2-kendi-calismam-lineer-regresyon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4d573bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10b362d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.adım: verileri okuma\n",
    "df_startup=pd.read_csv(\"resources/resources/50_Startups.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "205be7fd",
   "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>153773.43</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>122782.75</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>105751.03</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>99281.34</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>139553.16</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>182645.56</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>153032.06</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>115641.28</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>61136.38</td>\n",
       "      <td>152701.92</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>63408.86</td>\n",
       "      <td>129219.61</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>55493.95</td>\n",
       "      <td>103057.49</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>46426.07</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>46014.02</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>28663.76</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>44069.95</td>\n",
       "      <td>51283.14</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>20229.59</td>\n",
       "      <td>65947.93</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>38558.51</td>\n",
       "      <td>82982.09</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>28754.33</td>\n",
       "      <td>118546.05</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>27892.92</td>\n",
       "      <td>84710.77</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>96189.63</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>15505.73</td>\n",
       "      <td>127382.30</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       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        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       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verilere göz atalım\n",
    "df_startup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95df0c79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing Spend  50 non-null     float64\n",
      " 3   State            50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#2.adım: veriler hakkında genel bir bilgi sahibi olmak\n",
    "df_startup.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "935fb677",
   "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": [
    "#tüm alanlardaki verilerimiz dolu mu kontrol edelim\n",
    "df_startup.isnull().sum()\n",
    "#boş alan yok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9b5a5ec",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <td>50.0</td>\n",
       "      <td>73721.6156</td>\n",
       "      <td>45902.256482</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39936.3700</td>\n",
       "      <td>73051.080</td>\n",
       "      <td>101602.8000</td>\n",
       "      <td>165349.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>50.0</td>\n",
       "      <td>121344.6396</td>\n",
       "      <td>28017.802755</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>103730.8750</td>\n",
       "      <td>122699.795</td>\n",
       "      <td>144842.1800</td>\n",
       "      <td>182645.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Spend</th>\n",
       "      <td>50.0</td>\n",
       "      <td>211025.0978</td>\n",
       "      <td>122290.310726</td>\n",
       "      <td>0.00</td>\n",
       "      <td>129300.1325</td>\n",
       "      <td>212716.240</td>\n",
       "      <td>299469.0850</td>\n",
       "      <td>471784.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>50.0</td>\n",
       "      <td>112012.6392</td>\n",
       "      <td>40306.180338</td>\n",
       "      <td>14681.40</td>\n",
       "      <td>90138.9025</td>\n",
       "      <td>107978.190</td>\n",
       "      <td>139765.9775</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count         mean            std       min          25%  \\\n",
       "R&D Spend         50.0   73721.6156   45902.256482      0.00   39936.3700   \n",
       "Administration    50.0  121344.6396   28017.802755  51283.14  103730.8750   \n",
       "Marketing Spend   50.0  211025.0978  122290.310726      0.00  129300.1325   \n",
       "Profit            50.0  112012.6392   40306.180338  14681.40   90138.9025   \n",
       "\n",
       "                        50%          75%        max  \n",
       "R&D Spend         73051.080  101602.8000  165349.20  \n",
       "Administration   122699.795  144842.1800  182645.56  \n",
       "Marketing Spend  212716.240  299469.0850  471784.10  \n",
       "Profit           107978.190  139765.9775  192261.83  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3.adım: eğer nan alan varsa,bunlara nasıl davranılacağına karar verme aşaması\n",
    "#nan alanımız yok.\n",
    "#matematiksel verilere bakalım\n",
    "df_startup.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1d0db09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#max ve min değerler arasındaki fark büyük. Dataframe deki 0 olan alanları silersek bu farkı azaltabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67220622",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=df_startup[df_startup['R&D Spend']==0].index #0 olan değerlerin indexlerini bulmak\n",
    "a \n",
    "df_startup.drop(a,axis=0,inplace=True) #bu index değerlerini dataframeden silmek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39cc4619",
   "metadata": {},
   "outputs": [],
   "source": [
