Data_Exploration.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing all libraies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying the full dataset as **Startup_DF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Startup_DF = pd.read_csv('Data/50_Startups.csv')\n",
    "Startup_DF"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying all of the columns so i can select the columns i need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Startup_DF.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting all the columns i need and splitting the data into into a subset called **Startup_SS_st_Pro** i chose this to see if profit in new york is above 100000\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         State     Profit\n",
       "0     New York  192261.83\n",
       "1   California  191792.06\n",
       "2      Florida  191050.39\n",
       "3     New York  182901.99\n",
       "4      Florida  166187.94\n",
       "5     New York  156991.12\n",
       "6   California  156122.51\n",
       "7      Florida  155752.60\n",
       "8     New York  152211.77\n",
       "9   California  149759.96\n",
       "10     Florida  146121.95\n",
       "11  California  144259.40\n",
       "12     Florida  141585.52\n",
       "13  California  134307.35\n",
       "14     Florida  132602.65\n",
       "15    New York  129917.04\n",
       "16  California  126992.93\n",
       "17    New York  125370.37\n",
       "18     Florida  124266.90\n",
       "19    New York  122776.86\n",
       "20  California  118474.03\n",
       "21    New York  111313.02\n",
       "22     Florida  110352.25\n",
       "23     Florida  108733.99\n",
       "24    New York  108552.04\n",
       "25  California  107404.34\n",
       "26     Florida  105733.54\n",
       "27    New York  105008.31\n",
       "28     Florida  103282.38\n",
       "29    New York  101004.64\n",
       "30     Florida   99937.59\n",
       "31    New York   97483.56\n",
       "32  California   97427.84\n",
       "33     Florida   96778.92\n",
       "34  California   96712.80\n",
       "35    New York   96479.51\n",
       "36     Florida   90708.19\n",
       "37  California   89949.14\n",
       "38    New York   81229.06\n",
       "39  California   81005.76\n",
       "40  California   78239.91\n",
       "41     Florida   77798.83\n",
       "42  California   71498.49\n",
       "43    New York   69758.98\n",
       "44  California   65200.33\n",
       "45    New York   64926.08\n",
       "46     Florida   49490.75\n",
       "47  California   42559.73\n",
       "48    New York   35673.41\n",
       "49  California   14681.40"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Startup_SS_st_Pro = Startup_DF[[  'State', 'Profit']]\n",
    "Startup_SS_st_Pro"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will only show table values containing new york\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State     Profit\n",
       "0   New York  192261.83\n",
       "3   New York  182901.99\n",
       "5   New York  156991.12\n",
       "8   New York  152211.77\n",
       "15  New York  129917.04\n",
       "17  New York  125370.37\n",
       "19  New York  122776.86\n",
       "21  New York  111313.02\n",
       "24  New York  108552.04\n",
       "27  New York  105008.31\n",
       "29  New York  101004.64\n",
       "31  New York   97483.56\n",
       "35  New York   96479.51\n",
       "38  New York   81229.06\n",
       "43  New York   69758.98\n",
       "45  New York   64926.08\n",
       "48  New York   35673.41"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only show tables with state = New York\n",
    "Startup_SS_Ny = Startup_SS_st_Pro[Startup_SS_st_Pro['State'] == 'New York']\n",
    "Startup_SS_Ny"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at what data is missing from the subset i created"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State     0\n",
       "Profit    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values\n",
    "Startup_SS_Ny.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cardinality \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State      1\n",
       "Profit    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check cardinality of features\n",
    "Startup_SS_Ny.nunique()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(Startup_SS_Ny['State'], Startup_SS_Ny['Profit'])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split features and target\n",
    "X = Startup_SS_Ny.iloc[:, :-1].values\n",
    "y = Startup_SS_Ny.iloc[:, -1].values\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108816.95307692306"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, r2_score\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean apt price: 108816.95\n",
      "Baseline R-squared: 0.0\n"
     ]
    }
   ],
   "source": [
    "y_mean = y_train.mean()\n",
    "y_pred_baseline = [y_mean]*len(y_train)\n",
    "print(\"Mean apt price:\",round(y_mean,2))\n",
    "\n",
    "print(\"Baseline R-squared:\", round(r2_score(y_train,y_pred_baseline),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[70], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mplotly\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mexpress\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpx\u001b[39;00m\n\u001b[0;32m      3\u001b[0m fig \u001b[39m=\u001b[39m px\u001b[39m.\u001b[39mscatter(Startup_SS_Ny, x\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mState\u001b[39m\u001b[39m\"\u001b[39m, y\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mProfit\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m fig\u001b[39m.\u001b[39;49mshow()\n",
      "File \u001b[1;32mc:\\Users\\estia\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\plotly\\basedatatypes.py:3390\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3357\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   3358\u001b[0m \u001b[39mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[0;32m   3359\u001b[0m \u001b[39mspecified by the renderer argument\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3386\u001b[0m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m   3387\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   3388\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mplotly\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpio\u001b[39;00m\n\u001b[1;32m-> 3390\u001b[0m \u001b[39mreturn\u001b[39;00m pio\u001b[39m.\u001b[39;49mshow(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\estia\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\plotly\\io\\_renderers.py:396\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[0;32m    391\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    392\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    393\u001b[0m         )\n\u001b[0;32m    395\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m nbformat \u001b[39mor\u001b[39;00m Version(nbformat\u001b[39m.\u001b[39m__version__) \u001b[39m<\u001b[39m Version(\u001b[39m\"\u001b[39m\u001b[39m4.2.0\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m--> 396\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    397\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    398\u001b[0m         )\n\u001b[0;32m    400\u001b[0m     ipython_display\u001b[39m.\u001b[39mdisplay(bundle, raw\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m    402\u001b[0m \u001b[39m# external renderers\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "# CREAT A MAP OF THE DATA IN PLOTLY.EXPRESS\n",
    "import plotly.express as px\n",
    "fig = px.scatter(Startup_SS_Ny, x=\"State\", y=\"Profit\")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}