRs7d+7E008/jdKlS+Ppp59GjRo1cOvWLadfI7GN3969e5Vf07Dy9PRU4qamevXqWLp0KRITE3HgwAHMnz8ft2/fxnPPPQcASExMxNGjR532af3u4rZixQoMHjwY4eHhWLBggfKXN5KSkuDv72/33sDAQBQUFODixYv4888/sXXrVrvXK1SogGeeeUY5blG81q1bh+TkZISGhiIoKAht27YFALzzzjsYO3as7nHv2LEDPj4+eOmll1RfT0pKQpcuXVC+fHl89913ePbZZ+WDIikzMxNxcXEoKChQtnl4eODll1/G1atX4eHh4fQXk1555RUAQEZGBgCgYsWKKFeunNN7rly5AkBcPtatW4cbN26gadOmCAoKQlBQEABg3Lhxun85VEZRURH+85//FMsvlgMQHrtM/GTt3LkTTZo0QaVKley23717F1u3bnW6fy0Wi3INHMnUFTVq1FCO1fHYL126ZOjY9YhiWKNGDdSsWdPur0hZj+PixYtS7UZSUhJ8fHxQunRp5fXnn38eVapUQVpaGgCgXLlyqFixot0+LBaL8Dp9/vnn+Pjjj9G9e3dMnjwZHh7/TV/Kli2Lxx9/3GmfOTk5uH37tjA2so4cOaL88putWrVqIT09XbUurFWrFgAo5w9o16UykpKSULVqVTz99NPKtkceeQQ+Pj5IT0/X/Fx2djbeeecdbN68GdOnT0enTp2U10T1U3F46aWXsG7dOhw6dAjx8fGYPHkyMjIylPbOVQUFBdi5cycuX75st93T01PzPpV19epVdO3aFYcPH8bixYvRpEkT5TV32ih3qeVo1n/b5nSnTp3ChQsXXKqnZeo/mfvfkajtd7W8FyczjkmmvIhyKy3bt29Hz5498corr2D58uXSv8zrLr37w2r//v3KXzjXotUGy6hRowaqV6+OJ554Qtn21FNPoXLlypoxK4l6W4YZOZxMXSDKJWXrE6vKlSujQoUKUn3KnTt34sknn0RYWJheKFwm208S1YWdOnXCr7/+it27d2Pv3r3w9/cHEeHZZ59Vzslisdh95uWXX7YrY3ptrqMqVarg0UcfddrnK6+8gpycHGRmZirb9HIhdxw5cgRnz57FnDlzlPK1cOFCXL58GUFBQUhMTJTKA2Xzca1rcPnyZRw4cADr169XjsP6l6yDgoKwadMm1eN3tb40m0wcRbkkIL4PjY7l/J2I+iMbNmxATk4O2rVrp8T48uXLWLhwIdq0aWOoDN26dQsJCQmqdYE741/utGNmER2DTK6gtw/RuJXRvrmRelAmlyguWjERlUsrvfYFEPePypYtizFjxuC3337Dnj17sHnzZpQvXx5PPfUUKlSo4HS8D6OPp0Ym1xLlJ4B+rlmjRg0A/233AaBUqVJ46aWXlHbAzDGjkqA3Jm2ldz/I5nR6Oa7RMic75ijTb5Cldfz//Oc/Vc+/QoUKTmNHrsrPz8fw4cOxYMECxMTE4IMPPlBes5ZJUc5oSyZHsJ7HXyF+lSpVsmuvPTw88NJLLyn3bZkyZez6ZYB747Rt2rQR5r4y7YmZYzeu9hFlxoqt9M5ddlzNzPhZmdUeuxpDmesoGm8XtZMyub2jzMxMpKSkqPZNbt26pTkn6A69GJ47dw5vvvkmbty4gZUrVzodV0REBPbt24c9e/bg4MGDePPNN3H9+nXpuTHRGIyR8QJborJtZv0CqMdwz549uHbtGj766CPl+m/evBmJiYlKrgc86GNs27YN+/fvR3x8PD788ENcuHDB7j7UmzcTlWXZ43AkM//i+Mte1apVQ+XKlf9SMZS5p1JSUrB7926716tXr47KlSsrbdLjjz+OGTNmICkpCfv378eqVauQn5+vOQ9RXNfFSPwA+bKuNVYkU6+L8mhX6kJAHPMTJ04gPj7e7jPWY3BlDsfVGJZkP8JoDifqd7uyTz1aMTRz/lo0ViEapylTpozdOo9y5crh2Wef1R37K6l5UVfjJ9OHMmveQG+8TY2oTyBz/xjh6hokKzPWEGjNX7hS77uypsRdWjEU9e3XrVuHAwcOKOf2zjvvAADatm2LBQsWADDelqqRqdccPfnkk3a/5F6+fHk888wzdjE0c42SqzG0Kq45sC1btuDu3bvo1auXcp0SExOxefNm5Z53VFx1gx5X45eYmIiaNWviscceU/b1xBNPoGbNmkhPT39oOY2RuJt17d0pgzL9O722ViavBly750TzETLjoCKidl00ViqTF5i1XteV89WLoVn3srsxNLIeVSsvlBlTFdUZesxcC6qmpGKoV4aKY32V7Fi33ni5rOKe1zKjf+NqXSBamy6aQ5QhEz+9fqTM+n7Z/Fw0f2W03hJdO9n5FRF31/ebUYZlzsVoOSypcWuZelDm2uu1e6J5Z9k1XUaJ1rea1Ud2N4auzE86ksm/jbY3RtbxuLvuVRRDURkyqy0ye+1HcaxXVCPzjIkZ69T12r3imL8DxONDMuMjZsRQdOwyZVBmDY6oDLqy1lI092T2uL+VbJ5sxno9recNZdpSs8pQceCHxf8HeXl54bHHHsOhQ4eUbVlZWThx4gTq1q3r9P66desiIyPDriH/9ddfAQB16tQp/gMuYStWrMCnn36K0qVLo3r16vDw8MC2bdsQFBSEihUrok6dOigqKkJSUpLymdTUVFy5ckWJ38yZMzFv3jy7/R45cgSVK1fGU0895fSdRIR+/fphz549eOyxx/DEE08gOzsbBw4cUAYVly1bhkmTJjnts0yZMnjhhRdMjoK9VatW4dNPP0XXrl0xffp0lC1bVnmtevXqOH36tN37T58+jVKlSuH555/HqVOn8P777yMlJUV5PSsrC6mpqUqFKorX8uXLERcXh40bN2Ljxo2IjY0FAEyYMAHvv/++7rEnJCSgfv36qq8dPXoUffv2xSuvvIKVK1cW2wL769evY9iwYTh48KCyLT8/HydOnMBLL72EESNGoGfPnnafOXbsGIAHCd3Zs2dRu3Ztu3sWAP744w+8/PLLAMTl44svvsCWLVuUGG7cuBEAMGTIEEycONGt80tOTsbNmzfRoEEDt/ajRXTsovgZkZiYqFpeioqKMGzYMKeBkaNHj2p+h0xdUbduXRw9ehREpLznzJkzKF26NJ555hlDx65HFMO6devi1KlTuH//vt1xAA8e+KxatSpq1qxpVwYLCgqQmJionEv16tVx9uxZu3O5cuUKbt26hRdeeAFZWVkICQnB+vXr7Y7t2LFjToM6tj7//HN89dVXiI6OxsiRI+0mBokIr776KubMmeO0z3/84x+mPihp7VA71ndnzpzBCy+8oFkXAkDNmjUB6Nelssdw8+ZNu05XUVERkpOTNduBvLw89O/fH0ePHsXixYudBuNE9ZPZsrOzERUVhVOnTqFy5cp47LHHcPHiRZw4ccLtSbTSpUtjzJgx+Pbbb+22HzlyxHBdYOv27dvo0aMHMjMzsXLlSqdcyZ02yl1169ZFUlISCgsLlW3x8fGoWbMmqlatqmxLTExE1apV4eXlZfg7ZOo/0f2vRtT2u1Lei5sZxyRTXkS5lZr//Oc/GDp0KJo2bYrFixeb9iCEiOj+sEpMTISXl5duvazVBsuoW7cuLl++bHdtrl69ips3b2rGrCTqbRlm5HCiukAml5StT6xKlSqF2rVrK/1Cq0OHDiE4ONhuW2JiIkJCQlCmTBmD0ZEj20/Sqwu3bduGIUOGoFSpUqhevTrKlCmDbdu24emnn8ZLL70EX19fVKxYEUeOHLH73JkzZ5TBNFGb66h06dKoXbu20z5Pnz6NSpUqoXLlygD0cyF3BQQEYPv27fjhhx+U8tWlSxdUq1YNGzduhJ+fn9T4gWw+rnUNqlevju3bt2PTpk3KcQwZMgQAsHHjRkRERKgevyv1ZXGQiaMolwTE96HRsZy/E1F/5MMPP8RPP/1kVxdWq1YNXbp0QWxsrKEy9Pvvv4OIUK9ePdXjcHX8y512zCx6xyCbK2jtQ2bcymjfXLYetB6XKJcoLloxEZVLQNy+yPSPZsyYgfnz56Ns2bLKHy3Yvn27Zv+ppPt4WkS5lkx+Iso1g4ODUapUKRw+fFjZRkRITk5W2gEzx4yKm2hM2krvfpDJ6UQ5rtEyJ5OvyvYbZOgdf3BwsFN+Gh8fj9q1a5v2x3ZGjBiBrVu3Ytq0aU5lq3r16njuued0c0ZHMjnCXyV+e/fuRVBQEC5cuKC8XlBQgFOnTin3U7du3RATE2O3j2PHjuGRRx4x1JfPzs7GyZMnpcabZdoTs8Zu3OkjyowVA/rnLjuuZnb8rMxoj92Jocx1FI23i9pJmdze0RNPPIHy5curtnuVKlVyejjXXXoxvHDhAnr06KEsQHUcd09MTES3bt1QUFCAatWqoWzZsti+fTvKly+P2rVrS32/aAzGyHiBlUzZNqt+AbRj2Lx5c2zfvt2uTYuIiICfn5+S66Wnp6NLly7IzMxE1apVUa5cOSQmJuLWrVt295zevJmoLMschyOZ+ZcZM2agRYsWdvfHxYsXcfPmTcN5UXHGUOaeOnDgAIYMGYKsrCzl9fPnz+PmzZtKzvvRRx/h+++/R/ny5fHkk0+isLAQP//8s2ZOUxzXxWj8ALmyrjdWJFOvi/JoV+pCmZhv3rwZI0eOtFvwZj1Os8qgTAxLqh9hNIeT6XeXVF5o5vy1aLxENE5TUFCgxB4A7t+/jwsXLmiO/ZXUvKg78ZPpQ5k1b6A33qZG1CeQuX9kubMGCTBvDYHW/IUr9b4ra0rcoRdDUd9++/bt+PHHH5VzmzBhAgAgNjYWXbp0AWC8LXUkU685Wr16NUJDQ5GTk6Nsy87ORlpaml0MzVqj5E4MrYprDiwqKgrbtm2zK4N+fn6IiIhQ7nlHxVU3aHEnfjVq1EB6ejpyc3OVz+Tk5ODixYt44YUXHlpOYyTuZlx7d2Io27/Ta2tl8mrA+D0nmo+QHQcVEbXrorFSmbzAjPW6rpyvKIZm3cvuxtDIelStvFBmTFVUZ+gxcy2ompKIoagMFcf6Ktmxbr3xclnFPa9lRv/GlbpAtDZdNIcoSxQ/UT9SZn2/7LoM0fyV0XpLdO1k5ldkuLu+34wyLHMuRsthSY1by9SDomsvavdE884ya7pcIVrfalYf2d0YujI/6Ugm/zba3siu4zFj3asohqIyZMZ9XBxrP4pjvaIamWdM3F2nLmr3imP+TmZ8SGZ8xN0YfvHFF8JjlymDonIsUwaNrrWUmXsyc9zflmxfw4z1elrPG8q0pWaVoWJh+m+Vs7+F6dOnU0hICO3cuZNOnjxJvXv3psjISMrLy6OCggK6evUq3bt3j4iIioqKqEuXLtS+fXs6cuQIHTx4kMLDw2nkyJEP+SzMER0dTVFRUcq/Dxw4QD4+PrRhwwa6cOECLVy4kHx9fenQoUPKe4YNG0YREREUHx9PR44coTfeeMNuH3FxceTp6UnLly+ntLQ0Wr16Nfn7+9Py5cuV99y8eZNu3ryp/Hv48OHUtm1bOnbsGJ0+fZq6detGbdq0odzcXCIiSkpKIm9vb5o+fTqlpaXRli1bKCQkhKZMmVKM0SFKSUkhX19fGjRoEF29etXuv6ysLPruu+/Ix8eHvvnmGzp//jzt2LGDwsLCaPz48URElJubS+3ataN//etfdOzYMfrjjz+oR48eFBERQXfu3JGOl60LFy6QxWKh+Ph4ZVtubi5dvXpViRcRUUFBAfn6+tIPP/zgtI/8/Hxq3rw5NWvWjM6fP293Xjdu3DAzhERE1LdvX4qMjKRff/2VTp8+TcOGDaO6devSpUuXaOfOnWSxWGj27NmUnp5Ou3fvpoiICBo2bBgRERUWFlLHjh2pdevWlJCQQMnJyTRp0iTy8/Oj06dPE5Fr5cNisdC6deuUfzve+47Cw8Np1qxZTts3bNhAvr6+VFhY6E6IDLE9dlH8HDne81aXL18mi8VCiYmJqp8bN24c1atXj3bt2kXnzp2jiRMnkq+vLx07dkx5z9WrVyk7O1v5t6iuOHfuHNWqVYvGjBlDKSkptHfvXmrcuHGJ1K+2Mbxx4wY1aNCABg4cSGfOnKHExERq27YtdevWTXn/6tWrKSAggNavX09nz56l4cOHU2hoqHK/nDp1ivz8/GjUqFGUnJxMv//+O7355pv0+uuvU15eHhERDR48mBo2bEi7d++m1NRUWrhwIXl7e9PevXuV77GNYXx8PFksFvr000+d6h/re6ZMmUKBgYEUFxdH6enp9N1331FAQACtXr3a1HgVFhbSW2+9RS1btqSDBw9SamoqzZgxg7y9venw4cO0d+9e8vT0pBkzZlB6ejodOHCAWrRoQf369SMicV3qyHruFy5cULbdvXuXIiMjqXPnznT48GFKTk6mjz76iAIDA5X3OdaFs2bNIk9PT/rxxx+dvtf6Hr36qTi8/fbbFBUVRWfOnKGjR49S27ZtqVevXlIxsKVWJ8XGxlJAQABt2rTJrozt3LlTeh/37t2jq1evUkFBARE9qDN8fX3p4MGDTjG0vseWWhtlFsf66/r161S3bl2Kjo6ms2fP0rp168jf35/Wr19v97mYmBjq2bOn1Hc4tg1E4vpP5v7Pzs6mq1evKvsUtf0y5d0MWm2Cq/egaB+O1MqLKLciss8jb926RcHBwdSpUyfKyMiwK6O2uaYZHOMle390796dRo0apblfURtsa9asWRQeHm63LTc3l1q3bk1dunShY8eO0fHjx6lr167UsmVLpa5zvLfNrrdlOcbQjBxOVBfI5JIy9UlWVpZdnvzLL7+Qt7c3LVmyhJKTk2nq1KkUEBBAycnJdsfbrFkzmjdvnssxc+RqDPXqwuTkZPL19aVFixbRhQsX6Pvvv3fqR8ydO5eCgoJo8+bNlJ6eTvPmzSMvLy/l/pVpcx1jGB8fT97e3jRr1ixKT0+nuLg4qlOnDs2ePVt5XZQLuRs/R2r3mN74AZF8Pm6kPVq3bh1ZLBa7bY5tiUx9aSsqKoqio6Olvl/EaBxFuSSR3H0ouha2tHISx3JoSy3uttTyBFc4xk+mP+JIq39qpXUuc+bMoebNm6t+Rmb8y7E9IXK/HTNKrfzpHYNsriA6D9G4lUxdYLQetBLlEmYwGlc1juVSpn0R9Y/Wrl1LgYGB9J///IfOnz9PEyZMoMDAQDp37pzynhs3btjlJyXdxyNyjp8o15LJT2RyzY8++ojCwsJo9+7dlJKSQuPHj6eAgAAlPq6US1uietHMmMmMSRPp3w+itkQmx3WlzDlybC+M9qu1iI7/zJkz5OvrS59//jklJyfT4sWLycfHhw4cOKC6P73cQO3aW7d99dVXTt9vHU/duHEj+fj40IoVK+j8+fP07bffkq+vL23YsEHZj238ZHKEv0r87ty5Q+Hh4fTWW2/RsWPH6NSpU0r9cu3aNSIiWr58OXl7e9OqVavo/PnzFBcXR6GhoTR9+nRD1yAhIYEsFgtlZGSofs7V9sTKlbEbI31ErfEA0VixzLnLjKsVV/zcbY/NiKEttesoM95utJ1Uy98cYzht2jQKCgqiDRs20Pnz52nDhg0UFBREX331leo+RbmsFlEMo6KiqG7dunTy5Emn14ke1D9169alCRMmKH2oOnXq0Pz586XPXWYMRma8wLEtEZVto/WLqzF05Nhe5+fnU0REBH3wwQeUmppK8fHx1LhxYxo9erTd54zMm8nUSWq5quM8s2j+5dixY+Tr60tjx46llJQU+vXXX+mNN96gLl26UFFRkfA4rUoihqJ76ubNm9SoUSPq168fnTlzhhISEuj111+nf/3rX0rbOHPmTGrUqBH9+uuvlJKSQkOHDqWwsDDKzMxUvkdvfMXV6+Ju/ERlXWasSKZeF+XRjmTqQlHM09PTKTAwkGJiYpQ6Ojw8nAYPHlyiMZQ5fzPmnmVyOKPrRUoqLzQ6f+3q3DuR/jgNEVHPnj2pVatWlJCQQGfPnqXBgwdT/fr1lbL3MOZFzY4fkXMfyox5A5nxNsfxVpk+ldH6w5UYisZ9zVxDYGT+QqY9lllTYsvV8VZRDGX69rbU8m+ZtlR0LqJ6zXGN0uXLlyk4OJgGDRqk5IU9e/akV199le7fv6/s14w1SmbFULYMubL2wZFaH9roGI0rY/Fq3I3flStXKCQkhN599106efIknTx5kvr370+NGjVSzudh5TSOtMYu3J3/dDeGsv07vbZWJq8mEt9zRtcuyI6DytBr12Xm3UR5gdE2Wa2tkDlfozE06142I4aO1NoDUV4oGlOVqTNcmb8z2m5rKe4YisqQWWuZHPMa0XWRGS83I4a2RH11tTrbjP6NTF1g257IrE2XmUOUpRc/UT9SZn2/7LoMvfFjV/onomsnM79iRgxlxgiM9DPVyrHMuRgth0QlN26td/6iay/T7onmnYnEa7ocqbVXRte3EpnTR3Y3hkRy85O21K61KP92Zf26I8f5VzPXverFUKYMuXsfExXP2g8z1iu6wvYeMWOduky7Vxzzd0afMZGZn5SlN+ajduyi85cpx6IyaHRtOpF47smscX8ZjjE1Y72e3vOGRMbbUrUypNZXsSrOZ074YfH/UQUFBfTZZ59RvXr1KDAwkN555x2lQFoLnOMDCIMHD6bAwEAKDQ2lcePG2Q3E/p2pde7Wrl1LzZs3p4CAAOrQoYPdg4xEDxKeUaNGUXBwMAUHB9OwYcOcBsQ3btxIbdq0IT8/P4qMjKQ1a9bYvR4VFWX3vVlZWTRy5EgKCQmhunXr0rBhw5RFVlZ79+6lDh06UEBAADVt2pQWLFhQ7A/ozp8/nywWi+p/1o71hg0bqF27dlSrVi2KjIykuXPn2i1Wv3LlCg0bNoxCQ0MpKCiIBg8eTJcvX7b7HlG8bKlVitaK1Xbb9evXyWKx0J49e5z2kZSUpHle7i7cVpOVlUXjxo2jsLAwCggIoN69e9OZM2eU17ds2UJvvPEGBQQEUFhYGE2ZMsXuHrt27RqNHDmSwsLCyN/fnzp37kwJCQl232G0fDje52r3vi2tjmBsbCw1aNBAOhZmcDxOUfxsaU1YHzlyhCwWi9MDTla5ubk0ffp0atq0Kfn5+aleA4vFYhcjmbriyJEj1LVrV/L396cGDRrQlClT3ErQZTnGMDU1lfr160e1atWiunXrUkxMDN2+fdvuM1999RU1btyYAgIC6O2336YTJ07Yvf77779Tt27dqE6dOtSwYUMaOXKkXdJ9584dmjRpEjVp0oT8/Pzo9ddfpx07djgdlzWGo0eP1rxPre/Jz8+nOXPmULNmzcjX15datGhh+oPiVrdu3aKPP/6YmjZtSkFBQdS5c2e7wfvdu3dTp06dKDAwkMLDw2nSpEl09+5dIpKrS21pdTgyMjJo2LBhFBYWRsHBwdSrVy86efKk0+esdWFkZKTm91rfI6qfzJaRkUGDBg2iOnXqUP369WncuHGqC7JcmTAtLCykpUuXUvPmzcnPz4/atWvnVMZE+7AOSFy4cIEKCgrI399fM4Zqx1aSD4sTPahD3nzzTfLz86Pw8HDVP7TSt29f+uCDD6S+Q60dkKn/RPf/rFmznBbai9p+UXk3g5GHxY0ek6sLm4nEuZVtHrlp0ybNMmp0saKIbbyM3B+tWrWizz//XHO/ojbYllan/erVqzRs2DCqXbs2BQUF0XvvvWe3MN723rYys96WpVbm3M3hiMR1gUwuKdpHdHS0U+w3bNhAzZs3J39/f2rfvr3qgzgBAQG0atUq7aAY5GoMRXXhzz//TG3btqWAgABq3bq16gDQkiVLKCIignx9fZ3aGJk2Vy2Ge/fupfbt25Ovry81bdqUFi5cqBy7TC5klCsPi+uNH1jJ5ONG2iO1B7TU2hJRfWnrYT4sTiTOJYnE96HMtbDSamPUyqHVw3pYnEiuP2LL1YfFx40bR506ddL8nGj8S609MaMdM0IrJ9Q6BtlcQXQeMuNWorrAaD1oJcolzGA0rmrUyqWofZHpHy1YsICaNGlCgYGBFBUVRUeOHHH6Xtv6raT7eETq8dPLtYj08xPZXDMvL4+mT59ODRs2VPbhOBHkSrm0KsmHxYnEY9JEcrm1Vlsim+MaLXOObNsLV/rVWmSOf8+ePdS2bVvy8/Ojli1bUlxcnOb+jD4s3qtXL83vt20fN27cSK1atSJfX1/Vvq5j/PRyhL9a/NLT02nw4MEUEhJCtWrVot69eyt/4MFqxYoV1KpVK6UMzp8/X7N/o3UN4uLiyGKx6I7vutKeWLkydmOkj6g1HiAzViw6d5l2o7ji5257bEYMbWldR9F4u9F2Ui1/c4xhQUEBLVmyhFq2bEm1atWiNm3a0KpVqzQfwHV10Z1eDPXqKNv6LCkpiTp16kQBAQHUrFkzWrp0qaFzJxKPwRCJxwsc60KZsm2kftFidLxHrb1OTk6mHj16UGBgIDVu3JimT59O+fn5du8xMm/m6kPJjvPMMvMvBw4coM6dO1NgYCCFhIRQTEwM3bp1S+o4rUoihjL3VEpKCvXr14/q1KmjnIttHzIvL48mT55MDRo0oNq1a1P//v0pNTXV7jj0xleK62FxmfjplXXZsSJRvS6TR9uSqQtlYn7kyBGKioqiwMBAJTc3Oifqbgxlz9+duWfZHM7IepGSzguNzF+7OvdOJB6nuXPnDo0bN45CQ0OpVq1a1KtXLzp79qzy+sOYFzU7fkTqY25mzBuIxtvUxltFfQKj9Ycad9cgmbmGwMj8hUx7LDOnakvtusmQiaGob29LLf+WqddF5yIaT1Rbo/THH39Qr169qE6dOlS7dm3V9WVmrFEyK4ayZai4HhY3MkZjZXQsXo0Z8UtOTqb+/ftTSEgI1atXj9577z2n+DyMnMaR1tiFu/OfZsRQpn8namtFeTWR+J5zZe2CzDioDFG7Lpp3k8kLjLTJWnmR6HxdiaEZ97JMDGTWJNlSq3dk8kLRmKqoznBl/s5ou62lJGIoKkNmrGVyzGuI9K+L7Hi5DNkc3ZWHxYnM6d+I6gLb9kR2bbrMGhUZWvGT7UfKrO+XWZchGj92pX8iunYyazDciaGVaIzASD9TqxzLnIuRckhUcuPWovPXu/ay7Z5o3plIf02XI7W6ypX1rWb0kYnci6Htseq1pba0rrUo/za6ft2R4/yrmeteRTEUlSEz7uPiWPtB5P56RVc43iNmrFMXtXvFMX9n9BmTh/mwuMz5i8qxTBk0sjadSG7uyYxxfxlq5VLUz7DSmvPUe96QyHhbqlaG1PoqVsX5zEkpIqKH+9vmjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcaM8njYB8AYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjzDh+WJwxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHG/ob4YXHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYY+xvih8UZY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhj7G+IHxZnjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMsb8hflicMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxv6G+GFxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGPsb4ofFGWOMMcYYY4wxxhhjjDHGGGOMMTcR0cM+BMYYY4wxxhhjjDHGGGOMMcYYY/+D+GFxxhhjjDHGGGOMMcYYY4wxxhhjfwndunWDp6cnunTpovmeoUOHwtPTEyNHjnTruy5evAhPT0+sX7/erf1kZGSgX79+uHTpkrItIiLC7eNzR0FBAb7++mu0b98egYGBCAoKQvv27bFkyRLk5eU9tOPS4unpidmzZz/sw2CMMcYYY4wxxhhjjDHGGGOMsb+lMg/7ABhjjDHGGGOMMcYYY4wxxhhjjDErDw8PHD58GBkZGahRo4bdazk5Odi1a9dDOjJ1Bw4cwJ49e+y2zZkzB4899thDOiJgzJgx2L59O/r16wc/Pz8UFRUhMTERM2fORFJSEubOnfvQjo0xxhhjjDHGGGOMMcYYY4wxxpi5+GFxxhhjjDHGGGOMMcYYY4wxxhhjfxk+Pj5ITk7G1q1b0bNnT7vXdu3ahfLly6NSpUoP5+Ak+fj4PLTvvnz5MjZs2IBPPvkEb775prK9UaNGePLJJzFp0iQcPXoUAQEBD+0YGWOMMcYYY4wxxhhjjDHGGGOMmcfjYR8AY4wxxhhjjDHGGGOMMcYYY4wxZlWhQgU0adIEW7dudXpty5YtaNGiBcqU+e/fRc/MzMT48eMRHh4OPz8/hISEYNCgQbh48aLynm7duuHDDz/EkCFDEBgYiF69ejntm4gQExODgIAA7Nu3T9m+c+dOdOjQAf7+/ggLC8OECROQk5MDAFi/fj1iYmIAAM2aNcPIkSMBABEREcr/v3jxIjw9PfHTTz9hyJAhCAoKQkhICEaPHq3sBwDy8/PxxRdfoHHjxggICECfPn2wceNGeHp62p2LyPXr10FEKCoqcnrttddew7Bhw5SH7Q8dOgRPT0/s27cPXbt2RUBAACIjI7Fq1Sq7zxUVFSE2NhbNmzeHn58fWrRogeXLl9u9p1u3bhg1ahRiY2PRtGlT+Pv7o0uXLjh69Kjd+3799Vd07twZtWrVQosWLXDgwAHpc2OMMcYYY4wxxhhjjDHGGGOMMeaMHxZnjDHGGGOMMcYYY4wxxhhjjDH2l9K6dWscPnwYGRkZyrbs7Gzs3bsXbdu2VbYREfr374/9+/fjww8/xOLFi/Hee+/h4MGDGDdunN0+f/rpJ1SsWBHz589H3759nb5zwoQJ+PHHHzFnzhw0bNgQALB582YMGjQIL774IubOnYv33nsPmzZtwsCBA0FEaNq0KQYMGAAAmDNnDgYOHKh5TuPGjcP//d//Yd68eejTpw++//57zJ8/X3l97Nix+OabbxAVFYW5c+fiqaeewpgxYwzHzsvLC//85z8xefJkjB8/Hnv37kV2djYA4Mknn0T//v3xwgsv2H1m6NCh8PHxwdy5c9GgQQOMHz/e7oHxjz/+GLNmzUK7du2wYMECtGzZEpMmTcLcuXPt9rNt2zb8/PPPGD16NKZPn47r169j8ODBKCwsBAAcP34cvXv3xuOPP45Zs2ahe/fuGDZsmOFzZIwxxhhjjDHGGGOMMcYYY4wx9l9lxG9hjDHGGGOMMcYYY4wxxhhjjDHGSk7Tpk1Rvnx5bN26FT179gQA7NixA1WrVkWdOnWU9129ehXly5dHdHQ0goODAQChoaE4f/48Vq9ebbfPRx55BOPHj0fZsmUBwO7XuqdNm4bVq1djzpw5aNy4MYAHD6J/8cUXaNSoEb744gvlvS+88AJ69uyJPXv2oGnTpnjuuecAAN7e3njmmWc0z6lJkyaIjo4GANSvXx/79+/H7t278e9//xvnz5/Hhg0bEB0drfzqeaNGjXD9+nW7XzmXUbZsWcTGxmLEiBFYtWoVVq1aBQ8PD/j6+qJVq1bo2rUrypUrZ/eZ5s2bY9SoUcr3Xr16FfPmzcNbb72FtLQ0rFmzBsOGDUO/fv0AAA0bNkSpUqWwcOFCvP3226hSpQoAoKCgAIsXL8Zjjz0GALh79y6io6Nx8uRJ+Pn5YeHChahatSrmz5+PRx55BABQpUoVDB061NA5MsYYY4wxxhhjjDHGGGOMMcYY+y/+ZXHGGGOMMcYYY4wxxhhjjDHGGGN/KeXKlUNERAS2bt2qbIuLi0OrVq1QqlQpZVv16tWxbNky1KlTBxcvXsT+/fuxfPly/Pbbb8jLy7Pb54svvqg8KG5r5cqViI2NRZs2bdC0aVNle0pKCjIyMhAREYGCggLlv7p16+Kxxx7D/v37DZ1TYGCg3b9r1KiBnJwcAMChQ4dARGjZsqXde2x/Rd0Ii8WCjRs34vvvv8cHH3yA0NBQnD17Fp999hnat2+PzMxMu/e3b9/e7t+RkZG4du0aUlNTER8fDyJyikNERARyc3ORlJSkfO7ll19WHhQHHlwfALh37x4AICkpCY0aNVIeFLd+V+nSpV06T8YYY4wxxhhjjDHGGGOMMcYYY/zL4owxxhhjjDHGGGOMMcYYY4wxxv6CWrVqhffeew8ZGRl49NFHcfDgQXzwwQdO79u0aROmT5+OP//8E5UrV4a3t7fTL2cDQMWKFVW/59SpU2jYsCF+/PFH9OjRAz4+PgCAW7duAQDGjx+P8ePHO33u6tWrhs6nfPnydv/28PAAEQGA8vB21apV7d7j+G+j/P394e/vjwEDBuDevXtYsmQJZs2ahUWLFim/cg7896Fux++9ffu2Eoc2bdqofseVK1eU/692jgBQVFSk7M/6K+RWZcqUcdrGGGOMMcYYY4wxxhhjjDHGGGNMHj8szhhjjDHGGGOMMcYYY4wxxhhj7C+ncePGqFixIrZu3YoKFSrgmWeegZ+fn917EhMTER0djW7duqFPnz7KQ8+fffaZ3S9e63n//ffRvXt3tGnTBqNHj8batWtRunRpVKpUCQAwYsQIhISEOH3uiSeecPMM/8t63NevX8fTTz+tbHf8BXAZU6dOxa5du+x+lR148CD3oEGDsH37diQnJ9u9dvPmTTz33HPKv2/cuAHgwUPj1jh88803qg/c2x6vSOXKlXH9+nW7bUSE27dvS++DMcYYY4wxxhhjjDHGGGOMMcaYPY+HfQCMMcYYY4wxxhhjjDHGGGOMMcaYo7Jly+LVV1/Ftm3b8NNPP6n+svXvv/+OoqIiDB48WHngurCwEAcOHADw31+01vPUU0+hXLlyGDt2LI4fP46lS5cCAF588UVUrVoVFy9eVH6h29/fH9WrV8e0adNw4sQJAP/99Wx31KlTB6VLl8aOHTvstm/fvt3wvmrWrInU1FRs2bLF6bW7d+/i6tWrsFgsdtt37txp9++tW7fi//7v//Dcc88hODgYwIMHym3jkJmZiS+//FL55XEZ9evXx969e3Hv3j1l2y+//IL8/HwDZ8gYY4wxxhhjjDHGGGOMMcYYY8wW/7I4Y4wxxhhjjDHGGGOMMcYYY4yxv6TWrVujf//+8PDwwOjRo51eDwgIAAB88skn6NixI27fvo2VK1fi1KlTAICcnBw89thjUt/VpEkTtGzZErNnz0aLFi3w7LPPYujQoRg7dixKly6N8PBwZGVlYd68ebhy5Qp8fX0BQPnl7R07dqBx48Z46aWXDJ/ns88+i44dO2L69OnIz8+Hl5cXduzYgV27dgEw9kD6G2+8gc2bN2PEiBE4dOgQmjRpgkqVKiEtLQ3Lli1DuXLl0Lt3b7vPLF26FI8++igCAwOxfft27Nq1C9OmTQMAeHp6ol27dhgzZgwuXboEPz8/pKamYsaMGXjmmWfwwgsvSB/boEGDsHPnTvTp0wd9+/ZFZmYmZs6ciUceeUR6H4wxxhhjjDHGGGOMMcYYY4wxxuzxw+KMMcYYY4wxxhhjjDHGGGOMMcb+kho0aIBKlSrhn//8p+pD2KGhoRg7diyWLl2KrVu34qmnnkJoaCjmzJmDQYMGISkpCU2aNJH+vo8++gj79u3DmDFj8PXXX6NTp06oWLEivvrqK6xevRoVKlRA7dq18cUXX+DZZ59VjqFBgwaYNm0aDh48iNjYWJfOdcyYMahQoQKWLFmC7Oxs1K9fHwMGDMDcuXNRoUIF6f2ULVsWixcvxrJly7B161bExcXh/v37qFatGiIiIjBgwABUrVrV6bw3bNiAhQsX4sUXX8SsWbPQokUL5fXJkydj4cKF+O6775CRkYGqVauidevW+OCDD1C6dGnpY3vhhRewYsUKTJkyBUOHDkXVqlURHR2NKVOmSO+DMcYYY4wxxhhjjDHGGGOMMcaYvVJERA/7IBhjjDHGGGOMMcYYY4wxxhhjjLH/Vbdu3cLevXvRqFEjVKlSRdk+depUrF+/HocOHSqW7z106BC6d++OZcuWITQ0tFi+gzHGGGOMMcYYY4wxxhhjjDHGWPHiXxZnjDHGGGOMMcYYY4wxxhhjjDHGHqLy5ctj4sSJ8Pb2Ro8ePVChQgUcPnwYK1asQP/+/QEABQUFwv14eHjAw8OjuA+XMcYYY4wxxhhjjDHGGGOMMcbYXwg/LM4YY4wxxhhjjDHGGGOMMcYYY4w9RI8++ii+/vprzJw5EyNHjsS9e/fw3HPPITo6Gl27dgUA+Pr6CvfTvn17TJkypbgPlzHGGGOMMcYYY4wxxhhjjDHG2F9IKSKih30QjDHGGGOMMcYYY4wxxhhjjDHGGNN27Ngx4XuqVKmCZ555pgSOhjHGGGOMMcYYY4wxxhhjjDHG2F8FPyzOGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY39DHg/7ABhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMGccPizPGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcbY3xA/LM4YY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjf0P8sDhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOM/Q3xw+KMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wx9jfED4szxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHG2N8QPyzOGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY39D/LA4Y4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjP0N/T9y3OcIz0kNeAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 5000x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(50,30)})\n",