    "b=df_startup[df_startup['Marketing Spend']==0].index #0 olan değerlerin indexlerini bulmak\n",
    "b \n",
    "df_startup.drop(b,axis=0,inplace=True) #bu index değerlerini dataframeden silmek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "289f1a66",
   "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>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>153773.43</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>122782.75</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>105751.03</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>99281.34</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>139553.16</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>182645.56</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>153032.06</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>115641.28</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>61136.38</td>\n",
       "      <td>152701.92</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>63408.86</td>\n",
       "      <td>129219.61</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>55493.95</td>\n",
       "      <td>103057.49</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>46426.07</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>46014.02</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>28663.76</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>44069.95</td>\n",
       "      <td>51283.14</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>20229.59</td>\n",
       "      <td>65947.93</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>38558.51</td>\n",
       "      <td>82982.09</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>28754.33</td>\n",
       "      <td>118546.05</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>27892.92</td>\n",
       "      <td>84710.77</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>96189.63</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>15505.73</td>\n",
       "      <td>127382.30</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",
       "  </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",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        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       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         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"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_startup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08880ea1",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <td>46.0</td>\n",
       "      <td>78241.718043</td>\n",
       "      <td>43695.348335</td>\n",
       "      <td>1000.23</td>\n",
       "      <td>46117.0325</td>\n",
       "      <td>74661.715</td>\n",
       "      <td>111370.9775</td>\n",
       "      <td>165349.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>46.0</td>\n",
       "      <td>121947.043478</td>\n",
       "      <td>26812.767940</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>103730.8750</td>\n",
       "      <td>122699.795</td>\n",
       "      <td>144842.1800</td>\n",
       "      <td>182645.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Spend</th>\n",
       "      <td>46.0</td>\n",
       "      <td>228393.083261</td>\n",
       "      <td>111336.691801</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>142431.3850</td>\n",
       "      <td>239452.750</td>\n",
       "      <td>302423.7675</td>\n",
       "      <td>471784.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>46.0</td>\n",
       "      <td>117063.925217</td>\n",
       "      <td>35960.003971</td>\n",
       "      <td>49490.75</td>\n",
       "      <td>96537.8325</td>\n",
       "      <td>108643.015</td>\n",
       "      <td>143590.9300</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count           mean            std       min          25%  \\\n",
       "R&D Spend         46.0   78241.718043   43695.348335   1000.23   46117.0325   \n",
       "Administration    46.0  121947.043478   26812.767940  51283.14  103730.8750   \n",
       "Marketing Spend   46.0  228393.083261  111336.691801   1903.93  142431.3850   \n",
       "Profit            46.0  117063.925217   35960.003971  49490.75   96537.8325   \n",
       "\n",
       "                        50%          75%        max  \n",
       "R&D Spend         74661.715  111370.9775  165349.20  \n",
       "Administration   122699.795  144842.1800  182645.56  \n",
       "Marketing Spend  239452.750  302423.7675  471784.10  \n",
       "Profit           108643.015  143590.9300  192261.83  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_startup.describe().T #min max değerleri arasındaki fark da azalma oldu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "96f1961e",
   "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>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>153773.43</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>122782.75</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>105751.03</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>99281.34</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>139553.16</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>182645.56</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>153032.06</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>115641.28</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>61136.38</td>\n",
       "      <td>152701.92</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>63408.86</td>\n",
       "      <td>129219.61</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>55493.95</td>\n",
       "      <td>103057.49</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>46426.07</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>46014.02</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>28663.76</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>44069.95</td>\n",