    "\n",
    "sns.barplot(data=df, x= 'Marketing_Spend' , y='Profit' , hue = 'State')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ed772d-d108-4f51-a0e2-41e4f7f34e21",
   "metadata": {},
   "source": [
    "**Above Graph is showing that which country between newyork california and florida Spent money in the marketing in the most and how much money they generate from that marketing spent.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cfbc3604-6bbf-49ac-9266-926f1b6557bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marketing_Spend', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3, figsize = (10,4))\n",
    "\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "sns.kdeplot(x = 'RD_Spend', data = df , ax=axes[0])\n",
    "\n",
    "\n",
    "sns.kdeplot(x = 'Administration', data = df , ax=axes[1])\n",
    "\n",
    "sns.kdeplot(x = 'Marketing_Spend', data = df , ax=axes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "522f538f-0895-4392-b9f9-e066a45a0486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.298959528916"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Administration'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b19006d-583f-4d0c-8f48-3348aece05e1",
   "metadata": {},
   "source": [
    "**According to above graph , R&D Spend and Marketing Spend is almost Normal but in Administration outliers are present**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54bc0fcf-9c7e-4100-8848-72ea9e31bccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding the IQR\n",
    "percentile25= df['Administration'].quantile(0.25)\n",
    "percentile75= df['Administration'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dcdfc0a6-d52d-470e-b1ca-2590e377a5ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26303.753571428562"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#IQR\n",
    "\n",
    "iqr = percentile75 -percentile25\n",
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a7664c1-8a97-461e-b3a2-cbcb6c0075ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper Limit 184491.2914285714\n",
      "Lower Limit 79276.27714285716\n"
     ]
    }
   ],
   "source": [
    "upper_limit= percentile75 + 1.5*iqr\n",
    "lower_limit= percentile25 - 1.5*iqr\n",
    "print(\"Upper Limit\" , upper_limit)\n",
    "print(\"Lower Limit\",lower_limit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1e306e-3579-4873-a567-21d99af8a846",
   "metadata": {},
   "source": [
    "**FINDING OF OUTLIER**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01ae3749-003c-424a-8097-5d3c5ca85237",
   "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>RD_Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing_Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RD_Spend  Administration  Marketing_Spend  State  Profit\n",
       "48     False           False            False  False   False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[df['Administration'] >upper_limit]) | (df[df['Administration'] <lower_limit])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f348069d-0fe2-4eb9-b752-4148f0cca03f",
   "metadata": {},
   "source": [
    "**SOLVING OUTLIERS PROBLEM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9d32f510-6fc2-4078-b23a-ce61b24c554f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.copy()\n",
    "\n",
    "new_df['Administration'] = np.where(\n",
    "        df['Administration'] > upper_limit,\n",
    "        upper_limit,\n",
    "        np.where(\n",
    "            df['Administration'] < lower_limit,\n",
    "            lower_limit,\n",
    "            df['Administration']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b6bea508-8eb4-444b-812d-fdb6536a95ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        50.000000\n",
       "mean     129419.045500\n",
       "std       21233.370447\n",
       "min       51743.150000\n",
       "25%      118731.907500\n",
       "50%      134020.056250\n",
       "75%      145035.661071\n",
       "max      157693.920000\n",
       "Name: Administration, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Administration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1f1a15c4-66af-4cf7-9371-2e17736958d8",