       "      <td>51283.14</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>20229.59</td>\n",
       "      <td>65947.93</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>38558.51</td>\n",
       "      <td>82982.09</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>28754.33</td>\n",
       "      <td>118546.05</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>27892.92</td>\n",
       "      <td>84710.77</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>96189.63</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>15505.73</td>\n",
       "      <td>127382.30</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",
       "  </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",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        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       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X ve y dataframelerini belirleyelim ve bunları girişler sağda çıkış solda olacak şekilde düzenleyip birleştirelim\n",
    "df_X=df_startup.drop(\"Profit\",axis=1)\n",
    "df_y=df_startup[\"Profit\"]\n",
    "df_new=df_X.join(df_y)\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d959f14",
   "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>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.196068</td>\n",
       "      <td>0.708253</td>\n",
       "      <td>0.977703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.196068</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.085831</td>\n",
       "      <td>0.135076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Spend</th>\n",
       "      <td>0.708253</td>\n",
       "      <td>-0.085831</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.732277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.977703</td>\n",
       "      <td>0.135076</td>\n",
       "      <td>0.732277</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R&D Spend  Administration  Marketing Spend    Profit\n",
       "R&D Spend         1.000000        0.196068         0.708253  0.977703\n",
       "Administration    0.196068        1.000000        -0.085831  0.135076\n",
       "Marketing Spend   0.708253       -0.085831         1.000000  0.732277\n",
       "Profit            0.977703        0.135076         0.732277  1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Yukarıda dataframemizi düzenledik\n",
    "#Şimdi korelasyonu kontrol edebiliriz\n",
    "df_new.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ed198ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt #Grafik çizebilmek için matplotlib.pyplot kütüphanesini import ettik\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb2984bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.heatmap(df_new.corr(),annot=True) #seaborn ile sıcaklık haritasını çizdirdik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5ba0c9f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New York', 'California', 'Florida'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#veri setimizdeki sözel verileri sayısal veriye döndürmemiz gerekiyor. Çünkü sözel verilerle regresyon yapamayız.\n",
    "df_new[\"State\"].unique() #benzersiz değerleri gördük"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07566d78",
   "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>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    California  Florida  New York\n",
       "0            0        0         1\n",
       "1            1        0         0\n",
       "2            0        1         0\n",
       "3            0        0         1\n",
       "4            0        1         0\n",
       "5            0        0         1\n",
       "6            1        0         0\n",
       "7            0        1         0\n",
       "8            0        0         1\n",
       "9            1        0         0\n",
       "10           0        1         0\n",
       "11           1        0         0\n",
       "12           0        1         0\n",
       "13           1        0         0\n",
       "14           0        1         0\n",
       "15           0        0         1\n",
       "16           1        0         0\n",
       "17           0        0         1\n",
       "18           0        1         0\n",
       "20           1        0         0\n",
       "21           0        0         1\n",
       "22           0        1         0\n",
       "23           0        1         0\n",
       "24           0        0         1\n",
       "25           1        0         0\n",
       "26           0        1         0\n",
       "27           0        0         1\n",
       "28           0        1         0\n",
       "29           0        0         1\n",
       "30           0        1         0\n",
       "31           0        0         1\n",
       "32           1        0         0\n",
       "33           0        1         0\n",
       "34           1        0         0\n",
       "35           0        0         1\n",
       "36           0        1         0\n",
       "37           1        0         0\n",
       "38           0        0         1\n",
       "39           1        0         0\n",
       "40           1        0         0\n",
       "41           0        1         0\n",
       "42           1        0         0\n",
       "43           0        0         1\n",
       "44           1        0         0\n",
       "45           0        0         1\n",
       "46           0        1         0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df_new.State) #ne kadar benzersiz değer varsa ona göre matris oluşturuyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "40b68003",
   "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",
       "      <th>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "20   76253.86       113867.30        298664.47  California  118474.03   \n",
       "21   78389.47       153773.43        299737.29    New York  111313.02   \n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25   \n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99   \n",
       "24   77044.01        99281.34        140574.81    New York  108552.04   \n",
       "25   64664.71       139553.16        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       182645.56        118148.20     Florida  103282.38   \n",
       "29   65605.48       153032.06        107138.38    New York  101004.64   \n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59   \n",
       "31   61136.38       152701.92         88218.23    New York   97483.56   \n",
       "32   63408.86       129219.61         46085.25  California   97427.84   \n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92   \n",
       "34   46426.07       157693.92        210797.67  California   96712.80   \n",
       "35   46014.02        85047.44        205517.64    New York   96479.51   \n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19   \n",
       "37   44069.95        51283.14        197029.42  California   89949.14   \n",
       "38   20229.59        65947.93        185265.10    New York   81229.06   \n",
       "39   38558.51        82982.09        174999.30  California   81005.76   \n",
       "40   28754.33       118546.05        172795.67  California   78239.91   \n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83   \n",
       "42   23640.93        96189.63        148001.11  California   71498.49   \n",
       "43   15505.73       127382.30         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",
       "\n",
       "    California  Florida  New York  \n",
       "0            0        0         1  \n",
       "1            1        0         0  \n",
       "2            0        1         0  \n",
       "3            0        0         1  \n",
       "4            0        1         0  \n",
       "5            0        0         1  \n",
       "6            1        0         0  \n",
       "7            0        1         0  \n",
       "8            0        0         1  \n",
       "9            1        0         0  \n",
       "10           0        1         0  \n",
       "11           1        0         0  \n",
       "12           0        1         0  \n",
       "13           1        0         0  \n",
       "14           0        1         0  \n",
       "15           0        0         1  \n",
       "16           1        0         0  \n",
       "17           0        0         1  \n",
       "18           0        1         0  \n",
       "20           1        0         0  \n",
       "21           0        0         1  \n",
       "22           0        1         0  \n",
       "23           0        1         0  \n",
       "24           0        0         1  \n",
       "25           1        0         0  \n",
       "26           0        1         0  \n",
       "27           0        0         1  \n",
       "28           0        1         0  \n",
       "29           0        0         1  \n",
       "30           0        1         0  \n",
       "31           0        0         1  \n",
       "32           1        0         0  \n",
       "33           0        1         0  \n",
       "34           1        0         0  \n",
       "35           0        0         1  \n",
       "36           0        1         0  \n",
       "37           1        0         0  \n",
       "38           0        0         1  \n",
       "39           1        0         0  \n",
       "40           1        0         0  \n",
       "41           0        1         0  \n",
       "42           1        0         0  \n",
       "43           0        0         1  \n",
       "44           1        0         0  \n",
       "45           0        0         1  \n",
       "46           0        1         0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sözel verimizi yukarıda sayısala dönüştürdük. Şimdi bunu dataframemize eklememiz gerekiyor.\n",
    "df_new=df_new.join(pd.get_dummies(df_new.State))\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "77762e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Şimdi sözel olan sütunu silebiliriz.\n",
    "df_new.drop(\"State\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0618cb72",
   "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",
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       "\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>Profit</th>\n",
       "      <th>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</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>192261.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>191792.06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>191050.39</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>182901.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>166187.94</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>156991.12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>156122.51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>155752.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>152211.77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>149759.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>146121.95</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>144259.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>141585.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>134307.35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>132602.65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>129917.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>126992.93</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>125370.37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>124266.90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>118474.03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>111313.