   "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>RD_Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing_Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>136897.800000</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.700000</td>\n",
       "      <td>151377.590000</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.510000</td>\n",
       "      <td>101145.550000</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.410000</td>\n",
       "      <td>118671.850000</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.340000</td>\n",
       "      <td>91391.770000</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.900000</td>\n",
       "      <td>99814.710000</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.460000</td>\n",
       "      <td>147198.870000</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.130000</td>\n",
       "      <td>145530.060000</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.520000</td>\n",
       "      <td>148718.950000</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.880000</td>\n",
       "      <td>108679.170000</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.080000</td>\n",
       "      <td>110594.110000</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.960000</td>\n",
       "      <td>91790.610000</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.750000</td>\n",
       "      <td>127320.380000</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.390000</td>\n",
       "      <td>135495.070000</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.240000</td>\n",
       "      <td>156547.420000</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.610000</td>\n",
       "      <td>122616.840000</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.110000</td>\n",
       "      <td>121597.550000</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.160000</td>\n",
       "      <td>145077.580000</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.160000</td>\n",
       "      <td>114175.790000</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.700000</td>\n",
       "      <td>153514.110000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.860000</td>\n",
       "      <td>113867.300000</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.470000</td>\n",
       "      <td>118912.080000</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.560000</td>\n",
       "      <td>123956.860000</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.530000</td>\n",
       "      <td>129001.640000</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.010000</td>\n",
       "      <td>134046.420000</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.710000</td>\n",
       "      <td>139091.200000</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.870000</td>\n",
       "      <td>144135.980000</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.600000</td>\n",
       "      <td>127864.550000</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.520000</td>\n",
       "      <td>132125.888571</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.480000</td>\n",
       "      <td>136387.227143</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.480000</td>\n",
       "      <td>140648.565714</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>58798.350833</td>\n",
       "      <td>144909.904286</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>55602.221667</td>\n",
       "      <td>149171.242857</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>52406.092500</td>\n",
       "      <td>153432.581429</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>49209.963333</td>\n",
       "      <td>157693.920000</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46013.834167</td>\n",
       "      <td>85047.440000</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>42817.705000</td>\n",
       "      <td>127056.210000</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>39621.575833</td>\n",
       "      <td>130524.951250</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>36425.446667</td>\n",
       "      <td>133993.692500</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>33229.317500</td>\n",
       "      <td>137462.433750</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>30033.188333</td>\n",
       "      <td>140931.175000</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>26837.059167</td>\n",
       "      <td>144399.916250</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.930000</td>\n",
       "      <td>147868.657500</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22909.335000</td>\n",
       "      <td>151337.398750</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.740000</td>\n",
       "      <td>154806.140000</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.230000</td>\n",
       "      <td>124153.040000</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.460000</td>\n",
       "      <td>115816.210000</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>135426.920000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.050000</td>\n",
       "      <td>79276.277143</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>116983.800000</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RD_Spend  Administration  Marketing_Spend       State     Profit\n",
       "0   165349.200000   136897.800000        471784.10    New York  192261.83\n",
       "1   162597.700000   151377.590000        443898.53  California  191792.06\n",
       "2   153441.510000   101145.550000        407934.54     Florida  191050.39\n",
       "3   144372.410000   118671.850000        383199.62    New York  182901.99\n",
       "4   142107.340000    91391.770000        366168.42     Florida  166187.94\n",
       "5   131876.900000    99814.710000        362861.36    New York  156991.12\n",
       "6   134615.460000   147198.870000        127716.82  California  156122.51\n",
       "7   130298.130000   145530.060000        323876.68     Florida  155752.60\n",
       "8   120542.520000   148718.950000        311613.29    New York  152211.77\n",
       "9   123334.880000   108679.170000        304981.62  California  149759.96\n",
       "10  101913.080000   110594.110000        229160.95     Florida  146121.95\n",
       "11  100671.960000    91790.610000        249744.55  California  144259.40\n",
       "12   93863.750000   127320.380000        249839.44     Florida  141585.52\n",
       "13   91992.390000   135495.070000        252664.93  California  134307.35\n",
       "14  119943.240000   156547.420000        256512.92     Florida  132602.65\n",
       "15  114523.610000   122616.840000        261776.23    New York  129917.04\n",
       "16   78013.110000   121597.550000        264346.06  California  126992.93\n",
       "17   94657.160000   145077.580000        282574.31    New York  125370.37\n",
       "18   91749.160000   114175.790000        294919.57     Florida  124266.90\n",
       "19   86419.700000   153514.110000             0.00    New York  122776.86\n",
       "20   76253.860000   113867.300000        298664.47  California  118474.03\n",
       "21   78389.470000   118912.080000        299737.29    New York  111313.02\n",
       "22   73994.560000   123956.860000        303319.26     Florida  110352.25\n",
       "23   67532.530000   129001.640000        304768.73     Florida  108733.99\n",
       "24   77044.010000   134046.420000        140574.81    New York  108552.04\n",
       "25   64664.710000   139091.200000        137962.62  California  107404.34\n",
       "26   75328.870000   144135.980000        134050.07     Florida  105733.54\n",
       "27   72107.600000   127864.550000        353183.81    New York  105008.31\n",
       "28   66051.520000   132125.888571        118148.20     Florida  103282.38\n",
       "29   65605.480000   136387.227143        107138.38    New York  101004.64\n",
       "30   61994.480000   140648.565714         91131.24     Florida   99937.59\n",
       "31   58798.350833   144909.904286         88218.23    New York   97483.56\n",
       "32   55602.221667   149171.242857         46085.25  California   97427.84\n",
       "33   52406.092500   153432.581429        214634.81     Florida   96778.92\n",
       "34   49209.963333   157693.920000        210797.67  California   96712.80\n",
       "35   46013.834167    85047.440000        205517.64    New York   96479.51\n",