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>110352.25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>108733.99</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>108552.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>107404.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>105733.54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>105008.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>103282.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>101004.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>99937.59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>97483.56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>97427.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>96778.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>96712.80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>96479.51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>90708.19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>89949.14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>81229.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>81005.76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>78239.91</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>77798.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>71498.49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>69758.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>65200.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>64926.08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>49490.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend     Profit  California  \\\n",
       "0   165349.20       136897.80        471784.10  192261.83           0   \n",
       "1   162597.70       151377.59        443898.53  191792.06           1   \n",
       "2   153441.51       101145.55        407934.54  191050.39           0   \n",
       "3   144372.41       118671.85        383199.62  182901.99           0   \n",
       "4   142107.34        91391.77        366168.42  166187.94           0   \n",
       "5   131876.90        99814.71        362861.36  156991.12           0   \n",
       "6   134615.46       147198.87        127716.82  156122.51           1   \n",
       "7   130298.13       145530.06        323876.68  155752.60           0   \n",
       "8   120542.52       148718.95        311613.29  152211.77           0   \n",
       "9   123334.88       108679.17        304981.62  149759.96           1   \n",
       "10  101913.08       110594.11        229160.95  146121.95           0   \n",
       "11  100671.96        91790.61        249744.55  144259.40           1   \n",
       "12   93863.75       127320.38        249839.44  141585.52           0   \n",
       "13   91992.39       135495.07        252664.93  134307.35           1   \n",
       "14  119943.24       156547.42        256512.92  132602.65           0   \n",
       "15  114523.61       122616.84        261776.23  129917.04           0   \n",
       "16   78013.11       121597.55        264346.06  126992.93           1   \n",
       "17   94657.16       145077.58        282574.31  125370.37           0   \n",
       "18   91749.16       114175.79        294919.57  124266.90           0   \n",
       "20   76253.86       113867.30        298664.47  118474.03           1   \n",
       "21   78389.47       153773.43        299737.29  111313.02           0   \n",
       "22   73994.56       122782.75        303319.26  110352.25           0   \n",
       "23   67532.53       105751.03        304768.73  108733.99           0   \n",
       "24   77044.01        99281.34        140574.81  108552.04           0   \n",
       "25   64664.71       139553.16        137962.62  107404.34           1   \n",
       "26   75328.87       144135.98        134050.07  105733.54           0   \n",
       "27   72107.60       127864.55        353183.81  105008.31           0   \n",
       "28   66051.52       182645.56        118148.20  103282.38           0   \n",
       "29   65605.48       153032.06        107138.38  101004.64           0   \n",
       "30   61994.48       115641.28         91131.24   99937.59           0   \n",
       "31   61136.38       152701.92         88218.23   97483.56           0   \n",
       "32   63408.86       129219.61         46085.25   97427.84           1   \n",
       "33   55493.95       103057.49        214634.81   96778.92           0   \n",
       "34   46426.07       157693.92        210797.67   96712.80           1   \n",
       "35   46014.02        85047.44        205517.64   96479.51           0   \n",
       "36   28663.76       127056.21        201126.82   90708.19           0   \n",
       "37   44069.95        51283.14        197029.42   89949.14           1   \n",
       "38   20229.59        65947.93        185265.10   81229.06           0   \n",
       "39   38558.51        82982.09        174999.30   81005.76           1   \n",
       "40   28754.33       118546.05        172795.67   78239.91           1   \n",
       "41   27892.92        84710.77        164470.71   77798.83           0   \n",
       "42   23640.93        96189.63        148001.11   71498.49           1   \n",
       "43   15505.73       127382.30         35534.17   69758.98           0   \n",
       "44   22177.74       154806.14         28334.72   65200.33           1   \n",
       "45    1000.23       124153.04          1903.93   64926.08           0   \n",
       "46    1315.46       115816.21        297114.46   49490.75           0   \n",
       "\n",
       "    Florida  New York  \n",
       "0         0         1  \n",
       "1         0         0  \n",
       "2         1         0  \n",
       "3         0         1  \n",
       "4         1         0  \n",
       "5         0         1  \n",
       "6         0         0  \n",
       "7         1         0  \n",
       "8         0         1  \n",
       "9         0         0  \n",
       "10        1         0  \n",
       "11        0         0  \n",
       "12        1         0  \n",
       "13        0         0  \n",
       "14        1         0  \n",
       "15        0         1  \n",
       "16        0         0  \n",
       "17        0         1  \n",
       "18        1         0  \n",
       "20        0         0  \n",
       "21        0         1  \n",
       "22        1         0  \n",
       "23        1         0  \n",
       "24        0         1  \n",
       "25        0         0  \n",
       "26        1         0  \n",
       "27        0         1  \n",
       "28        1         0  \n",
       "29        0         1  \n",
       "30        1         0  \n",
       "31        0         1  \n",
       "32        0         0  \n",
       "33        1         0  \n",
       "34        0         0  \n",
       "35        0         1  \n",
       "36        1         0  \n",
       "37        0         0  \n",
       "38        0         1  \n",
       "39        0         0  \n",
       "40        0         0  \n",
       "41        1         0  \n",
       "42        0         0  \n",
       "43        0         1  \n",
       "44        0         0  \n",
       "45        0         1  \n",
       "46        1         0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new #Dataframe de sözel veri kalmadı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ee5b4a17",
   "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>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>124266.90</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend  California  Florida  New York  \\\n",
       "0   165349.20       136897.80        471784.10           0        0         1   \n",
       "1   162597.70       151377.59        443898.53           1        0         0   \n",
       "2   153441.51       101145.55        407934.54           0        1         0   \n",
       "3   144372.41       118671.85        383199.62           0        0         1   \n",
       "4   142107.34        91391.77        366168.42           0        1         0   \n",
       "5   131876.90        99814.71        362861.36           0        0         1   \n",
       "6   134615.46       147198.87        127716.82           1        0         0   \n",
       "7   130298.13       145530.06        323876.68           0        1         0   \n",
       "8   120542.52       148718.95        311613.29           0        0         1   \n",
       "9   123334.88       108679.17        304981.62           1        0         0   \n",
       "10  101913.08       110594.11        229160.95           0        1         0   \n",
       "11  100671.96        91790.61        249744.55           1        0         0   \n",
       "12   93863.75       127320.38        249839.44           0        1         0   \n",
       "13   91992.39       135495.07        252664.93           1        0         0   \n",
       "14  119943.24       156547.42        256512.92           0        1         0   \n",
       "15  114523.61       122616.84        261776.23           0        0         1   \n",
       "16   78013.11       121597.55        264346.06           1        0         0   \n",
       "17   94657.16       145077.58        282574.31           0        0         1   \n",
       "18   91749.16       114175.79        294919.57           0        1         0   \n",
       "20   76253.86       113867.30        298664.47           1        0         0   \n",
       "21   78389.47       153773.43        299737.29           0        0         1   \n",
       "22   73994.56       122782.75        303319.26           0        1         0   \n",
       "23   67532.53       105751.03        304768.73           0        1         0   \n",
       "24   77044.01        99281.34        140574.81           0        0         1   \n",
       "25   64664.71       139553.16        137962.62           1        0         0   \n",
       "26   75328.87       144135.98        134050.07           0        1         0   \n",
       "27   72107.60       127864.55        353183.81           0        0         1   \n",
       "28   66051.52       182645.56        118148.20           0        1         0   \n",
       "29   65605.48       153032.06        107138.38           0        0         1   \n",
       "30   61994.48       115641.28         91131.24           0        1         0   \n",
       "31   61136.38       152701.92         88218.23           0        0         1   \n",
       "32   63408.86       129219.61         46085.25           1        0         0   \n",
       "33   55493.95       103057.49        214634.81           0        1         0   \n",
       "34   46426.07       157693.92        210797.67           1        0         0   \n",
       "35   46014.02        85047.44        205517.64           0        0         1   \n",
       "36   28663.76       127056.21        201126.82           0        1         0   \n",
       "37   44069.95        51283.14        197029.42           1        0         0   \n",
       "38   20229.59        65947.93        185265.10           0        0         1   \n",
       "39   38558.51        82982.09        174999.30           1        0         0   \n",
       "40   28754.33       118546.05        172795.67           1        0         0   \n",
       "41   27892.92        84710.77        164470.71           0        1         0   \n",
       "42   23640.93        96189.63        148001.11           1        0         0   \n",
       "43   15505.73       127382.30         35534.17           0        0         1   \n",
       "44   22177.74       154806.14         28334.72           1        0         0   \n",
       "45    1000.23       124153.04          1903.93           0        0         1   \n",