       "36   42817.705000   127056.210000        201126.82     Florida   90708.19\n",
       "37   39621.575833   130524.951250        197029.42  California   89949.14\n",
       "38   36425.446667   133993.692500        185265.10    New York   81229.06\n",
       "39   33229.317500   137462.433750        174999.30  California   81005.76\n",
       "40   30033.188333   140931.175000        172795.67  California   78239.91\n",
       "41   26837.059167   144399.916250        164470.71     Florida   77798.83\n",
       "42   23640.930000   147868.657500        148001.11  California   71498.49\n",
       "43   22909.335000   151337.398750         35534.17    New York   69758.98\n",
       "44   22177.740000   154806.140000         28334.72  California   65200.33\n",
       "45    1000.230000   124153.040000          1903.93    New York   64926.08\n",
       "46    1315.460000   115816.210000        297114.46     Florida   49490.75\n",
       "47       0.000000   135426.920000             0.00  California   42559.73\n",
       "48     542.050000    79276.277143             0.00    New York   35673.41\n",
       "49       0.000000   116983.800000         45173.06  California   14681.40"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255a7a07-a7b7-4463-af48-ca7c7fe3890b",
   "metadata": {},
   "source": [
    "**TRAIN TEST SPLIT**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7dd66e39-9c01-43dd-a2ca-e216135836b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    new_df.RD_Spend,new_df.Profit ,test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1f824ca3-c50e-4c33-a05a-373a54369cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test Train Split')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,5\n",
    "                             )})\n",
    "\n",
    "plt.scatter(X_train,y_train,label='Training Data',color='r',alpha=.7)\n",
    "plt.scatter(X_test,y_test,label='Testing Data',color='g',alpha=.7)\n",
    "plt.legend()\n",
    "plt.title(\"Test Train Split\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659788eb-891e-4011-acaa-c4c424841081",
   "metadata": {},
   "source": [
    "**IMPLEMENTING LINEAR REGRESSION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4fdc9acc-5036-4295-adcb-6f156a92b9f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "regression=LinearRegression()\n",
    "\n",
    "regression.fit(X_train.values.reshape(-1,1),y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d1e8ad1e-8aa9-45f4-88b2-3f6f70d3f364",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use model to predict on Test data\n",
    "y_prediction = regression.predict(X_test.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "03e6414e-0e11-4c50-bc74-899854c32fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c57d6419a0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot Prediction line against actual test data\n",
    "\n",
    "plt.plot(X_test,y_prediction,label='Linear Regression',color='b')\n",
    "plt.scatter(X_test,y_test,label='Actual Test data',color='g',alpha=.7)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f1d9df7e-7807-42a0-980c-486d2c4fb212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66677.38525348305"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict Profit if R&D Spend is 23000\n",
    "\n",
    "regression.predict(np.array([[23000]]))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9d6e819c-56ba-46fd-bd9f-af4b69882615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([126689.52311481,  75575.22474038, 100596.22919541,  47541.16993818,\n",
       "       129007.43882419,  47142.62799282, 112194.97582496, 102918.89421516,\n",
       "        95036.00598318, 121842.19081427, 128317.30140033, 170281.36401659,\n",
       "        81135.44795261, 151523.48433167, 172251.60585204])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8916a940-db1c-4a18-a38e-747396263136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9531953268863746"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score this model\n",
    "\n",
    "regression.score(X_test.values.reshape(-1,1),y_test.values)"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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