       "46    1315.46       115816.21        297114.46           0        1         0   \n",
       "\n",
       "       Profit  \n",
       "0   192261.83  \n",
       "1   191792.06  \n",
       "2   191050.39  \n",
       "3   182901.99  \n",
       "4   166187.94  \n",
       "5   156991.12  \n",
       "6   156122.51  \n",
       "7   155752.60  \n",
       "8   152211.77  \n",
       "9   149759.96  \n",
       "10  146121.95  \n",
       "11  144259.40  \n",
       "12  141585.52  \n",
       "13  134307.35  \n",
       "14  132602.65  \n",
       "15  129917.04  \n",
       "16  126992.93  \n",
       "17  125370.37  \n",
       "18  124266.90  \n",
       "20  118474.03  \n",
       "21  111313.02  \n",
       "22  110352.25  \n",
       "23  108733.99  \n",
       "24  108552.04  \n",
       "25  107404.34  \n",
       "26  105733.54  \n",
       "27  105008.31  \n",
       "28  103282.38  \n",
       "29  101004.64  \n",
       "30   99937.59  \n",
       "31   97483.56  \n",
       "32   97427.84  \n",
       "33   96778.92  \n",
       "34   96712.80  \n",
       "35   96479.51  \n",
       "36   90708.19  \n",
       "37   89949.14  \n",
       "38   81229.06  \n",
       "39   81005.76  \n",
       "40   78239.91  \n",
       "41   77798.83  \n",
       "42   71498.49  \n",
       "43   69758.98  \n",
       "44   65200.33  \n",
       "45   64926.08  \n",
       "46   49490.75  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X ve y dataframelerini tekrar belirleyelim ve bunları girişler sağda çıkış solda olacak şekilde düzenleyip birleştirelim\n",
    "df_X=df_new.drop(\"Profit\",axis=1)\n",
    "df_y=df_new[\"Profit\"]\n",
    "df_new=df_X.join(df_y)\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5fba766d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#korelasyon sıcaklık haritasına tekrar bakalıö\n",
    "plt.figure(figsize=(14,8))\n",
    "sns.heatmap(df_new.corr(),annot=True) #seaborn ile sıcaklık haritasını çizdirdik\n",
    "#Görüyoruzki sütunlar arasındaki ilişki, daha önceki haritaya göre azalmış"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54c28f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Şimdi sırada train ve test verilerini ayarlamada\n",
    "from sklearn.model_selection import train_test_split #train ve test verilerini oluşturan modeli sağlar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37c922c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_new.drop(\"Profit\",axis=1) \n",
    "y=df_new[\"Profit\"]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "#train ve test verileri olmak üzere ikiye ayırdık.\n",
    "#test_size -> tüm verilerin yüzde kaçının test verisi olacağını belirtmemizi sağlar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2e9ef6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression #linear regresyon öğrenmesini sağlayan modelin kütüphanesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "08d04c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression() #modelleri oluşturmak için fit metodunu kullanmamızı sağlar\n",
    "model.fit(X_train,y_train) #fit ile modelimizi train verileriyle eğittik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ae6d17f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Şimdi modelimizin ne kadar başarılı olduğuna bakalım\n",
    "y_predict=model.predict(X_test) #Eğittiğimiz modeli(train), test verileriyle tahmin ettiriyoruz predict() sayesinde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f0b04f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#r^2 value yu bulmalıyız r^2 1 e ne kadar yakınsa o kadar iyi 0 a ne kadar yakınsa o kadar kötü bir modelimiz var diyoruz\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2eaa51e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9385926603829022"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_predict)\n",
    "#Gerçek değerlerle bizim x değerlerinden tahmin ettiğimiz değerleri karşılaştırıyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "74623adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Profit'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Regresyon grafiğini çizdirelim\n",
    "sns.regplot(x=y_test,y=y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48536f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 78934.51581113, 108773.81681548, 112985.26662189,  48934.05695492,\n",
       "        75312.70885203,  75816.41828965, 170187.68132042, 127275.86865172,\n",
       "       148222.30597197, 170600.92765981])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict #X_test verilerinden modelimizin tahmin ettiği"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5b19911d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40     78239.91\n",
       "26    105733.54\n",
       "27    105008.31\n",
       "45     64926.08\n",
       "36     90708.19\n",
       "42     71498.49\n",
       "4     166187.94\n",
       "12    141585.52\n",
       "8     152211.77\n",
       "3     182901.99\n",
       "Name: Profit, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test #Gerçek veriler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "86e3484c",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (244952287.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Input \u001b[1;32mIn [33]\u001b[1;36m\u001b[0m\n\u001b[1;33m    https://www.kaggle.com/datasets/karthickveerakumar/startup-logistic-regression\u001b[0m\n\u001b[1;37m          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "https://www.kaggle.com/datasets/karthickveerakumar/startup-logistic-regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "71e60b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.78185559e-01, -6.68184214e-02,  2.55194758e-02,  2.45752950e+03,\n",
       "       -1.24815783e+03, -1.20937167e+03])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
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